librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-11 18:29:29	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-11 18:25:24	card stringlengths 11 1.01M
abdusah/aradia-ctc-hubert-ft	abdusah	2022-03-31T20:56:27Z	14	0	transformers	[ "transformers", "pytorch", "hubert", "automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-31T08:14:31Z	--- tags: - automatic-speech-recognition - abdusahmbzuai/arabic_speech_massive_300hrs - generated_from_trainer model-index: - name: aradia-ctc-hubert-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aradia-ctc-hubert-ft This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.8536 - Wer: 0.3737 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.43 \| 100 \| 3.6934 \| 1.0 \| \| No log \| 0.87 \| 200 \| 3.0763 \| 1.0 \| \| No log \| 1.3 \| 300 \| 2.9737 \| 1.0 \| \| No log \| 1.74 \| 400 \| 2.5734 \| 1.0 \| \| 5.0957 \| 2.17 \| 500 \| 1.1900 \| 0.9011 \| \| 5.0957 \| 2.61 \| 600 \| 0.9726 \| 0.7572 \| \| 5.0957 \| 3.04 \| 700 \| 0.8960 \| 0.6209 \| \| 5.0957 \| 3.48 \| 800 \| 0.7851 \| 0.5515 \| \| 5.0957 \| 3.91 \| 900 \| 0.7271 \| 0.5115 \| \| 1.0312 \| 4.35 \| 1000 \| 0.7053 \| 0.4955 \| \| 1.0312 \| 4.78 \| 1100 \| 0.6823 \| 0.4737 \| \| 1.0312 \| 5.22 \| 1200 \| 0.6768 \| 0.4595 \| \| 1.0312 \| 5.65 \| 1300 \| 0.6635 \| 0.4488 \| \| 1.0312 \| 6.09 \| 1400 \| 0.6602 \| 0.4390 \| \| 0.6815 \| 6.52 \| 1500 \| 0.6464 \| 0.4310 \| \| 0.6815 \| 6.95 \| 1600 \| 0.6455 \| 0.4394 \| \| 0.6815 \| 7.39 \| 1700 \| 0.6630 \| 0.4312 \| \| 0.6815 \| 7.82 \| 1800 \| 0.6521 \| 0.4126 \| \| 0.6815 \| 8.26 \| 1900 \| 0.6282 \| 0.4284 \| \| 0.544 \| 8.69 \| 2000 \| 0.6248 \| 0.4178 \| \| 0.544 \| 9.13 \| 2100 \| 0.6510 \| 0.4104 \| \| 0.544 \| 9.56 \| 2200 \| 0.6527 \| 0.4013 \| \| 0.544 \| 10.0 \| 2300 \| 0.6511 \| 0.4064 \| \| 0.544 \| 10.43 \| 2400 \| 0.6734 \| 0.4061 \| \| 0.4478 \| 10.87 \| 2500 \| 0.6756 \| 0.4145 \| \| 0.4478 \| 11.3 \| 2600 \| 0.6727 \| 0.3990 \| \| 0.4478 \| 11.74 \| 2700 \| 0.6619 \| 0.4007 \| \| 0.4478 \| 12.17 \| 2800 \| 0.6614 \| 0.4019 \| \| 0.4478 \| 12.61 \| 2900 \| 0.6695 \| 0.4004 \| \| 0.3919 \| 13.04 \| 3000 \| 0.6778 \| 0.3966 \| \| 0.3919 \| 13.48 \| 3100 \| 0.6872 \| 0.3971 \| \| 0.3919 \| 13.91 \| 3200 \| 0.6882 \| 0.3945 \| \| 0.3919 \| 14.35 \| 3300 \| 0.7177 \| 0.4010 \| \| 0.3919 \| 14.78 \| 3400 \| 0.6888 \| 0.4043 \| \| 0.3767 \| 15.22 \| 3500 \| 0.7124 \| 0.4202 \| \| 0.3767 \| 15.65 \| 3600 \| 0.7276 \| 0.4120 \| \| 0.3767 \| 16.09 \| 3700 \| 0.7265 \| 0.4034 \| \| 0.3767 \| 16.52 \| 3800 \| 0.7392 \| 0.4077 \| \| 0.3767 \| 16.95 \| 3900 \| 0.7403 \| 0.3965 \| \| 0.3603 \| 17.39 \| 4000 \| 0.7445 \| 0.4016 \| \| 0.3603 \| 17.82 \| 4100 \| 0.7579 \| 0.4012 \| \| 0.3603 \| 18.26 \| 4200 \| 0.7225 \| 0.3963 \| \| 0.3603 \| 18.69 \| 4300 \| 0.7355 \| 0.3951 \| \| 0.3603 \| 19.13 \| 4400 \| 0.7482 \| 0.3925 \| \| 0.3153 \| 19.56 \| 4500 \| 0.7723 \| 0.3972 \| \| 0.3153 \| 20.0 \| 4600 \| 0.7469 \| 0.3898 \| \| 0.3153 \| 20.43 \| 4700 \| 0.7800 \| 0.3944 \| \| 0.3153 \| 20.87 \| 4800 \| 0.7827 \| 0.3897 \| \| 0.3153 \| 21.3 \| 4900 \| 0.7935 \| 0.3914 \| \| 0.286 \| 21.74 \| 5000 \| 0.7984 \| 0.3750 \| \| 0.286 \| 22.17 \| 5100 \| 0.7945 \| 0.3830 \| \| 0.286 \| 22.61 \| 5200 \| 0.8011 \| 0.3775 \| \| 0.286 \| 23.04 \| 5300 \| 0.7978 \| 0.3824 \| \| 0.286 \| 23.48 \| 5400 \| 0.8161 \| 0.3833 \| \| 0.2615 \| 23.91 \| 5500 \| 0.7823 \| 0.3858 \| \| 0.2615 \| 24.35 \| 5600 \| 0.8312 \| 0.3863 \| \| 0.2615 \| 24.78 \| 5700 \| 0.8427 \| 0.3819 \| \| 0.2615 \| 25.22 \| 5800 \| 0.8432 \| 0.3802 \| \| 0.2615 \| 25.65 \| 5900 \| 0.8286 \| 0.3794 \| \| 0.2408 \| 26.09 \| 6000 \| 0.8224 \| 0.3824 \| \| 0.2408 \| 26.52 \| 6100 \| 0.8228 \| 0.3823 \| \| 0.2408 \| 26.95 \| 6200 \| 0.8324 \| 0.3795 \| \| 0.2408 \| 27.39 \| 6300 \| 0.8564 \| 0.3744 \| \| 0.2408 \| 27.82 \| 6400 \| 0.8629 \| 0.3774 \| \| 0.2254 \| 28.26 \| 6500 \| 0.8545 \| 0.3778 \| \| 0.2254 \| 28.69 \| 6600 \| 0.8492 \| 0.3767 \| \| 0.2254 \| 29.13 \| 6700 \| 0.8511 \| 0.3751 \| \| 0.2254 \| 29.56 \| 6800 \| 0.8491 \| 0.3753 \| \| 0.2254 \| 30.0 \| 6900 \| 0.8536 \| 0.3737 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
magitz/distilbert-base-uncased-finetuned-emotion	magitz	2022-03-31T20:48:43Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-31T20:41:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9265 - name: F1 type: f1 value: 0.9267965474109292 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2235 - Accuracy: 0.9265 - F1: 0.9268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8101 \| 1.0 \| 250 \| 0.3177 \| 0.9045 \| 0.9010 \| \| 0.2472 \| 2.0 \| 500 \| 0.2235 \| 0.9265 \| 0.9268 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.8.1 - Datasets 1.18.3 - Tokenizers 0.11.0
ghees/FatimeFellowship	ghees	2022-03-31T20:47:24Z	0	0	null	[ "region:us" ]	null	2022-03-31T20:45:21Z	Preprocessing before feeding to model ``` from sentence_transformers import SentenceTransformer model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device='cuda') ... embeddings = model.encode([text]) return embeddings[0] ```
arampacha/gpt-neo-therapist-small	arampacha	2022-03-31T20:34:26Z	17	1	transformers	[ "transformers", "pytorch", "tensorboard", "onnx", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-30T08:40:54Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: gpt-neo-therapist-small results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-neo-therapist-small This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.6731 - Rouge1: 39.5028 - Rouge2: 6.43 - Rougel: 24.0091 - Rougelsum: 35.4481 - Gen Len: 204.1329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 24 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:--------:\| \| 9.9955 \| 0.97 \| 7 \| 6.8195 \| 18.6047 \| 1.0194 \| 14.8565 \| 17.9774 \| 212.0983 \| \| 6.9729 \| 1.97 \| 14 \| 5.6783 \| 26.3789 \| 3.0779 \| 18.5195 \| 24.8592 \| 203.0925 \| \| 5.2614 \| 2.97 \| 21 \| 5.0506 \| 34.9428 \| 4.921 \| 21.9741 \| 32.1122 \| 206.2775 \| \| 5.0599 \| 3.97 \| 28 \| 4.7372 \| 38.5235 \| 6.2251 \| 23.5923 \| 34.5633 \| 204.2428 \| \| 4.5479 \| 4.97 \| 35 \| 4.6731 \| 39.5028 \| 6.43 \| 24.0091 \| 35.4481 \| 204.1329 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
WENGSYX/Deberta-Chinese-Large	WENGSYX	2022-03-31T20:08:59Z	56	16	transformers	[ "transformers", "pytorch", "deberta", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	# Deberta-Chinese 本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。使用WWM与n-gramMLM 等预训练方法进行预训练。 \| 预训练模型 \| 学习率 \| batchsize \| 设备 \| 语料库 \| 时间 \| 优化器 \| \| --------------------- \| ------ \| --------- \| ------ \| ------ \| ---- \| ------ \| \| Deberta-Chinese-Large \| 1e-5 \| 512 \| 2*3090 \| 200G \| 14天 \| AdamW \| ### 加载与使用依托于huggingface-transformers ``` tokenizer = BertTokenizer.from_pretrained("WENGSYX/Deberta-Chinese-Large") model = AutoModel.from_pretrained("WENGSYX/Deberta-Chinese-Large") ``` #### 注意，请使用BertTokenizer加载中文词表
Tahsin-Mayeesha/distilbert-finetuned-fakenews	Tahsin-Mayeesha	2022-03-31T17:11:42Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-31T15:58:31Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-finetuned-fakenews results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-finetuned-fakenews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0049 - Accuracy: 0.9995 - F1: 0.9995 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.0392 \| 1.0 \| 500 \| 0.0059 \| 0.999 \| 0.999 \| \| 0.002 \| 2.0 \| 1000 \| 0.0047 \| 0.9995 \| 0.9995 \| \| 0.0001 \| 3.0 \| 1500 \| 0.0047 \| 0.9995 \| 0.9995 \| \| 0.0001 \| 4.0 \| 2000 \| 0.0049 \| 0.9995 \| 0.9995 \| \| 0.0 \| 5.0 \| 2500 \| 0.0049 \| 0.9995 \| 0.9995 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0
rahulacj/bertweet-base-finetuned-sentiment-analysis	rahulacj	2022-03-31T16:21:16Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-31T09:42:31Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bertweet-base-finetuned-sentiment-analysis results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bertweet-base-finetuned-sentiment-analysis This model is a fine-tuned version of [cardiffnlp/bertweet-base-sentiment](https://huggingface.co/cardiffnlp/bertweet-base-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8458 - Accuracy: 0.6426 - F1: 0.6397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8904 \| 1.0 \| 630 \| 0.8509 \| 0.6381 \| 0.6340 \| \| 0.7655 \| 2.0 \| 1260 \| 0.8345 \| 0.6579 \| 0.6559 \| \| 0.66 \| 3.0 \| 1890 \| 0.9199 \| 0.6548 \| 0.6514 \| \| 0.447 \| 4.0 \| 2520 \| 1.0324 \| 0.6429 \| 0.6417 \| \| 0.3585 \| 5.0 \| 3150 \| 1.1234 \| 0.6452 \| 0.6424 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0
eren23/pneumonia-bielefeld-dl-course	eren23	2022-03-31T15:55:27Z	61	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-27T12:17:21Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: pneumonia-bielefeld-dl-course results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8456632494926453 --- # pneumonia-bielefeld-dl-course This registry contains the model for making pneumonia predictions and was prepared for Bielefeld University Deep Learning course homework. The code used for this implementation mostly comes from here: https://github.com/nateraw/huggingpics it was a ready pipeline for model fine-tuning with huggingface and PyTorch Lightning for another dataset.
Nonem100/Test-Model	Nonem100	2022-03-31T15:19:38Z	62	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-31T15:19:30Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Test-Model results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9017857313156128 --- # Test-Model Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cotton candy ![cotton candy](images/cotton_candy.jpg) #### hamburger ![hamburger](images/hamburger.jpg) #### hot dog ![hot dog](images/hot_dog.jpg) #### nachos ![nachos](images/nachos.jpg) #### popcorn ![popcorn](images/popcorn.jpg)
huggingtweets/timdingmanlive	huggingtweets	2022-03-31T14:30:05Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-31T14:26:57Z	--- language: en thumbnail: http://www.huggingtweets.com/timdingmanlive/1648736999131/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2844974270/7bb6450b90b65f8712d9433b8d5e1971_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tim Dingman</div> <div style="text-align: center; font-size: 14px;">@timdingmanlive</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tim Dingman. \| Data \| Tim Dingman \| \| --- \| --- \| \| Tweets downloaded \| 3240 \| \| Retweets \| 555 \| \| Short tweets \| 138 \| \| Tweets kept \| 2547 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7yvdv2z7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @timdingmanlive's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/311pu3zj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/311pu3zj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/timdingmanlive') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
oferweintraub/bert-base-finance-sentiment-noisy-search	oferweintraub	2022-03-31T14:13:45Z	23	3	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "Finance-sentiment-analysis", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - Finance-sentiment-analysis - generated_from_trainer metrics: - f1 - accuracy - precision - recall model-index: - name: bert-base-finance-sentiment-noisy-search results: [] widget: - text: "Third quarter reported revenues were $10.9 billion, up 5 percent compared to prior year and up 8 percent on a currency-neutral basis" example_title: "Positive" - text: "The London-listed website for businesses reported a pretax loss of $26.6 million compared with a loss of $12.9 million the previous year" example_title: "Negative" - text: "Microsoft updates Outlook, Teams, and PowerPoint to be hybrid work ready" example_title: "Neutral" --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-finance-sentiment-noisy-search This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on Kaggle finance news sentiment analysis with data enhancement using noisy search. The process is explained below: 1. First "bert-base-uncased" was fine-tuned on Kaggle's finance news sentiment analysis https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news dataset achieving accuracy of about 88% 2. We then used a logistic-regression classifier on the same data. Here we looked at coefficients that contributed the most to the "Positive" and "Negative" classes by inspecting only bi-grams. 3. Using the top 25 bi-grams per class (i.e. "Positive" / "Negative") we invoked Bing news search with those bi-grams and retrieved up to 50 news items per bi-gram phrase. 4. We called it "noisy-search" because it is assumed the positive bi-grams (e.g. "profit rose" , "growth net") give rise to positive examples whereas negative bi-grams (e.g. "loss increase", "share loss") result in negative examples but note that we didn't test for the validity of this assumption (hence: noisy-search) 5. For each article we kept the title + excerpt and labeled it according to pre-assumptions on class associations. 6. We then trained the same model on the noisy data and apply it to an held-out test set from the original data set split. 7. Training with couple of thousands noisy "positives" and "negatives" examples yielded a test set accuracy of about 95%. 8. It shows that by automatically collecting noisy examples using search we can boost accuracy performance from about 88% to more than 95%. Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown in the attached pdf: https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing ## Model description BERT model trained on noisy data from search results. See PDF for more details. ## Intended uses & limitations Intended for use on finance news sentiment analysis with 3 options: "Positive", "Neutral" and "Negative" To get the best results feed the classifier with the title and either the 1st paragraph or a short news summarization e.g. of up to 64 tokens. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
huggingtweets/youtube	huggingtweets	2022-03-31T14:06:33Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-31T14:05:50Z	--- language: en thumbnail: http://www.huggingtweets.com/youtube/1648735587597/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427292844612595720/RC1YSvuT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">YouTube</div> <div style="text-align: center; font-size: 14px;">@youtube</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from YouTube. \| Data \| YouTube \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 23 \| \| Short tweets \| 104 \| \| Tweets kept \| 3123 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dx34obn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @youtube's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/youtube') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Edresson/wav2vec2-large-xlsr-coraa-portuguese	Edresson	2022-03-31T13:28:43Z	632	15	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "hf-asr-leaderboard", "PyTorch", "dataset:CORAA", "arxiv:2110.15731", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- language: pt datasets: - CORAA metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - hf-asr-leaderboard - speech - PyTorch license: apache-2.0 model-index: - name: Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: CORAA type: CORAA args: pt metrics: - name: Test CORAA WER type: wer value: 25.26 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: pt metrics: - name: Test WER on Common Voice 7 type: wer value: 20.08 --- # Wav2vec 2.0 trained with CORAA Portuguese Dataset This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following [CORAA dataset](https://github.com/nilc-nlp/CORAA) # Use this model ```python from transformers import AutoTokenizer, Wav2Vec2ForCTC tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese") model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese") ``` # Results For the results check the [CORAA article](https://arxiv.org/abs/2110.15731) # Example test with Common Voice Dataset ```python dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("â€™", "'") return batch ``` ```python ds = dataset.map(map_to_array) result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) print(wer.compute(predictions=result["predicted"], references=result["target"])) ```
Khalsuu/2nd-wav2vec2-l-xls-r-300m-turkish-test	Khalsuu	2022-03-31T12:09:32Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-31T08:45:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: 2nd-wav2vec2-l-xls-r-300m-turkish-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2nd-wav2vec2-l-xls-r-300m-turkish-test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6019 - Wer: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.0522 \| 3.67 \| 400 \| 0.7773 \| 0.7296 \| \| 0.5369 \| 7.34 \| 800 \| 0.6282 \| 0.5888 \| \| 0.276 \| 11.01 \| 1200 \| 0.5998 \| 0.5330 \| \| 0.1725 \| 14.68 \| 1600 \| 0.5859 \| 0.4908 \| \| 0.1177 \| 18.35 \| 2000 \| 0.6019 \| 0.4444 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
YiTian/wav2vec2-common_voice-tr-demo	YiTian	2022-03-31T11:40:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "tr", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-31T09:39:08Z	--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-tr-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-common_voice-tr-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 2.9841 - Wer: 0.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 7.14 \| 100 \| 3.6689 \| 1.0 \| \| No log \| 14.29 \| 200 \| 3.0280 \| 0.9999 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.0 - Datasets 1.18.0 - Tokenizers 0.11.6
scasutt/wav2vec2-base_toy_train_data_random_low_pass	scasutt	2022-03-31T10:42:02Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-31T08:21:35Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base_toy_train_data_random_low_pass results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base_toy_train_data_random_low_pass This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3227 - Wer: 0.7288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.0795 \| 2.1 \| 500 \| 3.2227 \| 0.9982 \| \| 1.21 \| 4.2 \| 1000 \| 1.3713 \| 0.8879 \| \| 0.742 \| 6.3 \| 1500 \| 1.2660 \| 0.8296 \| \| 0.5877 \| 8.4 \| 2000 \| 1.2921 \| 0.7794 \| \| 0.4823 \| 10.5 \| 2500 \| 1.2899 \| 0.7565 \| \| 0.4036 \| 12.6 \| 3000 \| 1.3486 \| 0.7494 \| \| 0.391 \| 14.7 \| 3500 \| 1.2701 \| 0.7466 \| \| 0.3426 \| 16.81 \| 4000 \| 1.3570 \| 0.7279 \| \| 0.3015 \| 18.91 \| 4500 \| 1.3227 \| 0.7288 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
unjustify/autotrain-IWant-689220804	unjustify	2022-03-31T06:46:48Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "unk", "dataset:unjustify/autotrain-data-IWant", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-31T06:09:55Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - unjustify/autotrain-data-IWant co2_eq_emissions: 39.40549299946679 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 689220804 - CO2 Emissions (in grams): 39.40549299946679 ## Validation Metrics - Loss: 2.0426149368286133 - Rouge1: 54.9813 - Rouge2: 44.923 - RougeL: 54.0399 - RougeLsum: 54.2553 - Gen Len: 16.6211 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/unjustify/autotrain-IWant-689220804 ```
michiyasunaga/BioLinkBERT-base	michiyasunaga	2022-03-31T00:51:21Z	6,225	36	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "exbert", "linkbert", "biolinkbert", "fill-mask", "question-answering", "text-classification", "token-classification", "en", "dataset:pubmed", "arxiv:2203.15827", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-classification	2022-03-08T07:22:12Z	--- license: apache-2.0 language: en datasets: - pubmed tags: - bert - exbert - linkbert - biolinkbert - feature-extraction - fill-mask - question-answering - text-classification - token-classification widget: - text: "Sunitinib is a tyrosine kinase inhibitor" --- ## BioLinkBERT-base BioLinkBERT-base model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT). This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA). ## Model description LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures document links such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document. LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for knowledge-intensive tasks (e.g. question answering) and cross-document tasks (e.g. reading comprehension, document retrieval). ## Intended uses & limitations The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification. You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text). ### How to use To use the model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base') model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-base') inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt") outputs = model(inputs) last_hidden_states = outputs.last_hidden_state ``` For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases. ## Evaluation results When fine-tuned on downstream tasks, LinkBERT achieves the following results. Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.): BioLinkBERT attains new state-of-the-art. \| \| BLURB score \| PubMedQA \| BioASQ \| MedQA-USMLE \| \| ---------------------- \| -------- \| -------- \| ------- \| -------- \| \| PubmedBERT-base \| 81.10 \| 55.8 \| 87.5 \| 38.1 \| \| BioLinkBERT-base \| 83.39 \| 70.2 \| 91.4 \| 40.0 \| \| BioLinkBERT-large \| 84.30 \| 72.2 \| 94.8 \| 44.6 \| \| \| MMLU-professional medicine \| \| ---------------------- \| -------- \| \| GPT-3 (175 params) \| 38.7 \| \| UnifiedQA (11B params) \| 43.2 \| \| BioLinkBERT-large (340M params) \| 50.7** \| ## Citation If you find LinkBERT useful in your project, please cite the following: ```bibtex @InProceedings{yasunaga2022linkbert, author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang}, title = {LinkBERT: Pretraining Language Models with Document Links}, year = {2022}, booktitle = {Association for Computational Linguistics (ACL)}, } ```
hoangbinhmta99/wav2vec-NCKH-2022	hoangbinhmta99	2022-03-31T00:28:52Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "feature-extraction", "audio", "speech", "Transformer", "automatic-speech-recognition", "vi", "dataset:vivos", "dataset:common_voice", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-30T04:39:46Z	--- language: vi datasets: - vivos - common_voice metrics: - wer pipeline_tag: automatic-speech-recognition tags: - audio - speech - Transformer license: cc-by-nc-4.0 model-index: - name: Wav2vec2 NCKH Vietnamese 2022 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice vi type: common_voice args: vi metrics: - name: Test WER type: wer value: No --- Convert from model .pt to transformer Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h Bash: ```bash pip install transformers[sentencepiece] pip install fairseq -U git clone https://github.com/huggingface/transformers.git cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py . wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt mkdir dict wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt mkdir outputs python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py --pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt --dict_path ./dict/dict.ltr.txt --not_finetuned ``` # install and upload model ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \| sudo bash git lfs install sudo apt-get install git-lfs git lfs install git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo ls cd wav2vec-demo/ git status git add . git commit -m "First model version" git config --global user.email [yourname] git config --global user.name [yourpass] git commit -m "First model version" git push ```
mrm8488/biomedtra-small-es	mrm8488	2022-03-30T21:07:50Z	3	2	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "pretraining", "Spanish", "Electra", "Bio", "Medical", "es", "dataset:cowese", "arxiv:1406.2661", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: es tags: - Spanish - Electra - Bio - Medical datasets: - cowese --- ## 🦠 BIOMEDtra 🏥 BIOMEDtra (small) is an Electra like model (discriminator in this case) trained on [Spanish Biomedical Crawled Corpus](https://zenodo.org/record/5510033#.Yhdk1ZHMLJx). As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ## Training details The model was trained using the Electra base code for 3 days on 1 GPU (Tesla V100 16GB). ## Dataset details The largest Spanish biomedical and heath corpus to date gathered from a massive Spanish health domain crawler over more than 3,000 URLs were downloaded and preprocessed. The collected data have been preprocessed to produce the CoWeSe (Corpus Web Salud Español) resource, a large-scale and high-quality corpus intended for biomedical and health NLP in Spanish. ## Model details ⚙ \|Param\| # Value\| \|-----\|--------\| \|Layers\| 12 \| \|Hidden \| 256 \| \|Params\| 14M \| ## Evaluation metrics (for discriminator) 🧾 \|Metric \| # Score \| \|-------\|---------\| \|Accuracy\| 0.9561\| \|Precision\| 0.808\| \|Recall \| 0.531 \| \|AUC \| 0.949\| ## Benchmarks 🔨 WIP 🚧 ## How to use the discriminator in `transformers` ```py from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("mrm8488/biomedtra-small-es") tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/biomedtra-small-es") sentence = "Los españoles tienden a sufir déficit de vitamina c" fake_sentence = "Los españoles tienden a déficit sufrir de vitamina c" fake_tokens = tokenizer.tokenize(fake_sentence) fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") discriminator_outputs = discriminator(fake_inputs) predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) [print("%7s" % token, end="") for token in fake_tokens] [print("%7s" % prediction, end="") for prediction in predictions.tolist()] ``` ## Acknowledgments TBA ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2022biomedtra, title={Spanish BioMedical Electra (small)}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/biomedtra-small-es}, year={2022} } ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
mrm8488/legalectra-small-spanish	mrm8488	2022-03-30T21:06:31Z	41	3	transformers	[ "transformers", "pytorch", "electra", "pretraining", "Spanish", "Electra", "Legal", "es", "dataset:Spanish-legal-corpora", "arxiv:1406.2661", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: es tags: - Spanish - Electra - Legal datasets: - Spanish-legal-corpora --- ## LEGALECTRA ⚖️ LEGALECTRA (small) is an Electra like model (discriminator in this case) trained on [A collection of corpora of Spanish legal domain](https://zenodo.org/record/5495529#.YZItp3vMLJw). As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ## Training details The model was trained using the Electra base code for 3 days on 1 Tesla V100 16GB. ## Model details ⚙ \|Param\| # Value\| \|-----\|--------\| \|Layers\| 12 \| \|Hidden \| 256 \| \|Params\| 14M \| ## Evaluation metrics (for discriminator) 🧾 \|Metric \| # Score \| \|-------\|---------\| \|Accuracy\| 0.955\| \|Precision\| 0.790\| \|AUC \| 0.971\| ## Benchmarks 🔨 WIP 🚧 ## How to use the discriminator in `transformers` TBA ## Acknowledgments TBA ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2022legalectra, title={Spanish Legal Electra (small)}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/legalectra-small-spanish}, year={2022} } ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
vlsb/autotrain-security-text-classification-albert-688320769	vlsb	2022-03-30T20:59:32Z	15	2	transformers	[ "transformers", "pytorch", "albert", "text-classification", "autotrain", "unk", "dataset:vlsb/autotrain-data-security-text-classification-albert", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T20:55:59Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - vlsb/autotrain-data-security-text-classification-albert co2_eq_emissions: 3.670416179055797 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 688320769 - CO2 Emissions (in grams): 3.670416179055797 ## Validation Metrics - Loss: 0.3046899139881134 - Accuracy: 0.8826530612244898 - Precision: 0.9181818181818182 - Recall: 0.8782608695652174 - AUC: 0.9423510466988727 - F1: 0.8977777777777778 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/vlsb/autotrain-security-text-classification-albert-688320769 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("vlsb/autotrain-security-text-classification-albert-688320769", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("vlsb/autotrain-security-text-classification-albert-688320769", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
mrm8488/electricidad-small-discriminator	mrm8488	2022-03-30T20:44:50Z	9	5	transformers	[ "transformers", "pytorch", "electra", "pretraining", "Spanish", "Electra", "es", "dataset:large_spanish_corpus", "arxiv:1406.2661", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: es thumbnail: https://i.imgur.com/uxAvBfh.png tags: - Spanish - Electra datasets: - large_spanish_corpus --- ## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh) ELECTRICIDAD is a small Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus). As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ## Model details ⚙ \|Param\| # Value\| \|-----\|--------\| \|Layers\|\t12 \| \|Hidden \|256 \t\| \|Params\| 14M\| ## Evaluation metrics (for discriminator) 🧾 \|Metric \| # Score \| \|-------\|---------\| \|Accuracy\| 0.94\| \|Precision\| 0.76\| \|AUC \| 0.92\| ## Benchmarks 🔨 WIP 🚧 ## How to use the discriminator in `transformers` ```python from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-small-discriminator") tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-small-discriminator") sentence = "el zorro rojo es muy rápido" fake_sentence = "el zorro rojo es muy ser" fake_tokens = tokenizer.tokenize(sentence) fake_inputs = tokenizer.encode(sentence, return_tensors="pt") discriminator_outputs = discriminator(fake_inputs) predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) [print("%7s" % token, end="") for token in fake_tokens] [print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]] # Output: ''' el zorro rojo es muy ser 0 0 0 0 0 1[None, None, None, None, None, None] ''' ``` As you can see there is a 1 in the place where the model detected the fake token (ser). So, it works! 🎉 [Electricidad-small fine-tuned models](https://huggingface.co/models?search=electricidad-small) ## Acknowledgments I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program. ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2020electricidad-small-discriminator, title={Spanish Electra (small) by Manuel Romero}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/electricidad-small-discriminator}}, year={2020} } ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
