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 2025-08-30 06:27:36	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 527 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-30 06:27:12	card stringlengths 11 1.01M
Aonodensetsu/codyblue-731	Aonodensetsu	2023-08-31T10:55:11Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T12:04:23Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist codyblue-731 trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry, femboy" - the recommended settings are epoch 11-15, strength 0.6-0.8. ![preview](preview.jpeg)
Aonodensetsu/cromachina	Aonodensetsu	2023-08-31T10:54:31Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T12:09:10Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist cromachina trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 11-15, strength 0.5-0.8. ![preview](preview.jpeg)
Aonodensetsu/delicious	Aonodensetsu	2023-08-31T10:54:12Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T12:42:21Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist delicious trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 12-15, strength 0.6-0.8. ![preview](preview.jpeg)
abhishek/llama-2-7b-hf-guanaco-sr-1	abhishek	2023-08-31T10:54:05Z	0	0	null	[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-08-31T08:09:34Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: llama-2-7b-hf-guanaco-sr-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. --> # llama-2-7b-hf-guanaco-sr-1 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown 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: 0.0002 - train_batch_size: 2 - 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_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
Aonodensetsu/darkmirage	Aonodensetsu	2023-08-31T10:53:59Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T12:24:00Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist darkmirage trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 13-14, strength 0.5-0.7. ![preview](preview.jpeg)
Aonodensetsu/frenky_hw	Aonodensetsu	2023-08-31T10:53:21Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T12:53:19Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist frenky_hw trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry, male, girly" - the recommended settings are epoch 11-13, strength 0.6-0.8. ![preview](preview.jpeg)
Aonodensetsu/gothbunnyboy	Aonodensetsu	2023-08-31T10:53:05Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T12:57:11Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist gothbunnyboy trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 11-15, strength 0.6-0.8. ![preview](preview.jpeg)
vishnuhaasan/q-FrozenLake-v1-4x4-noSlippery	vishnuhaasan	2023-08-31T10:52:54Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T10:52:48Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage model = load_from_hub(repo_id="vishnuhaasan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
Aonodensetsu/pumpkinspicelatte	Aonodensetsu	2023-08-31T10:52:39Z	0	0	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-15T13:04:44Z	--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist pumpkinspicelatte trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 10-15, strength 0.6-0.9. ![preview](preview.png)
ardt-multipart/ardt-multipart-ppo_train_walker2d_level-3108_0934-33	ardt-multipart	2023-08-31T10:39:20Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-31T08:36:19Z	--- tags: - generated_from_trainer model-index: - name: ardt-multipart-ppo_train_walker2d_level-3108_0934-33 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. --> # ardt-multipart-ppo_train_walker2d_level-3108_0934-33 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
SHENMU007/neunit_BASE_V9.5.9	SHENMU007	2023-08-31T10:36:34Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-08-31T09:35:39Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit 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. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
dt-and-vanilla-ardt/dt-ppo_train_hopper_level-3108_1003-66	dt-and-vanilla-ardt	2023-08-31T10:15:36Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-31T09:04:45Z	--- tags: - generated_from_trainer model-index: - name: dt-ppo_train_hopper_level-3108_1003-66 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. --> # dt-ppo_train_hopper_level-3108_1003-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
sumet/Test_Trocr_digit_handwriting	sumet	2023-08-31T09:52:03Z	201	2	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "trocr", "image-to-text", "endpoints_compatible", "region:us" ]	image-to-text	2023-08-30T02:28:52Z	--- tags: - trocr - image-to-text ---
phillipos99/ppo-LunarLander-v2	phillipos99	2023-08-31T09:51:38Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T09:51:20Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.40 +/- 17.75 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
vnktrmnb/MBERT_FT-TyDiQA_S67	vnktrmnb	2023-08-31T09:45:40Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-30T06:03:12Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S67 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. --> # vnktrmnb/MBERT_FT-TyDiQA_S67 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3185 - Train End Logits Accuracy: 0.9077 - Train Start Logits Accuracy: 0.9272 - Validation Loss: 0.5503 - Validation End Logits Accuracy: 0.875 - Validation Start Logits Accuracy: 0.9111 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 0.6586 \| 0.8284 \| 0.8598 \| 0.5000 \| 0.8737 \| 0.9124 \| 0 \| \| 0.4565 \| 0.8766 \| 0.8978 \| 0.5009 \| 0.8776 \| 0.9175 \| 1 \| \| 0.3185 \| 0.9077 \| 0.9272 \| 0.5503 \| 0.875 \| 0.9111 \| 2 \| ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
ukr-models/xlm-roberta-base-uk	ukr-models	2023-08-31T09:41:51Z	526	12	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-11T10:53:02Z	--- language: - uk tags: - ukrainian widget: - text: "Тарас Шевченко – великий український <mask>." license: mit --- This is a smaller version of the [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model with only Ukrainian and some English embeddings left. * The original model has 470M parameters, with 384M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 31K (top 25K Ukrainian tokens and top English tokens) the number of model parameters reduced to 134M parameters, and model size reduced from 1GB to 400MB.
ukr-models/uk-ner	ukr-models	2023-08-31T09:41:21Z	188	3	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-07T05:31:07Z	--- language: - uk tags: - ukrainian widget: - text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера." license: mit --- ## Model Description Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on [synthetic NER dataset](https://huggingface.co/datasets/ukr-models/Ukr-Synth) with B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags ## How to Use Huggingface pipeline way (returns tokens with labels): ```py from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-ner') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-ner') ner = pipeline('ner', model=model, tokenizer=tokenizer) ner("Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера.") ``` If you wish to get predictions split by words, not by tokens, you may use the following approach (download script get_predictions.py from the repository, it uses [package tokenize_uk](https://pypi.org/project/tokenize_uk/) for splitting) ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from get_predictions import get_word_predictions tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-ner') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-ner') get_word_predictions(model, tokenizer, ["Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."]) ```
ukr-models/uk-morph	ukr-models	2023-08-31T09:41:07Z	124	1	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-08T07:14:02Z	--- language: - uk tags: - ukrainian widget: - text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера." license: mit --- ## Model Description Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on [synthetic morphological dataset](https://huggingface.co/datasets/ukr-models/Ukr-Synth), returns both UPOS and morphological features (joined by double underscore symbol) ## How to Use Huggingface pipeline way (returns tokens with labels): ```py from transformers import TokenClassificationPipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-morph') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-morph') ppln = TokenClassificationPipeline(model=model, tokenizer=tokenizer) ppln("Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера.") ``` If you wish to get predictions split by words, not by tokens, you may use the following approach (download script get_predictions.py from the repository, it uses [package tokenize_uk](https://pypi.org/project/tokenize_uk/) for splitting) ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from get_predictions import get_word_predictions tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-morph') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-morph') get_word_predictions(model, tokenizer, ["Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."]) ```
ukr-models/uk-punctcase	ukr-models	2023-08-31T09:40:36Z	118	3	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-13T11:50:18Z	--- language: - uk tags: - ukrainian widget: - text: "упродовж 2012-2014 років національний природний парк «зачарований край» разом із всесвітнім фондом природи wwf успішно реалізували проект із відновлення болота «чорне багно» розташованого на схилах гори бужора у закарпатті водноболотне угіддя «чорне багно» є найбільшою болотною екосистемою регіону воно займає площу близько 15 га унікальністю цього високогірного болота розташованого на висоті 840 м над рівнем моря є велика потужність торфових покладів (глибиною до 59 м) і своєрідна рослинність у 50-х і на початку 60-х років минулого століття на природних потічках що протікали через болото побудували осушувальні канали це порушило природну рівновагу відтак змінилася екосистема болота" license: mit --- ## Model Description Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on Ukrainian texts to recover punctuation and case. ## How to Use Download script get_predictions.py from the repository. ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from get_predictions import recover_text tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-punctcase') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-punctcase') text = "..." recover_text(text_processed, model, tokenizer) ```
ukr-models/uk-summarizer	ukr-models	2023-08-31T09:40:08Z	132	4	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "ukrainian", "uk", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-29T13:21:16Z	--- language: - uk tags: - ukrainian license: mit --- ## Model Description Fine-tuning of [uk-mt5-base](https://huggingface.co/kravchenko/uk-mt5-base) model on summarization dataset. ## How to Use ```py from transformers import AutoTokenizer, T5ForConditionalGeneration, pipeline tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-summarizer') model = T5ForConditionalGeneration.from_pretrained('ukr-models/uk-summarizer') ppln = pipeline("summarization", model=model, tokenizer=tokenizer, device=0, max_length=128, num_beams=4, no_repeat_ngram_size=2, clean_up_tokenization_spaces=True) text = "..." ppln(text) ```
UholoDala/sentence_sentiments_analysis_roberta	UholoDala	2023-08-31T09:39:09Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-31T06:42:32Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: sentence_sentiments_analysis_roberta 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. --> # sentence_sentiments_analysis_roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2736 - F1-score: 0.9119 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3477 \| 1.0 \| 2500 \| 0.3307 \| 0.9112 \| \| 0.2345 \| 2.0 \| 5000 \| 0.2736 \| 0.9119 \| \| 0.175 \| 3.0 \| 7500 \| 0.3625 \| 0.9161 \| \| 0.1064 \| 4.0 \| 10000 \| 0.3272 \| 0.9358 \| \| 0.07 \| 5.0 \| 12500 \| 0.3291 \| 0.9380 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
AndrewL088/Pixelcopter-2	AndrewL088	2023-08-31T09:38:36Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T09:38:31Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 36.10 +/- 22.06 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Datactive/BERT_pap_queries_classification_2	Datactive	2023-08-31T09:37:52Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-29T20:36:30Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Datactive/BERT_pap_queries_classification_2 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. --> # Datactive/BERT_pap_queries_classification_2 This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1558 - Validation Loss: 0.1369 - Train F1: 0.9475 - Epoch: 0 ## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1463, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.1558 \| 0.1369 \| 0.9475 \| 0 \| ### Framework versions - Transformers 4.29.0.dev0 - TensorFlow 2.12.0 - Datasets 2.11.0 - Tokenizers 0.13.3
ardt-multipart/ardt-multipart-ppo_train_hopper_level-3108_0919-66	ardt-multipart	2023-08-31T09:36:54Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-31T08:21:10Z	--- tags: - generated_from_trainer model-index: - name: ardt-multipart-ppo_train_hopper_level-3108_0919-66 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. --> # ardt-multipart-ppo_train_hopper_level-3108_0919-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
lomahony/eleuther-pythia12b-hh-sft	lomahony	2023-08-31T09:34:04Z	16	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:Anthropic/hh-rlhf", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-25T10:52:10Z	--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) supervised finetuned with [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch. [wandb log](https://wandb.ai/pythia_dpo/Pythia_LOM/runs/hdct406x) Benchmark evaluations included in repo done using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor). See [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) for model details [(paper)](https://arxiv.org/abs/2101.00027).
AK-12/my_awesome_model	AK-12	2023-08-31T09:33:02Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-27T10:46:57Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.9475 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - Accuracy: 0.9475 ## 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: 9e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2804 \| 1.0 \| 100 \| 0.3327 \| 0.9475 \| \| 0.4089 \| 2.0 \| 200 \| 0.3448 \| 0.955 \| \| 0.0564 \| 3.0 \| 300 \| 0.3446 \| 0.95 \| \| 0.0 \| 4.0 \| 400 \| 0.3417 \| 0.9475 \| \| 0.0 \| 5.0 \| 500 \| 0.3616 \| 0.9475 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
aviroes/MAScIR_elderly_whisper-medium-LoRA	aviroes	2023-08-31T09:31:02Z	0	0	null	[ "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "region:us" ]	null	2023-08-31T07:02:39Z	--- license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer model-index: - name: MAScIR_elderly_whisper-medium-LoRA 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. --> # MAScIR_elderly_whisper-medium-LoRA This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0224 ## 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: 8 - 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: 200 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3209 \| 0.19 \| 100 \| 0.3262 \| \| 0.2482 \| 0.37 \| 200 \| 0.3101 \| \| 0.2726 \| 0.56 \| 300 \| 0.3030 \| \| 0.2288 \| 0.74 \| 400 \| 0.2848 \| \| 0.2014 \| 0.93 \| 500 \| 0.2586 \| \| 0.1277 \| 1.11 \| 600 \| 0.2098 \| \| 0.1054 \| 1.3 \| 700 \| 0.1857 \| \| 0.1056 \| 1.48 \| 800 \| 0.1449 \| \| 0.0842 \| 1.67 \| 900 \| 0.1069 \| \| 0.0692 \| 1.85 \| 1000 \| 0.0874 \| \| 0.0314 \| 2.04 \| 1100 \| 0.0628 \| \| 0.0265 \| 2.22 \| 1200 \| 0.0515 \| \| 0.0154 \| 2.41 \| 1300 \| 0.0443 \| \| 0.0127 \| 2.59 \| 1400 \| 0.0382 \| \| 0.0237 \| 2.78 \| 1500 \| 0.0290 \| \| 0.0119 \| 2.96 \| 1600 \| 0.0224 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Dala/mlc-chat-vicuna-13b-v1.5	Dala	2023-08-31T09:23:54Z	0	1	null	[ "license:llama2", "region:us" ]	null	2023-08-25T17:42:24Z	--- inference: false license: llama2 model_type: llama model_creator: lmsys model_link: https://huggingface.co/lmsys/vicuna-13b-v1.5 model_name: Vicuna 13B v1.5 quantized_by: Dala --- # Vicuna 13B v1.5 - MLC - Model creator: [lmsys](https://huggingface.co/lmsys) - Original model: [Vicuna 13B v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) ## Description This repo contains the [MLC](https://mlc.ai/mlc-llm/) compiled parameters for [lmsys's Vicuna 13B v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5). It contains several quantizations, each in its own branch: - main (q4f16_1) <-- You are currently on this branch - q4f16_2 - q8f16_1 - autogptq_llama_q4f16_1 To run the model, please check out the [MLC instructions](https://mlc.ai/mlc-llm/docs/get_started/try_out.html). In case the model libraries are not yet available in the [binary lib srepo](https://github.com/mlc-ai/binary-mlc-llm-libs), please obtain them from [this PR](https://github.com/mlc-ai/binary-mlc-llm-libs/pull/15/files)
dt-and-vanilla-ardt/ardt-vanilla-ppo_train_halfcheetah_level-3108_0816-99	dt-and-vanilla-ardt	2023-08-31T09:23:43Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-31T07:18:16Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-ppo_train_halfcheetah_level-3108_0816-99 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. --> # ardt-vanilla-ppo_train_halfcheetah_level-3108_0816-99 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
pvcodes/comment_toxicity_classifier	pvcodes	2023-08-31T09:22:36Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-08-28T14:03:39Z	--- license: mit --- <h1 align=center>Comment Toxicity Classification</h1> This model helps to predict the is a comment/sentence is hateful on various parameters such as toxicity, severe toxicity, obscene, threat, insult and racism. ### Test the model here : <a href="https://huggingface.co/spaces/pvcodes/comment_toxicity_classifier">pvcodes/comment_toxicity_classifier</a> <br> ## Working of the Model - #### Loading of Data The data is fetched from <a href='assets/jigsaw_toxic_challenge/train.csv/train.csv'>csv</a> file, which consist of the comment and attributes such as toxicity, severe toxicity, obscene, threat, insult and racism. - #### Preprocessing the comments Then the data is tokenized using the `TextVectorization` method of `keras` in and embeded - #### Creating of <emp>Deep NLP Model</emp> For this model we used `Keras sequential API` with a number of `LSTM` layers (because they are particulary good while working with sequences) - #### - The dataset used to train the model is from <a href=https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge>Toxic Comment Classification Challenge</a> from <a href=https://www.kaggle.com>Kaggle</a>. ##### Note: The compiled data model is available here: <a href='assets/toxicity.h5'>here</a>. <samp> <p align="center"> ════ ⋆★⋆ ════<br> From <a href="https://github.com/pvcodes/pvcodes">pvcodes</a> </p> </samp>
moro01525/mlm	moro01525	2023-08-31T09:16:14Z	6	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:moro01525/mlm", "base_model:finetune:moro01525/mlm", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-29T09:25:43Z	--- base_model: moro01525/mlm tags: - generated_from_trainer model-index: - name: mlm 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. --> # mlm This model is a fine-tuned version of [moro01525/mlm](https://huggingface.co/moro01525/mlm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.9361 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.248 \| 1.0 \| 582 \| 4.9240 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Norod78/SDXL-StickerSheet-Lora	Norod78	2023-08-31T09:12:04Z	248	33	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]	text-to-image	2023-08-31T09:04:29Z	--- license: mit base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: StickerSheet tags: - text-to-image - stable-diffusion - lora - diffusers widget: - text: Cute sparkle pink barbie StickerSheet - text: Cthulhu StickerSheet based on H.P Lovecraft stories - text: Cute sparkle rainbow kitten StickerSheet, Eric Wallis - text: Cute socially awkward potato StickerSheet inference: true language: - en --- # Trigger words Use "StickerSheet" in your prompts # Examples Cute sparkle pink barbie StickerSheet, Very detailed, clean, high quality, sharp image, Eric Wallis ![Sparkle pink barbie](https://huggingface.co/Norod78/SDXL-StickerSheet-Lora/resolve/main/Examples/00076-20230831113822-7778-Cute%20sparkle%20pink%20barbie%20StickerSheet%20%20%2C%20Very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp%20image%2C%20Eric%20Wallis-before-highres-fix.jpg) Cthulhu StickerSheet, based on H.P Lovecraft stories, Very detailed, clean, high quality, sharp image ![Cthulhu](https://huggingface.co/Norod78/SDXL-StickerSheet-Lora/resolve/main/Examples/00073-20230831113700-7780-Cthulhu%20StickerSheet%20%20_lora_SDXL-StickerSheet-Lora_1_%2C%20based%20on%20H.P%20Lovecraft%20stories%2C%20Very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp.jpg)
iloya/Taxi-v3	iloya	2023-08-31T09:08:28Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T09:08:25Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="iloya/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Hozier/sd-class-butterflies-32	Hozier	2023-08-31T08:57:55Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-08-31T08:53:04Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Hozier/sd-class-butterflies-32') image = pipeline().images[0] image ```
kimi0230/TestModel	kimi0230	2023-08-31T08:56:33Z	1	0	null	[ "tf", "generated_from_keras_callback", "dataset:fka/awesome-chatgpt-prompts", "license:mit", "region:us" ]	null	2023-08-31T07:44:10Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: chatgpt-gpt4-prompts-bart-large-cnn-samsum results: [] datasets: - fka/awesome-chatgpt-prompts --- <!-- 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. --> # chatgpt-gpt4-prompts-bart-large-cnn-samsum This model generates ChatGPT/BingChat & GPT-3 prompts and is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an [this](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2214 - Validation Loss: 2.7584 - Epoch: 4 ### Streamlit This model supports a [Streamlit](https://streamlit.io/) Web UI to run the chatgpt-gpt4-prompts-bart-large-cnn-samsum model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/ChatGPT-BingChat-GPT3-Prompt-Generator_App) ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.1982 \| 2.6801 \| 0 \| \| 2.3601 \| 2.5493 \| 1 \| \| 1.9225 \| 2.5377 \| 2 \| \| 1.5465 \| 2.6794 \| 3 \| \| 1.2214 \| 2.7584 \| 4 \| ### Framework versions - Transformers 4.27.3 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
iloya/q-FrozenLake-v1-4x4-noSlippery	iloya	2023-08-31T08:55:45Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T08:55:42Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="iloya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Alexa06/54yg	Alexa06	2023-08-31T08:53:31Z	0	0	null	[ "region:us" ]	null	2023-08-31T08:52:14Z	photo_5154810918662679307_x.jpg
vnktrmnb/MBERT_FT-TyDiQA_S59	vnktrmnb	2023-08-31T08:34:15Z	65	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-30T05:18:51Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S59 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. --> # vnktrmnb/MBERT_FT-TyDiQA_S59 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6175 - Train End Logits Accuracy: 0.8417 - Train Start Logits Accuracy: 0.8693 - Validation Loss: 0.4662 - Validation End Logits Accuracy: 0.8789 - Validation Start Logits Accuracy: 0.9162 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.4412 \| 0.6715 \| 0.7002 \| 0.4875 \| 0.8570 \| 0.8943 \| 0 \| \| 0.8493 \| 0.7898 \| 0.8229 \| 0.4547 \| 0.8686 \| 0.9137 \| 1 \| \| 0.6175 \| 0.8417 \| 0.8693 \| 0.4662 \| 0.8789 \| 0.9162 \| 2 \| ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
parksuna/xlm-roberta-base-finetuned-panx-de	parksuna	2023-08-31T08:29:59Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-31T08:25:49Z	--- license: mit base_model: xlm-roberta-base 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 config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8657241810026685 --- <!-- 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: 0.1338 - F1: 0.8657 ## 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 \| 0.1557 \| 0.8218 \| \| 0.126 \| 2.0 \| 1050 \| 0.1460 \| 0.8521 \| \| 0.0827 \| 3.0 \| 1575 \| 0.1338 \| 0.8657 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
vnktrmnb/MBERT_FT-TyDiQA_S531	vnktrmnb	2023-08-31T08:22:15Z	65	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-31T07:27:40Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S531 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. --> # vnktrmnb/MBERT_FT-TyDiQA_S531 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6202 - Train End Logits Accuracy: 0.8376 - Train Start Logits Accuracy: 0.8661 - Validation Loss: 0.4939 - Validation End Logits Accuracy: 0.8647 - Validation Start Logits Accuracy: 0.9046 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.4876 \| 0.6535 \| 0.6831 \| 0.5669 \| 0.8222 \| 0.8698 \| 0 \| \| 0.8473 \| 0.7841 \| 0.8173 \| 0.4769 \| 0.8647 \| 0.9059 \| 1 \| \| 0.6202 \| 0.8376 \| 0.8661 \| 0.4939 \| 0.8647 \| 0.9046 \| 2 \| ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
emotibot-inc/Zhuhai-13B	emotibot-inc	2023-08-31T08:13:01Z	0	0	null	[ "region:us" ]	null	2023-08-30T09:33:05Z	# README # Zhuhai-13B [Hugging Face](https://huggingface.co/emotibot-inc/Zhuhai-13B) \| [GitHub](https://github.com/emotibot-inc/Zhuhai-13B) \| [Model Scope](https://modelscope.cn/models/emotibotinc/Zhuhai-13B/summary) \| [Emotibrain](https://brain.emotibot.com/?source=zhuhai13b_huggingface) # 模型介绍 "竹海-13B"是竹间智能继“竹海-7B”之后开发的一款拥大模语言模型，以下是“竹海-13B”的四个主要特点： - 更大尺寸、更多数据：相比于“竹海-7B”，我们将参数量扩大到130亿，并在高质量语料上训练了1.2万亿tokens。Zhuhai-13B的上下文窗口长度为4096。 - 高效性能：基于Transformer结构，在大约1.2万亿tokens上训练出来的130亿参数模型，支持中英双语。 - 安全性：我们对“竹海-13B”进行了严格的安全控制和优化，确保其在实际应用中不会产生任何不适当或误导性的输出。通过精心设计和调整算法参数，“竹海-13B”可以有效地避免乱说话现象。 # Model benchmark ## 中文评测 - CMMLU ### Result \| Model 5-shot \| STEM \| Humanities \| Social Science \| Other \| China-specific \| Average \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Multilingual-oriented \| \| \| \| \| \| \| \| [GPT4](https://openai.com/gpt4) \| 65.23 \| 72.11 \| 72.06 \| 74.79 \| 66.12 \| 70.95 \| \| [ChatGPT](https://openai.com/chatgpt) \| 47.81 \| 55.68 \| 56.50 \| 62.66 \| 50.69 \| 55.51 \| \| [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) \| 33.33 \| 43.46 \| 44.28 \| 44.75 \| 39.46 \| 41.45 \| \| [LLaMA-65B](https://github.com/facebookresearch/llama) \| 34.47 \| 40.24 \| 41.55 \| 42.88 \| 37.00 \| 39.80 \| \| [BLOOMZ-7B](https://github.com/bigscience-workshop/xmtf) \| 30.56 \| 39.10 \| 38.59 \| 40.32 \| 37.15 \| 37.04 \| \| [Bactrian-LLaMA-13B](https://github.com/mbzuai-nlp/bactrian-x) \| 27.52 \| 32.47 \| 32.27 \| 35.77 \| 31.56 \| 31.88 \| \| Chinese-oriented \| \| \| \| \| \| \| \| [Zhuzhi-6B](https://github.com/emotibot-inc/Zhuzhi-6B) \| 40.30 \| 48.08 \| 46.72 \| 47.41 \| 45.51 \| 45.60 \| \| [Zhuhai-13B](https://github.com/emotibot-inc/Zhuhai-13B) \| 42.39 \| 61.57 \| 60.48 \| 58.57 \| 55.68 \| 55.74 \| \| [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) \| 42.38 \| 61.61 \| 60.44 \| 59.26 \| 56.62 \| 55.82 \| \| [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) \| 42.55 \| 50.98 \| 50.99 \| 50.80 \| 48.37 \| 48.80 \| \| [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) \| 35.25 \| 48.07 \| 47.88 \| 46.61 \| 44.14 \| 44.43 \| \| [ChatGLM-6B](https://github.com/THUDM/GLM-130B) \| 32.35 \| 39.22 \| 39.65 \| 38.62 \| 37.70 \| 37.48 \| \| [BatGPT-15B](https://github.com/haonan-li/CMMLU/blob/master) \| 34.96 \| 35.45 \| 36.31 \| 42.14 \| 37.89 \| 37.16 \| \| [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) \| 27.12 \| 33.18 \| 34.87 \| 35.10 \| 32.97 \| 32.63 \| \| [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) \| 27.23 \| 30.41 \| 28.84 \| 32.56 \| 28.68 \| 29.57 \| \| [Chinese-GLM-10B](https://github.com/THUDM/GLM) \| 25.49 \| 27.05 \| 27.42 \| 29.21 \| 28.05 \| 27.26 \| \| Random \| 25.00 \| 25.00 \| 25.00 \| 25.00 \| 25.00 \| 25.00 \| \| Model 0-shot \| STEM \| Humanities \| Social Science \| Other \| China-specific \| Average \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Multilingual-oriented \| \| \| \| \| \| \| \| [GPT4](https://openai.com/gpt4) \| 63.16 \| 69.19 \| 70.26 \| 73.16 \| 63.47 \| 68.9 \| \| [ChatGPT](https://openai.com/chatgpt) \| 44.8 \| 53.61 \| 54.22 \| 59.95 \| 49.74 \| 53.22 \| \| [BLOOMZ-7B](https://github.com/bigscience-workshop/xmtf) \| 33.03 \| 45.74 \| 45.74 \| 46.25 \| 41.58 \| 42.8 \| \| [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) \| 31.11 \| 41.3 \| 40.87 \| 40.61 \| 36.05 \| 38.5 \| \| [LLaMA-65B](https://github.com/facebookresearch/llama) \| 31.09 \| 34.45 \| 36.05 \| 37.94 \| 32.89 \| 34.88 \| \| [Bactrian-LLaMA-13B](https://github.com/mbzuai-nlp/bactrian-x) \| 26.46 \| 29.36 \| 31.81 \| 31.55 \| 29.17 \| 30.06 \| \| Chinese-oriented \| \| \| \| \| \| \| \| [Zhuzhi-6B](https://github.com/emotibot-inc/Zhuzhi-6B) \| 42.51 \| 48.91 \| 48.85 \| 50.25 \| 47.57 \| 47.62 \| \| [Zhuhai-13B](https://github.com/emotibot-inc/Zhuhai-13B) \| 42.37 \| 60.97 \| 59.71 \| 56.35 \| 54.81 \| 54.84 \| \| [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) \| 42.04 \| 60.49 \| 59.55 \| 56.6 \| 55.72 \| 54.63 \| \| [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) \| 41.28 \| 52.85 \| 53.37 \| 52.24 \| 50.58 \| 49.95 \| \| [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) \| 32.79 \| 44.43 \| 46.78 \| 44.79 \| 43.11 \| 42.33 \| \| [ChatGLM-6B](https://github.com/THUDM/GLM-130B) \| 32.22 \| 42.91 \| 44.81 \| 42.6 \| 41.93 \| 40.79 \| \| [BatGPT-15B](https://github.com/haonan-li/CMMLU/blob/master) \| 33.72 \| 36.53 \| 38.07 \| 46.94 \| 38.32 \| 38.51 \| \| [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) \| 26.76 \| 26.57 \| 27.42 \| 28.33 \| 26.73 \| 27.34 \| \| [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) \| 25.68 \| 26.35 \| 27.21 \| 27.92 \| 26.7 \| 26.88 \| \| [Chinese-GLM-10B](https://github.com/THUDM/GLM) \| 25.57 \| 25.01 \| 26.33 \| 25.94 \| 25.81 \| 25.8 \| \| Random \| 25 \| 25 \| 25 \| 25 \| 25 \| 25 \| # 推理对话您可以直接注册并登录竹间智能科技发布的大模型产品 [Emotibrain](https://brain.emotibot.com/?source=zhuhai13b_huggingface)，并选择 CoPilot（KKBot）进行的在线测试，注册即可立即使用； ![Untitled](./READMEjpg/Untitled.png) # 模型训练您可以直接注册并登录竹间智能科技发布的大模型产品 [Emotibrain](https://brain.emotibot.com/?source=zhuhai13b_huggingface)，并选择 Fine-tune 进行 0 代码微调，注册即可立即使用；详细的训练流程您可以浏览此文档：[Emotibrain 快速入门](https://brain.emotibot.com/supports/model-factory/dash-into.html)（大约 5 分钟） ![Untitled](./READMEjpg/Untitled1.png) ![Untitled](./READMEjpg/Untitled2.png) # 更多信息若您想了解更多大模型训练平台的相关信息，请访问 [Emotibrain 官网](https://brain.emotibot.com/?source=zhuhai13b_huggingface) 进行了解；
Geotrend/bert-base-en-fr-ar-cased	Geotrend	2023-08-31T08:03:30Z	114	0	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: multilingual datasets: wikipedia license: apache-2.0 --- # bert-base-en-fr-ar-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-ar-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-ar-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.
Geotrend/bert-base-en-pt-cased	Geotrend	2023-08-31T08:03:02Z	116	1	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "en", "pt", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - multilingual - en - pt datasets: wikipedia license: apache-2.0 --- # bert-base-en-pt-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-pt-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.
ThanhMai/green-clip-inpaint	ThanhMai	2023-08-31T08:01:58Z	21	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T08:01:05Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### green clip inpaint on Stable Diffusion via Dreambooth #### model by ThanhMai This your the Stable Diffusion model fine-tuned the green clip inpaint concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: <green-clip> clip You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/0.jpeg) ![image 3](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/5.jpeg) ![image 4](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/4.jpeg) ![image 5](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/2.jpeg) ![image 6](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/6.jpeg)
SCUT-DLVCLab/lilt-roberta-en-base	SCUT-DLVCLab	2023-08-31T07:59:36Z	19,575	18	transformers	[ "transformers", "pytorch", "safetensors", "lilt", "feature-extraction", "vision", "arxiv:2202.13669", "license:mit", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-29T14:06:32Z	--- license: mit tags: - vision --- # LiLT-RoBERTa (base-sized model) Language-Independent Layout Transformer - RoBERTa model by stitching a pre-trained RoBERTa (English) and a pre-trained Language-Independent Layout Transformer (LiLT) together. It was introduced in the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Wang et al. and first released in [this repository](https://github.com/jpwang/lilt). Disclaimer: The team releasing LiLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Language-Independent Layout Transformer (LiLT) allows to combine any pre-trained RoBERTa encoder from the hub (hence, in any language) with a lightweight Layout Transformer to have a LayoutLM-like model for any language. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg" alt="drawing" width="600"/> ## Intended uses & limitations The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the [model hub](https://huggingface.co/models?search=lilt) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/lilt.html). ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2202.13669, doi = {10.48550/ARXIV.2202.13669}, url = {https://arxiv.org/abs/2202.13669}, author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
yuekai/model_repo_whisper_large_v2	yuekai	2023-08-31T07:57:15Z	0	0	null	[ "onnx", "region:us" ]	null	2023-08-17T09:53:49Z	### Client https://huggingface.co/spaces/yuekai/triton-asr-client https://github.com/yuekaizhang/Triton-ASR-Client ### Server ```sh docker pull soar97/triton-whisper:23.06 docker run -it --name "whisper-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-whisper:23.06 apt-get install git-lfs git-lfs install git clone https://huggingface.co/yuekai/model_repo_whisper_large_v2.git export CUDA_VISIBLE_DEVICES="1" model_repo_path=./model_repo_whisper tritonserver --model-repository $model_repo_path \ --pinned-memory-pool-byte-size=2048000000 \ --cuda-memory-pool-byte-size=0:4096000000 \ --http-port 10086 \ --metrics-port 10087 ``` ### Benchmark Results Decoding on a single V100 GPU, audios are padding to 30s, using aishell1 test set files \| Model \| Backend \| Concurrency \| RTF \| \|-------\|-----------\|-----------------------\|---------\| \| Large-v2 \| ONNX FP16 \| 4 \| 0.14 \| \|Module\| Time Distribution\| \|--\|--\| \|feature_extractor\|0.8%\| \|encoder\|9.6%\| \|decoder\|67.4%\| \|greedy search\|22.2%\|
victornica/mini_molformer_gsf_6epochs	victornica	2023-08-31T07:56:29Z	153	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-30T22:56:12Z	--- license: mit tags: - generated_from_trainer model-index: - name: mini_molformer_gsf_6epochs 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. --> # mini_molformer_gsf_6epochs 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: 0.6470 ## 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: 0.0006 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.7953 \| 0.1 \| 1000 \| 1.0871 \| \| 1.0284 \| 0.19 \| 2000 \| 0.9575 \| \| 0.9463 \| 0.29 \| 3000 \| 0.9099 \| \| 0.9048 \| 0.39 \| 4000 \| 0.8758 \| \| 0.877 \| 0.48 \| 5000 \| 0.8517 \| \| 0.8573 \| 0.58 \| 6000 \| 0.8323 \| \| 0.8399 \| 0.68 \| 7000 \| 0.8176 \| \| 0.8276 \| 0.77 \| 8000 \| 0.8127 \| \| 0.8164 \| 0.87 \| 9000 \| 0.8037 \| \| 0.8071 \| 0.97 \| 10000 \| 0.7889 \| \| 0.7969 \| 1.07 \| 11000 \| 0.7815 \| \| 0.7901 \| 1.16 \| 12000 \| 0.7742 \| \| 0.7844 \| 1.26 \| 13000 \| 0.7710 \| \| 0.778 \| 1.36 \| 14000 \| 0.7633 \| \| 0.7732 \| 1.45 \| 15000 \| 0.7605 \| \| 0.7695 \| 1.55 \| 16000 \| 0.7567 \| \| 0.7646 \| 1.65 \| 17000 \| 0.7486 \| \| 0.7606 \| 1.74 \| 18000 \| 0.7462 \| \| 0.7576 \| 1.84 \| 19000 \| 0.7434 \| \| 0.7539 \| 1.94 \| 20000 \| 0.7376 \| \| 0.7484 \| 2.03 \| 21000 \| 0.7343 \| \| 0.7423 \| 2.13 \| 22000 \| 0.7318 \| \| 0.7403 \| 2.23 \| 23000 \| 0.7270 \| \| 0.7364 \| 2.32 \| 24000 \| 0.7274 \| \| 0.7341 \| 2.42 \| 25000 \| 0.7206 \| \| 0.7321 \| 2.52 \| 26000 \| 0.7204 \| \| 0.728 \| 2.61 \| 27000 \| 0.7152 \| \| 0.7253 \| 2.71 \| 28000 \| 0.7131 \| \| 0.7224 \| 2.81 \| 29000 \| 0.7099 \| \| 0.7198 \| 2.91 \| 30000 \| 0.7073 \| \| 0.7166 \| 3.0 \| 31000 \| 0.7039 \| \| 0.7079 \| 3.1 \| 32000 \| 0.7009 \| \| 0.7074 \| 3.2 \| 33000 \| 0.6980 \| \| 0.7051 \| 3.29 \| 34000 \| 0.6951 \| \| 0.703 \| 3.39 \| 35000 \| 0.6924 \| \| 0.7008 \| 3.49 \| 36000 \| 0.6895 \| \| 0.6971 \| 3.58 \| 37000 \| 0.6873 \| \| 0.6943 \| 3.68 \| 38000 \| 0.6854 \| \| 0.6931 \| 3.78 \| 39000 \| 0.6814 \| \| 0.6899 \| 3.87 \| 40000 \| 0.6799 \| \| 0.6874 \| 3.97 \| 41000 \| 0.6770 \| \| 0.6805 \| 4.07 \| 42000 \| 0.6740 \| \| 0.6762 \| 4.16 \| 43000 \| 0.6722 \| \| 0.6753 \| 4.26 \| 44000 \| 0.6689 \| \| 0.6721 \| 4.36 \| 45000 \| 0.6668 \| \| 0.671 \| 4.45 \| 46000 \| 0.6643 \| \| 0.6686 \| 4.55 \| 47000 \| 0.6627 \| \| 0.6664 \| 4.65 \| 48000 \| 0.6604 \| \| 0.6654 \| 4.75 \| 49000 \| 0.6581 \| \| 0.6635 \| 4.84 \| 50000 \| 0.6565 \| \| 0.6617 \| 4.94 \| 51000 \| 0.6548 \| \| 0.6577 \| 5.04 \| 52000 \| 0.6532 \| \| 0.6527 \| 5.13 \| 53000 \| 0.6522 \| \| 0.6514 \| 5.23 \| 54000 \| 0.6508 \| \| 0.6501 \| 5.33 \| 55000 \| 0.6498 \| \| 0.6494 \| 5.42 \| 56000 \| 0.6489 \| \| 0.6484 \| 5.52 \| 57000 \| 0.6483 \| \| 0.6484 \| 5.62 \| 58000 \| 0.6477 \| \| 0.6474 \| 5.71 \| 59000 \| 0.6473 \| \| 0.6478 \| 5.81 \| 60000 \| 0.6471 \| \| 0.6474 \| 5.91 \| 61000 \| 0.6470 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Edmon02/marian-finetuned-kde4-en-to-hy	Edmon02	2023-08-31T07:54:27Z	103	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:opus100", "base_model:Helsinki-NLP/opus-mt-en-hy", "base_model:finetune:Helsinki-NLP/opus-mt-en-hy", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-08-31T07:51:18Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-hy tags: - translation - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-hy results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 config: en-hy split: train args: en-hy metrics: - name: Bleu type: bleu value: 18.363987489312905 --- <!-- 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. --> # marian-finetuned-kde4-en-to-hy This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hy](https://huggingface.co/Helsinki-NLP/opus-mt-en-hy) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 1.4183 - Bleu: 18.3640 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