waboucay/camembert-base-finetuned-xnli_fr	waboucay	2022-03-30T17:47:05Z	5	0	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-11T08:54:07Z	--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: \| Set \| F1<sub>micro</sub> \| F1<sub>macro</sub> \| \|------------\|--------------------\|--------------------\| \| validation \| 89.2 \| 87.6 \| \| test \| 88.9 \| 87.4 \|
hoangbinhmta99/wav2vec-demo	hoangbinhmta99	2022-03-30T17:18:48Z	9	2	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	Convert from model .pt to transformer Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h Bash: ```bash pip install transformers[sentencepiece] pip install fairseq -U git clone https://github.com/huggingface/transformers.git cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py . wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt mkdir dict wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt mkdir outputs python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py --pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt --dict_path ./dict/dict.ltr.txt --not_finetuned ``` # install and upload model ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \| sudo bash git lfs install sudo apt-get install git-lfs git lfs install git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo ls cd wav2vec-demo/ git status git add . git commit -m "First model version" git config --global user.email [yourname] git config --global user.name [yourpass] git commit -m "First model version" git push ```
abdusah/aradia-ctc-v1	abdusah	2022-03-30T13:48:41Z	23	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-23T10:58:05Z	--- tags: - automatic-speech-recognition - abdusahmbzuai/arabic_speech_massive_300hrs - generated_from_trainer model-index: - name: aradia-ctc-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aradia-ctc-v1 This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.7171 - Wer: 0.3336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.22 \| 100 \| 5.1889 \| 1.0 \| \| No log \| 0.43 \| 200 \| 3.1129 \| 1.0 \| \| No log \| 0.65 \| 300 \| 3.0503 \| 1.0 \| \| No log \| 0.87 \| 400 \| 3.0279 \| 1.0 \| \| 6.2756 \| 1.09 \| 500 \| 2.9965 \| 1.0 \| \| 6.2756 \| 1.3 \| 600 \| 2.3618 \| 0.9993 \| \| 6.2756 \| 1.52 \| 700 \| 1.2715 \| 0.8758 \| \| 6.2756 \| 1.74 \| 800 \| 0.9971 \| 0.7156 \| \| 6.2756 \| 1.96 \| 900 \| 0.8927 \| 0.6382 \| \| 1.712 \| 2.17 \| 1000 \| 0.8252 \| 0.5926 \| \| 1.712 \| 2.39 \| 1100 \| 0.7794 \| 0.5434 \| \| 1.712 \| 2.61 \| 1200 \| 0.7557 \| 0.5092 \| \| 1.712 \| 2.83 \| 1300 \| 0.7347 \| 0.5203 \| \| 1.712 \| 3.04 \| 1400 \| 0.7189 \| 0.4929 \| \| 0.9305 \| 3.26 \| 1500 \| 0.6820 \| 0.4595 \| \| 0.9305 \| 3.48 \| 1600 \| 0.6792 \| 0.4504 \| \| 0.9305 \| 3.69 \| 1700 \| 0.6596 \| 0.4442 \| \| 0.9305 \| 3.91 \| 1800 \| 0.6756 \| 0.4432 \| \| 0.9305 \| 4.13 \| 1900 \| 0.6663 \| 0.4392 \| \| 0.737 \| 4.35 \| 2000 \| 0.6479 \| 0.4372 \| \| 0.737 \| 4.56 \| 2100 \| 0.6353 \| 0.4203 \| \| 0.737 \| 4.78 \| 2200 \| 0.6251 \| 0.4088 \| \| 0.737 \| 5.0 \| 2300 \| 0.6209 \| 0.4177 \| \| 0.737 \| 5.22 \| 2400 \| 0.6639 \| 0.4094 \| \| 0.6247 \| 5.43 \| 2500 \| 0.6408 \| 0.3970 \| \| 0.6247 \| 5.65 \| 2600 \| 0.6373 \| 0.3932 \| \| 0.6247 \| 5.87 \| 2700 \| 0.6411 \| 0.3928 \| \| 0.6247 \| 6.09 \| 2800 \| 0.6378 \| 0.3897 \| \| 0.6247 \| 6.3 \| 2900 \| 0.6396 \| 0.3929 \| \| 0.5443 \| 6.52 \| 3000 \| 0.6544 \| 0.3864 \| \| 0.5443 \| 6.74 \| 3100 \| 0.6218 \| 0.3786 \| \| 0.5443 \| 6.96 \| 3200 \| 0.6200 \| 0.3784 \| \| 0.5443 \| 7.17 \| 3300 \| 0.6157 \| 0.3791 \| \| 0.5443 \| 7.39 \| 3400 \| 0.6317 \| 0.3798 \| \| 0.4845 \| 7.61 \| 3500 \| 0.6540 \| 0.3771 \| \| 0.4845 \| 7.83 \| 3600 \| 0.6436 \| 0.3670 \| \| 0.4845 \| 8.04 \| 3700 \| 0.6335 \| 0.3695 \| \| 0.4845 \| 8.26 \| 3800 \| 0.6579 \| 0.3610 \| \| 0.4845 \| 8.48 \| 3900 \| 0.6170 \| 0.3613 \| \| 0.4279 \| 8.69 \| 4000 \| 0.6523 \| 0.3617 \| \| 0.4279 \| 8.91 \| 4100 \| 0.6349 \| 0.3577 \| \| 0.4279 \| 9.13 \| 4200 \| 0.6344 \| 0.3673 \| \| 0.4279 \| 9.35 \| 4300 \| 0.6215 \| 0.3641 \| \| 0.4279 \| 9.56 \| 4400 \| 0.6513 \| 0.3608 \| \| 0.3825 \| 9.78 \| 4500 \| 0.6386 \| 0.3605 \| \| 0.3825 \| 10.0 \| 4600 \| 0.6724 \| 0.3549 \| \| 0.3825 \| 10.22 \| 4700 \| 0.6776 \| 0.3602 \| \| 0.3825 \| 10.43 \| 4800 \| 0.6739 \| 0.3544 \| \| 0.3825 \| 10.65 \| 4900 \| 0.6688 \| 0.3557 \| \| 0.3477 \| 10.87 \| 5000 \| 0.6674 \| 0.3564 \| \| 0.3477 \| 11.09 \| 5100 \| 0.6786 \| 0.3476 \| \| 0.3477 \| 11.3 \| 5200 \| 0.6818 \| 0.3478 \| \| 0.3477 \| 11.52 \| 5300 \| 0.6874 \| 0.3470 \| \| 0.3477 \| 11.74 \| 5400 \| 0.6993 \| 0.3424 \| \| 0.3101 \| 11.96 \| 5500 \| 0.6950 \| 0.3404 \| \| 0.3101 \| 12.17 \| 5600 \| 0.6872 \| 0.3406 \| \| 0.3101 \| 12.39 \| 5700 \| 0.6846 \| 0.3424 \| \| 0.3101 \| 12.61 \| 5800 \| 0.7051 \| 0.3405 \| \| 0.3101 \| 12.83 \| 5900 \| 0.7051 \| 0.3378 \| \| 0.2859 \| 13.04 \| 6000 \| 0.6955 \| 0.3403 \| \| 0.2859 \| 13.26 \| 6100 \| 0.7115 \| 0.3390 \| \| 0.2859 \| 13.48 \| 6200 \| 0.7074 \| 0.3384 \| \| 0.2859 \| 13.69 \| 6300 \| 0.7002 \| 0.3376 \| \| 0.2859 \| 13.91 \| 6400 \| 0.7171 \| 0.3360 \| \| 0.2714 \| 14.13 \| 6500 \| 0.7193 \| 0.3341 \| \| 0.2714 \| 14.35 \| 6600 \| 0.7132 \| 0.3347 \| \| 0.2714 \| 14.56 \| 6700 \| 0.7184 \| 0.3353 \| \| 0.2714 \| 14.78 \| 6800 \| 0.7171 \| 0.3331 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
javilonso/classificationEsp3_Attraction	javilonso	2022-03-30T12:09:19Z	5	0	transformers	[ "transformers", "tf", "gpt2", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T11:07:40Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: javilonso/classificationEsp3_Attraction results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # javilonso/classificationEsp3_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0055 - Validation Loss: 0.0515 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17958, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0964 \| 0.0662 \| 0 \| \| 0.0265 \| 0.0500 \| 1 \| \| 0.0055 \| 0.0515 \| 2 \| ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6
yinde/dummy-model	yinde	2022-03-30T11:59:15Z	10	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T11:37:44Z	Fake news classifier This model trains a text classification model to detect fake news articles, it uses distilbert-base-uncased-finetuned-sst-2-english pretrained model to work on fake and real news dataset from kaggle (https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)
joe5campbell/Horovod_Tweet_Sentiment_1K_4eps	joe5campbell	2022-03-30T11:38:32Z	5	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-24T12:35:50Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Horovod_Tweet_Sentiment_1K_4eps results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Horovod_Tweet_Sentiment_1K_4eps This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6803332 - Train Accuracy: 0.57187504 - Validation Loss: 0.6883397 - Validation Accuracy: 0.54375 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.70931095 \| 0.5078125 \| 0.81717503 \| 0.528125 \| 0 \| \| 0.77384466 \| 0.5296875 \| 0.68696874 \| 0.51875 \| 1 \| \| 0.68944424 \| 0.53125 \| 0.6837756 \| 0.53125 \| 2 \| \| 0.6803332 \| 0.57187504 \| 0.6883397 \| 0.54375 \| 3 \| ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Tokenizers 0.11.6
mimicheng/codeparrot-ds-sample-2ep-29mar	mimicheng	2022-03-30T09:50:15Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-30T03:41:46Z	--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-2ep-29mar results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds-sample-2ep-29mar This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2585 \| 1.86 \| 5000 \| 1.6283 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.8.2+cpu - Datasets 2.0.0 - Tokenizers 0.11.6
jeniakim/hedgehog	jeniakim	2022-03-30T09:27:38Z	51	2	transformers	[ "transformers", "pytorch", "bert", "token-classification", "en", "license:mit", "autotrain_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: en license: mit inference: false --- 🦔 HEDGEhog 🦔: BERT-based multi-class uncertainty cues recognition ==================================================================== # Description A fine-tuned multi-class classification model that detects four different types of uncertainty cues (a.k.a hedges) on a token level. # Uncertainty types label \| type \| description \| example ---\| ---\| ---\| --- E \| Epistemic \| The proposition is possible, but its truth-value cannot be decided at the moment. \| She may be already asleep. I \| Investigation \| The proposition is in the process of having its truth-value determined. \| She examined the role of NF-kappaB in protein activation. D \| Doxatic \| The proposition expresses beliefs and hypotheses, which may be known as true or false by others. \| She believes that the Earth is flat. N \| Condition \| The proposition is true or false based on the truth-value of another proposition. \| If she gets the job, she will move to Utrecht. C \| certain \| n/a \| n/a # Intended uses and limitations - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. # How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.ner import NERModel model = NERModel( 'bert', 'jeniakim/hedgehog', use_cuda=False, labels=["C", "D", "E", "I", "N"], ) example = "As much as I definitely enjoy solitude, I wouldn't mind perhaps spending little time with you (Björk)" predictions, raw_outputs = model.predict([example]) ``` The predictions look like this: ``` [[{'As': 'C'}, {'much': 'C'}, {'as': 'C'}, {'I': 'C'}, {'definitely': 'C'}, {'enjoy': 'C'}, {'solitude,': 'C'}, {'I': 'C'}, {"wouldn't": 'C'}, {'mind': 'C'}, {'perhaps': 'E'}, {'spending': 'C'}, {'little': 'C'}, {'time': 'C'}, {'with': 'C'}, {'you': 'C'}, {'(Björk)': 'C'}]] ``` In other words, the token 'perhaps' is recognized as an epistemic uncertainty cue and all the other tokens are not uncertainty cues. # Training Data HEDGEhog is trained and evaluated on the [Szeged Uncertainty Corpus](https://rgai.inf.u-szeged.hu/node/160) (Szarvas et al. 2012<sup>1</sup>). The original sentence-level XML version of this dataset is available [here](https://rgai.inf.u-szeged.hu/node/160). The token-level version that was used for the training can be downloaded from [here](https://1drv.ms/u/s!AvPkt_QxBozXk7BiazucDqZkVxLo6g?e=IisuM6) in a form of pickled pandas DataFrame's. You can download either the split sets (```train.pkl``` 137MB, ```test.pkl``` 17MB, ```dev.pkl``` 17MB) or the full dataset (```szeged_fixed.pkl``` 172MB). Each row in the df contains a token, its features (these are not relevant for HEDGEhog; they were used to train the baseline CRF model, see [here](https://github.com/vanboefer/uncertainty_crf)), its sentence ID, and its label. # Training Procedure The following training parameters were used: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 16 # Evaluation Results class \| precision \| recall \| F1-score \| support ---\|---\|---\|---\|--- Epistemic \| 0.90 \| 0.85 \| 0.88 \| 624 Doxatic \| 0.88 \| 0.92 \| 0.90 \| 142 Investigation \| 0.83 \| 0.86 \| 0.84 \| 111 Condition \| 0.85 \| 0.87 \| 0.86 \| 86 Certain \| 1.00 \| 1.00 \| 1.00 \| 104,751 macro average \| 0.89 \| 0.90 \| 0.89 \| 105,714 # References <sup>1</sup> Szarvas, G., Vincze, V., Farkas, R., Móra, G., & Gurevych, I. (2012). Cross-genre and cross-domain detection of semantic uncertainty. Computational Linguistics, 38(2), 335-367.
markussagen/xlm-roberta-longformer-base-4096	markussagen	2022-03-30T09:24:39Z	9,277	36	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "longformer", "multilingual", "dataset:wikitext", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- tags: - longformer language: multilingual license: apache-2.0 datasets: - wikitext --- ## XLM-R Longformer Model / XLM-Long XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) on the English WikiText-103 corpus. The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at [Peltarion](https://peltarion.com/) and was fine-tuned on multilingual quesion-answering tasks, with code available [here](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer#xlm-r). Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps. ## How to Use The model can be used as expected to fine-tune on a downstream task. For instance for QA. ```python import torch from transformers import AutoModel, AutoTokenizer MAX_SEQUENCE_LENGTH = 4096 MODEL_NAME_OR_PATH = "markussagen/xlm-roberta-longformer-base-4096" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, max_length=MAX_SEQUENCE_LENGTH, padding="max_length", truncation=True, ) model = AutoModelForQuestionAnswering.from_pretrained( MODEL_NAME_OR_PATH, max_length=MAX_SEQUENCE_LENGTH, ) ``` ## Training Procedure The model have been trained on the WikiText-103 corpus, using a 48GB GPU with the following training script and parameters. The model was pre-trained for 6000 iterations and took ~5 days. See the full [training script](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer/blob/main/scripts/finetune_qa_models.py) and [Github repo](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer) for more information ```sh wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip unzip wikitext-103-raw-v1.zip export DATA_DIR=./wikitext-103-raw scripts/run_long_lm.py \ --model_name_or_path xlm-roberta-base \ --model_name xlm-roberta-to-longformer \ --output_dir ./output \ --logging_dir ./logs \ --val_file_path $DATA_DIR/wiki.valid.raw \ --train_file_path $DATA_DIR/wiki.train.raw \ --seed 42 \ --max_pos 4096 \ --adam_epsilon 1e-8 \ --warmup_steps 500 \ --learning_rate 3e-5 \ --weight_decay 0.01 \ --max_steps 6000 \ --evaluate_during_training \ --logging_steps 50 \ --eval_steps 50 \ --save_steps 6000 \ --max_grad_norm 1.0 \ --per_device_eval_batch_size 2 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 64 \ --overwrite_output_dir \ --fp16 \ --do_train \ --do_eval ```
Aureliano/electra-if	Aureliano	2022-03-30T09:07:27Z	6	0	transformers	[ "transformers", "pytorch", "tf", "electra", "feature-extraction", "en", "arxiv:1406.2661", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-11T15:40:21Z	--- language: en license: apache-2.0 --- ## ELECTRA for IF ELECTRA is a method for self-supervised language representation learning. They are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). For a detailed description and experimental results, please refer to the original paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). This repository contains a small ELECTRA discriminator finetuned on a corpus of interactive fiction commands labelled with the WordNet synset offset of the verb in the sentence. The original dataset has been collected from the list of action in the walkthroughs for the game included in the [Jericho](https://github.com/microsoft/jericho) framework and manually annotated. For more information visit https://github.com/aporporato/electra and https://github.com/aporporato/jericho-corpora. ## How to use the discriminator in `transformers` (Heavily based on: https://github.com/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb) ```python import math import numpy as np import tensorflow as tf from datasets import load_metric, Dataset, DatasetDict from transformers import TFAutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, create_optimizer from transformers.keras_callbacks import KerasMetricCallback # This example shows how this model can be used: # you should finetune the model of your specific corpus if commands, bigger than this dict_train = { "idx": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20"], "sentence": ["e", "get pen", "drop book", "x paper", "i", "south", "get paper", "drop the pen", "x book", "inventory", "n", "get the book", "drop paper", "look at Pen", "inv", "g", "s", "get sandwich", "drop sandwich", "x sandwich", "agin"], "label": ["travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "repeat.v.01", "travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "repeat.v.01"] } dict_val = { "idx": ["0", "1", "2", "3", "4", "5"], "sentence": ["w", "get shield", "drop sword", "x spikes", "i", "repeat"], "label": ["travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "repeat.v.01"] } raw_train_dataset = Dataset.from_dict(dict_train) raw_val_dataset = Dataset.from_dict(dict_val) raw_dataset = DatasetDict() raw_dataset["train"] = raw_train_dataset raw_dataset["val"] = raw_val_dataset raw_dataset = raw_dataset.class_encode_column("label") print(raw_dataset) print(raw_dataset["train"].features) print(raw_dataset["val"].features) print(raw_dataset["train"][1]) label2id = {} id2label = {} for i, l in enumerate(raw_dataset["train"].features["label"].names): label2id[l] = i id2label[i] = l discriminator = TFAutoModelForSequenceClassification.from_pretrained("Aureliano/electra-if", label2id=label2id, id2label=id2label) tokenizer = AutoTokenizer.from_pretrained("Aureliano/electra-if") tokenize_function = lambda example: tokenizer(example["sentence"], truncation=True) pre_tokenizer_columns = set(raw_dataset["train"].features) encoded_dataset = raw_dataset.map(tokenize_function, batched=True) tokenizer_columns = list(set(encoded_dataset["train"].features) - pre_tokenizer_columns) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") batch_size = len(encoded_dataset["train"]) tf_train_dataset = encoded_dataset["train"].to_tf_dataset( columns=tokenizer_columns, label_cols=["labels"], shuffle=True, batch_size=batch_size, collate_fn=data_collator ) tf_validation_dataset = encoded_dataset["val"].to_tf_dataset( columns=tokenizer_columns, label_cols=["labels"], shuffle=False, batch_size=batch_size, collate_fn=data_collator ) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) num_epochs = 25 batches_per_epoch = math.ceil(len(encoded_dataset["train"]) / batch_size) total_train_steps = int(batches_per_epoch * num_epochs) optimizer, schedule = create_optimizer( init_lr=5e-5, num_warmup_steps=total_train_steps // 5, num_train_steps=total_train_steps ) metric = load_metric("accuracy") def compute_metrics(eval_predictions): logits, labels = eval_predictions predictions = np.argmax(logits, axis=-1) return metric.compute(predictions=predictions, references=labels) metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_dataset) callbacks = [metric_callback] discriminator.compile(optimizer=optimizer, loss=loss, metrics=["sparse_categorical_accuracy"]) discriminator.fit( tf_train_dataset, epochs=num_epochs, validation_data=tf_validation_dataset, callbacks=callbacks ) print("Evaluate on test data") results = discriminator.evaluate(tf_validation_dataset) print("test loss, test acc:", results) text = "i" encoded_input = tokenizer(text, return_tensors='tf') output = discriminator(encoded_input) prediction = tf.nn.softmax(output["logits"][0], -1) label = id2label[tf.math.argmax(prediction).numpy()] print("\n", text, ":", label, "\n") # ideally 'inventory.v.01' (-> "make or include in an itemized record or report"), but probably only with a better finetuning dataset text = "get lamp" encoded_input = tokenizer(text, return_tensors='tf') output = discriminator(encoded_input) prediction = tf.nn.softmax(output["logits"][0], -1) label = id2label[tf.math.argmax(prediction).numpy()] print("\n", text, ":", label, "\n") # ideally 'take.v.04' (-> "get into one's hands, take physically"), but probably only with a better finetuning dataset text = "w" encoded_input = tokenizer(text, return_tensors='tf') output = discriminator(encoded_input) prediction = tf.nn.softmax(output["logits"][0], -1) label = id2label[tf.math.argmax(prediction).numpy()] print("\n", text, ":", label, "\n") # ideally 'travel.v.01' (-> "change location; move, travel, or proceed, also metaphorically"), but probably only with a better finetuning dataset ```
javilonso/classificationPolEsp1	javilonso	2022-03-30T09:02:50Z	3	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T07:49:20Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: javilonso/classificationPolEsp1 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # javilonso/classificationPolEsp1 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3728 - Validation Loss: 0.6217 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17958, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.6282 \| 0.6017 \| 0 \| \| 0.5129 \| 0.6177 \| 1 \| \| 0.3728 \| 0.6217 \| 2 \| ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6
neibla/distilbert-base-uncased-finetuned-emotion	neibla	2022-03-30T08:56:26Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T08:22:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9254917237562972 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2187 - Accuracy: 0.9255 - F1: 0.9255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.855 \| 1.0 \| 250 \| 0.3211 \| 0.905 \| 0.9017 \| \| 0.2561 \| 2.0 \| 500 \| 0.2187 \| 0.9255 \| 0.9255 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
nlp-waseda/gpt2-small-japanese	nlp-waseda	2022-03-30T04:28:17Z	26	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "ja", "dataset:wikipedia", "dataset:cc100", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-30T03:34:11Z	--- language: - ja license: cc-by-sa-4.0 datasets: - wikipedia - cc100 widget: - text: "早稲田大学で自然言語処理を" --- # nlp-waseda/gpt2-small-japanese This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. Note that the texts should be segmented into words using Juman++ in advance. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese') >>> set_seed(42) >>> generator("早稲田大学で自然言語処理を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5) [{'generated_text': '早稲田大学で自然言語処理を学び、帰国後、早稲田大学理工学部に入学します。卒業後、早稲田大学工学研究科、'}, {'generated_text': '早稲田大学で自然言語処理を学び、アメリカの大学で学士号を取得、修士の取得で博士号を取得。 2008 年'}, {'generated_text': '早稲田大学で自然言語処理を勉強しています。学部は日本語学科を専攻しています。英語が話せるという'}, {'generated_text': '早稲田大学で自然言語処理を専攻していた。 2011 年に第 26 回日本化学会学生委員会奨励賞 ( 第 2 年次審査'}, {'generated_text': '早稲田大学で自然言語処理を中心とする言語学研究を行っている。東京都・豊島区のお見合い相手。'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import ReformerTokenizer, GPT2Model tokenizer = ReformerTokenizer.from_pretrained('nlp-waseda/gpt2-small-japanese') model = GPT2Model.from_pretrained('nlp-waseda/gpt2-small-japanese') text = "早稲田大学で自然言語処理を" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Training data The GPT-2 model was pretrained on Japanese Wikipedia, dumped on 2022-03-20, and the Japanese portion of CC-100. ## Training procedure ### Preprocessing The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining. The model was trained on 8 NVIDIA A100 GPUs.
samayash/finetuning-financial-news-sentiment	samayash	2022-03-30T03:36:40Z	4	3	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T03:27:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-financial-news-sentiment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-financial-news-sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3345 - Accuracy: 0.8751 - F1: 0.8751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
scasutt/wav2vec2-large-xlsr-53_toy_train_data_masked_audio	scasutt	2022-03-30T03:35:01Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-29T11:30:40Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_masked_audio results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53_toy_train_data_masked_audio This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6445 - Wer: 0.4938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.3761 \| 1.05 \| 250 \| 3.4022 \| 0.9954 \| \| 3.0858 \| 2.1 \| 500 \| 3.4684 \| 0.9954 \| \| 2.6302 \| 3.15 \| 750 \| 1.7989 \| 0.9865 \| \| 1.1292 \| 4.2 \| 1000 \| 0.8558 \| 0.7355 \| \| 0.8371 \| 5.25 \| 1250 \| 0.7319 \| 0.6621 \| \| 0.5992 \| 6.3 \| 1500 \| 0.6848 \| 0.6147 \| \| 0.5189 \| 7.35 \| 1750 \| 0.6522 \| 0.5742 \| \| 0.454 \| 8.4 \| 2000 \| 0.6601 \| 0.5531 \| \| 0.3896 \| 9.45 \| 2250 \| 0.6138 \| 0.5439 \| \| 0.3678 \| 10.5 \| 2500 \| 0.6436 \| 0.5320 \| \| 0.3232 \| 11.55 \| 2750 \| 0.5920 \| 0.5174 \| \| 0.2926 \| 12.6 \| 3000 \| 0.6615 \| 0.5107 \| \| 0.3041 \| 13.65 \| 3250 \| 0.6311 \| 0.5015 \| \| 0.2882 \| 14.7 \| 3500 \| 0.6182 \| 0.5004 \| \| 0.2868 \| 15.75 \| 3750 \| 0.6266 \| 0.4943 \| \| 0.2508 \| 16.81 \| 4000 \| 0.6587 \| 0.4965 \| \| 0.2563 \| 17.86 \| 4250 \| 0.6634 \| 0.4939 \| \| 0.2213 \| 18.91 \| 4500 \| 0.6441 \| 0.4925 \| \| 0.2255 \| 19.96 \| 4750 \| 0.6445 \| 0.4938 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
javilonso/classificationEsp2_Attraction	javilonso	2022-03-30T03:04:09Z	5	0	transformers	[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-29T23:17:31Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: javilonso/classificationEsp2_Attraction results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # javilonso/classificationEsp2_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9927 - Validation Loss: 0.9926 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35916, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.8200 \| 0.9930 \| 0 \| \| 0.9942 \| 0.9947 \| 1 \| \| 0.9927 \| 0.9926 \| 2 \| ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6
aaraki/vit-base-patch16-224-in21k-finetuned-cifar10	aaraki	2022-03-30T01:41:47Z	8,239	10	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-30T00:18:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-finetuned-cifar10 results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9788 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.2564 - Accuracy: 0.9788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4291 \| 1.0 \| 390 \| 0.2564 \| 0.9788 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
BigSalmon/InformalToFormalLincoln33	BigSalmon	2022-03-30T01:24:08Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-30T01:19:07Z	``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln33") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln33") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. * - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. * - ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. * informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. * informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. * Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. * Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. * Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. * Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. * Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about * Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers * Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of * Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone * Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. * - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. * original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. * wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. * sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. * 1: ``` Keywords to sentences or sentence.
cammiemw/bert-marco-hdct	cammiemw	2022-03-30T01:21:38Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-30T01:09:55Z	--- license: cc-by-nc-4.0 ---
DrishtiSharma/poem-gen-spanish-t5-small-v7	DrishtiSharma	2022-03-30T00:34:41Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-29T19:14:40Z	--- license: mit tags: - generated_from_trainer model-index: - name: poem-gen-spanish-t5-small-v7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # poem-gen-spanish-t5-small-v7 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000333 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 3.1716 \| 0.73 \| 30000 \| 3.1114 \| \| 2.9666 \| 1.46 \| 60000 \| 3.0271 \| \| 2.8292 \| 2.19 \| 90000 \| 2.9531 \| \| 2.7264 \| 2.93 \| 120000 \| 2.9126 \| \| 2.6057 \| 3.66 \| 150000 \| 2.9175 \| \| 2.4876 \| 4.39 \| 180000 \| 2.9077 \| \| 2.3791 \| 5.12 \| 210000 \| 2.9240 \| \| 2.3515 \| 5.85 \| 240000 \| 2.9169 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
DrishtiSharma/poem-gen-spanish-t5-small-v6	DrishtiSharma	2022-03-29T23:45:09Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-29T18:58:46Z	--- license: mit tags: - generated_from_trainer model-index: - name: poem-gen-spanish-t5-small-v6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # poem-gen-spanish-t5-small-v6 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8831 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 2.8551 \| 0.73 \| 30000 \| 2.9296 \| \| 2.6961 \| 1.46 \| 60000 \| 2.9005 \| \| 2.5756 \| 2.19 \| 90000 \| 2.8786 \| \| 2.5095 \| 2.93 \| 120000 \| 2.8621 \| \| 2.4061 \| 3.66 \| 150000 \| 2.8830 \| \| 2.3161 \| 4.39 \| 180000 \| 2.8865 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
DrishtiSharma/poem-gen-spanish-t5-small-v5	DrishtiSharma	2022-03-29T23:25:30Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-29T18:54:38Z	--- license: mit tags: - generated_from_trainer model-index: - name: poem-gen-spanish-t5-small-v5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # poem-gen-spanish-t5-small-v5 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8881 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000125 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 2.9366 \| 0.73 \| 30000 \| 2.9656 \| \| 2.7518 \| 1.46 \| 60000 \| 2.9120 \| \| 2.6018 \| 2.19 \| 90000 \| 2.8870 \| \| 2.5262 \| 2.93 \| 120000 \| 2.8646 \| \| 2.3886 \| 3.66 \| 150000 \| 2.8816 \| \| 2.2758 \| 4.39 \| 180000 \| 2.8900 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