gillankrishna/ppo-LunarLander	gillankrishna	2023-08-31T07:54:09Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T07:53:51Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 238.64 +/- 59.44 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
redstonehero/arteyou_alpha1	redstonehero	2023-08-31T07:52:33Z	21	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T07:12:02Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/horridhentaimix_v10	redstonehero	2023-08-31T07:52:30Z	21	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T07:13:38Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/mistoonamethyst_v20	redstonehero	2023-08-31T07:52:29Z	19	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T07:13:29Z	--- license: creativeml-openrail-m library_name: diffusers ---
DopeorNope/A3_duck	DopeorNope	2023-08-31T07:37:42Z	2	0	peft	[ "peft", "region:us" ]	null	2023-08-31T07:35:36Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
sosuneko/Reinforce-CartPole-v1	sosuneko	2023-08-31T07:34:05Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T07:33:55Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
SasankVH/sample_data	SasankVH	2023-08-31T07:32:04Z	9	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "en", "dataset:localdataset", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-29T07:50:13Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - localdataset metrics: - wer model-index: - name: testing results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: localdataset type: localdataset config: default split: test args: 'config: data, split: test' metrics: - name: Wer type: wer value: 0.0 --- <!-- 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. --> # testing This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the localdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0 ## 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: 1e-05 - train_batch_size: 16 - 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: 62 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 0.0 \| 125.0 \| 125 \| 0.0000 \| 0.0 \| \| 0.0 \| 250.0 \| 250 \| 0.0000 \| 0.0 \| \| 0.0 \| 375.0 \| 375 \| 0.0000 \| 0.0 \| \| 0.0 \| 500.0 \| 500 \| 0.0000 \| 0.0 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
rossevine/Model_G_2	rossevine	2023-08-31T07:29:43Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-29T13:06:01Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: Model_G_2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: id split: test args: id metrics: - name: Wer type: wer value: 0.251258623904531 --- <!-- 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. --> # Model_G_2 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.3710 - Wer: 0.2513 - Cer: 0.0631 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 3.7484 \| 3.23 \| 400 \| 0.5706 \| 0.5698 \| 0.1477 \| \| 0.3419 \| 6.45 \| 800 \| 0.4120 \| 0.3758 \| 0.0924 \| \| 0.1796 \| 9.68 \| 1200 \| 0.3691 \| 0.3295 \| 0.0843 \| \| 0.125 \| 12.9 \| 1600 \| 0.3821 \| 0.3097 \| 0.0782 \| \| 0.0984 \| 16.13 \| 2000 \| 0.4085 \| 0.2947 \| 0.0742 \| \| 0.0827 \| 19.35 \| 2400 \| 0.3859 \| 0.2781 \| 0.0711 \| \| 0.0666 \| 22.58 \| 2800 \| 0.3813 \| 0.2663 \| 0.0684 \| \| 0.0558 \| 25.81 \| 3200 \| 0.3681 \| 0.2545 \| 0.0644 \| \| 0.0466 \| 29.03 \| 3600 \| 0.3710 \| 0.2513 \| 0.0631 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 1.18.3 - Tokenizers 0.13.3
ardt-multipart/ardt-multipart-ppo_train_halfcheetah_level-3108_0610-66	ardt-multipart	2023-08-31T07:22:42Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-31T05:11:57Z	--- tags: - generated_from_trainer model-index: - name: ardt-multipart-ppo_train_halfcheetah_level-3108_0610-66 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. --> # ardt-multipart-ppo_train_halfcheetah_level-3108_0610-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
vnktrmnb/MBERT_FT-TyDiQA_S431	vnktrmnb	2023-08-31T07:20:23Z	78	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-31T06:26:45Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S431 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. --> # vnktrmnb/MBERT_FT-TyDiQA_S431 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6089 - Train End Logits Accuracy: 0.8391 - Train Start Logits Accuracy: 0.8668 - Validation Loss: 0.5017 - Validation End Logits Accuracy: 0.8608 - Validation Start Logits Accuracy: 0.9085 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.4634 \| 0.6632 \| 0.6911 \| 0.5058 \| 0.8325 \| 0.8982 \| 0 \| \| 0.8321 \| 0.7907 \| 0.8249 \| 0.4951 \| 0.8531 \| 0.9085 \| 1 \| \| 0.6089 \| 0.8391 \| 0.8668 \| 0.5017 \| 0.8608 \| 0.9085 \| 2 \| ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
dt-and-vanilla-ardt/ardt-vanilla-ppo_train_halfcheetah_level-3108_0607-66	dt-and-vanilla-ardt	2023-08-31T07:16:25Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-31T05:09:28Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-ppo_train_halfcheetah_level-3108_0607-66 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. --> # ardt-vanilla-ppo_train_halfcheetah_level-3108_0607-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
vnktrmnb/MBERT_FT-TyDiQA_S41	vnktrmnb	2023-08-31T07:09:59Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-29T09:00:08Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S41 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. --> # vnktrmnb/MBERT_FT-TyDiQA_S41 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6256 - Train End Logits Accuracy: 0.8359 - Train Start Logits Accuracy: 0.8649 - Validation Loss: 0.4800 - Validation End Logits Accuracy: 0.8595 - Validation Start Logits Accuracy: 0.8995 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.4994 \| 0.6497 \| 0.6777 \| 0.4953 \| 0.8479 \| 0.8982 \| 0 \| \| 0.8529 \| 0.7875 \| 0.8176 \| 0.4775 \| 0.8544 \| 0.8892 \| 1 \| \| 0.6256 \| 0.8359 \| 0.8649 \| 0.4800 \| 0.8595 \| 0.8995 \| 2 \| ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
nisten/bigdoc-c13b-instruct-tf32	nisten	2023-08-31T06:54:29Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-31T06:51:42Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
redstonehero/cyberrealistic_v33_pruned	redstonehero	2023-08-31T06:53:52Z	23	1	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T06:08:48Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/revanimatedfp16_122_pruned	redstonehero	2023-08-31T06:53:35Z	19	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T06:07:35Z	--- license: creativeml-openrail-m library_name: diffusers ---
grammarly/pseudonymization-seq2seq	grammarly	2023-08-31T06:52:22Z	0	5	null	[ "text2text-generation", "en", "dataset:grammarly/pseudonymization-data", "dataset:cnn_dailymail", "dataset:imdb", "license:apache-2.0", "region:us" ]	text2text-generation	2023-07-05T18:35:11Z	--- license: apache-2.0 datasets: - grammarly/pseudonymization-data - cnn_dailymail - imdb language: - en metrics: - f1 - bleu pipeline_tag: text2text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This repository contains files for two Seq2Seq transformers-based models used in our paper: https://aclanthology.org/2023.trustnlp-1.20/. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Oleksandr Yermilov, Vipul Raheja, Artem Chernodub - Model type: Seq2Seq - Language (NLP): English - License: Apache license 2.0 - Finetuned from model: BART ### Model Sources - Paper: https://aclanthology.org/2023.trustnlp-1.20/ ## Uses These models can be used for anonymizing datasets in English language. ## Bias, Risks, and Limitations Please check the Limitations section in our paper. ## Training Details ### Training Data https://huggingface.co/datasets/grammarly/pseudonymization-data/tree/main/seq2seq ### Training Procedure 1. Gather text data from Wikipedia. 2. Preprocess it using NER-based pseudonymization. 3. Fine-tune BART model on translation task for translating text from "original" to "pseudonymized". #### Training Hyperparameters We train the models for 3 epochs using `AdamW` optimization with the learning rate α =210<sup>5</sup>, and the batch size is 8. ## Evaluation ### Factors & Metrics #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> There is no source truth of named entities for the data, on which this model was trained. We check whether the word is a named entity, using one of the NER systems (spaCy or FLAIR). #### Metrics We measure the amount of text, changed by our model. Specifically, we check for the following categories of translated text word by word: 1. True positive (TP) - Named entity, which was changed to another named entity. 2. True negative (TN) - Not a named entity, which was not changed. 3. False positive (FP) - Not a named entity, which was changed to another word. 4. False negative (FN) - Named entity, which was not changed to another named entity. We calculate F<sub>1</sub> score based on the abovementioned values. ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX:* ``` @inproceedings{yermilov-etal-2023-privacy, title = "Privacy- and Utility-Preserving {NLP} with Anonymized data: A case study of Pseudonymization", author = "Yermilov, Oleksandr and Raheja, Vipul and Chernodub, Artem", booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.trustnlp-1.20", doi = "10.18653/v1/2023.trustnlp-1.20", pages = "232--241", abstract = "This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques better to balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available.", } ``` ## Model Card Contact Oleksandr Yermilov (oleksandr.yermilov@ucu.edu.ua).
taufiq-lalokalabs/gpt2-test	taufiq-lalokalabs	2023-08-31T06:41:59Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-31T06:41:57Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
LarryAIDraw/Aria	LarryAIDraw	2023-08-31T06:40:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-30T05:43:37Z	--- license: creativeml-openrail-m --- https://civitai.com/models/136374/kurenaino-aria-occulticnine
LarryAIDraw/MGCM_eriza	LarryAIDraw	2023-08-31T06:27:03Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-31T05:05:11Z	--- license: creativeml-openrail-m ---
TrevorJS/CodeLlama-13b-mtg	TrevorJS	2023-08-31T06:25:31Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-31T06:05:03Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
LarryAIDraw/shizuka_hiratsuka_s2_v2	LarryAIDraw	2023-08-31T06:22:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-31T05:20:17Z	--- license: creativeml-openrail-m --- https://civitai.com/models/25866/shizuka-hiratsuka-season-1-season-2
yangdechuan/codeparrot-ds	yangdechuan	2023-08-31T06:22:06Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-28T12:33:55Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds 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 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: 1.0621 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 3.2102 \| 0.02 \| 1000 \| 2.7478 \| \| 2.359 \| 0.03 \| 2000 \| 2.2031 \| \| 2.0974 \| 0.05 \| 3000 \| 1.9751 \| \| 1.9383 \| 0.06 \| 4000 \| 1.8321 \| \| 1.8346 \| 0.08 \| 5000 \| 1.7406 \| \| 1.7547 \| 0.09 \| 6000 \| 1.6731 \| \| 1.6994 \| 0.11 \| 7000 \| 1.6212 \| \| 1.6632 \| 0.12 \| 8000 \| 1.5842 \| \| 1.6237 \| 0.14 \| 9000 \| 1.5506 \| \| 1.5986 \| 0.15 \| 10000 \| 1.5247 \| \| 1.5749 \| 0.17 \| 11000 \| 1.4994 \| \| 1.5466 \| 0.18 \| 12000 \| 1.4783 \| \| 1.5254 \| 0.2 \| 13000 \| 1.4579 \| \| 1.5085 \| 0.21 \| 14000 \| 1.4420 \| \| 1.4884 \| 0.23 \| 15000 \| 1.4235 \| \| 1.4842 \| 0.25 \| 16000 \| 1.4088 \| \| 1.4618 \| 0.26 \| 17000 \| 1.3957 \| \| 1.4479 \| 0.28 \| 18000 \| 1.3825 \| \| 1.4376 \| 0.29 \| 19000 \| 1.3716 \| \| 1.4225 \| 0.31 \| 20000 \| 1.3583 \| \| 1.4151 \| 0.32 \| 21000 \| 1.3476 \| \| 1.4021 \| 0.34 \| 22000 \| 1.3359 \| \| 1.3956 \| 0.35 \| 23000 \| 1.3245 \| \| 1.3839 \| 0.37 \| 24000 \| 1.3159 \| \| 1.3741 \| 0.38 \| 25000 \| 1.3060 \| \| 1.3635 \| 0.4 \| 26000 \| 1.2950 \| \| 1.3491 \| 0.41 \| 27000 \| 1.2844 \| \| 1.3462 \| 0.43 \| 28000 \| 1.2760 \| \| 1.3317 \| 0.44 \| 29000 \| 1.2676 \| \| 1.3249 \| 0.46 \| 30000 \| 1.2584 \| \| 1.3164 \| 0.48 \| 31000 \| 1.2486 \| \| 1.3055 \| 0.49 \| 32000 \| 1.2406 \| \| 1.3006 \| 0.51 \| 33000 \| 1.2327 \| \| 1.2906 \| 0.52 \| 34000 \| 1.2225 \| \| 1.2821 \| 0.54 \| 35000 \| 1.2135 \| \| 1.2677 \| 0.55 \| 36000 \| 1.2068 \| \| 1.2562 \| 0.57 \| 37000 \| 1.1981 \| \| 1.2541 \| 0.58 \| 38000 \| 1.1896 \| \| 1.2377 \| 0.6 \| 39000 \| 1.1814 \| \| 1.2346 \| 0.61 \| 40000 \| 1.1726 \| \| 1.2251 \| 0.63 \| 41000 \| 1.1647 \| \| 1.2175 \| 0.64 \| 42000 \| 1.1575 \| \| 1.2112 \| 0.66 \| 43000 \| 1.1486 \| \| 1.2021 \| 0.67 \| 44000 \| 1.1410 \| \| 1.1888 \| 0.69 \| 45000 \| 1.1339 \| \| 1.1939 \| 0.71 \| 46000 \| 1.1259 \| \| 1.18 \| 0.72 \| 47000 \| 1.1198 \| \| 1.1698 \| 0.74 \| 48000 \| 1.1130 \| \| 1.1634 \| 0.75 \| 49000 \| 1.1063 \| \| 1.1593 \| 0.77 \| 50000 \| 1.1006 \| \| 1.1545 \| 0.78 \| 51000 \| 1.0946 \| \| 1.1478 \| 0.8 \| 52000 \| 1.0896 \| \| 1.1443 \| 0.81 \| 53000 \| 1.0855 \| \| 1.1365 \| 0.83 \| 54000 \| 1.0808 \| \| 1.1332 \| 0.84 \| 55000 \| 1.0773 \| \| 1.1336 \| 0.86 \| 56000 \| 1.0736 \| \| 1.1276 \| 0.87 \| 57000 \| 1.0711 \| \| 1.1241 \| 0.89 \| 58000 \| 1.0686 \| \| 1.123 \| 0.9 \| 59000 \| 1.0665 \| \| 1.1187 \| 0.92 \| 60000 \| 1.0647 \| \| 1.1123 \| 0.93 \| 61000 \| 1.0636 \| \| 1.1159 \| 0.95 \| 62000 \| 1.0628 \| \| 1.1133 \| 0.97 \| 63000 \| 1.0623 \| \| 1.1181 \| 0.98 \| 64000 \| 1.0621 \| \| 1.1125 \| 1.0 \| 65000 \| 1.0621 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
LarryAIDraw/plymouth	LarryAIDraw	2023-08-31T06:21:50Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-30T05:44:15Z	--- license: creativeml-openrail-m --- https://civitai.com/models/135762/hms-plymouth-or-azur-lane
DogGoesBark/medical_en_zh_8_29	DogGoesBark	2023-08-31T06:21:24Z	6	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-zh", "base_model:finetune:Helsinki-NLP/opus-mt-en-zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-29T14:51:16Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-zh tags: - generated_from_trainer metrics: - bleu model-index: - name: medical_en_zh_8_29 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. --> # medical_en_zh_8_29 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6159 - Bleu: 41.4839 - Gen Len: 77.4048 ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.5915 \| 1.02 \| 3000 \| 1.4640 \| 30.8193 \| 76.572 \| \| 1.2908 \| 2.04 \| 6000 \| 1.2734 \| 32.3053 \| 76.897 \| \| 1.0814 \| 3.06 \| 9000 \| 1.1348 \| 34.3605 \| 77.2082 \| \| 0.9083 \| 4.08 \| 12000 \| 1.0246 \| 34.9139 \| 76.7213 \| \| 0.7507 \| 5.1 \| 15000 \| 0.9336 \| 36.2245 \| 76.6036 \| \| 0.6046 \| 6.12 \| 18000 \| 0.8291 \| 37.987 \| 77.326 \| \| 0.4838 \| 7.14 \| 21000 \| 0.7496 \| 38.7572 \| 77.2366 \| \| 0.3861 \| 8.16 \| 24000 \| 0.6730 \| 40.3566 \| 77.49 \| \| 0.3203 \| 9.19 \| 27000 \| 0.6159 \| 41.4839 \| 77.4048 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
rossevine/Check_Model_2	rossevine	2023-08-31T06:18:55Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-31T04:50:41Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: Check_Model_2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: id split: test args: id metrics: - name: Wer type: wer value: 0.2728883087823979 --- <!-- 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. --> # Check_Model_2 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: 0.3499 - Wer: 0.2729 - Cer: 0.0673 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 3.8708 \| 3.23 \| 400 \| 0.7345 \| 0.7259 \| 0.2034 \| \| 0.4247 \| 6.45 \| 800 \| 0.4128 \| 0.4268 \| 0.1102 \| \| 0.2047 \| 9.68 \| 1200 \| 0.3726 \| 0.3795 \| 0.0930 \| \| 0.1422 \| 12.9 \| 1600 \| 0.3690 \| 0.3514 \| 0.0884 \| \| 0.1139 \| 16.13 \| 2000 \| 0.3811 \| 0.3160 \| 0.0794 \| \| 0.089 \| 19.35 \| 2400 \| 0.3650 \| 0.2895 \| 0.0731 \| \| 0.0709 \| 22.58 \| 2800 \| 0.3629 \| 0.2944 \| 0.0727 \| \| 0.0594 \| 25.81 \| 3200 \| 0.3538 \| 0.2779 \| 0.0692 \| \| 0.0478 \| 29.03 \| 3600 \| 0.3499 \| 0.2729 \| 0.0673 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 1.18.3 - Tokenizers 0.13.3
sontn122/content	sontn122	2023-08-31T06:03:48Z	153	0	transformers	[ "transformers", "pytorch", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2023-08-31T05:59:52Z	--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer model-index: - name: microsoft/deberta-v3-large 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. --> # microsoft/deberta-v3-large This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6094 ## 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: 2 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.6123 \| 1.0 \| 3550 \| 1.6094 \| \| 1.6124 \| 2.0 \| 7100 \| 1.6094 \| \| 1.6106 \| 3.0 \| 10650 \| 1.6094 \| \| 1.6107 \| 4.0 \| 14200 \| 1.6094 \| \| 1.6104 \| 5.0 \| 17750 \| 1.6094 \| \| 1.6115 \| 6.0 \| 21300 \| 1.6094 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Glavin001/coqar-questions-llama-2-7b-v0.1-GPTQ	Glavin001	2023-08-31T06:02:02Z	4	0	transformers	[ "transformers", "llama", "text-generation", "en", "dataset:Glavin001/generate-questions-v0.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-08-27T01:20:27Z	--- datasets: - Glavin001/generate-questions-v0.1 language: - en library_name: transformers ---
beniben0/midjourney-falcon-7b	beniben0	2023-08-31T05:57:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-31T05:49:56Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
AdanLee/ppo-LunarLander-v2-CleanRL	AdanLee	2023-08-31T05:54:13Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T05:37:54Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -23.72 +/- 113.11 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'AdanLee/ppo-LunarLander-v2-CleanRL' 'batch_size': 512 'minibatch_size': 128} ```
mahimairaja/distilhubert-music-classifier-finetuned-gtzan	mahimairaja	2023-08-31T05:46:22Z	146	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-31T02:56:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: CTC-based-finetuned-gtzan 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. --> # CTC-based-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7057 - Accuracy: 0.79 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0608 \| 1.0 \| 57 \| 2.0361 \| 0.43 \| \| 1.663 \| 2.0 \| 114 \| 1.5387 \| 0.62 \| \| 1.2399 \| 3.0 \| 171 \| 1.2074 \| 0.68 \| \| 1.0662 \| 4.0 \| 228 \| 1.0805 \| 0.65 \| \| 0.7986 \| 5.0 \| 285 \| 0.8880 \| 0.75 \| \| 0.7328 \| 6.0 \| 342 \| 0.8037 \| 0.74 \| \| 0.5891 \| 7.0 \| 399 \| 0.7918 \| 0.78 \| \| 0.5227 \| 8.0 \| 456 \| 0.7232 \| 0.79 \| \| 0.5123 \| 9.0 \| 513 \| 0.7138 \| 0.78 \| \| 0.5578 \| 10.0 \| 570 \| 0.7057 \| 0.79 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
GCYY/speecht5_finetuned_fleurs_zh	GCYY	2023-08-31T05:39:23Z	82	1	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "audio", "text-to-speech", "dataset:fleurs", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-08-31T05:20:18Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - audio - text-to-speech datasets: - fleurs model-index: - name: speecht5_finetuned_fleurs_zh 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. --> # speecht5_finetuned_fleurs_zh This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.4343 ## 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: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6864 \| 1.09 \| 100 \| 0.6009 \| \| 0.5976 \| 2.19 \| 200 \| 0.5062 \| \| 0.543 \| 3.28 \| 300 \| 0.4577 \| \| 0.4786 \| 4.38 \| 400 \| 0.4343 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Bazaar/cv_canal_pollution_level	Bazaar	2023-08-31T05:34:23Z	183	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-08-31T03:17:43Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: cv_canal_pollution_level results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9027777910232544 --- # cv_canal_pollution_level 使用HuggingPics微调生成任务：河道污染等级分类（无污染、轻度污染、中度污染、重度污染）使用方法： ```python from transformers import pipeline classifier = pipeline('image-classification', model='Bazzar/cv_canal_pollution_level') print(classifier('http://图片地址')) ``` 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 #### no pollution ![no pollution](images/no_pollution.jpg) #### light pollution ![light pollution](images/light_pollution.jpg) #### moderate pollution ![moderate pollution](images/moderate_pollution.jpg) #### heavy pollution ![heavy pollution](images/heavy_pollution.webp)
redstonehero/realcartoonpixar_v2	redstonehero	2023-08-31T05:31:46Z	17	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T05:03:05Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/realcartoonrealistic_v6	redstonehero	2023-08-31T05:31:41Z	20	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-31T05:03:28Z	--- license: creativeml-openrail-m library_name: diffusers ---
dkqjrm/20230831092825	dkqjrm	2023-08-31T05:29:59Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-31T00:28:44Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230831092825' 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. --> # 20230831092825 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5298 - Accuracy: 0.6771 ## 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: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 340 \| 0.4989 \| 0.5 \| \| 0.5076 \| 2.0 \| 680 \| 0.4922 \| 0.5 \| \| 0.5029 \| 3.0 \| 1020 \| 0.4980 \| 0.5 \| \| 0.5029 \| 4.0 \| 1360 \| 0.4881 \| 0.5125 \| \| 0.4992 \| 5.0 \| 1700 \| 0.5067 \| 0.5 \| \| 0.4818 \| 6.0 \| 2040 \| 0.4919 \| 0.5251 \| \| 0.4818 \| 7.0 \| 2380 \| 0.5045 \| 0.5392 \| \| 0.4719 \| 8.0 \| 2720 \| 0.4695 \| 0.5 \| \| 0.4636 \| 9.0 \| 3060 \| 0.4805 \| 0.5 \| \| 0.4636 \| 10.0 \| 3400 \| 0.5002 \| 0.5 \| \| 0.4501 \| 11.0 \| 3740 \| 0.5665 \| 0.6646 \| \| 0.4418 \| 12.0 \| 4080 \| 0.5283 \| 0.6897 \| \| 0.4418 \| 13.0 \| 4420 \| 0.4705 \| 0.5 \| \| 0.4352 \| 14.0 \| 4760 \| 0.5644 \| 0.6630 \| \| 0.4302 \| 15.0 \| 5100 \| 0.5080 \| 0.6505 \| \| 0.4302 \| 16.0 \| 5440 \| 0.5084 \| 0.6897 \| \| 0.4305 \| 17.0 \| 5780 \| 0.5006 \| 0.6599 \| \| 0.4203 \| 18.0 \| 6120 \| 0.5246 \| 0.6928 \| \| 0.4203 \| 19.0 \| 6460 \| 0.4958 \| 0.6583 \| \| 0.4166 \| 20.0 \| 6800 \| 0.5595 \| 0.6630 \| \| 0.4117 \| 21.0 \| 7140 \| 0.4796 \| 0.5 \| \| 0.4117 \| 22.0 \| 7480 \| 0.4820 \| 0.5 \| \| 0.4131 \| 23.0 \| 7820 \| 0.5158 \| 0.6755 \| \| 0.406 \| 24.0 \| 8160 \| 0.4801 \| 0.5 \| \| 0.4062 \| 25.0 \| 8500 \| 0.5471 \| 0.6646 \| \| 0.4062 \| 26.0 \| 8840 \| 0.4904 \| 0.5 \| \| 0.4021 \| 27.0 \| 9180 \| 0.4880 \| 0.5 \| \| 0.3971 \| 28.0 \| 9520 \| 0.5019 \| 0.6646 \| \| 0.3971 \| 29.0 \| 9860 \| 0.4825 \| 0.5 \| \| 0.3936 \| 30.0 \| 10200 \| 0.5069 \| 0.6693 \| \| 0.3907 \| 31.0 \| 10540 \| 0.5472 \| 0.6693 \| \| 0.3907 \| 32.0 \| 10880 \| 0.4886 \| 0.5 \| \| 0.3906 \| 33.0 \| 11220 \| 0.5531 \| 0.6693 \| \| 0.3888 \| 34.0 \| 11560 \| 0.5023 \| 0.5266 \| \| 0.3888 \| 35.0 \| 11900 \| 0.4896 \| 0.5 \| \| 0.387 \| 36.0 \| 12240 \| 0.4985 \| 0.5 \| \| 0.3836 \| 37.0 \| 12580 \| 0.5309 \| 0.6834 \| \| 0.3836 \| 38.0 \| 12920 \| 0.5402 \| 0.6818 \| \| 0.3792 \| 39.0 \| 13260 \| 0.4854 \| 0.5 \| \| 0.3789 \| 40.0 \| 13600 \| 0.4971 \| 0.5 \| \| 0.3789 \| 41.0 \| 13940 \| 0.5368 \| 0.6803 \| \| 0.3775 \| 42.0 \| 14280 \| 0.4958 \| 0.5047 \| \| 0.3753 \| 43.0 \| 14620 \| 0.5139 \| 0.6897 \| \| 0.3753 \| 44.0 \| 14960 \| 0.5224 \| 0.6834 \| \| 0.3795 \| 45.0 \| 15300 \| 0.5119 \| 0.6865 \| \| 0.3743 \| 46.0 \| 15640 \| 0.5120 \| 0.6740 \| \| 0.3743 \| 47.0 \| 15980 \| 0.5049 \| 0.5204 \| \| 0.3726 \| 48.0 \| 16320 \| 0.5026 \| 0.5 \| \| 0.3683 \| 49.0 \| 16660 \| 0.5137 \| 0.6646 \| \| 0.3707 \| 50.0 \| 17000 \| 0.5088 \| 0.6129 \| \| 0.3707 \| 51.0 \| 17340 \| 0.5608 \| 0.6646 \| \| 0.3654 \| 52.0 \| 17680 \| 0.5217 \| 0.6803 \| \| 0.3684 \| 53.0 \| 18020 \| 0.5236 \| 0.6740 \| \| 0.3684 \| 54.0 \| 18360 \| 0.5135 \| 0.5016 \| \| 0.3663 \| 55.0 \| 18700 \| 0.5192 \| 0.6818 \| \| 0.3669 \| 56.0 \| 19040 \| 0.5212 \| 0.6160 \| \| 0.3669 \| 57.0 \| 19380 \| 0.5320 \| 0.6740 \| \| 0.3641 \| 58.0 \| 19720 \| 0.5344 \| 0.6646 \| \| 0.3628 \| 59.0 \| 20060 \| 0.4991 \| 0.5 \| \| 0.3628 \| 60.0 \| 20400 \| 0.5341 \| 0.6661 \| \| 0.3612 \| 61.0 \| 20740 \| 0.5039 \| 0.5 \| \| 0.3608 \| 62.0 \| 21080 \| 0.5267 \| 0.6379 \| \| 0.3608 \| 63.0 \| 21420 \| 0.5249 \| 0.6364 \| \| 0.3599 \| 64.0 \| 21760 \| 0.5226 \| 0.6599 \| \| 0.3616 \| 65.0 \| 22100 \| 0.5370 \| 0.6834 \| \| 0.3616 \| 66.0 \| 22440 \| 0.5109 \| 0.5 \| \| 0.3543 \| 67.0 \| 22780 \| 0.5368 \| 0.6740 \| \| 0.3616 \| 68.0 \| 23120 \| 0.5236 \| 0.5690 \| \| 0.3616 \| 69.0 \| 23460 \| 0.5300 \| 0.6693 \| \| 0.3578 \| 70.0 \| 23800 \| 0.5441 \| 0.6583 \| \| 0.3541 \| 71.0 \| 24140 \| 0.5310 \| 0.6724 \| \| 0.3541 \| 72.0 \| 24480 \| 0.5346 \| 0.6693 \| \| 0.354 \| 73.0 \| 24820 \| 0.5338 \| 0.6630 \| \| 0.355 \| 74.0 \| 25160 \| 0.5279 \| 0.6599 \| \| 0.3536 \| 75.0 \| 25500 \| 0.5280 \| 0.6552 \| \| 0.3536 \| 76.0 \| 25840 \| 0.5328 \| 0.6693 \| \| 0.3539 \| 77.0 \| 26180 \| 0.5231 \| 0.5376 \| \| 0.3527 \| 78.0 \| 26520 \| 0.5282 \| 0.6646 \| \| 0.3527 \| 79.0 \| 26860 \| 0.5250 \| 0.6364 \| \| 0.3535 \| 80.0 \| 27200 \| 0.5298 \| 0.6771 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
aaniket/aniket	aaniket	2023-08-31T05:28:04Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:timit_asr", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-30T17:29:32Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - timit_asr model-index: - name: aniket 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. --> # aniket This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the timit_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4216 - eval_wer: 1.0 - eval_runtime: 48.4566 - eval_samples_per_second: 33.576 - eval_steps_per_second: 4.21 - epoch: 19.86 - step: 2800 ## 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 ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
dhmeltzer/llama-7b-SFT-qlora-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16	dhmeltzer	2023-08-31T05:21:39Z	0	0	null	[ "generated_from_trainer", "base_model:dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged", "base_model:finetune:dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged", "region:us" ]	null	2023-08-31T03:53:40Z	--- base_model: dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged tags: - generated_from_trainer model-index: - name: llama-7b-SFT-qlora-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16 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. --> # llama-7b-SFT-qlora-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16 This model is a fine-tuned version of [dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged](https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6572 - Rewards/chosen: -0.1473 - Rewards/rejected: -0.2755 - Rewards/accuracies: 0.6128 - Rewards/margins: 0.1282 - Logps/rejected: -203.3539 - Logps/chosen: -207.2538 - Logits/rejected: 1.1534 - Logits/chosen: 1.1690 ## 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: 0.0002 - 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: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6925 \| 0.1 \| 19 \| 0.6761 \| -0.1021 \| -0.1593 \| 0.5697 \| 0.0573 \| -202.1919 \| -206.8013 \| 1.1506 \| 1.1664 \| \| 0.6754 \| 0.21 \| 38 \| 0.6738 \| -0.4156 \| -0.5460 \| 0.5701 \| 0.1303 \| -206.0580 \| -209.9368 \| 1.1257 \| 1.1406 \| \| 0.6799 \| 0.31 \| 57 \| 0.6666 \| -0.0458 \| -0.1454 \| 0.5932 \| 0.0996 \| -202.0523 \| -206.2388 \| 1.1176 \| 1.1327 \| \| 0.6618 \| 0.42 \| 76 \| 0.6637 \| -0.1458 \| -0.2745 \| 0.5971 \| 0.1286 \| -203.3434 \| -207.2391 \| 1.1195 \| 1.1333 \| \| 0.6706 \| 0.52 \| 95 \| 0.6607 \| -0.0386 \| -0.1827 \| 0.5971 \| 0.1440 \| -202.4252 \| -206.1670 \| 1.1334 \| 1.1484 \| \| 0.668 \| 0.63 \| 114 \| 0.6596 \| -0.1615 \| -0.2945 \| 0.6035 \| 0.1330 \| -203.5434 \| -207.3955 \| 1.1500 \| 1.1661 \| \| 0.6712 \| 0.73 \| 133 \| 0.6597 \| -0.1703 \| -0.2905 \| 0.5979 \| 0.1202 \| -203.5037 \| -207.4840 \| 1.1515 \| 1.1672 \| \| 0.6715 \| 0.84 \| 152 \| 0.6588 \| -0.1516 \| -0.2745 \| 0.6100 \| 0.1229 \| -203.3436 \| -207.2964 \| 1.1532 \| 1.1691 \| \| 0.673 \| 0.94 \| 171 \| 0.6572 \| -0.1473 \| -0.2755 \| 0.6128 \| 0.1282 \| -203.3539 \| -207.2538 \| 1.1534 \| 1.1690 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Saksham1234/helloitsme	Saksham1234	2023-08-31T05:05:59Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-08-31T05:05:59Z	--- license: bigscience-openrail-m ---
54data/llama_2_ko_7b_wiki_QA	54data	2023-08-31T05:00:25Z	0	0	null	[ "generated_from_trainer", "base_model:beomi/llama-2-ko-7b", "base_model:finetune:beomi/llama-2-ko-7b", "region:us" ]	null	2023-08-23T15:46:46Z	--- base_model: beomi/llama-2-ko-7b tags: - generated_from_trainer model-index: - name: llama_2_ko_7b_wiki_QA 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. --> # llama_2_ko_7b_wiki_QA This model is a fine-tuned version of [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1703 ## 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: 0.0002 - 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: 100 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8568 \| 0.33 \| 50 \| 1.4294 \| \| 1.2307 \| 0.67 \| 100 \| 1.2169 \| \| 1.1788 \| 1.0 \| 150 \| 1.1865 \| \| 1.0837 \| 1.33 \| 200 \| 1.1810 \| \| 1.1905 \| 1.67 \| 250 \| 1.1740 \| \| 1.161 \| 2.0 \| 300 \| 1.1703 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3
Yaxin1992/codellama-13b-multi-1800	Yaxin1992	2023-08-31T04:45:38Z	0	0	null	[ "generated_from_trainer", "base_model:codellama/CodeLlama-13b-hf", "base_model:finetune:codellama/CodeLlama-13b-hf", "license:llama2", "region:us" ]	null	2023-08-31T01:41:52Z	--- license: llama2 base_model: codellama/CodeLlama-13b-hf tags: - generated_from_trainer model-index: - name: codellama-13b-multi-1800 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. --> # codellama-13b-multi-1800 This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) on an unknown 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1800 ### Training results ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
soohmatthew/reddit-confidence-setfit-model-1	soohmatthew	2023-08-31T04:42:34Z	7	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-08-28T12:20:56Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # soohmatthew/reddit-confidence-setfit-model-1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("soohmatthew/reddit-confidence-setfit-model-1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
whywynn/poca-SoccerTwos	whywynn	2023-08-31T04:29:39Z	37	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-08-31T04:19:21Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: whywynn/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
soohmatthew/reddit-care-setfit-model-1	soohmatthew	2023-08-31T04:27:13Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-08-28T11:56:12Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # soohmatthew/reddit-care-setfit-model-1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("soohmatthew/reddit-care-setfit-model-1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
spsither/whisper-small-hi	spsither	2023-08-31T04:16:43Z	3	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-16T06:40:04Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-hi 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. --> # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3488 - Wer: 18.8573 ## 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: 1e-05 - train_batch_size: 16 - 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: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1899 \| 3.02 \| 1000 \| 0.3311 \| 22.3674 \| \| 0.0179 \| 6.04 \| 2000 \| 0.3252 \| 19.8309 \| \| 0.0026 \| 9.06 \| 3000 \| 0.3382 \| 18.8189 \| \| 0.0009 \| 12.08 \| 4000 \| 0.3488 \| 18.8573 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
rossevine/Check_Model_1	rossevine	2023-08-31T03:53:52Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-large", "base_model:finetune:facebook/wav2vec2-large", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-30T20:34:44Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: Check_Model_1 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: id split: test args: id metrics: - name: Wer type: wer value: 0.37479022934924483 --- <!-- 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. --> # Check_Model_1 This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5522 - Wer: 0.3748 - Cer: 0.1158 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 2.1839 \| 3.23 \| 400 \| 0.8796 \| 0.7306 \| 0.2332 \| \| 0.6388 \| 6.45 \| 800 \| 0.8702 \| 0.6410 \| 0.2200 \| \| 0.4695 \| 9.68 \| 1200 \| 0.7064 \| 0.5360 \| 0.1632 \| \| 0.3659 \| 12.9 \| 1600 \| 0.5814 \| 0.5211 \| 0.1662 \| \| 0.285 \| 16.13 \| 2000 \| 0.6394 \| 0.5041 \| 0.1663 \| \| 0.2254 \| 19.35 \| 2400 \| 0.5889 \| 0.4428 \| 0.1405 \| \| 0.1801 \| 22.58 \| 2800 \| 0.5712 \| 0.4013 \| 0.1182 \| \| 0.1392 \| 25.81 \| 3200 \| 0.5914 \| 0.3934 \| 0.1177 \| \| 0.1051 \| 29.03 \| 3600 \| 0.5522 \| 0.3748 \| 0.1158 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 1.18.3 - Tokenizers 0.13.3
MohanaSri/Taxi	MohanaSri	2023-08-31T03:48:45Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T03:48:43Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="MohanaSri/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Serotina/Reinforce-PixelCopter	Serotina	2023-08-31T03:43:59Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T02:03:50Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 34.00 +/- 29.07 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
beatwade/alpaca-bitcoin-tweets-sentiment	beatwade	2023-08-31T03:23:57Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-30T23:29:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
bwarshaw/dqn-SpaceInvadersNoFrameskip-v4	bwarshaw	2023-08-31T03:12:33Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-31T03:11:59Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 577.50 +/- 126.40 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bwarshaw -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bwarshaw -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bwarshaw ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
pensuke/distilbert-base-uncased-finetuned-emotion	pensuke	2023-08-31T03:10:05Z	103	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	2023-08-31T02:32:21Z	--- 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: split metrics: - name: Accuracy type: accuracy value: 0.9325 - name: F1 type: f1 value: 0.9324937609411934 --- <!-- 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: 0.1879 - Accuracy: 0.9325 - F1: 0.9325 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4344 \| 1.0 \| 250 \| 0.2242 \| 0.9185 \| 0.9176 \| \| 0.1857 \| 2.0 \| 500 \| 0.1879 \| 0.9325 \| 0.9325 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
batman555/layer_1_classifier	batman555	2023-08-31T03:09:03Z	104	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-31T02:46:49Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: layer_1_classifier 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. --> # layer_1_classifier 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: 0.1867 - Accuracy: 0.9457 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 0.1221 \| 1.0 \| \| No log \| 2.0 \| 8 \| 0.0832 \| 1.0 \| \| No log \| 3.0 \| 12 \| 0.0647 \| 1.0 \| \| No log \| 4.0 \| 16 \| 0.0591 \| 1.0 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
TigerResearch/tigerbot-7b-chat-8bit	TigerResearch	2023-08-31T02:55:52Z	4	0	transformers	[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-08-30T10:16:15Z	--- license: apache-2.0 --- <div style="width: 100%;"> <img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;"> </div> <p align="center"> <font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font> </p> <p align="center"> 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a> </p> This is a 8-bit GPTQ version of the [Tigerbot 13b chat](https://huggingface.co/TigerResearch/tigerbot-7b-chat). It was quantized to 8bit using: https://github.com/PanQiWei/AutoGPTQ ## How to download and use this model in github: https://github.com/TigerResearch/TigerBot Here are commands to clone the TigerBot and install. ``` conda create --name tigerbot python=3.8 conda activate tigerbot conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia git clone https://github.com/TigerResearch/TigerBot cd TigerBot pip install -r requirements.txt ``` Inference with command line interface ``` # 安装auto-gptq pip install auto-gptq # 启动推理 CUDA_VISIBLE_DEVICES=0 python other_infer/gptq_infer.py --model_path TigerResearch/tigerbot-7b-chat-8bit ```