BigSalmon/PointsToSentence	BigSalmon	2022-03-29T23:11:32Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-29T22:58:46Z	``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsToSentence") model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsToSentence") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. * - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. * - ``` It should also be able to do all that this can: https://huggingface.co/BigSalmon/InformalToFormalLincoln27 Keywords to sentences or sentence.
krinal214/augmented	krinal214	2022-03-29T16:58:16Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-29T15:02:50Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: augmented results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # augmented This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5104 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0609 \| 1.0 \| 9787 \| 0.5104 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
GleamEyeBeast/ascend	GleamEyeBeast	2022-03-29T16:49:48Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-29T01:37:59Z	--- tags: - generated_from_trainer model-index: - name: ascend results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ascend This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3718 - Wer: 0.6412 - Cer: 0.2428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| 0.5769 \| 1.0 \| 688 \| 1.1864 \| 0.7716 \| 0.3159 \| \| 0.5215 \| 2.0 \| 1376 \| 1.1613 \| 0.7504 \| 0.2965 \| \| 0.4188 \| 3.0 \| 2064 \| 1.1644 \| 0.7389 \| 0.2950 \| \| 0.3695 \| 4.0 \| 2752 \| 1.1937 \| 0.7184 \| 0.2815 \| \| 0.3404 \| 5.0 \| 3440 \| 1.1947 \| 0.7083 \| 0.2719 \| \| 0.2885 \| 6.0 \| 4128 \| 1.2314 \| 0.7108 \| 0.2685 \| \| 0.2727 \| 7.0 \| 4816 \| 1.2243 \| 0.6850 \| 0.2616 \| \| 0.2417 \| 8.0 \| 5504 \| 1.2506 \| 0.6767 \| 0.2608 \| \| 0.2207 \| 9.0 \| 6192 \| 1.2804 \| 0.6922 \| 0.2595 \| \| 0.2195 \| 10.0 \| 6880 \| 1.2582 \| 0.6818 \| 0.2575 \| \| 0.1896 \| 11.0 \| 7568 \| 1.3101 \| 0.6814 \| 0.2545 \| \| 0.1961 \| 12.0 \| 8256 \| 1.2793 \| 0.6706 \| 0.2526 \| \| 0.1752 \| 13.0 \| 8944 \| 1.2643 \| 0.6584 \| 0.2509 \| \| 0.1638 \| 14.0 \| 9632 \| 1.3152 \| 0.6588 \| 0.2482 \| \| 0.1522 \| 15.0 \| 10320 \| 1.3098 \| 0.6433 \| 0.2439 \| \| 0.1351 \| 16.0 \| 11008 \| 1.3253 \| 0.6537 \| 0.2447 \| \| 0.1266 \| 17.0 \| 11696 \| 1.3394 \| 0.6365 \| 0.2418 \| \| 0.1289 \| 18.0 \| 12384 \| 1.3718 \| 0.6412 \| 0.2443 \| \| 0.1204 \| 19.0 \| 13072 \| 1.3708 \| 0.6433 \| 0.2433 \| \| 0.1189 \| 20.0 \| 13760 \| 1.3718 \| 0.6412 \| 0.2428 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
gabitoo1234/autotrain-mut_all_text-680820343	gabitoo1234	2022-03-29T16:09:31Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "es", "dataset:gabitoo1234/autotrain-data-mut_all_text", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-29T14:22:14Z	--- tags: autotrain language: es widget: - text: "I love AutoTrain 🤗" datasets: - gabitoo1234/autotrain-data-mut_all_text co2_eq_emissions: 115.48848403681228 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 680820343 - CO2 Emissions (in grams): 115.48848403681228 ## Validation Metrics - Loss: 0.3041240870952606 - Accuracy: 0.9462770369425126 - Macro F1: 0.7836898686625933 - Micro F1: 0.9462770369425126 - Weighted F1: 0.9449148298990091 - Macro Precision: 0.8344505891491089 - Micro Precision: 0.9462770369425126 - Weighted Precision: 0.9451247372908952 - Macro Recall: 0.7568785255994025 - Micro Recall: 0.9462770369425126 - Weighted Recall: 0.9462770369425126 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/gabitoo1234/autotrain-mut_all_text-680820343 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("gabitoo1234/autotrain-mut_all_text-680820343", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("gabitoo1234/autotrain-mut_all_text-680820343", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
tbosse/bert-base-german-cased-finetuned-subj_v1	tbosse	2022-03-29T15:59:49Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-29T14:22:30Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-finetuned-subj_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-german-cased-finetuned-subj_v1 This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1594 - Precision: 0.1875 - Recall: 0.0077 - F1: 0.0147 - Accuracy: 0.9508 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 136 \| 0.1591 \| 1.0 \| 0.0051 \| 0.0102 \| 0.9523 \| \| No log \| 2.0 \| 272 \| 0.1571 \| 0.375 \| 0.0077 \| 0.015 \| 0.9518 \| \| No log \| 3.0 \| 408 \| 0.1594 \| 0.1875 \| 0.0077 \| 0.0147 \| 0.9508 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Daryaflp/roberta-retrained_ru_covid_papers	Daryaflp	2022-03-29T13:30:45Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-29T07:12:02Z	--- tags: - generated_from_trainer model-index: - name: roberta-retrained_ru_covid_papers results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-retrained_ru_covid_papers This model is a fine-tuned version of [Daryaflp/roberta-retrained_ru_covid](https://huggingface.co/Daryaflp/roberta-retrained_ru_covid) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9998 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
ArtemChistyakov-2/f	ArtemChistyakov-2	2022-03-29T12:21:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2022-03-29T12:21:18Z	--- license: apache-2.0 ---
gayanin/bart-med-term-conditional-masking-0	gayanin	2022-03-29T12:03:56Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-28T22:12:30Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-med-term-conditional-masking-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-med-term-conditional-masking-0 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5041 - Rouge2 Precision: 0.7497 - Rouge2 Recall: 0.5246 - Rouge2 Fmeasure: 0.5986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.6381 \| 1.0 \| 13915 \| 0.5595 \| 0.734 \| 0.5152 \| 0.5873 \| \| 0.5429 \| 2.0 \| 27830 \| 0.5243 \| 0.7441 \| 0.5225 \| 0.5956 \| \| 0.5002 \| 3.0 \| 41745 \| 0.5078 \| 0.7482 \| 0.5238 \| 0.5976 \| \| 0.4607 \| 4.0 \| 55660 \| 0.5041 \| 0.7497 \| 0.5246 \| 0.5986 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Rishav-hub/xlm-roberta-base-finetuned-panx-de	Rishav-hub	2022-03-29T11:05:37Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-29T10:26:12Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8591260810195721 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1352 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.257 \| 1.0 \| 525 \| 0.1512 \| 0.8302 \| \| 0.1305 \| 2.0 \| 1050 \| 0.1401 \| 0.8447 \| \| 0.0817 \| 3.0 \| 1575 \| 0.1352 \| 0.8591 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
KeithHorgan/TweetClimateAnalysis	KeithHorgan	2022-03-29T10:01:24Z	4	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:KeithHorgan98/autotrain-data-TweetClimateAnalysis", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-29T10:16:42Z	--- tags: autotrain language: unk widget: - text: "Climate Change is a hoax" - text: "It is freezing, where is global warming" datasets: - KeithHorgan98/autotrain-data-TweetClimateAnalysis co2_eq_emissions: 133.19491276284793 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 678720226 - CO2 Emissions (in grams): 133.19491276284793 ## Validation Metrics - Loss: 0.4864234924316406 - Accuracy: 0.865424430641822 - Macro F1: 0.7665472174344069 - Micro F1: 0.8654244306418221 - Weighted F1: 0.8586375445115083 - Macro Precision: 0.8281449061702826 - Micro Precision: 0.865424430641822 - Weighted Precision: 0.8619727477790186 - Macro Recall: 0.736576343905098 - Micro Recall: 0.865424430641822 - Weighted Recall: 0.865424430641822 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/KeithHorgan98/autotrain-TweetClimateAnalysis-678720226 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Davlan/m2m100_418M-eng-yor-mt	Davlan	2022-03-29T09:21:53Z	820	1	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "arxiv:2103.08647", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description m2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. Specifically, this model is a facebook/m2m100_418M model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves 13.39 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```
Davlan/m2m100_418M-yor-eng-mt	Davlan	2022-03-29T09:21:03Z	5	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "arxiv:2103.08647", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description m2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. Specifically, this model is a facebook/m2m100_418M model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves 16.76 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```
PereLluis13/wav2vec2-xls-r-1b-ca-lm	PereLluis13	2022-03-29T08:41:46Z	3,126	4	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "collectivat/tv3_parla", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "projecte-aina/parlament_parla", "robust-speech-event", "ca", "dataset:mozilla-foundation/common_voice_8_0", "dataset:collectivat/tv3_parla", "dataset:projecte-aina/parlament_parla", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- language: - ca license: apache-2.0 tags: - automatic-speech-recognition - collectivat/tv3_parla - generated_from_trainer - hf-asr-leaderboard - mozilla-foundation/common_voice_8_0 - projecte-aina/parlament_parla - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 - collectivat/tv3_parla - projecte-aina/parlament_parla model-index: - name: wav2vec2-xls-r-1b-ca-lm results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_8_0 ca type: mozilla-foundation/common_voice_8_0 args: ca metrics: - name: Test WER type: wer value: 6.0722669958130644 - name: Test CER type: cer value: 1.9180697705166526 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: projecte-aina/parlament_parla ca type: projecte-aina/parlament_parla args: clean metrics: - name: Test WER type: wer value: 5.139820371024042 - name: Test CER type: cer value: 2.0163620128164722 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: collectivat/tv3_parla ca type: collectivat/tv3_parla args: ca metrics: - name: Test WER type: wer value: 11.207991684952073 - name: Test CER type: cer value: 7.32119307305963 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Catalan Dev Data type: speech-recognition-community-v2/dev_data args: ca metrics: - name: Test WER type: wer value: 22.870153690468661 - name: Test CER type: cer value: 13.59039190897598 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: ca metrics: - name: Test WER type: wer value: 15.41 --- # wav2vec2-xls-r-1b-ca-lm This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets. ## Model description Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model. ## Intended uses & limitations As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language. ## Training and evaluation data ## Training procedure The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py). ### Training results Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 # Thanks Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.
STARBORN/MMC	STARBORN	2022-03-29T07:14:35Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-03-29T07:12:26Z	--- license: mit --- Metamodel Card (MMC) builds on MC and DC schemas by adding system level abstraction to the data. MMC instantiations follow
gayanin/t5-small-med-term-conditional-masking-0	gayanin	2022-03-29T03:19:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-28T22:04:47Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-med-term-conditional-masking-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-med-term-conditional-masking-0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6688 - Rouge2 Precision: 0.694 - Rouge2 Recall: 0.4781 - Rouge2 Fmeasure: 0.5479 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.9525 \| 1.0 \| 13915 \| 0.8148 \| 0.6657 \| 0.4581 \| 0.5252 \| \| 0.8541 \| 2.0 \| 27830 \| 0.7562 \| 0.6779 \| 0.4694 \| 0.5371 \| \| 0.8183 \| 3.0 \| 41745 \| 0.7268 \| 0.6827 \| 0.4722 \| 0.5405 \| \| 0.8033 \| 4.0 \| 55660 \| 0.7074 \| 0.6861 \| 0.4729 \| 0.5419 \| \| 0.7727 \| 5.0 \| 69575 \| 0.6934 \| 0.6872 \| 0.4726 \| 0.5419 \| \| 0.7704 \| 6.0 \| 83490 \| 0.6832 \| 0.6901 \| 0.4742 \| 0.544 \| \| 0.7485 \| 7.0 \| 97405 \| 0.6771 \| 0.6926 \| 0.4772 \| 0.5469 \| \| 0.7528 \| 8.0 \| 111320 \| 0.6722 \| 0.6934 \| 0.4782 \| 0.5478 \| \| 0.7535 \| 9.0 \| 125235 \| 0.6696 \| 0.6944 \| 0.4782 \| 0.5481 \| \| 0.7444 \| 10.0 \| 139150 \| 0.6688 \| 0.694 \| 0.4781 \| 0.5479 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
i-was-neo-first/hubert-large-ami-shard-experiment-colab	i-was-neo-first	2022-03-29T00:39:37Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-20T02:10:11Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: hubert-large-ami-shard-experiment-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert-large-ami-shard-experiment-colab This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: nan - eval_wer: 1.0 - eval_runtime: 6.0682 - eval_samples_per_second: 16.479 - eval_steps_per_second: 2.142 - epoch: 1.02 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
sanchit-gandhi/wav2vec2-2-bart-large-cnn	sanchit-gandhi	2022-03-29T00:24:41Z	25	0	transformers	[ "transformers", "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "generated_from_trainer", "dataset:librispeech_asr", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-22T16:26:40Z	--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.3524 - Wer: 0.1042 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.7605 \| 4.5 \| 500 \| 2.6299 \| 1.4451 \| \| 0.1177 \| 9.01 \| 1000 \| 0.3524 \| 0.1042 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
frtna/ted_mt-Spanish-to-Italian	frtna	2022-03-28T22:04:21Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:new_dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - new_dataset model-index: - name: ted_mt-Spanish-to-Italian results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ted_mt-Spanish-to-Italian This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-it](https://huggingface.co/Helsinki-NLP/opus-mt-es-it) on the new_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 46 \| 1.4873 \| 29.6133 \| 26.9081 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
Chikashi/t5-small-finetuned-cnndm1	Chikashi	2022-03-28T22:00:26Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-28T14:55:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: t5-small-finetuned-cnndm1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 24.4246 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-cnndm1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6853 - Rouge1: 24.4246 - Rouge2: 11.6944 - Rougel: 20.1717 - Rougelsum: 23.0424 - Gen Len: 18.9996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.912 \| 0.14 \| 5000 \| 1.7167 \| 24.4232 \| 11.7049 \| 20.1758 \| 23.0345 \| 18.9997 \| \| 1.8784 \| 0.28 \| 10000 \| 1.7018 \| 24.4009 \| 11.6918 \| 20.1561 \| 23.0073 \| 18.9997 \| \| 1.8628 \| 0.42 \| 15000 \| 1.6934 \| 24.385 \| 11.683 \| 20.1285 \| 22.9823 \| 18.9997 \| \| 1.8594 \| 0.56 \| 20000 \| 1.6902 \| 24.4407 \| 11.6835 \| 20.1734 \| 23.0369 \| 18.9996 \| \| 1.8537 \| 0.7 \| 25000 \| 1.6864 \| 24.3635 \| 11.658 \| 20.1318 \| 22.9782 \| 18.9993 \| \| 1.8505 \| 0.84 \| 30000 \| 1.6856 \| 24.4267 \| 11.6991 \| 20.1629 \| 23.0361 \| 18.9994 \| \| 1.8505 \| 0.98 \| 35000 \| 1.6853 \| 24.4246 \| 11.6944 \| 20.1717 \| 23.0424 \| 18.9996 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
DrishtiSharma/xls-r-es-test-lm-finetuned-sentiment-mesd	DrishtiSharma	2022-03-28T19:03:37Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-03-28T14:54:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: xls-r-es-test-lm-finetuned-sentiment-mesd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xls-r-es-test-lm-finetuned-sentiment-mesd This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7851 - Accuracy: 0.2385 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.25e-05 - train_batch_size: 64 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.86 \| 3 \| 1.7876 \| 0.1923 \| \| 1.9709 \| 1.86 \| 6 \| 1.7869 \| 0.2 \| \| 1.9709 \| 2.86 \| 9 \| 1.7859 \| 0.2308 \| \| 2.146 \| 3.86 \| 12 \| 1.7851 \| 0.2385 \| \| 1.9622 \| 4.86 \| 15 \| 1.7842 \| 0.1923 \| \| 1.9622 \| 5.86 \| 18 \| 1.7834 \| 0.1769 \| \| 2.137 \| 6.86 \| 21 \| 1.7823 \| 0.1923 \| \| 2.137 \| 7.86 \| 24 \| 1.7812 \| 0.1923 \| \| 2.1297 \| 8.86 \| 27 \| 1.7800 \| 0.1846 \| \| 1.9502 \| 9.86 \| 30 \| 1.7787 \| 0.1846 \| \| 1.9502 \| 10.86 \| 33 \| 1.7772 \| 0.1846 \| \| 2.1234 \| 11.86 \| 36 \| 1.7760 \| 0.1846 \| \| 2.1234 \| 12.86 \| 39 \| 1.7748 \| 0.1846 \| \| 2.1186 \| 13.86 \| 42 \| 1.7736 \| 0.1846 \| \| 1.9401 \| 14.86 \| 45 \| 1.7725 \| 0.1846 \| \| 1.9401 \| 15.86 \| 48 \| 1.7715 \| 0.1923 \| \| 2.112 \| 16.86 \| 51 \| 1.7706 \| 0.1923 \| \| 2.112 \| 17.86 \| 54 \| 1.7701 \| 0.1923 \| \| 2.1094 \| 18.86 \| 57 \| 1.7697 \| 0.2 \| \| 1.934 \| 19.86 \| 60 \| 1.7696 \| 0.2 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct	scasutt	2022-03-28T18:53:54Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-28T12:30:15Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_fast_10pct results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53_toy_train_data_fast_10pct This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6983 - Wer: 0.5026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.3619 \| 1.05 \| 250 \| 3.4334 \| 1.0 \| \| 3.0818 \| 2.1 \| 500 \| 3.4914 \| 1.0 \| \| 2.3245 \| 3.15 \| 750 \| 1.6483 \| 0.9486 \| \| 1.0233 \| 4.2 \| 1000 \| 0.8817 \| 0.7400 \| \| 0.7522 \| 5.25 \| 1250 \| 0.7374 \| 0.6529 \| \| 0.5343 \| 6.3 \| 1500 \| 0.6972 \| 0.6068 \| \| 0.4452 \| 7.35 \| 1750 \| 0.6757 \| 0.5740 \| \| 0.4275 \| 8.4 \| 2000 \| 0.6789 \| 0.5551 \| \| 0.3688 \| 9.45 \| 2250 \| 0.6468 \| 0.5394 \| \| 0.3363 \| 10.5 \| 2500 \| 0.6798 \| 0.5358 \| \| 0.3036 \| 11.55 \| 2750 \| 0.6439 \| 0.5265 \| \| 0.3173 \| 12.6 \| 3000 \| 0.6898 \| 0.5196 \| \| 0.2985 \| 13.65 \| 3250 \| 0.6791 \| 0.5169 \| \| 0.288 \| 14.7 \| 3500 \| 0.6442 \| 0.5090 \| \| 0.2673 \| 15.75 \| 3750 \| 0.6984 \| 0.5119 \| \| 0.2575 \| 16.81 \| 4000 \| 0.7146 \| 0.5084 \| \| 0.239 \| 17.86 \| 4250 \| 0.6847 \| 0.5040 \| \| 0.2266 \| 18.91 \| 4500 \| 0.6900 \| 0.5028 \| \| 0.22 \| 19.96 \| 4750 \| 0.6983 \| 0.5026 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
kingabzpro/CELEB-GANs	kingabzpro	2022-03-28T18:08:29Z	0	2	null	[ "huggan", "gan", "dcgans", "dataset:huggan/CelebA-faces", "license:apache-2.0", "region:us" ]	null	2022-03-28T16:05:34Z	--- tags: - huggan - gan - dcgans task: image-generation license: apache-2.0 datasets: - huggan/CelebA-faces --- # Fake Faces with DCGANs ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results - Generator_loss: 22.7 - Discriminator_loss: 7.9 ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
aapot/wav2vec2-large-xlsr-53-finnish	aapot	2022-03-28T17:56:36Z	9	0	transformers	[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "fi", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: fi datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Finnish by Aapo Tanskanen results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice fi type: common_voice args: fi metrics: - name: Test WER type: wer value: 32.378771 --- # NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) and [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm) # Wav2Vec2-Large-XLSR-53-Finnish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Finnish using the [Common Voice](https://huggingface.co/datasets/common_voice), [CSS10 Finnish](https://www.kaggle.com/bryanpark/finnish-single-speaker-speech-dataset) and [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) datasets. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import librosa import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "fi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Finnish test data of Common Voice. ```python import librosa import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "fi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\...\…\–\é]' resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 32.378771 % ## Training The Common Voice `train`, `validation` and `other` datasets were used for training as well as `CSS10 Finnish` and `Finnish parliament session 2` datasets. The script used for training can be found from [Google Colab](https://colab.research.google.com/drive/1vnEGC9BnNRmVyIHj-0UsVulh_cUYSGWA?usp=sharing)
aapot/wav2vec2-xlsr-1b-finnish-v2	aapot	2022-03-28T17:49:48Z	6	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-1b-finnish-v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 9.73 - name: Test CER type: cer value: 1.65 --- # Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). Note: there is a version with KenLM language model used in the decoding phase producing better transcriptions: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-v2/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. ## Training data This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets: \| Dataset \| Hours \| % of total hours \| \|:------------------------------------------------------------------------------------------------------------------------------ \|:--------:\|:----------------:\| \| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) \| 9.70 h \| 3.52 % \| \| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) \| 0.24 h \| 0.09 % \| \| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) \| 21.97 h \| 7.97 % \| \| [CSS10 Finnish](https://github.com/kyubyong/css10) \| 10.32 h \| 3.74 % \| \| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) \| 228.00 h \| 82.73 % \| \| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) \| 5.37 h \| 1.95 % \| Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.7778 \| 0.17 \| 500 \| 0.2851 \| 0.3572 \| \| 0.5506 \| 0.34 \| 1000 \| 0.1595 \| 0.2130 \| \| 0.6569 \| 0.5 \| 1500 \| 0.1458 \| 0.2046 \| \| 0.5997 \| 0.67 \| 2000 \| 0.1374 \| 0.1975 \| \| 0.542 \| 0.84 \| 2500 \| 0.1390 \| 0.1956 \| \| 0.4815 \| 1.01 \| 3000 \| 0.1266 \| 0.1813 \| \| 0.6982 \| 1.17 \| 3500 \| 0.1441 \| 0.1965 \| \| 0.4522 \| 1.34 \| 4000 \| 0.1232 \| 0.1822 \| \| 0.4655 \| 1.51 \| 4500 \| 0.1209 \| 0.1702 \| \| 0.4069 \| 1.68 \| 5000 \| 0.1149 \| 0.1688 \| \| 0.4226 \| 1.84 \| 5500 \| 0.1121 \| 0.1560 \| \| 0.3993 \| 2.01 \| 6000 \| 0.1091 \| 0.1557 \| \| 0.406 \| 2.18 \| 6500 \| 0.1115 \| 0.1553 \| \| 0.4098 \| 2.35 \| 7000 \| 0.1144 \| 0.1560 \| \| 0.3995 \| 2.51 \| 7500 \| 0.1028 \| 0.1476 \| \| 0.4101 \| 2.68 \| 8000 \| 0.1129 \| 0.1511 \| \| 0.3636 \| 2.85 \| 8500 \| 0.1025 \| 0.1517 \| \| 0.3534 \| 3.02 \| 9000 \| 0.1068 \| 0.1480 \| \| 0.3836 \| 3.18 \| 9500 \| 0.1072 \| 0.1459 \| \| 0.3531 \| 3.35 \| 10000 \| 0.0928 \| 0.1367 \| \| 0.3649 \| 3.52 \| 10500 \| 0.1042 \| 0.1426 \| \| 0.3645 \| 3.69 \| 11000 \| 0.0979 \| 0.1433 \| \| 0.3685 \| 3.85 \| 11500 \| 0.0947 \| 0.1346 \| \| 0.3325 \| 4.02 \| 12000 \| 0.0991 \| 0.1352 \| \| 0.3497 \| 4.19 \| 12500 \| 0.0919 \| 0.1358 \| \| 0.3303 \| 4.36 \| 13000 \| 0.0888 \| 0.1272 \| \| 0.3323 \| 4.52 \| 13500 \| 0.0888 \| 0.1277 \| \| 0.3452 \| 4.69 \| 14000 \| 0.0894 \| 0.1279 \| \| 0.337 \| 4.86 \| 14500 \| 0.0917 \| 0.1289 \| \| 0.3114 \| 5.03 \| 15000 \| 0.0942 \| 0.1313 \| \| 0.3099 \| 5.19 \| 15500 \| 0.0902 \| 0.1239 \| \| 0.3079 \| 5.36 \| 16000 \| 0.0871 \| 0.1256 \| \| 0.3293 \| 5.53 \| 16500 \| 0.0861 \| 0.1263 \| \| 0.3123 \| 5.7 \| 17000 \| 0.0876 \| 0.1203 \| \| 0.3093 \| 5.86 \| 17500 \| 0.0848 \| 0.1226 \| \| 0.2903 \| 6.03 \| 18000 \| 0.0914 \| 0.1221 \| \| 0.297 \| 6.2 \| 18500 \| 0.0841 \| 0.1185 \| \| 0.2797 \| 6.37 \| 19000 \| 0.0858 \| 0.1165 \| \| 0.2878 \| 6.53 \| 19500 \| 0.0874 \| 0.1161 \| \| 0.2974 \| 6.7 \| 20000 \| 0.0835 \| 0.1173 \| \| 0.3051 \| 6.87 \| 20500 \| 0.0835 \| 0.1178 \| \| 0.2941 \| 7.04 \| 21000 \| 0.0852 \| 0.1155 \| \| 0.258 \| 7.21 \| 21500 \| 0.0832 \| 0.1132 \| \| 0.2778 \| 7.37 \| 22000 \| 0.0829 \| 0.1110 \| \| 0.2751 \| 7.54 \| 22500 \| 0.0822 \| 0.1069 \| \| 0.2887 \| 7.71 \| 23000 \| 0.0819 \| 0.1103 \| \| 0.2509 \| 7.88 \| 23500 \| 0.0787 \| 0.1055 \| \| 0.2501 \| 8.04 \| 24000 \| 0.0807 \| 0.1076 \| \| 0.2399 \| 8.21 \| 24500 \| 0.0784 \| 0.1052 \| \| 0.2539 \| 8.38 \| 25000 \| 0.0772 \| 0.1075 \| \| 0.248 \| 8.55 \| 25500 \| 0.0772 \| 0.1055 \| \| 0.2689 \| 8.71 \| 26000 \| 0.0763 \| 0.1027 \| \| 0.2855 \| 8.88 \| 26500 \| 0.0756 \| 0.1035 \| \| 0.2421 \| 9.05 \| 27000 \| 0.0771 \| 0.0998 \| \| 0.2497 \| 9.22 \| 27500 \| 0.0756 \| 0.0971 \| \| 0.2367 \| 9.38 \| 28000 \| 0.0741 \| 0.0974 \| \| 0.2473 \| 9.55 \| 28500 \| 0.0739 \| 0.0982 \| \| 0.2396 \| 9.72 \| 29000 \| 0.0756 \| 0.0991 \| \| 0.2602 \| 9.89 \| 29500 \| 0.0737 \| 0.0975 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-v2 --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: \| \| WER (with LM) \| WER (without LM) \| CER (with LM) \| CER (without LM) \| \|-----------------------------------------\|---------------\|------------------\|---------------\|------------------\| \|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 \|4.09 \|9.73 \|0.88 \|1.65 \| \|aapot/wav2vec2-xlsr-1b-finnish-lm \|5.65 \|13.11 \|1.20 \|2.23 \| \|aapot/wav2vec2-xlsr-300m-finnish-lm \|8.16 \|17.92 \|1.97 \|3.36 \| ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
aapot/wav2vec2-xlsr-1b-finnish-lm	aapot	2022-03-28T17:31:03Z	7	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-1b-finnish-lm results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 5.65 - name: Test CER type: cer value: 1.2 --- # Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model. Note: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization. Note: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding. ## Training data This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets: \| Dataset \| Hours \| % of total hours \| \|:----------------------------------------------------------------------------------------------------------------------------------\|:--------:\|:----------------:\| \| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) \| 9.70 h \| 3.74 % \| \| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) \| 0.24 h \| 0.09 % \| \| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) \| 5.94 h \| 2.29 % \| \| [CSS10 Finnish](https://github.com/kyubyong/css10) \| 10.32 h \| 3.98 % \| \| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) \| 228.00 h \| 87.84 % \| \| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) \| 5.37 h \| 2.07 % \| Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.968 \| 0.18 \| 500 \| 0.4870 \| 0.4720 \| \| 0.6557 \| 0.36 \| 1000 \| 0.2450 \| 0.2931 \| \| 0.647 \| 0.54 \| 1500 \| 0.1818 \| 0.2255 \| \| 0.5297 \| 0.72 \| 2000 \| 0.1698 \| 0.2354 \| \| 0.5802 \| 0.9 \| 2500 \| 0.1581 \| 0.2355 \| \| 0.6351 \| 1.07 \| 3000 \| 0.1689 \| 0.2336 \| \| 0.4626 \| 1.25 \| 3500 \| 0.1719 \| 0.3099 \| \| 0.4526 \| 1.43 \| 4000 \| 0.1434 \| 0.2069 \| \| 0.4692 \| 1.61 \| 4500 \| 0.1645 \| 0.2192 \| \| 0.4584 \| 1.79 \| 5000 \| 0.1483 \| 0.1987 \| \| 0.4234 \| 1.97 \| 5500 \| 0.1499 \| 0.2178 \| \| 0.4243 \| 2.15 \| 6000 \| 0.1345 \| 0.2070 \| \| 0.4108 \| 2.33 \| 6500 \| 0.1383 \| 0.1850 \| \| 0.4048 \| 2.51 \| 7000 \| 0.1338 \| 0.1811 \| \| 0.4085 \| 2.69 \| 7500 \| 0.1290 \| 0.1780 \| \| 0.4026 \| 2.87 \| 8000 \| 0.1239 \| 0.1650 \| \| 0.4033 \| 3.04 \| 8500 \| 0.1346 \| 0.1657 \| \| 0.3986 \| 3.22 \| 9000 \| 0.1310 \| 0.1850 \| \| 0.3867 \| 3.4 \| 9500 \| 0.1273 \| 0.1741 \| \| 0.3658 \| 3.58 \| 10000 \| 0.1219 \| 0.1672 \| \| 0.382 \| 3.76 \| 10500 \| 0.1306 \| 0.1698 \| \| 0.3847 \| 3.94 \| 11000 \| 0.1230 \| 0.1577 \| \| 0.3691 \| 4.12 \| 11500 \| 0.1310 \| 0.1615 \| \| 0.3593 \| 4.3 \| 12000 \| 0.1296 \| 0.1622 \| \| 0.3619 \| 4.48 \| 12500 \| 0.1285 \| 0.1601 \| \| 0.3361 \| 4.66 \| 13000 \| 0.1261 \| 0.1569 \| \| 0.3603 \| 4.84 \| 13500 \| 0.1235 \| 0.1533 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: \| \| WER (with LM) \| WER (without LM) \| CER (with LM) \| CER (without LM) \| \|-----------------------------------------\|---------------\|------------------\|---------------\|------------------\| \|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 \|4.09 \|9.73 \|0.88 \|1.65 \| \|aapot/wav2vec2-xlsr-1b-finnish-lm \|5.65 \|13.11 \|1.20 \|2.23 \| \|aapot/wav2vec2-xlsr-300m-finnish-lm \|8.16 \|17.92 \|1.97 \|3.36 \| ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