Aonodensetsu/codyblue-731

Aonodensetsu

2023-08-31T10:55:11Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T12:04:23Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **codyblue-731** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry, femboy" - the recommended settings are epoch 11-15, strength 0.6-0.8. ![preview](preview.jpeg)

Aonodensetsu/cromachina

Aonodensetsu

2023-08-31T10:54:31Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T12:09:10Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **cromachina** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 11-15, strength 0.5-0.8. ![preview](preview.jpeg)

Aonodensetsu/delicious

Aonodensetsu

2023-08-31T10:54:12Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T12:42:21Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **delicious** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 12-15, strength 0.6-0.8. ![preview](preview.jpeg)

abhishek/llama-2-7b-hf-guanaco-sr-1

abhishek

2023-08-31T10:54:05Z

0

null

[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2023-08-31T08:09:34Z

--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: llama-2-7b-hf-guanaco-sr-1 results: [] ---  # llama-2-7b-hf-guanaco-sr-1 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown 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: 0.0002 - train_batch_size: 2 - 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_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3

Aonodensetsu/darkmirage

Aonodensetsu

2023-08-31T10:53:59Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T12:24:00Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **darkmirage** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 13-14, strength 0.5-0.7. ![preview](preview.jpeg)

Aonodensetsu/frenky_hw

Aonodensetsu

2023-08-31T10:53:21Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T12:53:19Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **frenky_hw** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry, male, girly" - the recommended settings are epoch 11-13, strength 0.6-0.8. ![preview](preview.jpeg)

Aonodensetsu/gothbunnyboy

Aonodensetsu

2023-08-31T10:53:05Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T12:57:11Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **gothbunnyboy** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 11-15, strength 0.6-0.8. ![preview](preview.jpeg)

vishnuhaasan/q-FrozenLake-v1-4x4-noSlippery

vishnuhaasan

2023-08-31T10:52:54Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T10:52:48Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage model = load_from_hub(repo_id="vishnuhaasan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])

Aonodensetsu/pumpkinspicelatte

Aonodensetsu

2023-08-31T10:52:39Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-15T13:04:44Z

--- license: gpl-3.0 --- This is a mirror of CivitAI. The style of artist **pumpkinspicelatte** trained for [Foxya v3](https://civitai.com/models/17138). The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 10-15, strength 0.6-0.9. ![preview](preview.png)

ardt-multipart/ardt-multipart-ppo_train_walker2d_level-3108_0934-33

ardt-multipart

2023-08-31T10:39:20Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-08-31T08:36:19Z

--- tags: - generated_from_trainer model-index: - name: ardt-multipart-ppo_train_walker2d_level-3108_0934-33 results: [] ---  # ardt-multipart-ppo_train_walker2d_level-3108_0934-33 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

SHENMU007/neunit_BASE_V9.5.9

SHENMU007

2023-08-31T10:36:34Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-08-31T09:35:39Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

dt-and-vanilla-ardt/dt-ppo_train_hopper_level-3108_1003-66

dt-and-vanilla-ardt

2023-08-31T10:15:36Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-08-31T09:04:45Z

--- tags: - generated_from_trainer model-index: - name: dt-ppo_train_hopper_level-3108_1003-66 results: [] ---  # dt-ppo_train_hopper_level-3108_1003-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

sumet/Test_Trocr_digit_handwriting

sumet

2023-08-31T09:52:03Z

201

2

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "trocr", "image-to-text", "endpoints_compatible", "region:us" ]

image-to-text

2023-08-30T02:28:52Z

--- tags: - trocr - image-to-text ---

phillipos99/ppo-LunarLander-v2

phillipos99

2023-08-31T09:51:38Z

2

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T09:51:20Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.40 +/- 17.75 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

vnktrmnb/MBERT_FT-TyDiQA_S67

vnktrmnb

2023-08-31T09:45:40Z

62

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-30T06:03:12Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S67 results: [] ---  # vnktrmnb/MBERT_FT-TyDiQA_S67 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3185 - Train End Logits Accuracy: 0.9077 - Train Start Logits Accuracy: 0.9272 - Validation Loss: 0.5503 - Validation End Logits Accuracy: 0.875 - Validation Start Logits Accuracy: 0.9111 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.6586 | 0.8284 | 0.8598 | 0.5000 | 0.8737 | 0.9124 | 0 | | 0.4565 | 0.8766 | 0.8978 | 0.5009 | 0.8776 | 0.9175 | 1 | | 0.3185 | 0.9077 | 0.9272 | 0.5503 | 0.875 | 0.9111 | 2 | ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

ukr-models/xlm-roberta-base-uk

ukr-models

2023-08-31T09:41:51Z

526

12

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-11T10:53:02Z

--- language: - uk tags: - ukrainian widget: - text: "Тарас Шевченко – великий український <mask>." license: mit --- This is a smaller version of the [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model with only Ukrainian and some English embeddings left. * The original model has 470M parameters, with 384M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 31K (top 25K Ukrainian tokens and top English tokens) the number of model parameters reduced to 134M parameters, and model size reduced from 1GB to 400MB.

ukr-models/uk-ner

ukr-models

2023-08-31T09:41:21Z

188

3

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-07T05:31:07Z

--- language: - uk tags: - ukrainian widget: - text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера." license: mit --- ## Model Description Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on [synthetic NER dataset](https://huggingface.co/datasets/ukr-models/Ukr-Synth) with B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags ## How to Use Huggingface pipeline way (returns tokens with labels): ```py from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-ner') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-ner') ner = pipeline('ner', model=model, tokenizer=tokenizer) ner("Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера.") ``` If you wish to get predictions split by words, not by tokens, you may use the following approach (download script get_predictions.py from the repository, it uses [package tokenize_uk](https://pypi.org/project/tokenize_uk/) for splitting) ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from get_predictions import get_word_predictions tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-ner') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-ner') get_word_predictions(model, tokenizer, ["Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."]) ```

ukr-models/uk-morph

ukr-models

2023-08-31T09:41:07Z

124

1

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-08T07:14:02Z

--- language: - uk tags: - ukrainian widget: - text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера." license: mit --- ## Model Description Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on [synthetic morphological dataset](https://huggingface.co/datasets/ukr-models/Ukr-Synth), returns both UPOS and morphological features (joined by double underscore symbol) ## How to Use Huggingface pipeline way (returns tokens with labels): ```py from transformers import TokenClassificationPipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-morph') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-morph') ppln = TokenClassificationPipeline(model=model, tokenizer=tokenizer) ppln("Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера.") ``` If you wish to get predictions split by words, not by tokens, you may use the following approach (download script get_predictions.py from the repository, it uses [package tokenize_uk](https://pypi.org/project/tokenize_uk/) for splitting) ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from get_predictions import get_word_predictions tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-morph') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-morph') get_word_predictions(model, tokenizer, ["Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."]) ```

ukr-models/uk-punctcase

ukr-models

2023-08-31T09:40:36Z

118

3

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "ukrainian", "uk", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-13T11:50:18Z

--- language: - uk tags: - ukrainian widget: - text: "упродовж 2012-2014 років національний природний парк «зачарований край» разом із всесвітнім фондом природи wwf успішно реалізували проект із відновлення болота «чорне багно» розташованого на схилах гори бужора у закарпатті водноболотне угіддя «чорне багно» є найбільшою болотною екосистемою регіону воно займає площу близько 15 га унікальністю цього високогірного болота розташованого на висоті 840 м над рівнем моря є велика потужність торфових покладів (глибиною до 59 м) і своєрідна рослинність у 50-х і на початку 60-х років минулого століття на природних потічках що протікали через болото побудували осушувальні канали це порушило природну рівновагу відтак змінилася екосистема болота" license: mit --- ## Model Description Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on Ukrainian texts to recover punctuation and case. ## How to Use Download script get_predictions.py from the repository. ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from get_predictions import recover_text tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-punctcase') model = AutoModelForTokenClassification.from_pretrained('ukr-models/uk-punctcase') text = "..." recover_text(text_processed, model, tokenizer) ```

ukr-models/uk-summarizer

ukr-models

2023-08-31T09:40:08Z

132

4

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "ukrainian", "uk", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-29T13:21:16Z

--- language: - uk tags: - ukrainian license: mit --- ## Model Description Fine-tuning of [uk-mt5-base](https://huggingface.co/kravchenko/uk-mt5-base) model on summarization dataset. ## How to Use ```py from transformers import AutoTokenizer, T5ForConditionalGeneration, pipeline tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-summarizer') model = T5ForConditionalGeneration.from_pretrained('ukr-models/uk-summarizer') ppln = pipeline("summarization", model=model, tokenizer=tokenizer, device=0, max_length=128, num_beams=4, no_repeat_ngram_size=2, clean_up_tokenization_spaces=True) text = "..." ppln(text) ```

UholoDala/sentence_sentiments_analysis_roberta

UholoDala

2023-08-31T09:39:09Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-31T06:42:32Z

--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: sentence_sentiments_analysis_roberta results: [] ---  # sentence_sentiments_analysis_roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2736 - F1-score: 0.9119 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1-score | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3477 | 1.0 | 2500 | 0.3307 | 0.9112 | | 0.2345 | 2.0 | 5000 | 0.2736 | 0.9119 | | 0.175 | 3.0 | 7500 | 0.3625 | 0.9161 | | 0.1064 | 4.0 | 10000 | 0.3272 | 0.9358 | | 0.07 | 5.0 | 12500 | 0.3291 | 0.9380 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

AndrewL088/Pixelcopter-2

AndrewL088

2023-08-31T09:38:36Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T09:38:31Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 36.10 +/- 22.06 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Datactive/BERT_pap_queries_classification_2

Datactive

2023-08-31T09:37:52Z

61

0

transformers

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

text-classification

2023-08-29T20:36:30Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Datactive/BERT_pap_queries_classification_2 results: [] ---  # Datactive/BERT_pap_queries_classification_2 This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1558 - Validation Loss: 0.1369 - Train F1: 0.9475 - Epoch: 0 ## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1463, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train F1 | Epoch | |:----------:|:---------------:|:--------:|:-----:| | 0.1558 | 0.1369 | 0.9475 | 0 | ### Framework versions - Transformers 4.29.0.dev0 - TensorFlow 2.12.0 - Datasets 2.11.0 - Tokenizers 0.13.3

ardt-multipart/ardt-multipart-ppo_train_hopper_level-3108_0919-66

ardt-multipart

2023-08-31T09:36:54Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-08-31T08:21:10Z

--- tags: - generated_from_trainer model-index: - name: ardt-multipart-ppo_train_hopper_level-3108_0919-66 results: [] ---  # ardt-multipart-ppo_train_hopper_level-3108_0919-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

lomahony/eleuther-pythia12b-hh-sft

lomahony

2023-08-31T09:34:04Z

16

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:Anthropic/hh-rlhf", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-25T10:52:10Z

--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) supervised finetuned with [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch. [wandb log](https://wandb.ai/pythia_dpo/Pythia_LOM/runs/hdct406x) Benchmark evaluations included in repo done using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor). See [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) for model details [(paper)](https://arxiv.org/abs/2101.00027).

AK-12/my_awesome_model

AK-12

2023-08-31T09:33:02Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-27T10:46:57Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.9475 ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - Accuracy: 0.9475 ## 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: 9e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2804 | 1.0 | 100 | 0.3327 | 0.9475 | | 0.4089 | 2.0 | 200 | 0.3448 | 0.955 | | 0.0564 | 3.0 | 300 | 0.3446 | 0.95 | | 0.0 | 4.0 | 400 | 0.3417 | 0.9475 | | 0.0 | 5.0 | 500 | 0.3616 | 0.9475 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

aviroes/MAScIR_elderly_whisper-medium-LoRA

aviroes

2023-08-31T09:31:02Z

0

null

[ "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "region:us" ]

null

2023-08-31T07:02:39Z

--- license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer model-index: - name: MAScIR_elderly_whisper-medium-LoRA results: [] ---  # MAScIR_elderly_whisper-medium-LoRA This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0224 ## 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: 8 - 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: 200 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3209 | 0.19 | 100 | 0.3262 | | 0.2482 | 0.37 | 200 | 0.3101 | | 0.2726 | 0.56 | 300 | 0.3030 | | 0.2288 | 0.74 | 400 | 0.2848 | | 0.2014 | 0.93 | 500 | 0.2586 | | 0.1277 | 1.11 | 600 | 0.2098 | | 0.1054 | 1.3 | 700 | 0.1857 | | 0.1056 | 1.48 | 800 | 0.1449 | | 0.0842 | 1.67 | 900 | 0.1069 | | 0.0692 | 1.85 | 1000 | 0.0874 | | 0.0314 | 2.04 | 1100 | 0.0628 | | 0.0265 | 2.22 | 1200 | 0.0515 | | 0.0154 | 2.41 | 1300 | 0.0443 | | 0.0127 | 2.59 | 1400 | 0.0382 | | 0.0237 | 2.78 | 1500 | 0.0290 | | 0.0119 | 2.96 | 1600 | 0.0224 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Dala/mlc-chat-vicuna-13b-v1.5

Dala

2023-08-31T09:23:54Z

0

1

null

[ "license:llama2", "region:us" ]

null

2023-08-25T17:42:24Z

--- inference: false license: llama2 model_type: llama model_creator: lmsys model_link: https://huggingface.co/lmsys/vicuna-13b-v1.5 model_name: Vicuna 13B v1.5 quantized_by: Dala --- # Vicuna 13B v1.5 - MLC - Model creator: [lmsys](https://huggingface.co/lmsys) - Original model: [Vicuna 13B v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) ## Description This repo contains the [MLC](https://mlc.ai/mlc-llm/) compiled parameters for [lmsys's Vicuna 13B v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5). It contains several quantizations, each in its own branch: - main (q4f16_1) <-- You are currently on this branch - q4f16_2 - q8f16_1 - autogptq_llama_q4f16_1 To run the model, please check out the [MLC instructions](https://mlc.ai/mlc-llm/docs/get_started/try_out.html). In case the model libraries are not yet available in the [binary lib srepo](https://github.com/mlc-ai/binary-mlc-llm-libs), please obtain them from [this PR](https://github.com/mlc-ai/binary-mlc-llm-libs/pull/15/files)

dt-and-vanilla-ardt/ardt-vanilla-ppo_train_halfcheetah_level-3108_0816-99

dt-and-vanilla-ardt

2023-08-31T09:23:43Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-08-31T07:18:16Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-ppo_train_halfcheetah_level-3108_0816-99 results: [] ---  # ardt-vanilla-ppo_train_halfcheetah_level-3108_0816-99 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

pvcodes/comment_toxicity_classifier

pvcodes

2023-08-31T09:22:36Z

0

null

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

null

2023-08-28T14:03:39Z

--- license: mit --- <h1 align=center>Comment Toxicity Classification</h1> This model helps to predict the is a comment/sentence is hateful on various parameters such as toxicity, severe toxicity, obscene, threat, insult and racism. ### Test the model here : <a href="https://huggingface.co/spaces/pvcodes/comment_toxicity_classifier">pvcodes/comment_toxicity_classifier</a> <br> ## Working of the Model - #### Loading of Data The data is fetched from <a href='assets/jigsaw_toxic_challenge/train.csv/train.csv'>csv</a> file, which consist of the comment and attributes such as toxicity, severe toxicity, obscene, threat, insult and racism. - #### Preprocessing the comments Then the data is tokenized using the `TextVectorization` method of `keras` in and embeded - #### Creating of <emp>Deep NLP Model</emp> For this model we used `Keras sequential API` with a number of `LSTM` layers (because they are particulary good while working with sequences) - #### - The dataset used to train the model is from <a href=https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge>Toxic Comment Classification Challenge</a> from <a href=https://www.kaggle.com>Kaggle</a>. ##### Note: The compiled data model is available here: <a href='assets/toxicity.h5'>here</a>. <samp> <p align="center"> ════ ⋆★⋆ ════<br> From <a href="https://github.com/pvcodes/pvcodes">pvcodes</a> </p> </samp>

moro01525/mlm

moro01525

2023-08-31T09:16:14Z

6

0

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:moro01525/mlm", "base_model:finetune:moro01525/mlm", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-08-29T09:25:43Z

--- base_model: moro01525/mlm tags: - generated_from_trainer model-index: - name: mlm results: [] ---  # mlm This model is a fine-tuned version of [moro01525/mlm](https://huggingface.co/moro01525/mlm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.9361 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.248 | 1.0 | 582 | 4.9240 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Norod78/SDXL-StickerSheet-Lora

Norod78

2023-08-31T09:12:04Z

248

33

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]

text-to-image

2023-08-31T09:04:29Z

--- license: mit base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: StickerSheet tags: - text-to-image - stable-diffusion - lora - diffusers widget: - text: Cute sparkle pink barbie StickerSheet - text: Cthulhu StickerSheet based on H.P Lovecraft stories - text: Cute sparkle rainbow kitten StickerSheet, Eric Wallis - text: Cute socially awkward potato StickerSheet inference: true language: - en --- # Trigger words Use "StickerSheet" in your prompts # Examples Cute sparkle pink barbie StickerSheet, Very detailed, clean, high quality, sharp image, Eric Wallis ![Sparkle pink barbie](https://huggingface.co/Norod78/SDXL-StickerSheet-Lora/resolve/main/Examples/00076-20230831113822-7778-Cute%20sparkle%20pink%20barbie%20StickerSheet%20%20%2C%20Very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp%20image%2C%20Eric%20Wallis-before-highres-fix.jpg) Cthulhu StickerSheet, based on H.P Lovecraft stories, Very detailed, clean, high quality, sharp image ![Cthulhu](https://huggingface.co/Norod78/SDXL-StickerSheet-Lora/resolve/main/Examples/00073-20230831113700-7780-Cthulhu%20StickerSheet%20%20_lora_SDXL-StickerSheet-Lora_1_%2C%20based%20on%20H.P%20Lovecraft%20stories%2C%20Very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp.jpg)

iloya/Taxi-v3

iloya

2023-08-31T09:08:28Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T09:08:25Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="iloya/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Hozier/sd-class-butterflies-32

Hozier

2023-08-31T08:57:55Z

44

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-08-31T08:53:04Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Hozier/sd-class-butterflies-32') image = pipeline().images[0] image ```

kimi0230/TestModel

kimi0230

2023-08-31T08:56:33Z

1

0

null

[ "tf", "generated_from_keras_callback", "dataset:fka/awesome-chatgpt-prompts", "license:mit", "region:us" ]

null

2023-08-31T07:44:10Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: chatgpt-gpt4-prompts-bart-large-cnn-samsum results: [] datasets: - fka/awesome-chatgpt-prompts ---  # chatgpt-gpt4-prompts-bart-large-cnn-samsum This model generates ChatGPT/BingChat & GPT-3 prompts and is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an [this](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2214 - Validation Loss: 2.7584 - Epoch: 4 ### Streamlit This model supports a [Streamlit](https://streamlit.io/) Web UI to run the chatgpt-gpt4-prompts-bart-large-cnn-samsum model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/ChatGPT-BingChat-GPT3-Prompt-Generator_App) ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.1982 | 2.6801 | 0 | | 2.3601 | 2.5493 | 1 | | 1.9225 | 2.5377 | 2 | | 1.5465 | 2.6794 | 3 | | 1.2214 | 2.7584 | 4 | ### Framework versions - Transformers 4.27.3 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

iloya/q-FrozenLake-v1-4x4-noSlippery

iloya

2023-08-31T08:55:45Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T08:55:42Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="iloya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Alexa06/54yg

Alexa06

2023-08-31T08:53:31Z

0

null

[ "region:us" ]

null

2023-08-31T08:52:14Z

photo_5154810918662679307_x.jpg

vnktrmnb/MBERT_FT-TyDiQA_S59

vnktrmnb

2023-08-31T08:34:15Z

65

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-30T05:18:51Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S59 results: [] ---  # vnktrmnb/MBERT_FT-TyDiQA_S59 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6175 - Train End Logits Accuracy: 0.8417 - Train Start Logits Accuracy: 0.8693 - Validation Loss: 0.4662 - Validation End Logits Accuracy: 0.8789 - Validation Start Logits Accuracy: 0.9162 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.4412 | 0.6715 | 0.7002 | 0.4875 | 0.8570 | 0.8943 | 0 | | 0.8493 | 0.7898 | 0.8229 | 0.4547 | 0.8686 | 0.9137 | 1 | | 0.6175 | 0.8417 | 0.8693 | 0.4662 | 0.8789 | 0.9162 | 2 | ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

parksuna/xlm-roberta-base-finetuned-panx-de

parksuna

2023-08-31T08:29:59Z

122

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-31T08:25:49Z

--- license: mit base_model: xlm-roberta-base 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 config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8657241810026685 ---  # 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: 0.1338 - F1: 0.8657 ## 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 | 0.1557 | 0.8218 | | 0.126 | 2.0 | 1050 | 0.1460 | 0.8521 | | 0.0827 | 3.0 | 1575 | 0.1338 | 0.8657 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

vnktrmnb/MBERT_FT-TyDiQA_S531

vnktrmnb

2023-08-31T08:22:15Z

65

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-31T07:27:40Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S531 results: [] ---  # vnktrmnb/MBERT_FT-TyDiQA_S531 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6202 - Train End Logits Accuracy: 0.8376 - Train Start Logits Accuracy: 0.8661 - Validation Loss: 0.4939 - Validation End Logits Accuracy: 0.8647 - Validation Start Logits Accuracy: 0.9046 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.4876 | 0.6535 | 0.6831 | 0.5669 | 0.8222 | 0.8698 | 0 | | 0.8473 | 0.7841 | 0.8173 | 0.4769 | 0.8647 | 0.9059 | 1 | | 0.6202 | 0.8376 | 0.8661 | 0.4939 | 0.8647 | 0.9046 | 2 | ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

emotibot-inc/Zhuhai-13B

emotibot-inc

2023-08-31T08:13:01Z

0

null

[ "region:us" ]