aapot/wav2vec2-xlsr-300m-finnish-lm	aapot	2022-03-28T17:22:08Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-300m-finnish-lm results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 8.16 - name: Test CER type: cer value: 1.97 --- # Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model. Note: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization. ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-300m-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding. ## Training data This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets: \| Dataset \| Hours \| % of total hours \| \|:------------------------------------------------------------------------------------------------------------------------------ \|:--------:\|:----------------:\| \| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) \| 9.70 h \| 3.52 % \| \| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) \| 0.24 h \| 0.09 % \| \| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) \| 21.97 h \| 7.97 % \| \| [CSS10 Finnish](https://github.com/kyubyong/css10) \| 10.32 h \| 3.74 % \| \| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) \| 228.00 h \| 82.73 % \| \| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) \| 5.37 h \| 1.95 % \| Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned [Finnish Wikipedia](https://huggingface.co/datasets/wikipedia) (August 2021) dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-04 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-300m` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.973 \| 0.17 \| 500 \| 0.5750 \| 0.6844 \| \| 0.713 \| 0.34 \| 1000 \| 0.3356 \| 0.4518 \| \| 0.6563 \| 0.5 \| 1500 \| 0.3007 \| 0.4039 \| \| 0.642 \| 0.67 \| 2000 \| 0.2619 \| 0.3674 \| \| 0.6203 \| 0.84 \| 2500 \| 0.2488 \| 0.3558 \| \| 0.6016 \| 1.01 \| 3000 \| 0.2795 \| 0.3835 \| \| 0.5423 \| 1.17 \| 3500 \| 0.2652 \| 0.3310 \| \| 0.5639 \| 1.34 \| 4000 \| 0.2479 \| 0.3462 \| \| 0.586 \| 1.51 \| 4500 \| 0.2409 \| 0.3295 \| \| 0.5169 \| 1.68 \| 5000 \| 0.2728 \| 0.3352 \| \| 0.5176 \| 1.84 \| 5500 \| 0.2254 \| 0.3149 \| \| 0.4983 \| 2.01 \| 6000 \| 0.2169 \| 0.3009 \| \| 0.4982 \| 2.18 \| 6500 \| 0.2215 \| 0.3079 \| \| 0.4898 \| 2.35 \| 7000 \| 0.2174 \| 0.3023 \| \| 0.4922 \| 2.51 \| 7500 \| 0.2217 \| 0.3081 \| \| 0.5025 \| 2.68 \| 8000 \| 0.2002 \| 0.2710 \| \| 0.4745 \| 2.85 \| 8500 \| 0.1935 \| 0.2783 \| \| 0.4377 \| 3.02 \| 9000 \| 0.1859 \| 0.2742 \| \| 0.4511 \| 3.18 \| 9500 \| 0.2038 \| 0.2786 \| \| 0.4411 \| 3.35 \| 10000 \| 0.1863 \| 0.2651 \| \| 0.4501 \| 3.52 \| 10500 \| 0.1948 \| 0.2605 \| \| 0.4557 \| 3.69 \| 11000 \| 0.1872 \| 0.2695 \| \| 0.4493 \| 3.85 \| 11500 \| 0.1888 \| 0.2632 \| \| 0.4047 \| 4.02 \| 12000 \| 0.1818 \| 0.2559 \| \| 0.4319 \| 4.19 \| 12500 \| 0.1896 \| 0.2648 \| \| 0.4162 \| 4.36 \| 13000 \| 0.1953 \| 0.2595 \| \| 0.4046 \| 4.52 \| 13500 \| 0.1864 \| 0.2606 \| \| 0.4195 \| 4.69 \| 14000 \| 0.1843 \| 0.2467 \| \| 0.4146 \| 4.86 \| 14500 \| 0.1686 \| 0.2450 \| \| 0.378 \| 5.03 \| 15000 \| 0.1731 \| 0.2401 \| \| 0.3792 \| 5.19 \| 15500 \| 0.1676 \| 0.2325 \| \| 0.3855 \| 5.36 \| 16000 \| 0.1740 \| 0.2326 \| \| 0.4029 \| 5.53 \| 16500 \| 0.1674 \| 0.2345 \| \| 0.386 \| 5.7 \| 17000 \| 0.1735 \| 0.2280 \| \| 0.3811 \| 5.86 \| 17500 \| 0.1692 \| 0.2258 \| \| 0.3607 \| 6.03 \| 18000 \| 0.1797 \| 0.2279 \| \| 0.3604 \| 6.2 \| 18500 \| 0.1651 \| 0.2206 \| \| 0.3362 \| 6.37 \| 19000 \| 0.1627 \| 0.2199 \| \| 0.3611 \| 6.53 \| 19500 \| 0.1652 \| 0.2172 \| \| 0.3671 \| 6.7 \| 20000 \| 0.1564 \| 0.2140 \| \| 0.3769 \| 6.87 \| 20500 \| 0.1525 \| 0.2101 \| \| 0.3539 \| 7.04 \| 21000 \| 0.1639 \| 0.2096 \| \| 0.3225 \| 7.21 \| 21500 \| 0.1611 \| 0.2087 \| \| 0.3323 \| 7.37 \| 22000 \| 0.1633 \| 0.2008 \| \| 0.3327 \| 7.54 \| 22500 \| 0.1692 \| 0.1975 \| \| 0.3456 \| 7.71 \| 23000 \| 0.1555 \| 0.1991 \| \| 0.3058 \| 7.88 \| 23500 \| 0.1590 \| 0.1959 \| \| 0.3034 \| 8.04 \| 24000 \| 0.1531 \| 0.1973 \| \| 0.2925 \| 8.21 \| 24500 \| 0.1583 \| 0.1978 \| \| 0.2967 \| 8.38 \| 25000 \| 0.1546 \| 0.1906 \| \| 0.2974 \| 8.55 \| 25500 \| 0.1540 \| 0.1869 \| \| 0.3131 \| 8.71 \| 26000 \| 0.1534 \| 0.1850 \| \| 0.3306 \| 8.88 \| 26500 \| 0.1482 \| 0.1844 \| \| 0.2842 \| 9.05 \| 27000 \| 0.1490 \| 0.1854 \| \| 0.2879 \| 9.22 \| 27500 \| 0.1463 \| 0.1799 \| \| 0.27 \| 9.38 \| 28000 \| 0.1454 \| 0.1798 \| \| 0.2874 \| 9.55 \| 28500 \| 0.1504 \| 0.1787 \| \| 0.2757 \| 9.72 \| 29000 \| 0.1512 \| 0.1784 \| \| 0.3017 \| 9.89 \| 29500 \| 0.1484 \| 0.1800 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-300m-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: \| \| WER (with LM) \| WER (without LM) \| CER (with LM) \| CER (without LM) \| \|-----------------------------------------\|---------------\|------------------\|---------------\|------------------\| \|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 \|4.09 \|9.73 \|0.88 \|1.65 \| \|aapot/wav2vec2-xlsr-1b-finnish-lm \|5.65 \|13.11 \|1.20 \|2.23 \| \|aapot/wav2vec2-xlsr-300m-finnish-lm \|8.16 \|17.92 \|1.97 \|3.36 \| ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
ntoldalagi/C0_LID_DEV	ntoldalagi	2022-03-28T15:46:21Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-21T21:34:38Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: name: C0_LID_DEV --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # C0_LID_DEV This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.8267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 0.0 \| 25 \| inf \| 0.8426 \| \| 1.5354 \| 0.17 \| 2000 \| inf \| 0.8198 \| \| 1.5688 \| 0.33 \| 4000 \| inf \| 0.8271 \| \| 1.5294 \| 0.5 \| 6000 \| inf \| 0.8339 \| \| 1.1947 \| 0.67 \| 8000 \| inf \| 0.8260 \| \| 1.1534 \| 0.83 \| 10000 \| inf \| 0.8267 \| \| 1.1484 \| 1.0 \| 12000 \| inf \| 0.8267 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
mikeadimech/punctuation-test-4	mikeadimech	2022-03-28T15:09:06Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-28T14:31:52Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: punctuation-test-4 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 39.1294 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # punctuation-test-4 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 0.3411 - Bleu: 39.1294 - Gen Len: 18.4812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.3331 \| 1.0 \| 625 \| 0.3411 \| 39.1294 \| 18.4812 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
dhlee347/distilbert-imdb	dhlee347	2022-03-28T14:07:15Z	4	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-28T14:01:20Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: distilbert-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9302 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1796 - Accuracy: 0.9302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2808 \| 1.0 \| 782 \| 0.1796 \| 0.9302 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.6
Chikashi/t5-small-finetuned-cnndm	Chikashi	2022-03-28T14:04:38Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-28T09:07:17Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: t5-small-finetuned-cnndm results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 24.417 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-cnndm This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6854 - Rouge1: 24.417 - Rouge2: 11.6924 - Rougel: 20.1756 - Rougelsum: 23.0414 - Gen Len: 18.9996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.8522 \| 1.0 \| 35890 \| 1.6854 \| 24.417 \| 11.6924 \| 20.1756 \| 23.0414 \| 18.9996 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
dennisowusuk/wav2vec2-large-xls-r-300m-turkish-colab	dennisowusuk	2022-03-28T13:28:30Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-28T05:29:48Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3863 - Wer: 0.3095 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.8284 \| 3.67 \| 400 \| 0.6782 \| 0.6739 \| \| 0.4174 \| 7.34 \| 800 \| 0.4524 \| 0.4811 \| \| 0.2015 \| 11.01 \| 1200 \| 0.4736 \| 0.4311 \| \| 0.1371 \| 14.68 \| 1600 \| 0.4254 \| 0.3929 \| \| 0.0997 \| 18.35 \| 2000 \| 0.4254 \| 0.3636 \| \| 0.082 \| 22.02 \| 2400 \| 0.3807 \| 0.3474 \| \| 0.0665 \| 25.69 \| 2800 \| 0.3987 \| 0.3236 \| \| 0.0523 \| 29.36 \| 3200 \| 0.3863 \| 0.3095 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
huggingtweets/hirox246	huggingtweets	2022-03-28T13:12:56Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/hirox246/1648473171015/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/646595746905620480/oeKI14gB_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ひろゆき, Hiroyuki Nishimura</div> <div style="text-align: center; font-size: 14px;">@hirox246</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ひろゆき, Hiroyuki Nishimura. \| Data \| ひろゆき, Hiroyuki Nishimura \| \| --- \| --- \| \| Tweets downloaded \| 3246 \| \| Retweets \| 288 \| \| Short tweets \| 2002 \| \| Tweets kept \| 956 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fs862rv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hirox246's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ktc28kc0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ktc28kc0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/hirox246') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
scasutt/wav2vec2-large-xlsr-53_toy_train_data_augmented	scasutt	2022-03-28T12:29:16Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-27T17:08:43Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_augmented results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53_toy_train_data_augmented This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5016 - Wer: 0.4656 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.418 \| 1.05 \| 250 \| 3.4171 \| 1.0 \| \| 3.0886 \| 2.1 \| 500 \| 3.4681 \| 1.0 \| \| 2.9422 \| 3.15 \| 750 \| 2.6151 \| 1.0 \| \| 1.3195 \| 4.2 \| 1000 \| 0.8789 \| 0.7739 \| \| 0.9154 \| 5.25 \| 1250 \| 0.6364 \| 0.6518 \| \| 0.6519 \| 6.3 \| 1500 \| 0.5682 \| 0.5949 \| \| 0.5622 \| 7.35 \| 1750 \| 0.5273 \| 0.5625 \| \| 0.4965 \| 8.4 \| 2000 \| 0.4891 \| 0.5283 \| \| 0.4283 \| 9.45 \| 2250 \| 0.5018 \| 0.5260 \| \| 0.4019 \| 10.5 \| 2500 \| 0.5016 \| 0.5006 \| \| 0.3585 \| 11.55 \| 2750 \| 0.5047 \| 0.5003 \| \| 0.3275 \| 12.6 \| 3000 \| 0.5148 \| 0.4866 \| \| 0.3427 \| 13.65 \| 3250 \| 0.5035 \| 0.4786 \| \| 0.3229 \| 14.7 \| 3500 \| 0.4855 \| 0.4768 \| \| 0.3332 \| 15.75 \| 3750 \| 0.5040 \| 0.4769 \| \| 0.2861 \| 16.81 \| 4000 \| 0.5138 \| 0.4669 \| \| 0.3029 \| 17.86 \| 4250 \| 0.5133 \| 0.4670 \| \| 0.2633 \| 18.91 \| 4500 \| 0.5063 \| 0.4637 \| \| 0.2621 \| 19.96 \| 4750 \| 0.5016 \| 0.4656 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
Champion/SA-models	Champion	2022-03-28T11:56:45Z	0	0	null	[ "pchampio", "audio", "region:us" ]	null	2022-03-10T18:32:17Z	--- tags: - pchampio - audio inference: false ---
VincentC12/rh_classification_kara	VincentC12	2022-03-28T11:53:41Z	9	0	pytorch	[ "pytorch", "distilbert", "sentiment-analysis", "en", "region:us" ]	null	2022-03-23T16:19:02Z	--- language: - en library_name: pytorch metrics: - satisfaction - culture organisationnelle - leadership - conditions de travail tags: - sentiment-analysis widget: - text: "My work is recognized by my superiors and I would even say that I feel like I have more recognition since we are on telework." example_title: "Exemple leadership" - text: "For Working conditions and wages in particular." example_title: "Exemple conditions de travail" - text: "A climate of overperformance is in place in the company." example_title: "Exemple culture organisationnelle" - text: "With regard to telework, I look forward to setting up the hybrid week, so 2 3 days at home and at the office." example_title: "Exemple satisfaction" --- Ce modèle est développé pour KARA. Ce modèle est : - Un outil de classification thématique des commentaires RH - Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits) - Spécialisé pour des commentaires entre 10 et 512 charactères Ce modèle n'est pas : - Utilisable pour détecter un discours haineux ou bien une lettre de suicide Étiquettes : - Label_0 = Satisfaction - Label_1 = Culture Organisationnelle - Label_2 = Leadership - Label_3 = Conditions de travail version 0.0.1 Performances sur le jeux de données du HRM : 84.3% de précision
robvanderg/Sem-RemmmBERT	robvanderg	2022-03-28T11:29:41Z	5	0	transformers	[ "transformers", "pytorch", "rembert", "feature-extraction", "STILT", "retraining", "multi-task learning", "multilingual", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-28T11:20:13Z	--- language: - multilingual tags: - STILT - retraining - multi-task learning datasets: - SemEval 2022 --- ## Sem-RemmmBERT This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from remBERT (https://huggingface.co/google/rembertased). The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (https://machamp-nlp.github.io/), a toolkit focusing on multi-task learning for NLP. More information can be found in the paper (which should be released when the SemEval proceedings are online).
robvanderg/Sem-mmmBERT	robvanderg	2022-03-28T11:28:17Z	4	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "STILT", "retraining", "multi-task learning", "multilingual", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-28T11:15:17Z	--- language: - multilingual tags: - STILT - retraining - multi-task learning datasets: - SemEval 2022 --- ## Sem-mmmBERT This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (https://huggingface.co/bert-base-multilingual-cased). The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (https://machamp-nlp.github.io/), a toolkit focusing on multi-task learning for NLP. More information can be found in the paper (which should be released when the SemEval proceedings are online).
sanchit-gandhi/wav2vec2-2-bart-large-cnn-no-adapter	sanchit-gandhi	2022-03-28T11:26:30Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "generated_from_trainer", "dataset:librispeech_asr", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-26T17:08:05Z	--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 3.9938 - Wer: 0.9745 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 4.9301 \| 2.24 \| 500 \| 4.6291 \| 0.9601 \| \| 4.4562 \| 4.48 \| 1000 \| 4.3604 \| 0.9608 \| \| 3.8356 \| 6.73 \| 1500 \| 4.0728 \| 0.9530 \| \| 3.2716 \| 8.97 \| 2000 \| 3.9938 \| 0.9745 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
21iridescent/distilroberta-base-finetuned-squad2-lwt	21iridescent	2022-03-28T11:18:44Z	31	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-28T08:54:28Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilroberta-base-finetuned-squad2-lwt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-squad2-lwt This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.1356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.1702 \| 1.0 \| 4120 \| 1.1220 \| \| 0.9787 \| 2.0 \| 8240 \| 1.0500 \| \| 0.8153 \| 3.0 \| 12360 \| 1.1356 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6 {'HasAns_exact': 71.39001349527665, 'HasAns_f1': 77.71740687727831, 'HasAns_total': 5928, 'NoAns_exact': 68.59545836837678, 'NoAns_f1': 68.59545836837678, 'NoAns_total': 5945, 'best_exact': 69.9991577528847, 'best_exact_thresh': 0.0, 'best_f1': 73.1583245993857, 'best_f1_thresh': 0.0, 'exact': 69.99073528173166, 'f1': 73.1499021282327, 'total': 11873}
mrm8488/t5-base-iterater	mrm8488	2022-03-28T11:00:41Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "IteraTeR", "en", "dataset:wanyu/IteraTeR_full_sent", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-27T18:48:43Z	--- license: apache-2.0 language: - en datasets: - wanyu/IteraTeR_full_sent tags: - generated_from_trainer - IteraTeR widget: - text: "<clarity> Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." model-index: - name: t5-base-iterater results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # T5 (base) fine-tuned on IteraTeR This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an [IteraTeR](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset. It achieves the following results on the evaluation set: - Loss: 0.2580 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.3286 \| 0.09 \| 2000 \| 0.3010 \| \| 0.3194 \| 0.18 \| 4000 \| 0.2872 \| \| 0.3208 \| 0.27 \| 6000 \| 0.2792 \| \| 0.3091 \| 0.36 \| 8000 \| 0.2731 \| \| 0.3164 \| 0.45 \| 10000 \| 0.2678 \| \| 0.2941 \| 0.54 \| 12000 \| 0.2682 \| \| 0.2981 \| 0.63 \| 14000 \| 0.2696 \| \| 0.2975 \| 0.72 \| 16000 \| 0.2643 \| \| 0.3109 \| 0.81 \| 18000 \| 0.2624 \| \| 0.2965 \| 0.9 \| 20000 \| 0.2648 \| \| 0.3053 \| 0.99 \| 22000 \| 0.2627 \| \| 0.2779 \| 1.08 \| 24000 \| 0.2632 \| \| 0.2692 \| 1.17 \| 26000 \| 0.2608 \| \| 0.2755 \| 1.26 \| 28000 \| 0.2600 \| \| 0.2771 \| 1.35 \| 30000 \| 0.2584 \| \| 0.2774 \| 1.44 \| 32000 \| 0.2609 \| \| 0.2976 \| 1.53 \| 34000 \| 0.2593 \| \| 0.2646 \| 1.62 \| 36000 \| 0.2616 \| \| 0.2705 \| 1.71 \| 38000 \| 0.2574 \| \| 0.2714 \| 1.8 \| 40000 \| 0.2577 \| \| 0.2857 \| 1.9 \| 42000 \| 0.2576 \| \| 0.2832 \| 1.99 \| 44000 \| 0.2580 \| ### How to use ```py from transformers import T5ForConditionalGeneration, T5TokenizerFast MODEL_CKPT = 'mrm8488/t5-base-iterater' tokenizer = T5TokenizerFast.from_pretrained(MODEL_CKPT) model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT) def predict(intent, text): input_text = f"<{intent}> {text}" features = tokenizer([input_text], return_tensors='pt') output = model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'], max_length=128, num_beams=8) return tokenizer.decode(output[0], skip_special_tokens=True) text = "Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." intent = "clarity" predict(intent, text) # Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. ``` ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
SAGAR4REAL/wav2vec2hindia	SAGAR4REAL	2022-03-28T08:32:52Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-28T07:17:45Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2hindia results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2hindia This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
huggingtweets/nsawaikar	huggingtweets	2022-03-28T07:54:11Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-28T07:52:56Z	--- language: en thumbnail: http://www.huggingtweets.com/nsawaikar/1648454046318/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1508184022052184064/yqLU6MxW_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nathan.eth</div> <div style="text-align: center; font-size: 14px;">@nsawaikar</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nathan.eth. \| Data \| Nathan.eth \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 336 \| \| Short tweets \| 621 \| \| Tweets kept \| 2293 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/pn1domem/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nsawaikar's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g9hqx5dx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g9hqx5dx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nsawaikar') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
jkhan447/sentiment-model-sample-offline-goemotion	jkhan447	2022-03-28T06:50:10Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-28T06:33:49Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: sentiment-model-sample-offline-goemotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-model-sample-offline-goemotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0183 - Accuracy: 0.7109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
timhbach/Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract	timhbach	2022-03-28T06:27:50Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-28T03:21:43Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0231 - eval_precision: 0.7448 - eval_recall: 0.75 - eval_f1: 0.7474 - eval_accuracy: 0.9942 - eval_runtime: 61.7618 - eval_samples_per_second: 27.201 - eval_steps_per_second: 3.4 - epoch: 3.0 - step: 5670 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6
haddadalwi/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad	haddadalwi	2022-03-28T05:04:56Z	32	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-10T14:03:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 40 \| 0.4082 \| \| No log \| 2.0 \| 80 \| 0.3855 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
BigSalmon/InformalToFormalLincoln31	BigSalmon	2022-03-28T00:48:44Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-27T23:08:12Z	``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln31") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln31") ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. * informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. * informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. * Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. * Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. * Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. * Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. * Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about * Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers * Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of * Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone * Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. * - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. * original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. * wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. * sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. * 1: ```
huggingtweets/jacobe	huggingtweets	2022-03-27T23:02:12Z	5	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-27T23:01:35Z	--- language: en thumbnail: http://www.huggingtweets.com/jacobe/1648422127637/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1025926108984664064/2ZHTSIof_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Rowel Atienza</div> <div style="text-align: center; font-size: 14px;">@jacobe</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Rowel Atienza. \| Data \| Rowel Atienza \| \| --- \| --- \| \| Tweets downloaded \| 100 \| \| Retweets \| 29 \| \| Short tweets \| 4 \| \| Tweets kept \| 67 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1uzq4b7w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jacobe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ouo6sis) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ouo6sis/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/jacobe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/baguioni	huggingtweets	2022-03-27T22:55:21Z	4	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-27T22:54:40Z	--- language: en thumbnail: http://www.huggingtweets.com/baguioni/1648421716784/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506662013707046914/hVtCPrPL_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">baguio</div> <div style="text-align: center; font-size: 14px;">@baguioni</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from baguio. \| Data \| baguio \| \| --- \| --- \| \| Tweets downloaded \| 3012 \| \| Retweets \| 1090 \| \| Short tweets \| 527 \| \| Tweets kept \| 1395 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1z9nh9v8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @baguioni's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s53fr1o) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s53fr1o/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/baguioni') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/baguioni-elonmusk-jacobe	huggingtweets	2022-03-27T22:44:21Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-27T22:43:39Z	--- language: en thumbnail: http://www.huggingtweets.com/baguioni-elonmusk-jacobe/1648421056394/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1025926108984664064/2ZHTSIof_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506662013707046914/hVtCPrPL_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Rowel Atienza & baguio</div> <div style="text-align: center; font-size: 14px;">@baguioni-elonmusk-jacobe</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Rowel Atienza & baguio. \| Data \| Elon Musk \| Rowel Atienza \| baguio \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 1621 \| 100 \| 3012 \| \| Retweets \| 69 \| 29 \| 1090 \| \| Short tweets \| 520 \| 4 \| 527 \| \| Tweets kept \| 1032 \| 67 \| 1395 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xuj1tda/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @baguioni-elonmusk-jacobe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fpkbu3i) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fpkbu3i/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/baguioni-elonmusk-jacobe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
leonadase/bert-base-chinese-finetuned-fdRE	leonadase	2022-03-27T20:52:06Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:sem_eval2010_task8", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-27T19:04:51Z	--- tags: - generated_from_trainer datasets: - sem_eval2010_task8 metrics: - accuracy model-index: - name: bert-base-chinese-finetuned-fdRE results: - task: name: Text Classification type: text-classification dataset: name: sem_eval2010_task8 type: sem_eval2010_task8 args: default metrics: - name: Accuracy type: accuracy value: 0.9080962800875274 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-chinese-finetuned-fdRE This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the sem_eval2010_task8 dataset. It achieves the following results on the evaluation set: - Loss: 0.2716 - Accuracy: 0.9081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 46 \| 0.5571 \| 0.7812 \| \| No log \| 2.0 \| 92 \| 0.4030 \| 0.8621 \| \| No log \| 3.0 \| 138 \| 0.3139 \| 0.8928 \| \| No log \| 4.0 \| 184 \| 0.2716 \| 0.9081 \| \| No log \| 5.0 \| 230 \| 0.2564 \| 0.9081 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
ikram54/autotrain-harassement-675420038	ikram54	2022-03-27T18:08:30Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:ikram54/autotrain-data-harassement", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-27T18:06:02Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ikram54/autotrain-data-harassement co2_eq_emissions: 2.6332836871905054 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 675420038 - CO2 Emissions (in grams): 2.6332836871905054 ## Validation Metrics - Loss: 0.8747465014457703 - Accuracy: 0.7085201793721974 - Macro F1: 0.579743989078862 - Micro F1: 0.7085201793721974 - Weighted F1: 0.6913786522271296 - Macro Precision: 0.5669375905888698 - Micro Precision: 0.7085201793721974 - Weighted Precision: 0.6760144007300164 - Macro Recall: 0.5940655209452201 - Micro Recall: 0.7085201793721974 - Weighted Recall: 0.7085201793721974 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ikram54/autotrain-harassement-675420038 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
willcai/wav2vec2_common_voice_accents_indian_only_rerun	willcai	2022-03-27T18:00:16Z	2	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-27T06:51:10Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2_common_voice_accents_indian_only_rerun results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2_common_voice_accents_indian_only_rerun This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2807 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 48 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 384 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 588 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.6205 \| 25.0 \| 400 \| 1.4584 \| \| 0.3427 \| 50.0 \| 800 \| 1.8377 \| \| 0.1213 \| 75.0 \| 1200 \| 1.6086 \| \| 0.0643 \| 100.0 \| 1600 \| 1.5136 \| \| 0.0433 \| 125.0 \| 2000 \| 1.4882 \| \| 0.0323 \| 150.0 \| 2400 \| 1.2204 \| \| 0.0265 \| 175.0 \| 2800 \| 1.3034 \| \| 0.0206 \| 200.0 \| 3200 \| 1.2866 \| \| 0.0191 \| 225.0 \| 3600 \| 1.2337 \| \| 0.0148 \| 250.0 \| 4000 \| 1.1729 \| \| 0.0121 \| 275.0 \| 4400 \| 1.2059 \| \| 0.0105 \| 300.0 \| 4800 \| 1.1246 \| \| 0.01 \| 325.0 \| 5200 \| 1.1397 \| \| 0.0098 \| 350.0 \| 5600 \| 1.1684 \| \| 0.0073 \| 375.0 \| 6000 \| 1.1030 \| \| 0.0061 \| 400.0 \| 6400 \| 1.2077 \| \| 0.0049 \| 425.0 \| 6800 \| 1.2653 \| \| 0.0044 \| 450.0 \| 7200 \| 1.1587 \| \| 0.0037 \| 475.0 \| 7600 \| 1.2283 \| \| 0.0033 \| 500.0 \| 8000 \| 1.1897 \| \| 0.0026 \| 525.0 \| 8400 \| 1.2633 \| \| 0.0023 \| 550.0 \| 8800 \| 1.2571 \| \| 0.002 \| 575.0 \| 9200 \| 1.2807 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6
scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1	scasutt	2022-03-27T17:07:53Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-25T17:45:52Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_augment_0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53_toy_train_data_augment_0.1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4658 - Wer: 0.5037 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.447 \| 1.05 \| 250 \| 3.3799 \| 1.0 \| \| 3.089 \| 2.1 \| 500 \| 3.4868 \| 1.0 \| \| 3.063 \| 3.15 \| 750 \| 3.3155 \| 1.0 \| \| 2.4008 \| 4.2 \| 1000 \| 1.2934 \| 0.8919 \| \| 1.618 \| 5.25 \| 1250 \| 0.7847 \| 0.7338 \| \| 1.3038 \| 6.3 \| 1500 \| 0.6459 \| 0.6712 \| \| 1.2074 \| 7.35 \| 1750 \| 0.5705 \| 0.6269 \| \| 1.1062 \| 8.4 \| 2000 \| 0.5267 \| 0.5843 \| \| 1.026 \| 9.45 \| 2250 \| 0.5108 \| 0.5683 \| \| 0.9505 \| 10.5 \| 2500 \| 0.5066 \| 0.5568 \| \| 0.893 \| 11.55 \| 2750 \| 0.5161 \| 0.5532 \| \| 0.8535 \| 12.6 \| 3000 \| 0.4994 \| 0.5341 \| \| 0.8462 \| 13.65 \| 3250 \| 0.4626 \| 0.5262 \| \| 0.8334 \| 14.7 \| 3500 \| 0.4593 \| 0.5197 \| \| 0.842 \| 15.75 \| 3750 \| 0.4651 \| 0.5126 \| \| 0.7678 \| 16.81 \| 4000 \| 0.4687 \| 0.5120 \| \| 0.7873 \| 17.86 \| 4250 \| 0.4716 \| 0.5070 \| \| 0.7486 \| 18.91 \| 4500 \| 0.4657 \| 0.5033 \| \| 0.7073 \| 19.96 \| 4750 \| 0.4658 \| 0.5037 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
csukuangfj/icefall-asr-librispeech-stateless-transducer-2022-03-27-2	csukuangfj	2022-03-27T15:59:24Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-03-27T13:27:21Z	## Introduction Please see <https://github.com/k2-fsa/icefall/pull/271> for more details.
EMBO/bio-lm	EMBO	2022-03-27T15:46:51Z	8	0	transformers	[ "transformers", "pytorch", "jax", "roberta", "fill-mask", "language model", "dataset:EMBO/biolang", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - english thumbnail: tags: - language model license: datasets: - EMBO/biolang metrics: - --- # bio-lm ## Model description This model is a [RoBERTa base pre-trained model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). ## Intended uses & limitations #### How to use The intended use of this model is to be fine-tuned for downstream tasks, token classification in particular. To have a quick check of the model as-is in a fill-mask task: ```python from transformers import pipeline, RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) text = "Let us try this model to see if it <mask>." fill_mask = pipeline( "fill-mask", model='EMBO/bio-lm', tokenizer=tokenizer ) fill_mask(text) ``` #### Limitations and bias This model should be fine-tuned on a specifi task like token classification. The model must be used with the `roberta-base` tokenizer. ## Training data The model was trained with a masked language modeling taskon the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes 12Mio examples from abstracts and figure legends extracted from papers published in life sciences. ## Training procedure The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Command: `python -m lm.train /data/json/oapmc_abstracts_figs/ MLM` - Tokenizer vocab size: 50265 - Training data: EMBO/biolang MLM - Training with: 12005390 examples - Evaluating on: 36713 examples - Epochs: 3.0 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - tensorboard run: lm-MLM-2021-01-27T15-17-43.113766 End of training: ``` trainset: 'loss': 0.8653350830078125 validation set: 'eval_loss': 0.8192330598831177, 'eval_recall': 0.8154601116513597 ``` ## Eval results Eval on test set: ``` recall: 0.814471959728645 ```