null

2023-08-30T09:33:05Z

# README # Zhuhai-13B [Hugging Face](https://huggingface.co/emotibot-inc/Zhuhai-13B) | [GitHub](https://github.com/emotibot-inc/Zhuhai-13B) | [Model Scope](https://modelscope.cn/models/emotibotinc/Zhuhai-13B/summary) | [Emotibrain](https://brain.emotibot.com/?source=zhuhai13b_huggingface) # **模型介绍** "竹海-13B"是竹间智能继“竹海-7B”之后开发的一款拥大模语言模型，以下是“竹海-13B”的四个主要特点： - 更大尺寸、更多数据：相比于“竹海-7B”，我们将参数量扩大到130亿，并在高质量语料上训练了1.2万亿tokens。Zhuhai-13B的上下文窗口长度为4096。 - 高效性能：基于Transformer结构，在大约1.2万亿tokens上训练出来的130亿参数模型，支持中英双语。 - 安全性：我们对“竹海-13B”进行了严格的安全控制和优化，确保其在实际应用中不会产生任何不适当或误导性的输出。通过精心设计和调整算法参数，“竹海-13B”可以有效地避免乱说话现象。 # Model **benchmark** ## **中文评测** - **CMMLU** ### Result | Model 5-shot | STEM | Humanities | Social Science | Other | China-specific | Average | | --- | --- | --- | --- | --- | --- | --- | | Multilingual-oriented | | | | | | | | [GPT4](https://openai.com/gpt4) | 65.23 | 72.11 | 72.06 | 74.79 | 66.12 | 70.95 | | [ChatGPT](https://openai.com/chatgpt) | 47.81 | 55.68 | 56.50 | 62.66 | 50.69 | 55.51 | | [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) | 33.33 | 43.46 | 44.28 | 44.75 | 39.46 | 41.45 | | [LLaMA-65B](https://github.com/facebookresearch/llama) | 34.47 | 40.24 | 41.55 | 42.88 | 37.00 | 39.80 | | [BLOOMZ-7B](https://github.com/bigscience-workshop/xmtf) | 30.56 | 39.10 | 38.59 | 40.32 | 37.15 | 37.04 | | [Bactrian-LLaMA-13B](https://github.com/mbzuai-nlp/bactrian-x) | 27.52 | 32.47 | 32.27 | 35.77 | 31.56 | 31.88 | | Chinese-oriented | | | | | | | | [Zhuzhi-6B](https://github.com/emotibot-inc/Zhuzhi-6B) | 40.30 | 48.08 | 46.72 | 47.41 | 45.51 | 45.60 | | [Zhuhai-13B](https://github.com/emotibot-inc/Zhuhai-13B) | 42.39 | 61.57 | 60.48 | 58.57 | 55.68 | 55.74 | | [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) | 42.38 | 61.61 | 60.44 | 59.26 | 56.62 | 55.82 | | [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) | 42.55 | 50.98 | 50.99 | 50.80 | 48.37 | 48.80 | | [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) | 35.25 | 48.07 | 47.88 | 46.61 | 44.14 | 44.43 | | [ChatGLM-6B](https://github.com/THUDM/GLM-130B) | 32.35 | 39.22 | 39.65 | 38.62 | 37.70 | 37.48 | | [BatGPT-15B](https://github.com/haonan-li/CMMLU/blob/master) | 34.96 | 35.45 | 36.31 | 42.14 | 37.89 | 37.16 | | [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 27.12 | 33.18 | 34.87 | 35.10 | 32.97 | 32.63 | | [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) | 27.23 | 30.41 | 28.84 | 32.56 | 28.68 | 29.57 | | [Chinese-GLM-10B](https://github.com/THUDM/GLM) | 25.49 | 27.05 | 27.42 | 29.21 | 28.05 | 27.26 | | Random | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | | Model 0-shot | STEM | Humanities | Social Science | Other | China-specific | Average | | --- | --- | --- | --- | --- | --- | --- | | Multilingual-oriented | | | | | | | | [GPT4](https://openai.com/gpt4) | 63.16 | 69.19 | 70.26 | 73.16 | 63.47 | 68.9 | | [ChatGPT](https://openai.com/chatgpt) | 44.8 | 53.61 | 54.22 | 59.95 | 49.74 | 53.22 | | [BLOOMZ-7B](https://github.com/bigscience-workshop/xmtf) | 33.03 | 45.74 | 45.74 | 46.25 | 41.58 | 42.8 | | [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) | 31.11 | 41.3 | 40.87 | 40.61 | 36.05 | 38.5 | | [LLaMA-65B](https://github.com/facebookresearch/llama) | 31.09 | 34.45 | 36.05 | 37.94 | 32.89 | 34.88 | | [Bactrian-LLaMA-13B](https://github.com/mbzuai-nlp/bactrian-x) | 26.46 | 29.36 | 31.81 | 31.55 | 29.17 | 30.06 | | Chinese-oriented | | | | | | | | [Zhuzhi-6B](https://github.com/emotibot-inc/Zhuzhi-6B) | 42.51 | 48.91 | 48.85 | 50.25 | 47.57 | 47.62 | | [Zhuhai-13B](https://github.com/emotibot-inc/Zhuhai-13B) | 42.37 | 60.97 | 59.71 | 56.35 | 54.81 | 54.84 | | [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) | 42.04 | 60.49 | 59.55 | 56.6 | 55.72 | 54.63 | | [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) | 41.28 | 52.85 | 53.37 | 52.24 | 50.58 | 49.95 | | [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) | 32.79 | 44.43 | 46.78 | 44.79 | 43.11 | 42.33 | | [ChatGLM-6B](https://github.com/THUDM/GLM-130B) | 32.22 | 42.91 | 44.81 | 42.6 | 41.93 | 40.79 | | [BatGPT-15B](https://github.com/haonan-li/CMMLU/blob/master) | 33.72 | 36.53 | 38.07 | 46.94 | 38.32 | 38.51 | | [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 26.76 | 26.57 | 27.42 | 28.33 | 26.73 | 27.34 | | [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) | 25.68 | 26.35 | 27.21 | 27.92 | 26.7 | 26.88 | | [Chinese-GLM-10B](https://github.com/THUDM/GLM) | 25.57 | 25.01 | 26.33 | 25.94 | 25.81 | 25.8 | | Random | 25 | 25 | 25 | 25 | 25 | 25 | # **推理对话** 您可以直接注册并登录竹间智能科技发布的大模型产品 [Emotibrain](https://brain.emotibot.com/?source=zhuhai13b_huggingface)，并选择 **CoPilot**（**KKBot**）进行的在线测试，注册即可立即使用； ![Untitled](./READMEjpg/Untitled.png) # **模型训练** 您可以直接注册并登录竹间智能科技发布的大模型产品 [Emotibrain](https://brain.emotibot.com/?source=zhuhai13b_huggingface)，并选择 Fine-tune 进行 **0 代码微调**，注册即可立即使用；详细的训练流程您可以浏览此文档：[Emotibrain 快速入门](https://brain.emotibot.com/supports/model-factory/dash-into.html)（大约 5 分钟） ![Untitled](./READMEjpg/Untitled1.png) ![Untitled](./READMEjpg/Untitled2.png) # **更多信息** 若您想了解更多大模型训练平台的相关信息，请访问 [Emotibrain 官网](https://brain.emotibot.com/?source=zhuhai13b_huggingface) 进行了解；

Geotrend/bert-base-en-fr-ar-cased

Geotrend

2023-08-31T08:03:30Z

114

0

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: multilingual datasets: wikipedia license: apache-2.0 --- # bert-base-en-fr-ar-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-ar-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-ar-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

Geotrend/bert-base-en-pt-cased

Geotrend

2023-08-31T08:03:02Z

116

1

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "en", "pt", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: - multilingual - en - pt datasets: wikipedia license: apache-2.0 --- # bert-base-en-pt-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-pt-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

ThanhMai/green-clip-inpaint

ThanhMai

2023-08-31T08:01:58Z

21

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T08:01:05Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### green clip inpaint on Stable Diffusion via Dreambooth #### model by ThanhMai This your the Stable Diffusion model fine-tuned the green clip inpaint concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **<green-clip> clip** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/0.jpeg) ![image 3](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/5.jpeg) ![image 4](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/4.jpeg) ![image 5](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/2.jpeg) ![image 6](https://huggingface.co/ThanhMai/green-clip-inpaint/resolve/main/concept_images/6.jpeg)

SCUT-DLVCLab/lilt-roberta-en-base

SCUT-DLVCLab

2023-08-31T07:59:36Z

19,575

18

transformers

[ "transformers", "pytorch", "safetensors", "lilt", "feature-extraction", "vision", "arxiv:2202.13669", "license:mit", "endpoints_compatible", "region:us" ]

feature-extraction

2022-09-29T14:06:32Z

--- license: mit tags: - vision --- # LiLT-RoBERTa (base-sized model) Language-Independent Layout Transformer - RoBERTa model by stitching a pre-trained RoBERTa (English) and a pre-trained Language-Independent Layout Transformer (LiLT) together. It was introduced in the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Wang et al. and first released in [this repository](https://github.com/jpwang/lilt). Disclaimer: The team releasing LiLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Language-Independent Layout Transformer (LiLT) allows to combine any pre-trained RoBERTa encoder from the hub (hence, in any language) with a lightweight Layout Transformer to have a LayoutLM-like model for any language. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg" alt="drawing" width="600"/> ## Intended uses & limitations The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the [model hub](https://huggingface.co/models?search=lilt) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/lilt.html). ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2202.13669, doi = {10.48550/ARXIV.2202.13669}, url = {https://arxiv.org/abs/2202.13669}, author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

yuekai/model_repo_whisper_large_v2

yuekai

2023-08-31T07:57:15Z

0

null

[ "onnx", "region:us" ]

null

2023-08-17T09:53:49Z

### Client https://huggingface.co/spaces/yuekai/triton-asr-client https://github.com/yuekaizhang/Triton-ASR-Client ### Server ```sh docker pull soar97/triton-whisper:23.06 docker run -it --name "whisper-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-whisper:23.06 apt-get install git-lfs git-lfs install git clone https://huggingface.co/yuekai/model_repo_whisper_large_v2.git export CUDA_VISIBLE_DEVICES="1" model_repo_path=./model_repo_whisper tritonserver --model-repository $model_repo_path \ --pinned-memory-pool-byte-size=2048000000 \ --cuda-memory-pool-byte-size=0:4096000000 \ --http-port 10086 \ --metrics-port 10087 ``` ### Benchmark Results Decoding on a single V100 GPU, audios are padding to 30s, using aishell1 test set files | Model | Backend | Concurrency | RTF | |-------|-----------|-----------------------|---------| | Large-v2 | ONNX FP16 | 4 | 0.14 | |Module| Time Distribution| |--|--| |feature_extractor|0.8%| |encoder|9.6%| |decoder|67.4%| |greedy search|22.2%|

victornica/mini_molformer_gsf_6epochs

victornica

2023-08-31T07:56:29Z

153

0

transformers

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

text-generation

2023-08-30T22:56:12Z

--- license: mit tags: - generated_from_trainer model-index: - name: mini_molformer_gsf_6epochs results: [] ---  # mini_molformer_gsf_6epochs 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: 0.6470 ## 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: 0.0006 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7953 | 0.1 | 1000 | 1.0871 | | 1.0284 | 0.19 | 2000 | 0.9575 | | 0.9463 | 0.29 | 3000 | 0.9099 | | 0.9048 | 0.39 | 4000 | 0.8758 | | 0.877 | 0.48 | 5000 | 0.8517 | | 0.8573 | 0.58 | 6000 | 0.8323 | | 0.8399 | 0.68 | 7000 | 0.8176 | | 0.8276 | 0.77 | 8000 | 0.8127 | | 0.8164 | 0.87 | 9000 | 0.8037 | | 0.8071 | 0.97 | 10000 | 0.7889 | | 0.7969 | 1.07 | 11000 | 0.7815 | | 0.7901 | 1.16 | 12000 | 0.7742 | | 0.7844 | 1.26 | 13000 | 0.7710 | | 0.778 | 1.36 | 14000 | 0.7633 | | 0.7732 | 1.45 | 15000 | 0.7605 | | 0.7695 | 1.55 | 16000 | 0.7567 | | 0.7646 | 1.65 | 17000 | 0.7486 | | 0.7606 | 1.74 | 18000 | 0.7462 | | 0.7576 | 1.84 | 19000 | 0.7434 | | 0.7539 | 1.94 | 20000 | 0.7376 | | 0.7484 | 2.03 | 21000 | 0.7343 | | 0.7423 | 2.13 | 22000 | 0.7318 | | 0.7403 | 2.23 | 23000 | 0.7270 | | 0.7364 | 2.32 | 24000 | 0.7274 | | 0.7341 | 2.42 | 25000 | 0.7206 | | 0.7321 | 2.52 | 26000 | 0.7204 | | 0.728 | 2.61 | 27000 | 0.7152 | | 0.7253 | 2.71 | 28000 | 0.7131 | | 0.7224 | 2.81 | 29000 | 0.7099 | | 0.7198 | 2.91 | 30000 | 0.7073 | | 0.7166 | 3.0 | 31000 | 0.7039 | | 0.7079 | 3.1 | 32000 | 0.7009 | | 0.7074 | 3.2 | 33000 | 0.6980 | | 0.7051 | 3.29 | 34000 | 0.6951 | | 0.703 | 3.39 | 35000 | 0.6924 | | 0.7008 | 3.49 | 36000 | 0.6895 | | 0.6971 | 3.58 | 37000 | 0.6873 | | 0.6943 | 3.68 | 38000 | 0.6854 | | 0.6931 | 3.78 | 39000 | 0.6814 | | 0.6899 | 3.87 | 40000 | 0.6799 | | 0.6874 | 3.97 | 41000 | 0.6770 | | 0.6805 | 4.07 | 42000 | 0.6740 | | 0.6762 | 4.16 | 43000 | 0.6722 | | 0.6753 | 4.26 | 44000 | 0.6689 | | 0.6721 | 4.36 | 45000 | 0.6668 | | 0.671 | 4.45 | 46000 | 0.6643 | | 0.6686 | 4.55 | 47000 | 0.6627 | | 0.6664 | 4.65 | 48000 | 0.6604 | | 0.6654 | 4.75 | 49000 | 0.6581 | | 0.6635 | 4.84 | 50000 | 0.6565 | | 0.6617 | 4.94 | 51000 | 0.6548 | | 0.6577 | 5.04 | 52000 | 0.6532 | | 0.6527 | 5.13 | 53000 | 0.6522 | | 0.6514 | 5.23 | 54000 | 0.6508 | | 0.6501 | 5.33 | 55000 | 0.6498 | | 0.6494 | 5.42 | 56000 | 0.6489 | | 0.6484 | 5.52 | 57000 | 0.6483 | | 0.6484 | 5.62 | 58000 | 0.6477 | | 0.6474 | 5.71 | 59000 | 0.6473 | | 0.6478 | 5.81 | 60000 | 0.6471 | | 0.6474 | 5.91 | 61000 | 0.6470 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

Edmon02/marian-finetuned-kde4-en-to-hy

Edmon02

2023-08-31T07:54:27Z

103

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:opus100", "base_model:Helsinki-NLP/opus-mt-en-hy", "base_model:finetune:Helsinki-NLP/opus-mt-en-hy", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-08-31T07:51:18Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-hy tags: - translation - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-hy results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 config: en-hy split: train args: en-hy metrics: - name: Bleu type: bleu value: 18.363987489312905 ---  # marian-finetuned-kde4-en-to-hy This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hy](https://huggingface.co/Helsinki-NLP/opus-mt-en-hy) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 1.4183 - Bleu: 18.3640 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

gillankrishna/ppo-LunarLander

gillankrishna

2023-08-31T07:54:09Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T07:53:51Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 238.64 +/- 59.44 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

redstonehero/arteyou_alpha1

redstonehero

2023-08-31T07:52:33Z

21

0

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T07:12:02Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/horridhentaimix_v10

redstonehero

2023-08-31T07:52:30Z

21

0

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T07:13:38Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/mistoonamethyst_v20

redstonehero

2023-08-31T07:52:29Z

19

0

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T07:13:29Z

--- license: creativeml-openrail-m library_name: diffusers ---

DopeorNope/A3_duck

DopeorNope

2023-08-31T07:37:42Z

2

0

peft

[ "peft", "region:us" ]

null

2023-08-31T07:35:36Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

sosuneko/Reinforce-CartPole-v1

sosuneko

2023-08-31T07:34:05Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T07:33:55Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

SasankVH/sample_data

SasankVH

2023-08-31T07:32:04Z

9

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "en", "dataset:localdataset", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-29T07:50:13Z

--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - localdataset metrics: - wer model-index: - name: testing results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: localdataset type: localdataset config: default split: test args: 'config: data, split: test' metrics: - name: Wer type: wer value: 0.0 ---  # testing This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the localdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0 ## 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: 1e-05 - train_batch_size: 16 - 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: 62 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 0.0 | 125.0 | 125 | 0.0000 | 0.0 | | 0.0 | 250.0 | 250 | 0.0000 | 0.0 | | 0.0 | 375.0 | 375 | 0.0000 | 0.0 | | 0.0 | 500.0 | 500 | 0.0000 | 0.0 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

rossevine/Model_G_2

rossevine

2023-08-31T07:29:43Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-29T13:06:01Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: Model_G_2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: id split: test args: id metrics: - name: Wer type: wer value: 0.251258623904531 ---  # Model_G_2 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.3710 - Wer: 0.2513 - Cer: 0.0631 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.7484 | 3.23 | 400 | 0.5706 | 0.5698 | 0.1477 | | 0.3419 | 6.45 | 800 | 0.4120 | 0.3758 | 0.0924 | | 0.1796 | 9.68 | 1200 | 0.3691 | 0.3295 | 0.0843 | | 0.125 | 12.9 | 1600 | 0.3821 | 0.3097 | 0.0782 | | 0.0984 | 16.13 | 2000 | 0.4085 | 0.2947 | 0.0742 | | 0.0827 | 19.35 | 2400 | 0.3859 | 0.2781 | 0.0711 | | 0.0666 | 22.58 | 2800 | 0.3813 | 0.2663 | 0.0684 | | 0.0558 | 25.81 | 3200 | 0.3681 | 0.2545 | 0.0644 | | 0.0466 | 29.03 | 3600 | 0.3710 | 0.2513 | 0.0631 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 1.18.3 - Tokenizers 0.13.3

ardt-multipart/ardt-multipart-ppo_train_halfcheetah_level-3108_0610-66

ardt-multipart

2023-08-31T07:22:42Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-08-31T05:11:57Z