abdusah/aradia-ctc-hubert-ft

abdusah

2022-03-31T20:56:27Z

14

0

transformers

[ "transformers", "pytorch", "hubert", "automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-31T08:14:31Z

--- tags: - automatic-speech-recognition - abdusahmbzuai/arabic_speech_massive_300hrs - generated_from_trainer model-index: - name: aradia-ctc-hubert-ft results: [] ---  # aradia-ctc-hubert-ft This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.8536 - Wer: 0.3737 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.43 | 100 | 3.6934 | 1.0 | | No log | 0.87 | 200 | 3.0763 | 1.0 | | No log | 1.3 | 300 | 2.9737 | 1.0 | | No log | 1.74 | 400 | 2.5734 | 1.0 | | 5.0957 | 2.17 | 500 | 1.1900 | 0.9011 | | 5.0957 | 2.61 | 600 | 0.9726 | 0.7572 | | 5.0957 | 3.04 | 700 | 0.8960 | 0.6209 | | 5.0957 | 3.48 | 800 | 0.7851 | 0.5515 | | 5.0957 | 3.91 | 900 | 0.7271 | 0.5115 | | 1.0312 | 4.35 | 1000 | 0.7053 | 0.4955 | | 1.0312 | 4.78 | 1100 | 0.6823 | 0.4737 | | 1.0312 | 5.22 | 1200 | 0.6768 | 0.4595 | | 1.0312 | 5.65 | 1300 | 0.6635 | 0.4488 | | 1.0312 | 6.09 | 1400 | 0.6602 | 0.4390 | | 0.6815 | 6.52 | 1500 | 0.6464 | 0.4310 | | 0.6815 | 6.95 | 1600 | 0.6455 | 0.4394 | | 0.6815 | 7.39 | 1700 | 0.6630 | 0.4312 | | 0.6815 | 7.82 | 1800 | 0.6521 | 0.4126 | | 0.6815 | 8.26 | 1900 | 0.6282 | 0.4284 | | 0.544 | 8.69 | 2000 | 0.6248 | 0.4178 | | 0.544 | 9.13 | 2100 | 0.6510 | 0.4104 | | 0.544 | 9.56 | 2200 | 0.6527 | 0.4013 | | 0.544 | 10.0 | 2300 | 0.6511 | 0.4064 | | 0.544 | 10.43 | 2400 | 0.6734 | 0.4061 | | 0.4478 | 10.87 | 2500 | 0.6756 | 0.4145 | | 0.4478 | 11.3 | 2600 | 0.6727 | 0.3990 | | 0.4478 | 11.74 | 2700 | 0.6619 | 0.4007 | | 0.4478 | 12.17 | 2800 | 0.6614 | 0.4019 | | 0.4478 | 12.61 | 2900 | 0.6695 | 0.4004 | | 0.3919 | 13.04 | 3000 | 0.6778 | 0.3966 | | 0.3919 | 13.48 | 3100 | 0.6872 | 0.3971 | | 0.3919 | 13.91 | 3200 | 0.6882 | 0.3945 | | 0.3919 | 14.35 | 3300 | 0.7177 | 0.4010 | | 0.3919 | 14.78 | 3400 | 0.6888 | 0.4043 | | 0.3767 | 15.22 | 3500 | 0.7124 | 0.4202 | | 0.3767 | 15.65 | 3600 | 0.7276 | 0.4120 | | 0.3767 | 16.09 | 3700 | 0.7265 | 0.4034 | | 0.3767 | 16.52 | 3800 | 0.7392 | 0.4077 | | 0.3767 | 16.95 | 3900 | 0.7403 | 0.3965 | | 0.3603 | 17.39 | 4000 | 0.7445 | 0.4016 | | 0.3603 | 17.82 | 4100 | 0.7579 | 0.4012 | | 0.3603 | 18.26 | 4200 | 0.7225 | 0.3963 | | 0.3603 | 18.69 | 4300 | 0.7355 | 0.3951 | | 0.3603 | 19.13 | 4400 | 0.7482 | 0.3925 | | 0.3153 | 19.56 | 4500 | 0.7723 | 0.3972 | | 0.3153 | 20.0 | 4600 | 0.7469 | 0.3898 | | 0.3153 | 20.43 | 4700 | 0.7800 | 0.3944 | | 0.3153 | 20.87 | 4800 | 0.7827 | 0.3897 | | 0.3153 | 21.3 | 4900 | 0.7935 | 0.3914 | | 0.286 | 21.74 | 5000 | 0.7984 | 0.3750 | | 0.286 | 22.17 | 5100 | 0.7945 | 0.3830 | | 0.286 | 22.61 | 5200 | 0.8011 | 0.3775 | | 0.286 | 23.04 | 5300 | 0.7978 | 0.3824 | | 0.286 | 23.48 | 5400 | 0.8161 | 0.3833 | | 0.2615 | 23.91 | 5500 | 0.7823 | 0.3858 | | 0.2615 | 24.35 | 5600 | 0.8312 | 0.3863 | | 0.2615 | 24.78 | 5700 | 0.8427 | 0.3819 | | 0.2615 | 25.22 | 5800 | 0.8432 | 0.3802 | | 0.2615 | 25.65 | 5900 | 0.8286 | 0.3794 | | 0.2408 | 26.09 | 6000 | 0.8224 | 0.3824 | | 0.2408 | 26.52 | 6100 | 0.8228 | 0.3823 | | 0.2408 | 26.95 | 6200 | 0.8324 | 0.3795 | | 0.2408 | 27.39 | 6300 | 0.8564 | 0.3744 | | 0.2408 | 27.82 | 6400 | 0.8629 | 0.3774 | | 0.2254 | 28.26 | 6500 | 0.8545 | 0.3778 | | 0.2254 | 28.69 | 6600 | 0.8492 | 0.3767 | | 0.2254 | 29.13 | 6700 | 0.8511 | 0.3751 | | 0.2254 | 29.56 | 6800 | 0.8491 | 0.3753 | | 0.2254 | 30.0 | 6900 | 0.8536 | 0.3737 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6

magitz/distilbert-base-uncased-finetuned-emotion

magitz

2022-03-31T20:48:43Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-31T20:41:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9265 - name: F1 type: f1 value: 0.9267965474109292 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2235 - Accuracy: 0.9265 - F1: 0.9268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8101 | 1.0 | 250 | 0.3177 | 0.9045 | 0.9010 | | 0.2472 | 2.0 | 500 | 0.2235 | 0.9265 | 0.9268 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.8.1 - Datasets 1.18.3 - Tokenizers 0.11.0

ghees/FatimeFellowship

ghees

2022-03-31T20:47:24Z

0

null

[ "region:us" ]

null

2022-03-31T20:45:21Z

Preprocessing before feeding to model ``` from sentence_transformers import SentenceTransformer model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device='cuda') ... embeddings = model.encode([text]) return embeddings[0] ```

arampacha/gpt-neo-therapist-small

arampacha

2022-03-31T20:34:26Z

17

1

transformers

[ "transformers", "pytorch", "tensorboard", "onnx", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-03-30T08:40:54Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: gpt-neo-therapist-small results: [] ---  # gpt-neo-therapist-small This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.6731 - Rouge1: 39.5028 - Rouge2: 6.43 - Rougel: 24.0091 - Rougelsum: 35.4481 - Gen Len: 204.1329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 24 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 9.9955 | 0.97 | 7 | 6.8195 | 18.6047 | 1.0194 | 14.8565 | 17.9774 | 212.0983 | | 6.9729 | 1.97 | 14 | 5.6783 | 26.3789 | 3.0779 | 18.5195 | 24.8592 | 203.0925 | | 5.2614 | 2.97 | 21 | 5.0506 | 34.9428 | 4.921 | 21.9741 | 32.1122 | 206.2775 | | 5.0599 | 3.97 | 28 | 4.7372 | 38.5235 | 6.2251 | 23.5923 | 34.5633 | 204.2428 | | 4.5479 | 4.97 | 35 | 4.6731 | 39.5028 | 6.43 | 24.0091 | 35.4481 | 204.1329 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

WENGSYX/Deberta-Chinese-Large

WENGSYX

2022-03-31T20:08:59Z

56

16

transformers

[ "transformers", "pytorch", "deberta", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

# Deberta-Chinese 本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。使用WWM与n-gramMLM 等预训练方法进行预训练。 | 预训练模型 | 学习率 | batchsize | 设备 | 语料库 | 时间 | 优化器 | | --------------------- | ------ | --------- | ------ | ------ | ---- | ------ | | Deberta-Chinese-Large | 1e-5 | 512 | 2*3090 | 200G | 14天 | AdamW | ### 加载与使用依托于huggingface-transformers ``` tokenizer = BertTokenizer.from_pretrained("WENGSYX/Deberta-Chinese-Large") model = AutoModel.from_pretrained("WENGSYX/Deberta-Chinese-Large") ``` #### 注意，请使用BertTokenizer加载中文词表

Tahsin-Mayeesha/distilbert-finetuned-fakenews

Tahsin-Mayeesha

2022-03-31T17:11:42Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-31T15:58:31Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-finetuned-fakenews results: [] ---  # distilbert-finetuned-fakenews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0049 - Accuracy: 0.9995 - F1: 0.9995 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0392 | 1.0 | 500 | 0.0059 | 0.999 | 0.999 | | 0.002 | 2.0 | 1000 | 0.0047 | 0.9995 | 0.9995 | | 0.0001 | 3.0 | 1500 | 0.0047 | 0.9995 | 0.9995 | | 0.0001 | 4.0 | 2000 | 0.0049 | 0.9995 | 0.9995 | | 0.0 | 5.0 | 2500 | 0.0049 | 0.9995 | 0.9995 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0

rahulacj/bertweet-base-finetuned-sentiment-analysis

rahulacj

2022-03-31T16:21:16Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-31T09:42:31Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bertweet-base-finetuned-sentiment-analysis results: [] ---  # bertweet-base-finetuned-sentiment-analysis This model is a fine-tuned version of [cardiffnlp/bertweet-base-sentiment](https://huggingface.co/cardiffnlp/bertweet-base-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8458 - Accuracy: 0.6426 - F1: 0.6397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8904 | 1.0 | 630 | 0.8509 | 0.6381 | 0.6340 | | 0.7655 | 2.0 | 1260 | 0.8345 | 0.6579 | 0.6559 | | 0.66 | 3.0 | 1890 | 0.9199 | 0.6548 | 0.6514 | | 0.447 | 4.0 | 2520 | 1.0324 | 0.6429 | 0.6417 | | 0.3585 | 5.0 | 3150 | 1.1234 | 0.6452 | 0.6424 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0

eren23/pneumonia-bielefeld-dl-course

eren23

2022-03-31T15:55:27Z

61

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-27T12:17:21Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: pneumonia-bielefeld-dl-course results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8456632494926453 --- # pneumonia-bielefeld-dl-course This registry contains the model for making pneumonia predictions and was prepared for Bielefeld University Deep Learning course homework. The code used for this implementation mostly comes from here: https://github.com/nateraw/huggingpics it was a ready pipeline for model fine-tuning with huggingface and PyTorch Lightning for another dataset.

Nonem100/Test-Model

Nonem100

2022-03-31T15:19:38Z

62

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-31T15:19:30Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Test-Model results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9017857313156128 --- # Test-Model Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cotton candy ![cotton candy](images/cotton_candy.jpg) #### hamburger ![hamburger](images/hamburger.jpg) #### hot dog ![hot dog](images/hot_dog.jpg) #### nachos ![nachos](images/nachos.jpg) #### popcorn ![popcorn](images/popcorn.jpg)

huggingtweets/timdingmanlive

huggingtweets

2022-03-31T14:30:05Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-31T14:26:57Z

--- language: en thumbnail: http://www.huggingtweets.com/timdingmanlive/1648736999131/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2844974270/7bb6450b90b65f8712d9433b8d5e1971_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tim Dingman</div> <div style="text-align: center; font-size: 14px;">@timdingmanlive</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tim Dingman. | Data | Tim Dingman | | --- | --- | | Tweets downloaded | 3240 | | Retweets | 555 | | Short tweets | 138 | | Tweets kept | 2547 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7yvdv2z7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @timdingmanlive's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/311pu3zj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/311pu3zj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/timdingmanlive') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

oferweintraub/bert-base-finance-sentiment-noisy-search

oferweintraub

2022-03-31T14:13:45Z

23

3

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "Finance-sentiment-analysis", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - Finance-sentiment-analysis - generated_from_trainer metrics: - f1 - accuracy - precision - recall model-index: - name: bert-base-finance-sentiment-noisy-search results: [] widget: - text: "Third quarter reported revenues were $10.9 billion, up 5 percent compared to prior year and up 8 percent on a currency-neutral basis" example_title: "Positive" - text: "The London-listed website for businesses reported a pretax loss of $26.6 million compared with a loss of $12.9 million the previous year" example_title: "Negative" - text: "Microsoft updates Outlook, Teams, and PowerPoint to be hybrid work ready" example_title: "Neutral" ---  # bert-base-finance-sentiment-noisy-search This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on Kaggle finance news sentiment analysis with data enhancement using noisy search. The process is explained below: 1. First "bert-base-uncased" was fine-tuned on Kaggle's finance news sentiment analysis https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news dataset achieving accuracy of about 88% 2. We then used a logistic-regression classifier on the same data. Here we looked at coefficients that contributed the most to the "Positive" and "Negative" classes by inspecting only bi-grams. 3. Using the top 25 bi-grams per class (i.e. "Positive" / "Negative") we invoked Bing news search with those bi-grams and retrieved up to 50 news items per bi-gram phrase. 4. We called it "noisy-search" because it is assumed the positive bi-grams (e.g. "profit rose" , "growth net") give rise to positive examples whereas negative bi-grams (e.g. "loss increase", "share loss") result in negative examples but note that we didn't test for the validity of this assumption (hence: noisy-search) 5. For each article we kept the title + excerpt and labeled it according to pre-assumptions on class associations. 6. We then trained the same model on the noisy data and apply it to an held-out test set from the original data set split. 7. Training with couple of thousands noisy "positives" and "negatives" examples yielded a test set accuracy of about 95%. 8. It shows that by automatically collecting noisy examples using search we can boost accuracy performance from about 88% to more than 95%. Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown in the attached pdf: https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing ## Model description BERT model trained on noisy data from search results. See PDF for more details. ## Intended uses & limitations Intended for use on finance news sentiment analysis with 3 options: "Positive", "Neutral" and "Negative" To get the best results feed the classifier with the title and either the 1st paragraph or a short news summarization e.g. of up to 64 tokens. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

huggingtweets/youtube

huggingtweets

2022-03-31T14:06:33Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-31T14:05:50Z

--- language: en thumbnail: http://www.huggingtweets.com/youtube/1648735587597/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427292844612595720/RC1YSvuT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">YouTube</div> <div style="text-align: center; font-size: 14px;">@youtube</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from YouTube. | Data | YouTube | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 23 | | Short tweets | 104 | | Tweets kept | 3123 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dx34obn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @youtube's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/youtube') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Edresson/wav2vec2-large-xlsr-coraa-portuguese

Edresson

2022-03-31T13:28:43Z

632

15

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "hf-asr-leaderboard", "PyTorch", "dataset:CORAA", "arxiv:2110.15731", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:04Z

--- language: pt datasets: - CORAA metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - hf-asr-leaderboard - speech - PyTorch license: apache-2.0 model-index: - name: Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: CORAA type: CORAA args: pt metrics: - name: Test CORAA WER type: wer value: 25.26 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: pt metrics: - name: Test WER on Common Voice 7 type: wer value: 20.08 --- # Wav2vec 2.0 trained with CORAA Portuguese Dataset This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following [CORAA dataset](https://github.com/nilc-nlp/CORAA) # Use this model ```python from transformers import AutoTokenizer, Wav2Vec2ForCTC tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese") model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese") ``` # Results For the results check the [CORAA article](https://arxiv.org/abs/2110.15731) # Example test with Common Voice Dataset ```python dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("â€™", "'") return batch ``` ```python ds = dataset.map(map_to_array) result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) print(wer.compute(predictions=result["predicted"], references=result["target"])) ```

Khalsuu/2nd-wav2vec2-l-xls-r-300m-turkish-test

Khalsuu

2022-03-31T12:09:32Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-31T08:45:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: 2nd-wav2vec2-l-xls-r-300m-turkish-test results: [] ---  # 2nd-wav2vec2-l-xls-r-300m-turkish-test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6019 - Wer: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0522 | 3.67 | 400 | 0.7773 | 0.7296 | | 0.5369 | 7.34 | 800 | 0.6282 | 0.5888 | | 0.276 | 11.01 | 1200 | 0.5998 | 0.5330 | | 0.1725 | 14.68 | 1600 | 0.5859 | 0.4908 | | 0.1177 | 18.35 | 2000 | 0.6019 | 0.4444 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

YiTian/wav2vec2-common_voice-tr-demo

YiTian

2022-03-31T11:40:04Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "tr", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-31T09:39:08Z

--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-tr-demo results: [] ---  # wav2vec2-common_voice-tr-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 2.9841 - Wer: 0.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 7.14 | 100 | 3.6689 | 1.0 | | No log | 14.29 | 200 | 3.0280 | 0.9999 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.0 - Datasets 1.18.0 - Tokenizers 0.11.6

scasutt/wav2vec2-base_toy_train_data_random_low_pass

scasutt

2022-03-31T10:42:02Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-31T08:21:35Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base_toy_train_data_random_low_pass results: [] ---  # wav2vec2-base_toy_train_data_random_low_pass This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3227 - Wer: 0.7288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0795 | 2.1 | 500 | 3.2227 | 0.9982 | | 1.21 | 4.2 | 1000 | 1.3713 | 0.8879 | | 0.742 | 6.3 | 1500 | 1.2660 | 0.8296 | | 0.5877 | 8.4 | 2000 | 1.2921 | 0.7794 | | 0.4823 | 10.5 | 2500 | 1.2899 | 0.7565 | | 0.4036 | 12.6 | 3000 | 1.3486 | 0.7494 | | 0.391 | 14.7 | 3500 | 1.2701 | 0.7466 | | 0.3426 | 16.81 | 4000 | 1.3570 | 0.7279 | | 0.3015 | 18.91 | 4500 | 1.3227 | 0.7288 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

unjustify/autotrain-IWant-689220804

unjustify

2022-03-31T06:46:48Z

4

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "unk", "dataset:unjustify/autotrain-data-IWant", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-31T06:09:55Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - unjustify/autotrain-data-IWant co2_eq_emissions: 39.40549299946679 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 689220804 - CO2 Emissions (in grams): 39.40549299946679 ## Validation Metrics - Loss: 2.0426149368286133 - Rouge1: 54.9813 - Rouge2: 44.923 - RougeL: 54.0399 - RougeLsum: 54.2553 - Gen Len: 16.6211 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/unjustify/autotrain-IWant-689220804 ```

michiyasunaga/BioLinkBERT-base

michiyasunaga

2022-03-31T00:51:21Z

6,225

36

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "exbert", "linkbert", "biolinkbert", "fill-mask", "question-answering", "text-classification", "token-classification", "en", "dataset:pubmed", "arxiv:2203.15827", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-classification

2022-03-08T07:22:12Z

--- license: apache-2.0 language: en datasets: - pubmed tags: - bert - exbert - linkbert - biolinkbert - feature-extraction - fill-mask - question-answering - text-classification - token-classification widget: - text: "Sunitinib is a tyrosine kinase inhibitor" --- ## BioLinkBERT-base BioLinkBERT-base model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT). This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA). ## Model description LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document. LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval). ## Intended uses & limitations The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification. You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text). ### How to use To use the model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base') model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-base') inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases. ## Evaluation results When fine-tuned on downstream tasks, LinkBERT achieves the following results. **Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art. | | BLURB score | PubMedQA | BioASQ | MedQA-USMLE | | ---------------------- | -------- | -------- | ------- | -------- | | PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 | | **BioLinkBERT-base** | **83.39** | **70.2** | **91.4** | **40.0** | | **BioLinkBERT-large** | **84.30** | **72.2** | **94.8** | **44.6** | | | MMLU-professional medicine | | ---------------------- | -------- | | GPT-3 (175 params) | 38.7 | | UnifiedQA (11B params) | 43.2 | | **BioLinkBERT-large (340M params)** | **50.7** | ## Citation If you find LinkBERT useful in your project, please cite the following: ```bibtex @InProceedings{yasunaga2022linkbert, author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang}, title = {LinkBERT: Pretraining Language Models with Document Links}, year = {2022}, booktitle = {Association for Computational Linguistics (ACL)}, } ```

hoangbinhmta99/wav2vec-NCKH-2022

hoangbinhmta99

2022-03-31T00:28:52Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "feature-extraction", "audio", "speech", "Transformer", "automatic-speech-recognition", "vi", "dataset:vivos", "dataset:common_voice", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-30T04:39:46Z

--- language: vi datasets: - vivos - common_voice metrics: - wer pipeline_tag: automatic-speech-recognition tags: - audio - speech - Transformer license: cc-by-nc-4.0 model-index: - name: Wav2vec2 NCKH Vietnamese 2022 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice vi type: common_voice args: vi metrics: - name: Test WER type: wer value: No --- Convert from model .pt to transformer Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h Bash: ```bash pip install transformers[sentencepiece] pip install fairseq -U git clone https://github.com/huggingface/transformers.git cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py . wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt mkdir dict wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt mkdir outputs python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py --pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt --dict_path ./dict/dict.ltr.txt --not_finetuned ``` # install and upload model ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash git lfs install sudo apt-get install git-lfs git lfs install git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo ls cd wav2vec-demo/ git status git add . git commit -m "First model version" git config --global user.email [yourname] git config --global user.name [yourpass] git commit -m "First model version" git push ```

mrm8488/biomedtra-small-es

mrm8488

2022-03-30T21:07:50Z

3

2

transformers

[ "transformers", "pytorch", "tensorboard", "electra", "pretraining", "Spanish", "Electra", "Bio", "Medical", "es", "dataset:cowese", "arxiv:1406.2661", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: es tags: - Spanish - Electra - Bio - Medical datasets: - cowese --- ## 🦠 BIOMEDtra 🏥 **BIOMEDtra** (small) is an Electra like model (discriminator in this case) trained on [Spanish Biomedical Crawled Corpus](https://zenodo.org/record/5510033#.Yhdk1ZHMLJx). As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ## Training details The model was trained using the Electra base code for 3 days on 1 GPU (Tesla V100 16GB). ## Dataset details The largest Spanish biomedical and heath corpus to date gathered from a massive Spanish health domain crawler over more than 3,000 URLs were downloaded and preprocessed. The collected data have been preprocessed to produce the **CoWeSe** (Corpus Web Salud Español) resource, a large-scale and high-quality corpus intended for biomedical and health NLP in Spanish. ## Model details ⚙ |Param| # Value| |-----|--------| |Layers| 12 | |Hidden | 256 | |Params| 14M | ## Evaluation metrics (for discriminator) 🧾 |Metric | # Score | |-------|---------| |Accuracy| 0.9561| |Precision| 0.808| |Recall | 0.531 | |AUC | 0.949| ## Benchmarks 🔨 WIP 🚧 ## How to use the discriminator in `transformers` ```py from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("mrm8488/biomedtra-small-es") tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/biomedtra-small-es") sentence = "Los españoles tienden a sufir déficit de vitamina c" fake_sentence = "Los españoles tienden a déficit sufrir de vitamina c" fake_tokens = tokenizer.tokenize(fake_sentence) fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") discriminator_outputs = discriminator(fake_inputs) predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) [print("%7s" % token, end="") for token in fake_tokens] [print("%7s" % prediction, end="") for prediction in predictions.tolist()] ``` ## Acknowledgments TBA ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2022biomedtra, title={Spanish BioMedical Electra (small)}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/biomedtra-small-es}, year={2022} } ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with <span style="color: #e25555;">&hearts;</span> in Spain

mrm8488/legalectra-small-spanish

mrm8488

2022-03-30T21:06:31Z

41

3

transformers

[ "transformers", "pytorch", "electra", "pretraining", "Spanish", "Electra", "Legal", "es", "dataset:Spanish-legal-corpora", "arxiv:1406.2661", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: es tags: - Spanish - Electra - Legal datasets: - Spanish-legal-corpora --- ## LEGALECTRA ⚖️ **LEGALECTRA** (small) is an Electra like model (discriminator in this case) trained on [A collection of corpora of Spanish legal domain](https://zenodo.org/record/5495529#.YZItp3vMLJw). As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ## Training details The model was trained using the Electra base code for 3 days on 1 Tesla V100 16GB. ## Model details ⚙ |Param| # Value| |-----|--------| |Layers| 12 | |Hidden | 256 | |Params| 14M | ## Evaluation metrics (for discriminator) 🧾 |Metric | # Score | |-------|---------| |Accuracy| 0.955| |Precision| 0.790| |AUC | 0.971| ## Benchmarks 🔨 WIP 🚧 ## How to use the discriminator in `transformers` TBA ## Acknowledgments TBA ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2022legalectra, title={Spanish Legal Electra (small)}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/legalectra-small-spanish}, year={2022} } ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with <span style="color: #e25555;">&hearts;</span> in Spain

vlsb/autotrain-security-text-classification-albert-688320769

vlsb

2022-03-30T20:59:32Z

15

2

transformers

[ "transformers", "pytorch", "albert", "text-classification", "autotrain", "unk", "dataset:vlsb/autotrain-data-security-text-classification-albert", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T20:55:59Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - vlsb/autotrain-data-security-text-classification-albert co2_eq_emissions: 3.670416179055797 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 688320769 - CO2 Emissions (in grams): 3.670416179055797 ## Validation Metrics - Loss: 0.3046899139881134 - Accuracy: 0.8826530612244898 - Precision: 0.9181818181818182 - Recall: 0.8782608695652174 - AUC: 0.9423510466988727 - F1: 0.8977777777777778 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/vlsb/autotrain-security-text-classification-albert-688320769 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("vlsb/autotrain-security-text-classification-albert-688320769", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("vlsb/autotrain-security-text-classification-albert-688320769", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

mrm8488/electricidad-small-discriminator

mrm8488

2022-03-30T20:44:50Z

9

5

transformers

[ "transformers", "pytorch", "electra", "pretraining", "Spanish", "Electra", "es", "dataset:large_spanish_corpus", "arxiv:1406.2661", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: es thumbnail: https://i.imgur.com/uxAvBfh.png tags: - Spanish - Electra datasets: - large_spanish_corpus --- ## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh) **ELECTRICIDAD** is a small Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus). As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ## Model details ⚙ |Param| # Value| |-----|--------| |Layers|\t12 | |Hidden |256 \t| |Params| 14M| ## Evaluation metrics (for discriminator) 🧾 |Metric | # Score | |-------|---------| |Accuracy| 0.94| |Precision| 0.76| |AUC | 0.92| ## Benchmarks 🔨 WIP 🚧 ## How to use the discriminator in `transformers` ```python from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-small-discriminator") tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-small-discriminator") sentence = "el zorro rojo es muy rápido" fake_sentence = "el zorro rojo es muy ser" fake_tokens = tokenizer.tokenize(sentence) fake_inputs = tokenizer.encode(sentence, return_tensors="pt") discriminator_outputs = discriminator(fake_inputs) predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) [print("%7s" % token, end="") for token in fake_tokens] [print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]] # Output: ''' el zorro rojo es muy ser 0 0 0 0 0 1[None, None, None, None, None, None] ''' ``` As you can see there is a **1** in the place where the model detected the fake token (**ser**). So, it works! 🎉 [Electricidad-small fine-tuned models](https://huggingface.co/models?search=electricidad-small) ## Acknowledgments I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program. ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2020electricidad-small-discriminator, title={Spanish Electra (small) by Manuel Romero}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/electricidad-small-discriminator}}, year={2020} } ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) > Made with <span style="color: #e25555;">&hearts;</span> in Spain

waboucay/camembert-base-finetuned-xnli_fr

waboucay

2022-03-30T17:47:05Z

5

0

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-11T08:54:07Z

--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | F1<sub>micro</sub> | F1<sub>macro</sub> | |------------|--------------------|--------------------| | validation | 89.2 | 87.6 | | test | 88.9 | 87.4 |

hoangbinhmta99/wav2vec-demo

hoangbinhmta99

2022-03-30T17:18:48Z

9

2

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

Convert from model .pt to transformer Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h Bash: ```bash pip install transformers[sentencepiece] pip install fairseq -U git clone https://github.com/huggingface/transformers.git cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py . wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt mkdir dict wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt mkdir outputs python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py --pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt --dict_path ./dict/dict.ltr.txt --not_finetuned ``` # install and upload model ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash git lfs install sudo apt-get install git-lfs git lfs install git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo ls cd wav2vec-demo/ git status git add . git commit -m "First model version" git config --global user.email [yourname] git config --global user.name [yourpass] git commit -m "First model version" git push ```

abdusah/aradia-ctc-v1

abdusah

2022-03-30T13:48:41Z

23

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-23T10:58:05Z

--- tags: - automatic-speech-recognition - abdusahmbzuai/arabic_speech_massive_300hrs - generated_from_trainer model-index: - name: aradia-ctc-v1 results: [] ---  # aradia-ctc-v1 This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.7171 - Wer: 0.3336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.22 | 100 | 5.1889 | 1.0 | | No log | 0.43 | 200 | 3.1129 | 1.0 | | No log | 0.65 | 300 | 3.0503 | 1.0 | | No log | 0.87 | 400 | 3.0279 | 1.0 | | 6.2756 | 1.09 | 500 | 2.9965 | 1.0 | | 6.2756 | 1.3 | 600 | 2.3618 | 0.9993 | | 6.2756 | 1.52 | 700 | 1.2715 | 0.8758 | | 6.2756 | 1.74 | 800 | 0.9971 | 0.7156 | | 6.2756 | 1.96 | 900 | 0.8927 | 0.6382 | | 1.712 | 2.17 | 1000 | 0.8252 | 0.5926 | | 1.712 | 2.39 | 1100 | 0.7794 | 0.5434 | | 1.712 | 2.61 | 1200 | 0.7557 | 0.5092 | | 1.712 | 2.83 | 1300 | 0.7347 | 0.5203 | | 1.712 | 3.04 | 1400 | 0.7189 | 0.4929 | | 0.9305 | 3.26 | 1500 | 0.6820 | 0.4595 | | 0.9305 | 3.48 | 1600 | 0.6792 | 0.4504 | | 0.9305 | 3.69 | 1700 | 0.6596 | 0.4442 | | 0.9305 | 3.91 | 1800 | 0.6756 | 0.4432 | | 0.9305 | 4.13 | 1900 | 0.6663 | 0.4392 | | 0.737 | 4.35 | 2000 | 0.6479 | 0.4372 | | 0.737 | 4.56 | 2100 | 0.6353 | 0.4203 | | 0.737 | 4.78 | 2200 | 0.6251 | 0.4088 | | 0.737 | 5.0 | 2300 | 0.6209 | 0.4177 | | 0.737 | 5.22 | 2400 | 0.6639 | 0.4094 | | 0.6247 | 5.43 | 2500 | 0.6408 | 0.3970 | | 0.6247 | 5.65 | 2600 | 0.6373 | 0.3932 | | 0.6247 | 5.87 | 2700 | 0.6411 | 0.3928 | | 0.6247 | 6.09 | 2800 | 0.6378 | 0.3897 | | 0.6247 | 6.3 | 2900 | 0.6396 | 0.3929 | | 0.5443 | 6.52 | 3000 | 0.6544 | 0.3864 | | 0.5443 | 6.74 | 3100 | 0.6218 | 0.3786 | | 0.5443 | 6.96 | 3200 | 0.6200 | 0.3784 | | 0.5443 | 7.17 | 3300 | 0.6157 | 0.3791 | | 0.5443 | 7.39 | 3400 | 0.6317 | 0.3798 | | 0.4845 | 7.61 | 3500 | 0.6540 | 0.3771 | | 0.4845 | 7.83 | 3600 | 0.6436 | 0.3670 | | 0.4845 | 8.04 | 3700 | 0.6335 | 0.3695 | | 0.4845 | 8.26 | 3800 | 0.6579 | 0.3610 | | 0.4845 | 8.48 | 3900 | 0.6170 | 0.3613 | | 0.4279 | 8.69 | 4000 | 0.6523 | 0.3617 | | 0.4279 | 8.91 | 4100 | 0.6349 | 0.3577 | | 0.4279 | 9.13 | 4200 | 0.6344 | 0.3673 | | 0.4279 | 9.35 | 4300 | 0.6215 | 0.3641 | | 0.4279 | 9.56 | 4400 | 0.6513 | 0.3608 | | 0.3825 | 9.78 | 4500 | 0.6386 | 0.3605 | | 0.3825 | 10.0 | 4600 | 0.6724 | 0.3549 | | 0.3825 | 10.22 | 4700 | 0.6776 | 0.3602 | | 0.3825 | 10.43 | 4800 | 0.6739 | 0.3544 | | 0.3825 | 10.65 | 4900 | 0.6688 | 0.3557 | | 0.3477 | 10.87 | 5000 | 0.6674 | 0.3564 | | 0.3477 | 11.09 | 5100 | 0.6786 | 0.3476 | | 0.3477 | 11.3 | 5200 | 0.6818 | 0.3478 | | 0.3477 | 11.52 | 5300 | 0.6874 | 0.3470 | | 0.3477 | 11.74 | 5400 | 0.6993 | 0.3424 | | 0.3101 | 11.96 | 5500 | 0.6950 | 0.3404 | | 0.3101 | 12.17 | 5600 | 0.6872 | 0.3406 | | 0.3101 | 12.39 | 5700 | 0.6846 | 0.3424 | | 0.3101 | 12.61 | 5800 | 0.7051 | 0.3405 | | 0.3101 | 12.83 | 5900 | 0.7051 | 0.3378 | | 0.2859 | 13.04 | 6000 | 0.6955 | 0.3403 | | 0.2859 | 13.26 | 6100 | 0.7115 | 0.3390 | | 0.2859 | 13.48 | 6200 | 0.7074 | 0.3384 | | 0.2859 | 13.69 | 6300 | 0.7002 | 0.3376 | | 0.2859 | 13.91 | 6400 | 0.7171 | 0.3360 | | 0.2714 | 14.13 | 6500 | 0.7193 | 0.3341 | | 0.2714 | 14.35 | 6600 | 0.7132 | 0.3347 | | 0.2714 | 14.56 | 6700 | 0.7184 | 0.3353 | | 0.2714 | 14.78 | 6800 | 0.7171 | 0.3331 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6

javilonso/classificationEsp3_Attraction

javilonso

2022-03-30T12:09:19Z

5

0

transformers

[ "transformers", "tf", "gpt2", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T11:07:40Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: javilonso/classificationEsp3_Attraction results: [] ---  # javilonso/classificationEsp3_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0055 - Validation Loss: 0.0515 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17958, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0964 | 0.0662 | 0 | | 0.0265 | 0.0500 | 1 | | 0.0055 | 0.0515 | 2 | ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6

yinde/dummy-model

yinde

2022-03-30T11:59:15Z

10

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T11:37:44Z

Fake news classifier This model trains a text classification model to detect fake news articles, it uses distilbert-base-uncased-finetuned-sst-2-english pretrained model to work on fake and real news dataset from kaggle (https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)

joe5campbell/Horovod_Tweet_Sentiment_1K_4eps

joe5campbell

2022-03-30T11:38:32Z

5

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-24T12:35:50Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Horovod_Tweet_Sentiment_1K_4eps results: [] ---  # Horovod_Tweet_Sentiment_1K_4eps This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6803332 - Train Accuracy: 0.57187504 - Validation Loss: 0.6883397 - Validation Accuracy: 0.54375 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.70931095 | 0.5078125 | 0.81717503 | 0.528125 | 0 | | 0.77384466 | 0.5296875 | 0.68696874 | 0.51875 | 1 | | 0.68944424 | 0.53125 | 0.6837756 | 0.53125 | 2 | | 0.6803332 | 0.57187504 | 0.6883397 | 0.54375 | 3 | ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Tokenizers 0.11.6

mimicheng/codeparrot-ds-sample-2ep-29mar

mimicheng

2022-03-30T09:50:15Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-30T03:41:46Z

--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-2ep-29mar results: [] ---  # codeparrot-ds-sample-2ep-29mar This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2585 | 1.86 | 5000 | 1.6283 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.8.2+cpu - Datasets 2.0.0 - Tokenizers 0.11.6

jeniakim/hedgehog

jeniakim

2022-03-30T09:27:38Z

51

2

transformers

[ "transformers", "pytorch", "bert", "token-classification", "en", "license:mit", "autotrain_compatible", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- language: en license: mit inference: false --- 🦔 HEDGEhog 🦔: BERT-based multi-class uncertainty cues recognition ==================================================================== # Description A fine-tuned multi-class classification model that detects four different types of uncertainty cues (a.k.a hedges) on a token level. # Uncertainty types label | type | description | example ---| ---| ---| --- E | Epistemic | The proposition is possible, but its truth-value cannot be decided at the moment. | She **may** be already asleep. I | Investigation | The proposition is in the process of having its truth-value determined. | She **examined** the role of NF-kappaB in protein activation. D | Doxatic | The proposition expresses beliefs and hypotheses, which may be known as true or false by others. | She **believes** that the Earth is flat. N | Condition | The proposition is true or false based on the truth-value of another proposition. | **If** she gets the job, she will move to Utrecht. C | *certain* | *n/a* | *n/a* # Intended uses and limitations - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. # How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.ner import NERModel model = NERModel( 'bert', 'jeniakim/hedgehog', use_cuda=False, labels=["C", "D", "E", "I", "N"], ) example = "As much as I definitely enjoy solitude, I wouldn't mind perhaps spending little time with you (Björk)" predictions, raw_outputs = model.predict([example]) ``` The predictions look like this: ``` [[{'As': 'C'}, {'much': 'C'}, {'as': 'C'}, {'I': 'C'}, {'definitely': 'C'}, {'enjoy': 'C'}, {'solitude,': 'C'}, {'I': 'C'}, {"wouldn't": 'C'}, {'mind': 'C'}, {'perhaps': 'E'}, {'spending': 'C'}, {'little': 'C'}, {'time': 'C'}, {'with': 'C'}, {'you': 'C'}, {'(Björk)': 'C'}]] ``` In other words, the token 'perhaps' is recognized as an **epistemic uncertainty cue** and all the other tokens are not uncertainty cues. # Training Data HEDGEhog is trained and evaluated on the [Szeged Uncertainty Corpus](https://rgai.inf.u-szeged.hu/node/160) (Szarvas et al. 2012<sup>1</sup>). The original sentence-level XML version of this dataset is available [here](https://rgai.inf.u-szeged.hu/node/160). The token-level version that was used for the training can be downloaded from [here](https://1drv.ms/u/s!AvPkt_QxBozXk7BiazucDqZkVxLo6g?e=IisuM6) in a form of pickled pandas DataFrame's. You can download either the split sets (```train.pkl``` 137MB, ```test.pkl``` 17MB, ```dev.pkl``` 17MB) or the full dataset (```szeged_fixed.pkl``` 172MB). Each row in the df contains a token, its features (these are not relevant for HEDGEhog; they were used to train the baseline CRF model, see [here](https://github.com/vanboefer/uncertainty_crf)), its sentence ID, and its label. # Training Procedure The following training parameters were used: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 16 # Evaluation Results class | precision | recall | F1-score | support ---|---|---|---|--- Epistemic | 0.90 | 0.85 | 0.88 | 624 Doxatic | 0.88 | 0.92 | 0.90 | 142 Investigation | 0.83 | 0.86 | 0.84 | 111 Condition | 0.85 | 0.87 | 0.86 | 86 Certain | 1.00 | 1.00 | 1.00 | 104,751 **macro average** | **0.89** | **0.90** | **0.89** | 105,714 # References <sup>1</sup> Szarvas, G., Vincze, V., Farkas, R., Móra, G., & Gurevych, I. (2012). Cross-genre and cross-domain detection of semantic uncertainty. *Computational Linguistics, 38*(2), 335-367.

markussagen/xlm-roberta-longformer-base-4096

markussagen

2022-03-30T09:24:39Z

9,277

36

transformers

[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "longformer", "multilingual", "dataset:wikitext", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- tags: - longformer language: multilingual license: apache-2.0 datasets: - wikitext --- ## XLM-R Longformer Model / XLM-Long XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) on the English WikiText-103 corpus. The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at [Peltarion](https://peltarion.com/) and was fine-tuned on multilingual quesion-answering tasks, with code available [here](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer#xlm-r). Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps. ## How to Use The model can be used as expected to fine-tune on a downstream task. For instance for QA. ```python import torch from transformers import AutoModel, AutoTokenizer MAX_SEQUENCE_LENGTH = 4096 MODEL_NAME_OR_PATH = "markussagen/xlm-roberta-longformer-base-4096" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, max_length=MAX_SEQUENCE_LENGTH, padding="max_length", truncation=True, ) model = AutoModelForQuestionAnswering.from_pretrained( MODEL_NAME_OR_PATH, max_length=MAX_SEQUENCE_LENGTH, ) ``` ## Training Procedure The model have been trained on the WikiText-103 corpus, using a **48GB** GPU with the following training script and parameters. The model was pre-trained for 6000 iterations and took ~5 days. See the full [training script](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer/blob/main/scripts/finetune_qa_models.py) and [Github repo](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer) for more information ```sh wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip unzip wikitext-103-raw-v1.zip export DATA_DIR=./wikitext-103-raw scripts/run_long_lm.py \ --model_name_or_path xlm-roberta-base \ --model_name xlm-roberta-to-longformer \ --output_dir ./output \ --logging_dir ./logs \ --val_file_path $DATA_DIR/wiki.valid.raw \ --train_file_path $DATA_DIR/wiki.train.raw \ --seed 42 \ --max_pos 4096 \ --adam_epsilon 1e-8 \ --warmup_steps 500 \ --learning_rate 3e-5 \ --weight_decay 0.01 \ --max_steps 6000 \ --evaluate_during_training \ --logging_steps 50 \ --eval_steps 50 \ --save_steps 6000 \ --max_grad_norm 1.0 \ --per_device_eval_batch_size 2 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 64 \ --overwrite_output_dir \ --fp16 \ --do_train \ --do_eval ```

Aureliano/electra-if

Aureliano

2022-03-30T09:07:27Z

6

0

transformers

[ "transformers", "pytorch", "tf", "electra", "feature-extraction", "en", "arxiv:1406.2661", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2022-03-11T15:40:21Z

--- language: en license: apache-2.0 --- ## ELECTRA for IF **ELECTRA** is a method for self-supervised language representation learning. They are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). For a detailed description and experimental results, please refer to the original paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). This repository contains a small ELECTRA discriminator finetuned on a corpus of interactive fiction commands labelled with the WordNet synset offset of the verb in the sentence. The original dataset has been collected from the list of action in the walkthroughs for the game included in the [Jericho](https://github.com/microsoft/jericho) framework and manually annotated. For more information visit https://github.com/aporporato/electra and https://github.com/aporporato/jericho-corpora. ## How to use the discriminator in `transformers` (Heavily based on: https://github.com/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb) ```python import math import numpy as np import tensorflow as tf from datasets import load_metric, Dataset, DatasetDict from transformers import TFAutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, create_optimizer from transformers.keras_callbacks import KerasMetricCallback # This example shows how this model can be used: # you should finetune the model of your specific corpus if commands, bigger than this dict_train = { "idx": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20"], "sentence": ["e", "get pen", "drop book", "x paper", "i", "south", "get paper", "drop the pen", "x book", "inventory", "n", "get the book", "drop paper", "look at Pen", "inv", "g", "s", "get sandwich", "drop sandwich", "x sandwich", "agin"], "label": ["travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "repeat.v.01", "travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "repeat.v.01"] } dict_val = { "idx": ["0", "1", "2", "3", "4", "5"], "sentence": ["w", "get shield", "drop sword", "x spikes", "i", "repeat"], "label": ["travel.v.01", "take.v.04", "drop.v.01", "examine.v.02", "inventory.v.01", "repeat.v.01"] } raw_train_dataset = Dataset.from_dict(dict_train) raw_val_dataset = Dataset.from_dict(dict_val) raw_dataset = DatasetDict() raw_dataset["train"] = raw_train_dataset raw_dataset["val"] = raw_val_dataset raw_dataset = raw_dataset.class_encode_column("label") print(raw_dataset) print(raw_dataset["train"].features) print(raw_dataset["val"].features) print(raw_dataset["train"][1]) label2id = {} id2label = {} for i, l in enumerate(raw_dataset["train"].features["label"].names): label2id[l] = i id2label[i] = l discriminator = TFAutoModelForSequenceClassification.from_pretrained("Aureliano/electra-if", label2id=label2id, id2label=id2label) tokenizer = AutoTokenizer.from_pretrained("Aureliano/electra-if") tokenize_function = lambda example: tokenizer(example["sentence"], truncation=True) pre_tokenizer_columns = set(raw_dataset["train"].features) encoded_dataset = raw_dataset.map(tokenize_function, batched=True) tokenizer_columns = list(set(encoded_dataset["train"].features) - pre_tokenizer_columns) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") batch_size = len(encoded_dataset["train"]) tf_train_dataset = encoded_dataset["train"].to_tf_dataset( columns=tokenizer_columns, label_cols=["labels"], shuffle=True, batch_size=batch_size, collate_fn=data_collator ) tf_validation_dataset = encoded_dataset["val"].to_tf_dataset( columns=tokenizer_columns, label_cols=["labels"], shuffle=False, batch_size=batch_size, collate_fn=data_collator ) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) num_epochs = 25 batches_per_epoch = math.ceil(len(encoded_dataset["train"]) / batch_size) total_train_steps = int(batches_per_epoch * num_epochs) optimizer, schedule = create_optimizer( init_lr=5e-5, num_warmup_steps=total_train_steps // 5, num_train_steps=total_train_steps ) metric = load_metric("accuracy") def compute_metrics(eval_predictions): logits, labels = eval_predictions predictions = np.argmax(logits, axis=-1) return metric.compute(predictions=predictions, references=labels) metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_dataset) callbacks = [metric_callback] discriminator.compile(optimizer=optimizer, loss=loss, metrics=["sparse_categorical_accuracy"]) discriminator.fit( tf_train_dataset, epochs=num_epochs, validation_data=tf_validation_dataset, callbacks=callbacks ) print("Evaluate on test data") results = discriminator.evaluate(tf_validation_dataset) print("test loss, test acc:", results) text = "i" encoded_input = tokenizer(text, return_tensors='tf') output = discriminator(encoded_input) prediction = tf.nn.softmax(output["logits"][0], -1) label = id2label[tf.math.argmax(prediction).numpy()] print("\n", text, ":", label, "\n") # ideally 'inventory.v.01' (-> "make or include in an itemized record or report"), but probably only with a better finetuning dataset text = "get lamp" encoded_input = tokenizer(text, return_tensors='tf') output = discriminator(encoded_input) prediction = tf.nn.softmax(output["logits"][0], -1) label = id2label[tf.math.argmax(prediction).numpy()] print("\n", text, ":", label, "\n") # ideally 'take.v.04' (-> "get into one's hands, take physically"), but probably only with a better finetuning dataset text = "w" encoded_input = tokenizer(text, return_tensors='tf') output = discriminator(encoded_input) prediction = tf.nn.softmax(output["logits"][0], -1) label = id2label[tf.math.argmax(prediction).numpy()] print("\n", text, ":", label, "\n") # ideally 'travel.v.01' (-> "change location; move, travel, or proceed, also metaphorically"), but probably only with a better finetuning dataset ```

javilonso/classificationPolEsp1

javilonso

2022-03-30T09:02:50Z

3

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T07:49:20Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: javilonso/classificationPolEsp1 results: [] ---  # javilonso/classificationPolEsp1 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3728 - Validation Loss: 0.6217 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17958, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6282 | 0.6017 | 0 | | 0.5129 | 0.6177 | 1 | | 0.3728 | 0.6217 | 2 | ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6

neibla/distilbert-base-uncased-finetuned-emotion

neibla

2022-03-30T08:56:26Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T08:22:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9254917237562972 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2187 - Accuracy: 0.9255 - F1: 0.9255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.855 | 1.0 | 250 | 0.3211 | 0.905 | 0.9017 | | 0.2561 | 2.0 | 500 | 0.2187 | 0.9255 | 0.9255 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

nlp-waseda/gpt2-small-japanese

nlp-waseda

2022-03-30T04:28:17Z

26

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "ja", "dataset:wikipedia", "dataset:cc100", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-30T03:34:11Z

--- language: - ja license: cc-by-sa-4.0 datasets: - wikipedia - cc100 widget: - text: "早稲田大学で自然言語処理を" --- # nlp-waseda/gpt2-small-japanese This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. Note that the texts should be segmented into words using Juman++ in advance. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese') >>> set_seed(42) >>> generator("早稲田大学で自然言語処理を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5) [{'generated_text': '早稲田大学で自然言語処理を学び、帰国後、早稲田大学理工学部に入学します。卒業後、早稲田大学工学研究科、'}, {'generated_text': '早稲田大学で自然言語処理を学び、アメリカの大学で学士号を取得、修士の取得で博士号を取得。 2008 年'}, {'generated_text': '早稲田大学で自然言語処理を勉強しています。学部は日本語学科を専攻しています。英語が話せるという'}, {'generated_text': '早稲田大学で自然言語処理を専攻していた。 2011 年に第 26 回日本化学会学生委員会奨励賞 ( 第 2 年次審査'}, {'generated_text': '早稲田大学で自然言語処理を中心とする言語学研究を行っている。東京都・豊島区のお見合い相手。'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import ReformerTokenizer, GPT2Model tokenizer = ReformerTokenizer.from_pretrained('nlp-waseda/gpt2-small-japanese') model = GPT2Model.from_pretrained('nlp-waseda/gpt2-small-japanese') text = "早稲田大学で自然言語処理を" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Training data The GPT-2 model was pretrained on Japanese Wikipedia, dumped on 2022-03-20, and the Japanese portion of CC-100. ## Training procedure ### Preprocessing The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining. The model was trained on 8 NVIDIA A100 GPUs.

samayash/finetuning-financial-news-sentiment

samayash

2022-03-30T03:36:40Z

4

3

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T03:27:02Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-financial-news-sentiment results: [] ---  # finetuning-financial-news-sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3345 - Accuracy: 0.8751 - F1: 0.8751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

scasutt/wav2vec2-large-xlsr-53_toy_train_data_masked_audio

scasutt

2022-03-30T03:35:01Z

3

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-29T11:30:40Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_masked_audio results: [] ---  # wav2vec2-large-xlsr-53_toy_train_data_masked_audio This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6445 - Wer: 0.4938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3761 | 1.05 | 250 | 3.4022 | 0.9954 | | 3.0858 | 2.1 | 500 | 3.4684 | 0.9954 | | 2.6302 | 3.15 | 750 | 1.7989 | 0.9865 | | 1.1292 | 4.2 | 1000 | 0.8558 | 0.7355 | | 0.8371 | 5.25 | 1250 | 0.7319 | 0.6621 | | 0.5992 | 6.3 | 1500 | 0.6848 | 0.6147 | | 0.5189 | 7.35 | 1750 | 0.6522 | 0.5742 | | 0.454 | 8.4 | 2000 | 0.6601 | 0.5531 | | 0.3896 | 9.45 | 2250 | 0.6138 | 0.5439 | | 0.3678 | 10.5 | 2500 | 0.6436 | 0.5320 | | 0.3232 | 11.55 | 2750 | 0.5920 | 0.5174 | | 0.2926 | 12.6 | 3000 | 0.6615 | 0.5107 | | 0.3041 | 13.65 | 3250 | 0.6311 | 0.5015 | | 0.2882 | 14.7 | 3500 | 0.6182 | 0.5004 | | 0.2868 | 15.75 | 3750 | 0.6266 | 0.4943 | | 0.2508 | 16.81 | 4000 | 0.6587 | 0.4965 | | 0.2563 | 17.86 | 4250 | 0.6634 | 0.4939 | | 0.2213 | 18.91 | 4500 | 0.6441 | 0.4925 | | 0.2255 | 19.96 | 4750 | 0.6445 | 0.4938 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

javilonso/classificationEsp2_Attraction

javilonso

2022-03-30T03:04:09Z

5

0

transformers

[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-29T23:17:31Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: javilonso/classificationEsp2_Attraction results: [] ---  # javilonso/classificationEsp2_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9927 - Validation Loss: 0.9926 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35916, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.8200 | 0.9930 | 0 | | 0.9942 | 0.9947 | 1 | | 0.9927 | 0.9926 | 2 | ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6

aaraki/vit-base-patch16-224-in21k-finetuned-cifar10

aaraki

2022-03-30T01:41:47Z

8,239

10

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-30T00:18:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-finetuned-cifar10 results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9788 ---  # vit-base-patch16-224-in21k-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.2564 - Accuracy: 0.9788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4291 | 1.0 | 390 | 0.2564 | 0.9788 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

BigSalmon/InformalToFormalLincoln33

BigSalmon

2022-03-30T01:24:08Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-30T01:19:07Z

``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln33") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln33") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ``` Keywords to sentences or sentence.

cammiemw/bert-marco-hdct

cammiemw

2022-03-30T01:21:38Z

3

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-30T01:09:55Z

--- license: cc-by-nc-4.0 ---

DrishtiSharma/poem-gen-spanish-t5-small-v7

DrishtiSharma

2022-03-30T00:34:41Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-29T19:14:40Z

--- license: mit tags: - generated_from_trainer model-index: - name: poem-gen-spanish-t5-small-v7 results: [] ---  # poem-gen-spanish-t5-small-v7 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000333 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.1716 | 0.73 | 30000 | 3.1114 | | 2.9666 | 1.46 | 60000 | 3.0271 | | 2.8292 | 2.19 | 90000 | 2.9531 | | 2.7264 | 2.93 | 120000 | 2.9126 | | 2.6057 | 3.66 | 150000 | 2.9175 | | 2.4876 | 4.39 | 180000 | 2.9077 | | 2.3791 | 5.12 | 210000 | 2.9240 | | 2.3515 | 5.85 | 240000 | 2.9169 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

DrishtiSharma/poem-gen-spanish-t5-small-v6

DrishtiSharma

2022-03-29T23:45:09Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-29T18:58:46Z

--- license: mit tags: - generated_from_trainer model-index: - name: poem-gen-spanish-t5-small-v6 results: [] ---  # poem-gen-spanish-t5-small-v6 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8831 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.8551 | 0.73 | 30000 | 2.9296 | | 2.6961 | 1.46 | 60000 | 2.9005 | | 2.5756 | 2.19 | 90000 | 2.8786 | | 2.5095 | 2.93 | 120000 | 2.8621 | | 2.4061 | 3.66 | 150000 | 2.8830 | | 2.3161 | 4.39 | 180000 | 2.8865 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

DrishtiSharma/poem-gen-spanish-t5-small-v5

DrishtiSharma

2022-03-29T23:25:30Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-29T18:54:38Z

--- license: mit tags: - generated_from_trainer model-index: - name: poem-gen-spanish-t5-small-v5 results: [] ---  # poem-gen-spanish-t5-small-v5 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8881 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000125 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.9366 | 0.73 | 30000 | 2.9656 | | 2.7518 | 1.46 | 60000 | 2.9120 | | 2.6018 | 2.19 | 90000 | 2.8870 | | 2.5262 | 2.93 | 120000 | 2.8646 | | 2.3886 | 3.66 | 150000 | 2.8816 | | 2.2758 | 4.39 | 180000 | 2.8900 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

BigSalmon/PointsToSentence

BigSalmon

2022-03-29T23:11:32Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-29T22:58:46Z

``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsToSentence") model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsToSentence") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ``` It should also be able to do all that this can: https://huggingface.co/BigSalmon/InformalToFormalLincoln27 Keywords to sentences or sentence.

krinal214/augmented

krinal214

2022-03-29T16:58:16Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-29T15:02:50Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: augmented results: [] ---  # augmented This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5104 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.0609 | 1.0 | 9787 | 0.5104 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6

GleamEyeBeast/ascend

GleamEyeBeast

2022-03-29T16:49:48Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-29T01:37:59Z

--- tags: - generated_from_trainer model-index: - name: ascend results: [] ---  # ascend This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3718 - Wer: 0.6412 - Cer: 0.2428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 0.5769 | 1.0 | 688 | 1.1864 | 0.7716 | 0.3159 | | 0.5215 | 2.0 | 1376 | 1.1613 | 0.7504 | 0.2965 | | 0.4188 | 3.0 | 2064 | 1.1644 | 0.7389 | 0.2950 | | 0.3695 | 4.0 | 2752 | 1.1937 | 0.7184 | 0.2815 | | 0.3404 | 5.0 | 3440 | 1.1947 | 0.7083 | 0.2719 | | 0.2885 | 6.0 | 4128 | 1.2314 | 0.7108 | 0.2685 | | 0.2727 | 7.0 | 4816 | 1.2243 | 0.6850 | 0.2616 | | 0.2417 | 8.0 | 5504 | 1.2506 | 0.6767 | 0.2608 | | 0.2207 | 9.0 | 6192 | 1.2804 | 0.6922 | 0.2595 | | 0.2195 | 10.0 | 6880 | 1.2582 | 0.6818 | 0.2575 | | 0.1896 | 11.0 | 7568 | 1.3101 | 0.6814 | 0.2545 | | 0.1961 | 12.0 | 8256 | 1.2793 | 0.6706 | 0.2526 | | 0.1752 | 13.0 | 8944 | 1.2643 | 0.6584 | 0.2509 | | 0.1638 | 14.0 | 9632 | 1.3152 | 0.6588 | 0.2482 | | 0.1522 | 15.0 | 10320 | 1.3098 | 0.6433 | 0.2439 | | 0.1351 | 16.0 | 11008 | 1.3253 | 0.6537 | 0.2447 | | 0.1266 | 17.0 | 11696 | 1.3394 | 0.6365 | 0.2418 | | 0.1289 | 18.0 | 12384 | 1.3718 | 0.6412 | 0.2443 | | 0.1204 | 19.0 | 13072 | 1.3708 | 0.6433 | 0.2433 | | 0.1189 | 20.0 | 13760 | 1.3718 | 0.6412 | 0.2428 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

gabitoo1234/autotrain-mut_all_text-680820343

gabitoo1234

2022-03-29T16:09:31Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "es", "dataset:gabitoo1234/autotrain-data-mut_all_text", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-29T14:22:14Z

--- tags: autotrain language: es widget: - text: "I love AutoTrain 🤗" datasets: - gabitoo1234/autotrain-data-mut_all_text co2_eq_emissions: 115.48848403681228 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 680820343 - CO2 Emissions (in grams): 115.48848403681228 ## Validation Metrics - Loss: 0.3041240870952606 - Accuracy: 0.9462770369425126 - Macro F1: 0.7836898686625933 - Micro F1: 0.9462770369425126 - Weighted F1: 0.9449148298990091 - Macro Precision: 0.8344505891491089 - Micro Precision: 0.9462770369425126 - Weighted Precision: 0.9451247372908952 - Macro Recall: 0.7568785255994025 - Micro Recall: 0.9462770369425126 - Weighted Recall: 0.9462770369425126 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/gabitoo1234/autotrain-mut_all_text-680820343 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("gabitoo1234/autotrain-mut_all_text-680820343", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("gabitoo1234/autotrain-mut_all_text-680820343", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

tbosse/bert-base-german-cased-finetuned-subj_v1

tbosse

2022-03-29T15:59:49Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-29T14:22:30Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-finetuned-subj_v1 results: [] ---  # bert-base-german-cased-finetuned-subj_v1 This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1594 - Precision: 0.1875 - Recall: 0.0077 - F1: 0.0147 - Accuracy: 0.9508 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 136 | 0.1591 | 1.0 | 0.0051 | 0.0102 | 0.9523 | | No log | 2.0 | 272 | 0.1571 | 0.375 | 0.0077 | 0.015 | 0.9518 | | No log | 3.0 | 408 | 0.1594 | 0.1875 | 0.0077 | 0.0147 | 0.9508 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Daryaflp/roberta-retrained_ru_covid_papers

Daryaflp

2022-03-29T13:30:45Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-29T07:12:02Z

--- tags: - generated_from_trainer model-index: - name: roberta-retrained_ru_covid_papers results: [] ---  # roberta-retrained_ru_covid_papers This model is a fine-tuned version of [Daryaflp/roberta-retrained_ru_covid](https://huggingface.co/Daryaflp/roberta-retrained_ru_covid) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9998 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

ArtemChistyakov-2/f

ArtemChistyakov-2

2022-03-29T12:21:18Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2022-03-29T12:21:18Z

--- license: apache-2.0 ---

gayanin/bart-med-term-conditional-masking-0

gayanin

2022-03-29T12:03:56Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-28T22:12:30Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-med-term-conditional-masking-0 results: [] ---  # bart-med-term-conditional-masking-0 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5041 - Rouge2 Precision: 0.7497 - Rouge2 Recall: 0.5246 - Rouge2 Fmeasure: 0.5986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6381 | 1.0 | 13915 | 0.5595 | 0.734 | 0.5152 | 0.5873 | | 0.5429 | 2.0 | 27830 | 0.5243 | 0.7441 | 0.5225 | 0.5956 | | 0.5002 | 3.0 | 41745 | 0.5078 | 0.7482 | 0.5238 | 0.5976 | | 0.4607 | 4.0 | 55660 | 0.5041 | 0.7497 | 0.5246 | 0.5986 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Rishav-hub/xlm-roberta-base-finetuned-panx-de

Rishav-hub

2022-03-29T11:05:37Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-29T10:26:12Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8591260810195721 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1352 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.257 | 1.0 | 525 | 0.1512 | 0.8302 | | 0.1305 | 2.0 | 1050 | 0.1401 | 0.8447 | | 0.0817 | 3.0 | 1575 | 0.1352 | 0.8591 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

KeithHorgan/TweetClimateAnalysis

KeithHorgan

2022-03-29T10:01:24Z

4

1

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:KeithHorgan98/autotrain-data-TweetClimateAnalysis", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-29T10:16:42Z

--- tags: autotrain language: unk widget: - text: "Climate Change is a hoax" - text: "It is freezing, where is global warming" datasets: - KeithHorgan98/autotrain-data-TweetClimateAnalysis co2_eq_emissions: 133.19491276284793 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 678720226 - CO2 Emissions (in grams): 133.19491276284793 ## Validation Metrics - Loss: 0.4864234924316406 - Accuracy: 0.865424430641822 - Macro F1: 0.7665472174344069 - Micro F1: 0.8654244306418221 - Weighted F1: 0.8586375445115083 - Macro Precision: 0.8281449061702826 - Micro Precision: 0.865424430641822 - Weighted Precision: 0.8619727477790186 - Macro Recall: 0.736576343905098 - Micro Recall: 0.865424430641822 - Weighted Recall: 0.865424430641822 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/KeithHorgan98/autotrain-TweetClimateAnalysis-678720226 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Davlan/m2m100_418M-eng-yor-mt

Davlan

2022-03-29T09:21:53Z

820

1

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "arxiv:2103.08647", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description **m2m100_418M-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```

Davlan/m2m100_418M-yor-eng-mt

Davlan

2022-03-29T09:21:03Z

5

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "arxiv:2103.08647", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description **m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves **16.76 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```

PereLluis13/wav2vec2-xls-r-1b-ca-lm

PereLluis13

2022-03-29T08:41:46Z

3,126

4

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "collectivat/tv3_parla", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "projecte-aina/parlament_parla", "robust-speech-event", "ca", "dataset:mozilla-foundation/common_voice_8_0", "dataset:collectivat/tv3_parla", "dataset:projecte-aina/parlament_parla", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:04Z

--- language: - ca license: apache-2.0 tags: - automatic-speech-recognition - collectivat/tv3_parla - generated_from_trainer - hf-asr-leaderboard - mozilla-foundation/common_voice_8_0 - projecte-aina/parlament_parla - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 - collectivat/tv3_parla - projecte-aina/parlament_parla model-index: - name: wav2vec2-xls-r-1b-ca-lm results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_8_0 ca type: mozilla-foundation/common_voice_8_0 args: ca metrics: - name: Test WER type: wer value: 6.0722669958130644 - name: Test CER type: cer value: 1.9180697705166526 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: projecte-aina/parlament_parla ca type: projecte-aina/parlament_parla args: clean metrics: - name: Test WER type: wer value: 5.139820371024042 - name: Test CER type: cer value: 2.0163620128164722 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: collectivat/tv3_parla ca type: collectivat/tv3_parla args: ca metrics: - name: Test WER type: wer value: 11.207991684952073 - name: Test CER type: cer value: 7.32119307305963 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Catalan Dev Data type: speech-recognition-community-v2/dev_data args: ca metrics: - name: Test WER type: wer value: 22.870153690468661 - name: Test CER type: cer value: 13.59039190897598 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: ca metrics: - name: Test WER type: wer value: 15.41 --- # wav2vec2-xls-r-1b-ca-lm This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets. ## Model description Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model. ## Intended uses & limitations As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language. ## Training and evaluation data ## Training procedure The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py). ### Training results Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 # Thanks Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.

STARBORN/MMC

STARBORN

2022-03-29T07:14:35Z

0

1

null

[ "license:mit", "region:us" ]

null

2022-03-29T07:12:26Z

--- license: mit --- Metamodel Card (MMC) builds on MC and DC schemas by adding system level abstraction to the data. MMC instantiations follow

gayanin/t5-small-med-term-conditional-masking-0

gayanin

2022-03-29T03:19:04Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-28T22:04:47Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-med-term-conditional-masking-0 results: [] ---  # t5-small-med-term-conditional-masking-0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6688 - Rouge2 Precision: 0.694 - Rouge2 Recall: 0.4781 - Rouge2 Fmeasure: 0.5479 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.9525 | 1.0 | 13915 | 0.8148 | 0.6657 | 0.4581 | 0.5252 | | 0.8541 | 2.0 | 27830 | 0.7562 | 0.6779 | 0.4694 | 0.5371 | | 0.8183 | 3.0 | 41745 | 0.7268 | 0.6827 | 0.4722 | 0.5405 | | 0.8033 | 4.0 | 55660 | 0.7074 | 0.6861 | 0.4729 | 0.5419 | | 0.7727 | 5.0 | 69575 | 0.6934 | 0.6872 | 0.4726 | 0.5419 | | 0.7704 | 6.0 | 83490 | 0.6832 | 0.6901 | 0.4742 | 0.544 | | 0.7485 | 7.0 | 97405 | 0.6771 | 0.6926 | 0.4772 | 0.5469 | | 0.7528 | 8.0 | 111320 | 0.6722 | 0.6934 | 0.4782 | 0.5478 | | 0.7535 | 9.0 | 125235 | 0.6696 | 0.6944 | 0.4782 | 0.5481 | | 0.7444 | 10.0 | 139150 | 0.6688 | 0.694 | 0.4781 | 0.5479 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

i-was-neo-first/hubert-large-ami-shard-experiment-colab

i-was-neo-first

2022-03-29T00:39:37Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-20T02:10:11Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: hubert-large-ami-shard-experiment-colab results: [] ---  # hubert-large-ami-shard-experiment-colab This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: nan - eval_wer: 1.0 - eval_runtime: 6.0682 - eval_samples_per_second: 16.479 - eval_steps_per_second: 2.142 - epoch: 1.02 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

sanchit-gandhi/wav2vec2-2-bart-large-cnn

sanchit-gandhi

2022-03-29T00:24:41Z

25

0

transformers

[ "transformers", "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "generated_from_trainer", "dataset:librispeech_asr", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-22T16:26:40Z

--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' results: [] ---  # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.3524 - Wer: 0.1042 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.7605 | 4.5 | 500 | 2.6299 | 1.4451 | | 0.1177 | 9.01 | 1000 | 0.3524 | 0.1042 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

frtna/ted_mt-Spanish-to-Italian

frtna

2022-03-28T22:04:21Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:new_dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - new_dataset model-index: - name: ted_mt-Spanish-to-Italian results: [] ---  # ted_mt-Spanish-to-Italian This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-it](https://huggingface.co/Helsinki-NLP/opus-mt-es-it) on the new_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | No log | 1.0 | 46 | 1.4873 | 29.6133 | 26.9081 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6

Chikashi/t5-small-finetuned-cnndm1

Chikashi

2022-03-28T22:00:26Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-28T14:55:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: t5-small-finetuned-cnndm1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 24.4246 ---  # t5-small-finetuned-cnndm1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6853 - Rouge1: 24.4246 - Rouge2: 11.6944 - Rougel: 20.1717 - Rougelsum: 23.0424 - Gen Len: 18.9996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.912 | 0.14 | 5000 | 1.7167 | 24.4232 | 11.7049 | 20.1758 | 23.0345 | 18.9997 | | 1.8784 | 0.28 | 10000 | 1.7018 | 24.4009 | 11.6918 | 20.1561 | 23.0073 | 18.9997 | | 1.8628 | 0.42 | 15000 | 1.6934 | 24.385 | 11.683 | 20.1285 | 22.9823 | 18.9997 | | 1.8594 | 0.56 | 20000 | 1.6902 | 24.4407 | 11.6835 | 20.1734 | 23.0369 | 18.9996 | | 1.8537 | 0.7 | 25000 | 1.6864 | 24.3635 | 11.658 | 20.1318 | 22.9782 | 18.9993 | | 1.8505 | 0.84 | 30000 | 1.6856 | 24.4267 | 11.6991 | 20.1629 | 23.0361 | 18.9994 | | 1.8505 | 0.98 | 35000 | 1.6853 | 24.4246 | 11.6944 | 20.1717 | 23.0424 | 18.9996 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

DrishtiSharma/xls-r-es-test-lm-finetuned-sentiment-mesd

DrishtiSharma

2022-03-28T19:03:37Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2022-03-28T14:54:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: xls-r-es-test-lm-finetuned-sentiment-mesd results: [] ---  # xls-r-es-test-lm-finetuned-sentiment-mesd This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7851 - Accuracy: 0.2385 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.25e-05 - train_batch_size: 64 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.86 | 3 | 1.7876 | 0.1923 | | 1.9709 | 1.86 | 6 | 1.7869 | 0.2 | | 1.9709 | 2.86 | 9 | 1.7859 | 0.2308 | | 2.146 | 3.86 | 12 | 1.7851 | 0.2385 | | 1.9622 | 4.86 | 15 | 1.7842 | 0.1923 | | 1.9622 | 5.86 | 18 | 1.7834 | 0.1769 | | 2.137 | 6.86 | 21 | 1.7823 | 0.1923 | | 2.137 | 7.86 | 24 | 1.7812 | 0.1923 | | 2.1297 | 8.86 | 27 | 1.7800 | 0.1846 | | 1.9502 | 9.86 | 30 | 1.7787 | 0.1846 | | 1.9502 | 10.86 | 33 | 1.7772 | 0.1846 | | 2.1234 | 11.86 | 36 | 1.7760 | 0.1846 | | 2.1234 | 12.86 | 39 | 1.7748 | 0.1846 | | 2.1186 | 13.86 | 42 | 1.7736 | 0.1846 | | 1.9401 | 14.86 | 45 | 1.7725 | 0.1846 | | 1.9401 | 15.86 | 48 | 1.7715 | 0.1923 | | 2.112 | 16.86 | 51 | 1.7706 | 0.1923 | | 2.112 | 17.86 | 54 | 1.7701 | 0.1923 | | 2.1094 | 18.86 | 57 | 1.7697 | 0.2 | | 1.934 | 19.86 | 60 | 1.7696 | 0.2 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct

scasutt

2022-03-28T18:53:54Z

3

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-28T12:30:15Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_fast_10pct results: [] ---  # wav2vec2-large-xlsr-53_toy_train_data_fast_10pct This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6983 - Wer: 0.5026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3619 | 1.05 | 250 | 3.4334 | 1.0 | | 3.0818 | 2.1 | 500 | 3.4914 | 1.0 | | 2.3245 | 3.15 | 750 | 1.6483 | 0.9486 | | 1.0233 | 4.2 | 1000 | 0.8817 | 0.7400 | | 0.7522 | 5.25 | 1250 | 0.7374 | 0.6529 | | 0.5343 | 6.3 | 1500 | 0.6972 | 0.6068 | | 0.4452 | 7.35 | 1750 | 0.6757 | 0.5740 | | 0.4275 | 8.4 | 2000 | 0.6789 | 0.5551 | | 0.3688 | 9.45 | 2250 | 0.6468 | 0.5394 | | 0.3363 | 10.5 | 2500 | 0.6798 | 0.5358 | | 0.3036 | 11.55 | 2750 | 0.6439 | 0.5265 | | 0.3173 | 12.6 | 3000 | 0.6898 | 0.5196 | | 0.2985 | 13.65 | 3250 | 0.6791 | 0.5169 | | 0.288 | 14.7 | 3500 | 0.6442 | 0.5090 | | 0.2673 | 15.75 | 3750 | 0.6984 | 0.5119 | | 0.2575 | 16.81 | 4000 | 0.7146 | 0.5084 | | 0.239 | 17.86 | 4250 | 0.6847 | 0.5040 | | 0.2266 | 18.91 | 4500 | 0.6900 | 0.5028 | | 0.22 | 19.96 | 4750 | 0.6983 | 0.5026 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

kingabzpro/CELEB-GANs

kingabzpro

2022-03-28T18:08:29Z

0

2

null

[ "huggan", "gan", "dcgans", "dataset:huggan/CelebA-faces", "license:apache-2.0", "region:us" ]

null

2022-03-28T16:05:34Z

--- tags: - huggan - gan - dcgans task: image-generation license: apache-2.0 datasets: - huggan/CelebA-faces --- # Fake Faces with DCGANs ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results - Generator_loss: 22.7 - Discriminator_loss: 7.9 ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

aapot/wav2vec2-large-xlsr-53-finnish

aapot

2022-03-28T17:56:36Z

9

0

transformers

[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "fi", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: fi datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Finnish by Aapo Tanskanen results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice fi type: common_voice args: fi metrics: - name: Test WER type: wer value: 32.378771 --- # NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) and [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm) # Wav2Vec2-Large-XLSR-53-Finnish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Finnish using the [Common Voice](https://huggingface.co/datasets/common_voice), [CSS10 Finnish](https://www.kaggle.com/bryanpark/finnish-single-speaker-speech-dataset) and [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) datasets. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import librosa import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "fi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Finnish test data of Common Voice. ```python import librosa import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "fi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\...\…\–\é]' resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 32.378771 % ## Training The Common Voice `train`, `validation` and `other` datasets were used for training as well as `CSS10 Finnish` and `Finnish parliament session 2` datasets. The script used for training can be found from [Google Colab](https://colab.research.google.com/drive/1vnEGC9BnNRmVyIHj-0UsVulh_cUYSGWA?usp=sharing)

aapot/wav2vec2-xlsr-1b-finnish-v2

aapot

2022-03-28T17:49:48Z

6

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-1b-finnish-v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 9.73 - name: Test CER type: cer value: 1.65 --- # Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). **Note**: there is a version with KenLM language model used in the decoding phase producing better transcriptions: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-v2/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. ## Training data This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:| | [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % | | [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % | | [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % | | [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % | | [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % | | [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % | Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.7778 | 0.17 | 500 | 0.2851 | 0.3572 | | 0.5506 | 0.34 | 1000 | 0.1595 | 0.2130 | | 0.6569 | 0.5 | 1500 | 0.1458 | 0.2046 | | 0.5997 | 0.67 | 2000 | 0.1374 | 0.1975 | | 0.542 | 0.84 | 2500 | 0.1390 | 0.1956 | | 0.4815 | 1.01 | 3000 | 0.1266 | 0.1813 | | 0.6982 | 1.17 | 3500 | 0.1441 | 0.1965 | | 0.4522 | 1.34 | 4000 | 0.1232 | 0.1822 | | 0.4655 | 1.51 | 4500 | 0.1209 | 0.1702 | | 0.4069 | 1.68 | 5000 | 0.1149 | 0.1688 | | 0.4226 | 1.84 | 5500 | 0.1121 | 0.1560 | | 0.3993 | 2.01 | 6000 | 0.1091 | 0.1557 | | 0.406 | 2.18 | 6500 | 0.1115 | 0.1553 | | 0.4098 | 2.35 | 7000 | 0.1144 | 0.1560 | | 0.3995 | 2.51 | 7500 | 0.1028 | 0.1476 | | 0.4101 | 2.68 | 8000 | 0.1129 | 0.1511 | | 0.3636 | 2.85 | 8500 | 0.1025 | 0.1517 | | 0.3534 | 3.02 | 9000 | 0.1068 | 0.1480 | | 0.3836 | 3.18 | 9500 | 0.1072 | 0.1459 | | 0.3531 | 3.35 | 10000 | 0.0928 | 0.1367 | | 0.3649 | 3.52 | 10500 | 0.1042 | 0.1426 | | 0.3645 | 3.69 | 11000 | 0.0979 | 0.1433 | | 0.3685 | 3.85 | 11500 | 0.0947 | 0.1346 | | 0.3325 | 4.02 | 12000 | 0.0991 | 0.1352 | | 0.3497 | 4.19 | 12500 | 0.0919 | 0.1358 | | 0.3303 | 4.36 | 13000 | 0.0888 | 0.1272 | | 0.3323 | 4.52 | 13500 | 0.0888 | 0.1277 | | 0.3452 | 4.69 | 14000 | 0.0894 | 0.1279 | | 0.337 | 4.86 | 14500 | 0.0917 | 0.1289 | | 0.3114 | 5.03 | 15000 | 0.0942 | 0.1313 | | 0.3099 | 5.19 | 15500 | 0.0902 | 0.1239 | | 0.3079 | 5.36 | 16000 | 0.0871 | 0.1256 | | 0.3293 | 5.53 | 16500 | 0.0861 | 0.1263 | | 0.3123 | 5.7 | 17000 | 0.0876 | 0.1203 | | 0.3093 | 5.86 | 17500 | 0.0848 | 0.1226 | | 0.2903 | 6.03 | 18000 | 0.0914 | 0.1221 | | 0.297 | 6.2 | 18500 | 0.0841 | 0.1185 | | 0.2797 | 6.37 | 19000 | 0.0858 | 0.1165 | | 0.2878 | 6.53 | 19500 | 0.0874 | 0.1161 | | 0.2974 | 6.7 | 20000 | 0.0835 | 0.1173 | | 0.3051 | 6.87 | 20500 | 0.0835 | 0.1178 | | 0.2941 | 7.04 | 21000 | 0.0852 | 0.1155 | | 0.258 | 7.21 | 21500 | 0.0832 | 0.1132 | | 0.2778 | 7.37 | 22000 | 0.0829 | 0.1110 | | 0.2751 | 7.54 | 22500 | 0.0822 | 0.1069 | | 0.2887 | 7.71 | 23000 | 0.0819 | 0.1103 | | 0.2509 | 7.88 | 23500 | 0.0787 | 0.1055 | | 0.2501 | 8.04 | 24000 | 0.0807 | 0.1076 | | 0.2399 | 8.21 | 24500 | 0.0784 | 0.1052 | | 0.2539 | 8.38 | 25000 | 0.0772 | 0.1075 | | 0.248 | 8.55 | 25500 | 0.0772 | 0.1055 | | 0.2689 | 8.71 | 26000 | 0.0763 | 0.1027 | | 0.2855 | 8.88 | 26500 | 0.0756 | 0.1035 | | 0.2421 | 9.05 | 27000 | 0.0771 | 0.0998 | | 0.2497 | 9.22 | 27500 | 0.0756 | 0.0971 | | 0.2367 | 9.38 | 28000 | 0.0741 | 0.0974 | | 0.2473 | 9.55 | 28500 | 0.0739 | 0.0982 | | 0.2396 | 9.72 | 29000 | 0.0756 | 0.0991 | | 0.2602 | 9.89 | 29500 | 0.0737 | 0.0975 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-v2 --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: | | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-----------------------------------------|---------------|------------------|---------------|------------------| |aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** | |aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 | |aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 | ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗

aapot/wav2vec2-xlsr-1b-finnish-lm

aapot

2022-03-28T17:31:03Z

7

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-1b-finnish-lm results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 5.65 - name: Test CER type: cer value: 1.2 --- # Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model. **Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization. **Note**: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding. ## Training data This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:----------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:| | [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.74 % | | [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % | | [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 5.94 h | 2.29 % | | [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.98 % | | [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 87.84 % | | [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 2.07 % | Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.968 | 0.18 | 500 | 0.4870 | 0.4720 | | 0.6557 | 0.36 | 1000 | 0.2450 | 0.2931 | | 0.647 | 0.54 | 1500 | 0.1818 | 0.2255 | | 0.5297 | 0.72 | 2000 | 0.1698 | 0.2354 | | 0.5802 | 0.9 | 2500 | 0.1581 | 0.2355 | | 0.6351 | 1.07 | 3000 | 0.1689 | 0.2336 | | 0.4626 | 1.25 | 3500 | 0.1719 | 0.3099 | | 0.4526 | 1.43 | 4000 | 0.1434 | 0.2069 | | 0.4692 | 1.61 | 4500 | 0.1645 | 0.2192 | | 0.4584 | 1.79 | 5000 | 0.1483 | 0.1987 | | 0.4234 | 1.97 | 5500 | 0.1499 | 0.2178 | | 0.4243 | 2.15 | 6000 | 0.1345 | 0.2070 | | 0.4108 | 2.33 | 6500 | 0.1383 | 0.1850 | | 0.4048 | 2.51 | 7000 | 0.1338 | 0.1811 | | 0.4085 | 2.69 | 7500 | 0.1290 | 0.1780 | | 0.4026 | 2.87 | 8000 | 0.1239 | 0.1650 | | 0.4033 | 3.04 | 8500 | 0.1346 | 0.1657 | | 0.3986 | 3.22 | 9000 | 0.1310 | 0.1850 | | 0.3867 | 3.4 | 9500 | 0.1273 | 0.1741 | | 0.3658 | 3.58 | 10000 | 0.1219 | 0.1672 | | 0.382 | 3.76 | 10500 | 0.1306 | 0.1698 | | 0.3847 | 3.94 | 11000 | 0.1230 | 0.1577 | | 0.3691 | 4.12 | 11500 | 0.1310 | 0.1615 | | 0.3593 | 4.3 | 12000 | 0.1296 | 0.1622 | | 0.3619 | 4.48 | 12500 | 0.1285 | 0.1601 | | 0.3361 | 4.66 | 13000 | 0.1261 | 0.1569 | | 0.3603 | 4.84 | 13500 | 0.1235 | 0.1533 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: | | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-----------------------------------------|---------------|------------------|---------------|------------------| |aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** | |aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 | |aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 | ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗

aapot/wav2vec2-xlsr-300m-finnish-lm

aapot

2022-03-28T17:22:08Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-300m-finnish-lm results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 8.16 - name: Test CER type: cer value: 1.97 --- # Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model. **Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization. ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-300m-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding. ## Training data This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:| | [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % | | [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % | | [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % | | [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % | | [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % | | [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % | Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned [Finnish Wikipedia](https://huggingface.co/datasets/wikipedia) (August 2021) dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-04 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-300m` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.973 | 0.17 | 500 | 0.5750 | 0.6844 | | 0.713 | 0.34 | 1000 | 0.3356 | 0.4518 | | 0.6563 | 0.5 | 1500 | 0.3007 | 0.4039 | | 0.642 | 0.67 | 2000 | 0.2619 | 0.3674 | | 0.6203 | 0.84 | 2500 | 0.2488 | 0.3558 | | 0.6016 | 1.01 | 3000 | 0.2795 | 0.3835 | | 0.5423 | 1.17 | 3500 | 0.2652 | 0.3310 | | 0.5639 | 1.34 | 4000 | 0.2479 | 0.3462 | | 0.586 | 1.51 | 4500 | 0.2409 | 0.3295 | | 0.5169 | 1.68 | 5000 | 0.2728 | 0.3352 | | 0.5176 | 1.84 | 5500 | 0.2254 | 0.3149 | | 0.4983 | 2.01 | 6000 | 0.2169 | 0.3009 | | 0.4982 | 2.18 | 6500 | 0.2215 | 0.3079 | | 0.4898 | 2.35 | 7000 | 0.2174 | 0.3023 | | 0.4922 | 2.51 | 7500 | 0.2217 | 0.3081 | | 0.5025 | 2.68 | 8000 | 0.2002 | 0.2710 | | 0.4745 | 2.85 | 8500 | 0.1935 | 0.2783 | | 0.4377 | 3.02 | 9000 | 0.1859 | 0.2742 | | 0.4511 | 3.18 | 9500 | 0.2038 | 0.2786 | | 0.4411 | 3.35 | 10000 | 0.1863 | 0.2651 | | 0.4501 | 3.52 | 10500 | 0.1948 | 0.2605 | | 0.4557 | 3.69 | 11000 | 0.1872 | 0.2695 | | 0.4493 | 3.85 | 11500 | 0.1888 | 0.2632 | | 0.4047 | 4.02 | 12000 | 0.1818 | 0.2559 | | 0.4319 | 4.19 | 12500 | 0.1896 | 0.2648 | | 0.4162 | 4.36 | 13000 | 0.1953 | 0.2595 | | 0.4046 | 4.52 | 13500 | 0.1864 | 0.2606 | | 0.4195 | 4.69 | 14000 | 0.1843 | 0.2467 | | 0.4146 | 4.86 | 14500 | 0.1686 | 0.2450 | | 0.378 | 5.03 | 15000 | 0.1731 | 0.2401 | | 0.3792 | 5.19 | 15500 | 0.1676 | 0.2325 | | 0.3855 | 5.36 | 16000 | 0.1740 | 0.2326 | | 0.4029 | 5.53 | 16500 | 0.1674 | 0.2345 | | 0.386 | 5.7 | 17000 | 0.1735 | 0.2280 | | 0.3811 | 5.86 | 17500 | 0.1692 | 0.2258 | | 0.3607 | 6.03 | 18000 | 0.1797 | 0.2279 | | 0.3604 | 6.2 | 18500 | 0.1651 | 0.2206 | | 0.3362 | 6.37 | 19000 | 0.1627 | 0.2199 | | 0.3611 | 6.53 | 19500 | 0.1652 | 0.2172 | | 0.3671 | 6.7 | 20000 | 0.1564 | 0.2140 | | 0.3769 | 6.87 | 20500 | 0.1525 | 0.2101 | | 0.3539 | 7.04 | 21000 | 0.1639 | 0.2096 | | 0.3225 | 7.21 | 21500 | 0.1611 | 0.2087 | | 0.3323 | 7.37 | 22000 | 0.1633 | 0.2008 | | 0.3327 | 7.54 | 22500 | 0.1692 | 0.1975 | | 0.3456 | 7.71 | 23000 | 0.1555 | 0.1991 | | 0.3058 | 7.88 | 23500 | 0.1590 | 0.1959 | | 0.3034 | 8.04 | 24000 | 0.1531 | 0.1973 | | 0.2925 | 8.21 | 24500 | 0.1583 | 0.1978 | | 0.2967 | 8.38 | 25000 | 0.1546 | 0.1906 | | 0.2974 | 8.55 | 25500 | 0.1540 | 0.1869 | | 0.3131 | 8.71 | 26000 | 0.1534 | 0.1850 | | 0.3306 | 8.88 | 26500 | 0.1482 | 0.1844 | | 0.2842 | 9.05 | 27000 | 0.1490 | 0.1854 | | 0.2879 | 9.22 | 27500 | 0.1463 | 0.1799 | | 0.27 | 9.38 | 28000 | 0.1454 | 0.1798 | | 0.2874 | 9.55 | 28500 | 0.1504 | 0.1787 | | 0.2757 | 9.72 | 29000 | 0.1512 | 0.1784 | | 0.3017 | 9.89 | 29500 | 0.1484 | 0.1800 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-300m-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: | | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-----------------------------------------|---------------|------------------|---------------|------------------| |aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** | |aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 | |aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 | ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗

ntoldalagi/C0_LID_DEV

ntoldalagi

2022-03-28T15:46:21Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-21T21:34:38Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: name: C0_LID_DEV ---  # C0_LID_DEV This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.8267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.0 | 25 | inf | 0.8426 | | 1.5354 | 0.17 | 2000 | inf | 0.8198 | | 1.5688 | 0.33 | 4000 | inf | 0.8271 | | 1.5294 | 0.5 | 6000 | inf | 0.8339 | | 1.1947 | 0.67 | 8000 | inf | 0.8260 | | 1.1534 | 0.83 | 10000 | inf | 0.8267 | | 1.1484 | 1.0 | 12000 | inf | 0.8267 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

mikeadimech/punctuation-test-4

mikeadimech

2022-03-28T15:09:06Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-28T14:31:52Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: punctuation-test-4 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 39.1294 ---  # punctuation-test-4 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 0.3411 - Bleu: 39.1294 - Gen Len: 18.4812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.3331 | 1.0 | 625 | 0.3411 | 39.1294 | 18.4812 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

dhlee347/distilbert-imdb

dhlee347

2022-03-28T14:07:15Z

4

1

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-28T14:01:20Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: distilbert-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9302 ---  # distilbert-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1796 - Accuracy: 0.9302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2808 | 1.0 | 782 | 0.1796 | 0.9302 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.6

Chikashi/t5-small-finetuned-cnndm

Chikashi

2022-03-28T14:04:38Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-28T09:07:17Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: t5-small-finetuned-cnndm results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 24.417 ---  # t5-small-finetuned-cnndm This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6854 - Rouge1: 24.417 - Rouge2: 11.6924 - Rougel: 20.1756 - Rougelsum: 23.0414 - Gen Len: 18.9996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:| | 1.8522 | 1.0 | 35890 | 1.6854 | 24.417 | 11.6924 | 20.1756 | 23.0414 | 18.9996 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

dennisowusuk/wav2vec2-large-xls-r-300m-turkish-colab

dennisowusuk

2022-03-28T13:28:30Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-28T05:29:48Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] ---  # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3863 - Wer: 0.3095 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.8284 | 3.67 | 400 | 0.6782 | 0.6739 | | 0.4174 | 7.34 | 800 | 0.4524 | 0.4811 | | 0.2015 | 11.01 | 1200 | 0.4736 | 0.4311 | | 0.1371 | 14.68 | 1600 | 0.4254 | 0.3929 | | 0.0997 | 18.35 | 2000 | 0.4254 | 0.3636 | | 0.082 | 22.02 | 2400 | 0.3807 | 0.3474 | | 0.0665 | 25.69 | 2800 | 0.3987 | 0.3236 | | 0.0523 | 29.36 | 3200 | 0.3863 | 0.3095 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

huggingtweets/hirox246

huggingtweets

2022-03-28T13:12:56Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/hirox246/1648473171015/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/646595746905620480/oeKI14gB_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ひろゆき, Hiroyuki Nishimura</div> <div style="text-align: center; font-size: 14px;">@hirox246</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ひろゆき, Hiroyuki Nishimura. | Data | ひろゆき, Hiroyuki Nishimura | | --- | --- | | Tweets downloaded | 3246 | | Retweets | 288 | | Short tweets | 2002 | | Tweets kept | 956 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fs862rv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hirox246's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ktc28kc0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ktc28kc0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/hirox246') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

scasutt/wav2vec2-large-xlsr-53_toy_train_data_augmented

scasutt

2022-03-28T12:29:16Z

3

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-27T17:08:43Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_augmented results: [] ---  # wav2vec2-large-xlsr-53_toy_train_data_augmented This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5016 - Wer: 0.4656 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.418 | 1.05 | 250 | 3.4171 | 1.0 | | 3.0886 | 2.1 | 500 | 3.4681 | 1.0 | | 2.9422 | 3.15 | 750 | 2.6151 | 1.0 | | 1.3195 | 4.2 | 1000 | 0.8789 | 0.7739 | | 0.9154 | 5.25 | 1250 | 0.6364 | 0.6518 | | 0.6519 | 6.3 | 1500 | 0.5682 | 0.5949 | | 0.5622 | 7.35 | 1750 | 0.5273 | 0.5625 | | 0.4965 | 8.4 | 2000 | 0.4891 | 0.5283 | | 0.4283 | 9.45 | 2250 | 0.5018 | 0.5260 | | 0.4019 | 10.5 | 2500 | 0.5016 | 0.5006 | | 0.3585 | 11.55 | 2750 | 0.5047 | 0.5003 | | 0.3275 | 12.6 | 3000 | 0.5148 | 0.4866 | | 0.3427 | 13.65 | 3250 | 0.5035 | 0.4786 | | 0.3229 | 14.7 | 3500 | 0.4855 | 0.4768 | | 0.3332 | 15.75 | 3750 | 0.5040 | 0.4769 | | 0.2861 | 16.81 | 4000 | 0.5138 | 0.4669 | | 0.3029 | 17.86 | 4250 | 0.5133 | 0.4670 | | 0.2633 | 18.91 | 4500 | 0.5063 | 0.4637 | | 0.2621 | 19.96 | 4750 | 0.5016 | 0.4656 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

Champion/SA-models

Champion

2022-03-28T11:56:45Z

0

null

[ "pchampio", "audio", "region:us" ]

null

2022-03-10T18:32:17Z

--- tags: - pchampio - audio inference: false ---

VincentC12/rh_classification_kara

VincentC12

2022-03-28T11:53:41Z

9

0

pytorch

[ "pytorch", "distilbert", "sentiment-analysis", "en", "region:us" ]

null

2022-03-23T16:19:02Z

--- language: - en library_name: pytorch metrics: - satisfaction - culture organisationnelle - leadership - conditions de travail tags: - sentiment-analysis widget: - text: "My work is recognized by my superiors and I would even say that I feel like I have more recognition since we are on telework." example_title: "Exemple leadership" - text: "For Working conditions and wages in particular." example_title: "Exemple conditions de travail" - text: "A climate of overperformance is in place in the company." example_title: "Exemple culture organisationnelle" - text: "With regard to telework, I look forward to setting up the hybrid week, so 2 3 days at home and at the office." example_title: "Exemple satisfaction" --- Ce modèle est développé pour KARA. Ce modèle est : - Un outil de classification thématique des commentaires RH - Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits) - Spécialisé pour des commentaires entre 10 et 512 charactères Ce modèle n'est pas : - Utilisable pour détecter un discours haineux ou bien une lettre de suicide Étiquettes : - Label_0 = Satisfaction - Label_1 = Culture Organisationnelle - Label_2 = Leadership - Label_3 = Conditions de travail version 0.0.1 Performances sur le jeux de données du HRM : 84.3% de précision

robvanderg/Sem-RemmmBERT

robvanderg

2022-03-28T11:29:41Z

5

0

transformers

[ "transformers", "pytorch", "rembert", "feature-extraction", "STILT", "retraining", "multi-task learning", "multilingual", "endpoints_compatible", "region:us" ]

feature-extraction

2022-03-28T11:20:13Z

--- language: - multilingual tags: - STILT - retraining - multi-task learning datasets: - SemEval 2022 --- ## Sem-RemmmBERT This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from remBERT (https://huggingface.co/google/rembertased). The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (https://machamp-nlp.github.io/), a toolkit focusing on multi-task learning for NLP. More information can be found in the paper (which should be released when the SemEval proceedings are online).

robvanderg/Sem-mmmBERT

robvanderg

2022-03-28T11:28:17Z

4

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "STILT", "retraining", "multi-task learning", "multilingual", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-03-28T11:15:17Z

--- language: - multilingual tags: - STILT - retraining - multi-task learning datasets: - SemEval 2022 --- ## Sem-mmmBERT This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (https://huggingface.co/bert-base-multilingual-cased). The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (https://machamp-nlp.github.io/), a toolkit focusing on multi-task learning for NLP. More information can be found in the paper (which should be released when the SemEval proceedings are online).

sanchit-gandhi/wav2vec2-2-bart-large-cnn-no-adapter

sanchit-gandhi

2022-03-28T11:26:30Z

11

0

transformers

[ "transformers", "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "generated_from_trainer", "dataset:librispeech_asr", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-26T17:08:05Z

--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' results: [] ---  # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 3.9938 - Wer: 0.9745 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.9301 | 2.24 | 500 | 4.6291 | 0.9601 | | 4.4562 | 4.48 | 1000 | 4.3604 | 0.9608 | | 3.8356 | 6.73 | 1500 | 4.0728 | 0.9530 | | 3.2716 | 8.97 | 2000 | 3.9938 | 0.9745 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

21iridescent/distilroberta-base-finetuned-squad2-lwt

21iridescent

2022-03-28T11:18:44Z

31

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-28T08:54:28Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilroberta-base-finetuned-squad2-lwt results: [] ---  # distilroberta-base-finetuned-squad2-lwt This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.1356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1702 | 1.0 | 4120 | 1.1220 | | 0.9787 | 2.0 | 8240 | 1.0500 | | 0.8153 | 3.0 | 12360 | 1.1356 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6 {'HasAns_exact': 71.39001349527665, 'HasAns_f1': 77.71740687727831, 'HasAns_total': 5928, 'NoAns_exact': 68.59545836837678, 'NoAns_f1': 68.59545836837678, 'NoAns_total': 5945, 'best_exact': 69.9991577528847, 'best_exact_thresh': 0.0, 'best_f1': 73.1583245993857, 'best_f1_thresh': 0.0, 'exact': 69.99073528173166, 'f1': 73.1499021282327, 'total': 11873}

mrm8488/t5-base-iterater

mrm8488

2022-03-28T11:00:41Z

5

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "IteraTeR", "en", "dataset:wanyu/IteraTeR_full_sent", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-27T18:48:43Z

--- license: apache-2.0 language: - en datasets: - wanyu/IteraTeR_full_sent tags: - generated_from_trainer - IteraTeR widget: - text: "<clarity> Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." model-index: - name: t5-base-iterater results: [] ---  # T5 (base) fine-tuned on IteraTeR This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an [IteraTeR](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset. It achieves the following results on the evaluation set: - Loss: 0.2580 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.3286 | 0.09 | 2000 | 0.3010 | | 0.3194 | 0.18 | 4000 | 0.2872 | | 0.3208 | 0.27 | 6000 | 0.2792 | | 0.3091 | 0.36 | 8000 | 0.2731 | | 0.3164 | 0.45 | 10000 | 0.2678 | | 0.2941 | 0.54 | 12000 | 0.2682 | | 0.2981 | 0.63 | 14000 | 0.2696 | | 0.2975 | 0.72 | 16000 | 0.2643 | | 0.3109 | 0.81 | 18000 | 0.2624 | | 0.2965 | 0.9 | 20000 | 0.2648 | | 0.3053 | 0.99 | 22000 | 0.2627 | | 0.2779 | 1.08 | 24000 | 0.2632 | | 0.2692 | 1.17 | 26000 | 0.2608 | | 0.2755 | 1.26 | 28000 | 0.2600 | | 0.2771 | 1.35 | 30000 | 0.2584 | | 0.2774 | 1.44 | 32000 | 0.2609 | | 0.2976 | 1.53 | 34000 | 0.2593 | | 0.2646 | 1.62 | 36000 | 0.2616 | | 0.2705 | 1.71 | 38000 | 0.2574 | | 0.2714 | 1.8 | 40000 | 0.2577 | | 0.2857 | 1.9 | 42000 | 0.2576 | | 0.2832 | 1.99 | 44000 | 0.2580 | ### How to use ```py from transformers import T5ForConditionalGeneration, T5TokenizerFast MODEL_CKPT = 'mrm8488/t5-base-iterater' tokenizer = T5TokenizerFast.from_pretrained(MODEL_CKPT) model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT) def predict(intent, text): input_text = f"<{intent}> {text}" features = tokenizer([input_text], return_tensors='pt') output = model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'], max_length=128, num_beams=8) return tokenizer.decode(output[0], skip_special_tokens=True) text = "Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." intent = "clarity" predict(intent, text) # Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. ``` ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

SAGAR4REAL/wav2vec2hindia

SAGAR4REAL

2022-03-28T08:32:52Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-28T07:17:45Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2hindia results: [] ---  # wav2vec2hindia This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

huggingtweets/nsawaikar

huggingtweets

2022-03-28T07:54:11Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-28T07:52:56Z

--- language: en thumbnail: http://www.huggingtweets.com/nsawaikar/1648454046318/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1508184022052184064/yqLU6MxW_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nathan.eth</div> <div style="text-align: center; font-size: 14px;">@nsawaikar</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nathan.eth. | Data | Nathan.eth | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 336 | | Short tweets | 621 | | Tweets kept | 2293 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/pn1domem/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nsawaikar's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g9hqx5dx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g9hqx5dx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nsawaikar') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

jkhan447/sentiment-model-sample-offline-goemotion

jkhan447

2022-03-28T06:50:10Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-28T06:33:49Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: sentiment-model-sample-offline-goemotion results: [] ---  # sentiment-model-sample-offline-goemotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0183 - Accuracy: 0.7109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

timhbach/Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract

timhbach

2022-03-28T06:27:50Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-28T03:21:43Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract results: [] ---  # Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0231 - eval_precision: 0.7448 - eval_recall: 0.75 - eval_f1: 0.7474 - eval_accuracy: 0.9942 - eval_runtime: 61.7618 - eval_samples_per_second: 27.201 - eval_steps_per_second: 3.4 - epoch: 3.0 - step: 5670 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6

haddadalwi/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad

haddadalwi

2022-03-28T05:04:56Z

32

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-10T14:03:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad results: [] ---  # bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-islamic-squad This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 40 | 0.4082 | | No log | 2.0 | 80 | 0.3855 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

BigSalmon/InformalToFormalLincoln31

BigSalmon

2022-03-28T00:48:44Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-27T23:08:12Z

``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln31") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln31") ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ```

huggingtweets/jacobe

huggingtweets

2022-03-27T23:02:12Z

5

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-27T23:01:35Z

--- language: en thumbnail: http://www.huggingtweets.com/jacobe/1648422127637/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1025926108984664064/2ZHTSIof_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Rowel Atienza</div> <div style="text-align: center; font-size: 14px;">@jacobe</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Rowel Atienza. | Data | Rowel Atienza | | --- | --- | | Tweets downloaded | 100 | | Retweets | 29 | | Short tweets | 4 | | Tweets kept | 67 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1uzq4b7w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jacobe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ouo6sis) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ouo6sis/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/jacobe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/baguioni

huggingtweets

2022-03-27T22:55:21Z

4

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-27T22:54:40Z

--- language: en thumbnail: http://www.huggingtweets.com/baguioni/1648421716784/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506662013707046914/hVtCPrPL_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">baguio</div> <div style="text-align: center; font-size: 14px;">@baguioni</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from baguio. | Data | baguio | | --- | --- | | Tweets downloaded | 3012 | | Retweets | 1090 | | Short tweets | 527 | | Tweets kept | 1395 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1z9nh9v8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @baguioni's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s53fr1o) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s53fr1o/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/baguioni') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/baguioni-elonmusk-jacobe

huggingtweets

2022-03-27T22:44:21Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-27T22:43:39Z

--- language: en thumbnail: http://www.huggingtweets.com/baguioni-elonmusk-jacobe/1648421056394/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1025926108984664064/2ZHTSIof_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506662013707046914/hVtCPrPL_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Rowel Atienza & baguio</div> <div style="text-align: center; font-size: 14px;">@baguioni-elonmusk-jacobe</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Rowel Atienza & baguio. | Data | Elon Musk | Rowel Atienza | baguio | | --- | --- | --- | --- | | Tweets downloaded | 1621 | 100 | 3012 | | Retweets | 69 | 29 | 1090 | | Short tweets | 520 | 4 | 527 | | Tweets kept | 1032 | 67 | 1395 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xuj1tda/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @baguioni-elonmusk-jacobe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fpkbu3i) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fpkbu3i/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/baguioni-elonmusk-jacobe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

leonadase/bert-base-chinese-finetuned-fdRE

leonadase

2022-03-27T20:52:06Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:sem_eval2010_task8", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-27T19:04:51Z

--- tags: - generated_from_trainer datasets: - sem_eval2010_task8 metrics: - accuracy model-index: - name: bert-base-chinese-finetuned-fdRE results: - task: name: Text Classification type: text-classification dataset: name: sem_eval2010_task8 type: sem_eval2010_task8 args: default metrics: - name: Accuracy type: accuracy value: 0.9080962800875274 ---  # bert-base-chinese-finetuned-fdRE This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the sem_eval2010_task8 dataset. It achieves the following results on the evaluation set: - Loss: 0.2716 - Accuracy: 0.9081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 46 | 0.5571 | 0.7812 | | No log | 2.0 | 92 | 0.4030 | 0.8621 | | No log | 3.0 | 138 | 0.3139 | 0.8928 | | No log | 4.0 | 184 | 0.2716 | 0.9081 | | No log | 5.0 | 230 | 0.2564 | 0.9081 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

ikram54/autotrain-harassement-675420038

ikram54

2022-03-27T18:08:30Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:ikram54/autotrain-data-harassement", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-27T18:06:02Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ikram54/autotrain-data-harassement co2_eq_emissions: 2.6332836871905054 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 675420038 - CO2 Emissions (in grams): 2.6332836871905054 ## Validation Metrics - Loss: 0.8747465014457703 - Accuracy: 0.7085201793721974 - Macro F1: 0.579743989078862 - Micro F1: 0.7085201793721974 - Weighted F1: 0.6913786522271296 - Macro Precision: 0.5669375905888698 - Micro Precision: 0.7085201793721974 - Weighted Precision: 0.6760144007300164 - Macro Recall: 0.5940655209452201 - Micro Recall: 0.7085201793721974 - Weighted Recall: 0.7085201793721974 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ikram54/autotrain-harassement-675420038 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

willcai/wav2vec2_common_voice_accents_indian_only_rerun

willcai

2022-03-27T18:00:16Z

2

1

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-27T06:51:10Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2_common_voice_accents_indian_only_rerun results: [] ---  # wav2vec2_common_voice_accents_indian_only_rerun This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2807 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 48 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 384 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 588 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.6205 | 25.0 | 400 | 1.4584 | | 0.3427 | 50.0 | 800 | 1.8377 | | 0.1213 | 75.0 | 1200 | 1.6086 | | 0.0643 | 100.0 | 1600 | 1.5136 | | 0.0433 | 125.0 | 2000 | 1.4882 | | 0.0323 | 150.0 | 2400 | 1.2204 | | 0.0265 | 175.0 | 2800 | 1.3034 | | 0.0206 | 200.0 | 3200 | 1.2866 | | 0.0191 | 225.0 | 3600 | 1.2337 | | 0.0148 | 250.0 | 4000 | 1.1729 | | 0.0121 | 275.0 | 4400 | 1.2059 | | 0.0105 | 300.0 | 4800 | 1.1246 | | 0.01 | 325.0 | 5200 | 1.1397 | | 0.0098 | 350.0 | 5600 | 1.1684 | | 0.0073 | 375.0 | 6000 | 1.1030 | | 0.0061 | 400.0 | 6400 | 1.2077 | | 0.0049 | 425.0 | 6800 | 1.2653 | | 0.0044 | 450.0 | 7200 | 1.1587 | | 0.0037 | 475.0 | 7600 | 1.2283 | | 0.0033 | 500.0 | 8000 | 1.1897 | | 0.0026 | 525.0 | 8400 | 1.2633 | | 0.0023 | 550.0 | 8800 | 1.2571 | | 0.002 | 575.0 | 9200 | 1.2807 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6

scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1

scasutt

2022-03-27T17:07:53Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-25T17:45:52Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_toy_train_data_augment_0.1 results: [] ---  # wav2vec2-large-xlsr-53_toy_train_data_augment_0.1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4658 - Wer: 0.5037 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.447 | 1.05 | 250 | 3.3799 | 1.0 | | 3.089 | 2.1 | 500 | 3.4868 | 1.0 | | 3.063 | 3.15 | 750 | 3.3155 | 1.0 | | 2.4008 | 4.2 | 1000 | 1.2934 | 0.8919 | | 1.618 | 5.25 | 1250 | 0.7847 | 0.7338 | | 1.3038 | 6.3 | 1500 | 0.6459 | 0.6712 | | 1.2074 | 7.35 | 1750 | 0.5705 | 0.6269 | | 1.1062 | 8.4 | 2000 | 0.5267 | 0.5843 | | 1.026 | 9.45 | 2250 | 0.5108 | 0.5683 | | 0.9505 | 10.5 | 2500 | 0.5066 | 0.5568 | | 0.893 | 11.55 | 2750 | 0.5161 | 0.5532 | | 0.8535 | 12.6 | 3000 | 0.4994 | 0.5341 | | 0.8462 | 13.65 | 3250 | 0.4626 | 0.5262 | | 0.8334 | 14.7 | 3500 | 0.4593 | 0.5197 | | 0.842 | 15.75 | 3750 | 0.4651 | 0.5126 | | 0.7678 | 16.81 | 4000 | 0.4687 | 0.5120 | | 0.7873 | 17.86 | 4250 | 0.4716 | 0.5070 | | 0.7486 | 18.91 | 4500 | 0.4657 | 0.5033 | | 0.7073 | 19.96 | 4750 | 0.4658 | 0.5037 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

csukuangfj/icefall-asr-librispeech-stateless-transducer-2022-03-27-2

csukuangfj

2022-03-27T15:59:24Z

0

null

[ "tensorboard", "region:us" ]

null

2022-03-27T13:27:21Z

## Introduction Please see <https://github.com/k2-fsa/icefall/pull/271> for more details.

EMBO/bio-lm

EMBO

2022-03-27T15:46:51Z

8

0

transformers

[ "transformers", "pytorch", "jax", "roberta", "fill-mask", "language model", "dataset:EMBO/biolang", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: - english thumbnail: tags: - language model license: datasets: - EMBO/biolang metrics: - --- # bio-lm ## Model description This model is a [RoBERTa base pre-trained model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). ## Intended uses & limitations #### How to use The intended use of this model is to be fine-tuned for downstream tasks, token classification in particular. To have a quick check of the model as-is in a fill-mask task: ```python from transformers import pipeline, RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) text = "Let us try this model to see if it <mask>." fill_mask = pipeline( "fill-mask", model='EMBO/bio-lm', tokenizer=tokenizer ) fill_mask(text) ``` #### Limitations and bias This model should be fine-tuned on a specifi task like token classification. The model must be used with the `roberta-base` tokenizer. ## Training data The model was trained with a masked language modeling taskon the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes 12Mio examples from abstracts and figure legends extracted from papers published in life sciences. ## Training procedure The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Command: `python -m lm.train /data/json/oapmc_abstracts_figs/ MLM` - Tokenizer vocab size: 50265 - Training data: EMBO/biolang MLM - Training with: 12005390 examples - Evaluating on: 36713 examples - Epochs: 3.0 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - tensorboard run: lm-MLM-2021-01-27T15-17-43.113766 End of training: ``` trainset: 'loss': 0.8653350830078125 validation set: 'eval_loss': 0.8192330598831177, 'eval_recall': 0.8154601116513597 ``` ## Eval results Eval on test set: ``` recall: 0.814471959728645 ```