--- tags: - generated_from_trainer model-index: - name: ardt-multipart-ppo_train_halfcheetah_level-3108_0610-66 results: [] ---  # ardt-multipart-ppo_train_halfcheetah_level-3108_0610-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

vnktrmnb/MBERT_FT-TyDiQA_S431

vnktrmnb

2023-08-31T07:20:23Z

78

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-31T06:26:45Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S431 results: [] ---  # vnktrmnb/MBERT_FT-TyDiQA_S431 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6089 - Train End Logits Accuracy: 0.8391 - Train Start Logits Accuracy: 0.8668 - Validation Loss: 0.5017 - Validation End Logits Accuracy: 0.8608 - Validation Start Logits Accuracy: 0.9085 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.4634 | 0.6632 | 0.6911 | 0.5058 | 0.8325 | 0.8982 | 0 | | 0.8321 | 0.7907 | 0.8249 | 0.4951 | 0.8531 | 0.9085 | 1 | | 0.6089 | 0.8391 | 0.8668 | 0.5017 | 0.8608 | 0.9085 | 2 | ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

dt-and-vanilla-ardt/ardt-vanilla-ppo_train_halfcheetah_level-3108_0607-66

dt-and-vanilla-ardt

2023-08-31T07:16:25Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-08-31T05:09:28Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-ppo_train_halfcheetah_level-3108_0607-66 results: [] ---  # ardt-vanilla-ppo_train_halfcheetah_level-3108_0607-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

vnktrmnb/MBERT_FT-TyDiQA_S41

vnktrmnb

2023-08-31T07:09:59Z

62

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-29T09:00:08Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/MBERT_FT-TyDiQA_S41 results: [] ---  # vnktrmnb/MBERT_FT-TyDiQA_S41 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6256 - Train End Logits Accuracy: 0.8359 - Train Start Logits Accuracy: 0.8649 - Validation Loss: 0.4800 - Validation End Logits Accuracy: 0.8595 - Validation Start Logits Accuracy: 0.8995 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.4994 | 0.6497 | 0.6777 | 0.4953 | 0.8479 | 0.8982 | 0 | | 0.8529 | 0.7875 | 0.8176 | 0.4775 | 0.8544 | 0.8892 | 1 | | 0.6256 | 0.8359 | 0.8649 | 0.4800 | 0.8595 | 0.8995 | 2 | ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

nisten/bigdoc-c13b-instruct-tf32

nisten

2023-08-31T06:54:29Z

0

peft

[ "peft", "region:us" ]

null

2023-08-31T06:51:42Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

redstonehero/cyberrealistic_v33_pruned

redstonehero

2023-08-31T06:53:52Z

23

1

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T06:08:48Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/revanimatedfp16_122_pruned

redstonehero

2023-08-31T06:53:35Z

19

0

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T06:07:35Z

--- license: creativeml-openrail-m library_name: diffusers ---

grammarly/pseudonymization-seq2seq

grammarly

2023-08-31T06:52:22Z

0

5

null

[ "text2text-generation", "en", "dataset:grammarly/pseudonymization-data", "dataset:cnn_dailymail", "dataset:imdb", "license:apache-2.0", "region:us" ]

text2text-generation

2023-07-05T18:35:11Z

--- license: apache-2.0 datasets: - grammarly/pseudonymization-data - cnn_dailymail - imdb language: - en metrics: - f1 - bleu pipeline_tag: text2text-generation --- # Model Card for Model ID  This repository contains files for two Seq2Seq transformers-based models used in our paper: https://aclanthology.org/2023.trustnlp-1.20/. ## Model Details ### Model Description  - **Developed by:** Oleksandr Yermilov, Vipul Raheja, Artem Chernodub - **Model type:** Seq2Seq - **Language (NLP):** English - **License:** Apache license 2.0 - **Finetuned from model:** BART ### Model Sources - **Paper:** https://aclanthology.org/2023.trustnlp-1.20/ ## Uses These models can be used for anonymizing datasets in English language. ## Bias, Risks, and Limitations Please check the Limitations section in our paper. ## Training Details ### Training Data https://huggingface.co/datasets/grammarly/pseudonymization-data/tree/main/seq2seq ### Training Procedure 1. Gather text data from Wikipedia. 2. Preprocess it using NER-based pseudonymization. 3. Fine-tune BART model on translation task for translating text from "original" to "pseudonymized". #### Training Hyperparameters We train the models for 3 epochs using `AdamW` optimization with the learning rate α =2*10<sup>5</sup>, and the batch size is 8. ## Evaluation ### Factors & Metrics #### Factors  There is no source truth of named entities for the data, on which this model was trained. We check whether the word is a named entity, using one of the NER systems (spaCy or FLAIR). #### Metrics We measure the amount of text, changed by our model. Specifically, we check for the following categories of translated text word by word: 1. True positive (TP) - Named entity, which was changed to another named entity. 2. True negative (TN) - Not a named entity, which was not changed. 3. False positive (FP) - Not a named entity, which was changed to another word. 4. False negative (FN) - Named entity, which was not changed to another named entity. We calculate F<sub>1</sub> score based on the abovementioned values. ## Citation  **BibTeX:** ``` @inproceedings{yermilov-etal-2023-privacy, title = "Privacy- and Utility-Preserving {NLP} with Anonymized data: A case study of Pseudonymization", author = "Yermilov, Oleksandr and Raheja, Vipul and Chernodub, Artem", booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.trustnlp-1.20", doi = "10.18653/v1/2023.trustnlp-1.20", pages = "232--241", abstract = "This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques better to balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available.", } ``` ## Model Card Contact Oleksandr Yermilov (oleksandr.yermilov@ucu.edu.ua).

taufiq-lalokalabs/gpt2-test

taufiq-lalokalabs

2023-08-31T06:41:59Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-31T06:41:57Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0

LarryAIDraw/Aria

LarryAIDraw

2023-08-31T06:40:41Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-08-30T05:43:37Z

--- license: creativeml-openrail-m --- https://civitai.com/models/136374/kurenaino-aria-occulticnine

LarryAIDraw/MGCM_eriza

LarryAIDraw

2023-08-31T06:27:03Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-08-31T05:05:11Z

--- license: creativeml-openrail-m ---

TrevorJS/CodeLlama-13b-mtg

TrevorJS

2023-08-31T06:25:31Z

0

peft

[ "peft", "region:us" ]

null

2023-08-31T06:05:03Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

LarryAIDraw/shizuka_hiratsuka_s2_v2

LarryAIDraw

2023-08-31T06:22:10Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-08-31T05:20:17Z

--- license: creativeml-openrail-m --- https://civitai.com/models/25866/shizuka-hiratsuka-season-1-season-2

yangdechuan/codeparrot-ds

yangdechuan

2023-08-31T06:22:06Z

6

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-28T12:33:55Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] ---  # codeparrot-ds 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: 1.0621 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.2102 | 0.02 | 1000 | 2.7478 | | 2.359 | 0.03 | 2000 | 2.2031 | | 2.0974 | 0.05 | 3000 | 1.9751 | | 1.9383 | 0.06 | 4000 | 1.8321 | | 1.8346 | 0.08 | 5000 | 1.7406 | | 1.7547 | 0.09 | 6000 | 1.6731 | | 1.6994 | 0.11 | 7000 | 1.6212 | | 1.6632 | 0.12 | 8000 | 1.5842 | | 1.6237 | 0.14 | 9000 | 1.5506 | | 1.5986 | 0.15 | 10000 | 1.5247 | | 1.5749 | 0.17 | 11000 | 1.4994 | | 1.5466 | 0.18 | 12000 | 1.4783 | | 1.5254 | 0.2 | 13000 | 1.4579 | | 1.5085 | 0.21 | 14000 | 1.4420 | | 1.4884 | 0.23 | 15000 | 1.4235 | | 1.4842 | 0.25 | 16000 | 1.4088 | | 1.4618 | 0.26 | 17000 | 1.3957 | | 1.4479 | 0.28 | 18000 | 1.3825 | | 1.4376 | 0.29 | 19000 | 1.3716 | | 1.4225 | 0.31 | 20000 | 1.3583 | | 1.4151 | 0.32 | 21000 | 1.3476 | | 1.4021 | 0.34 | 22000 | 1.3359 | | 1.3956 | 0.35 | 23000 | 1.3245 | | 1.3839 | 0.37 | 24000 | 1.3159 | | 1.3741 | 0.38 | 25000 | 1.3060 | | 1.3635 | 0.4 | 26000 | 1.2950 | | 1.3491 | 0.41 | 27000 | 1.2844 | | 1.3462 | 0.43 | 28000 | 1.2760 | | 1.3317 | 0.44 | 29000 | 1.2676 | | 1.3249 | 0.46 | 30000 | 1.2584 | | 1.3164 | 0.48 | 31000 | 1.2486 | | 1.3055 | 0.49 | 32000 | 1.2406 | | 1.3006 | 0.51 | 33000 | 1.2327 | | 1.2906 | 0.52 | 34000 | 1.2225 | | 1.2821 | 0.54 | 35000 | 1.2135 | | 1.2677 | 0.55 | 36000 | 1.2068 | | 1.2562 | 0.57 | 37000 | 1.1981 | | 1.2541 | 0.58 | 38000 | 1.1896 | | 1.2377 | 0.6 | 39000 | 1.1814 | | 1.2346 | 0.61 | 40000 | 1.1726 | | 1.2251 | 0.63 | 41000 | 1.1647 | | 1.2175 | 0.64 | 42000 | 1.1575 | | 1.2112 | 0.66 | 43000 | 1.1486 | | 1.2021 | 0.67 | 44000 | 1.1410 | | 1.1888 | 0.69 | 45000 | 1.1339 | | 1.1939 | 0.71 | 46000 | 1.1259 | | 1.18 | 0.72 | 47000 | 1.1198 | | 1.1698 | 0.74 | 48000 | 1.1130 | | 1.1634 | 0.75 | 49000 | 1.1063 | | 1.1593 | 0.77 | 50000 | 1.1006 | | 1.1545 | 0.78 | 51000 | 1.0946 | | 1.1478 | 0.8 | 52000 | 1.0896 | | 1.1443 | 0.81 | 53000 | 1.0855 | | 1.1365 | 0.83 | 54000 | 1.0808 | | 1.1332 | 0.84 | 55000 | 1.0773 | | 1.1336 | 0.86 | 56000 | 1.0736 | | 1.1276 | 0.87 | 57000 | 1.0711 | | 1.1241 | 0.89 | 58000 | 1.0686 | | 1.123 | 0.9 | 59000 | 1.0665 | | 1.1187 | 0.92 | 60000 | 1.0647 | | 1.1123 | 0.93 | 61000 | 1.0636 | | 1.1159 | 0.95 | 62000 | 1.0628 | | 1.1133 | 0.97 | 63000 | 1.0623 | | 1.1181 | 0.98 | 64000 | 1.0621 | | 1.1125 | 1.0 | 65000 | 1.0621 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

LarryAIDraw/plymouth

LarryAIDraw

2023-08-31T06:21:50Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-08-30T05:44:15Z

--- license: creativeml-openrail-m --- https://civitai.com/models/135762/hms-plymouth-or-azur-lane

DogGoesBark/medical_en_zh_8_29

DogGoesBark

2023-08-31T06:21:24Z

6

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-zh", "base_model:finetune:Helsinki-NLP/opus-mt-en-zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-29T14:51:16Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-zh tags: - generated_from_trainer metrics: - bleu model-index: - name: medical_en_zh_8_29 results: [] ---  # medical_en_zh_8_29 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6159 - Bleu: 41.4839 - Gen Len: 77.4048 ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.5915 | 1.02 | 3000 | 1.4640 | 30.8193 | 76.572 | | 1.2908 | 2.04 | 6000 | 1.2734 | 32.3053 | 76.897 | | 1.0814 | 3.06 | 9000 | 1.1348 | 34.3605 | 77.2082 | | 0.9083 | 4.08 | 12000 | 1.0246 | 34.9139 | 76.7213 | | 0.7507 | 5.1 | 15000 | 0.9336 | 36.2245 | 76.6036 | | 0.6046 | 6.12 | 18000 | 0.8291 | 37.987 | 77.326 | | 0.4838 | 7.14 | 21000 | 0.7496 | 38.7572 | 77.2366 | | 0.3861 | 8.16 | 24000 | 0.6730 | 40.3566 | 77.49 | | 0.3203 | 9.19 | 27000 | 0.6159 | 41.4839 | 77.4048 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

rossevine/Check_Model_2

rossevine

2023-08-31T06:18:55Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-31T04:50:41Z

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: Check_Model_2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: id split: test args: id metrics: - name: Wer type: wer value: 0.2728883087823979 ---  # Check_Model_2 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: 0.3499 - Wer: 0.2729 - Cer: 0.0673 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.8708 | 3.23 | 400 | 0.7345 | 0.7259 | 0.2034 | | 0.4247 | 6.45 | 800 | 0.4128 | 0.4268 | 0.1102 | | 0.2047 | 9.68 | 1200 | 0.3726 | 0.3795 | 0.0930 | | 0.1422 | 12.9 | 1600 | 0.3690 | 0.3514 | 0.0884 | | 0.1139 | 16.13 | 2000 | 0.3811 | 0.3160 | 0.0794 | | 0.089 | 19.35 | 2400 | 0.3650 | 0.2895 | 0.0731 | | 0.0709 | 22.58 | 2800 | 0.3629 | 0.2944 | 0.0727 | | 0.0594 | 25.81 | 3200 | 0.3538 | 0.2779 | 0.0692 | | 0.0478 | 29.03 | 3600 | 0.3499 | 0.2729 | 0.0673 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 1.18.3 - Tokenizers 0.13.3

sontn122/content

sontn122

2023-08-31T06:03:48Z

153

0

transformers

[ "transformers", "pytorch", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2023-08-31T05:59:52Z

--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer model-index: - name: microsoft/deberta-v3-large results: [] ---  # microsoft/deberta-v3-large This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6094 ## 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: 2 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.6123 | 1.0 | 3550 | 1.6094 | | 1.6124 | 2.0 | 7100 | 1.6094 | | 1.6106 | 3.0 | 10650 | 1.6094 | | 1.6107 | 4.0 | 14200 | 1.6094 | | 1.6104 | 5.0 | 17750 | 1.6094 | | 1.6115 | 6.0 | 21300 | 1.6094 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Glavin001/coqar-questions-llama-2-7b-v0.1-GPTQ

Glavin001

2023-08-31T06:02:02Z

4

0

transformers

[ "transformers", "llama", "text-generation", "en", "dataset:Glavin001/generate-questions-v0.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-08-27T01:20:27Z

--- datasets: - Glavin001/generate-questions-v0.1 language: - en library_name: transformers ---

beniben0/midjourney-falcon-7b

beniben0

2023-08-31T05:57:07Z

0

peft

[ "peft", "region:us" ]

null

2023-08-31T05:49:56Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0

AdanLee/ppo-LunarLander-v2-CleanRL

AdanLee

2023-08-31T05:54:13Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T05:37:54Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -23.72 +/- 113.11 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'AdanLee/ppo-LunarLander-v2-CleanRL' 'batch_size': 512 'minibatch_size': 128} ```

mahimairaja/distilhubert-music-classifier-finetuned-gtzan

mahimairaja

2023-08-31T05:46:22Z

146

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-08-31T02:56:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: CTC-based-finetuned-gtzan results: [] ---  # CTC-based-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7057 - Accuracy: 0.79 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0608 | 1.0 | 57 | 2.0361 | 0.43 | | 1.663 | 2.0 | 114 | 1.5387 | 0.62 | | 1.2399 | 3.0 | 171 | 1.2074 | 0.68 | | 1.0662 | 4.0 | 228 | 1.0805 | 0.65 | | 0.7986 | 5.0 | 285 | 0.8880 | 0.75 | | 0.7328 | 6.0 | 342 | 0.8037 | 0.74 | | 0.5891 | 7.0 | 399 | 0.7918 | 0.78 | | 0.5227 | 8.0 | 456 | 0.7232 | 0.79 | | 0.5123 | 9.0 | 513 | 0.7138 | 0.78 | | 0.5578 | 10.0 | 570 | 0.7057 | 0.79 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

GCYY/speecht5_finetuned_fleurs_zh

GCYY

2023-08-31T05:39:23Z

82

1

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "audio", "text-to-speech", "dataset:fleurs", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-08-31T05:20:18Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - audio - text-to-speech datasets: - fleurs model-index: - name: speecht5_finetuned_fleurs_zh results: [] ---  # speecht5_finetuned_fleurs_zh This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.4343 ## 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: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6864 | 1.09 | 100 | 0.6009 | | 0.5976 | 2.19 | 200 | 0.5062 | | 0.543 | 3.28 | 300 | 0.4577 | | 0.4786 | 4.38 | 400 | 0.4343 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Bazaar/cv_canal_pollution_level

Bazaar

2023-08-31T05:34:23Z

183

0

transformers

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

image-classification

2023-08-31T03:17:43Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: cv_canal_pollution_level results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9027777910232544 --- # cv_canal_pollution_level 使用HuggingPics微调生成任务：河道污染等级分类（无污染、轻度污染、中度污染、重度污染）使用方法： ```python from transformers import pipeline classifier = pipeline('image-classification', model='Bazzar/cv_canal_pollution_level') print(classifier('http://图片地址')) ``` 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 #### no pollution ![no pollution](images/no_pollution.jpg) #### light pollution ![light pollution](images/light_pollution.jpg) #### moderate pollution ![moderate pollution](images/moderate_pollution.jpg) #### heavy pollution ![heavy pollution](images/heavy_pollution.webp)

redstonehero/realcartoonpixar_v2

redstonehero

2023-08-31T05:31:46Z

17

0

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T05:03:05Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/realcartoonrealistic_v6

redstonehero

2023-08-31T05:31:41Z

20

0

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-31T05:03:28Z

--- license: creativeml-openrail-m library_name: diffusers ---

dkqjrm/20230831092825

dkqjrm

2023-08-31T05:29:59Z

105

0

transformers

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

text-classification

2023-08-31T00:28:44Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230831092825' results: [] ---  # 20230831092825 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5298 - Accuracy: 0.6771 ## 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: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 340 | 0.4989 | 0.5 | | 0.5076 | 2.0 | 680 | 0.4922 | 0.5 | | 0.5029 | 3.0 | 1020 | 0.4980 | 0.5 | | 0.5029 | 4.0 | 1360 | 0.4881 | 0.5125 | | 0.4992 | 5.0 | 1700 | 0.5067 | 0.5 | | 0.4818 | 6.0 | 2040 | 0.4919 | 0.5251 | | 0.4818 | 7.0 | 2380 | 0.5045 | 0.5392 | | 0.4719 | 8.0 | 2720 | 0.4695 | 0.5 | | 0.4636 | 9.0 | 3060 | 0.4805 | 0.5 | | 0.4636 | 10.0 | 3400 | 0.5002 | 0.5 | | 0.4501 | 11.0 | 3740 | 0.5665 | 0.6646 | | 0.4418 | 12.0 | 4080 | 0.5283 | 0.6897 | | 0.4418 | 13.0 | 4420 | 0.4705 | 0.5 | | 0.4352 | 14.0 | 4760 | 0.5644 | 0.6630 | | 0.4302 | 15.0 | 5100 | 0.5080 | 0.6505 | | 0.4302 | 16.0 | 5440 | 0.5084 | 0.6897 | | 0.4305 | 17.0 | 5780 | 0.5006 | 0.6599 | | 0.4203 | 18.0 | 6120 | 0.5246 | 0.6928 | | 0.4203 | 19.0 | 6460 | 0.4958 | 0.6583 | | 0.4166 | 20.0 | 6800 | 0.5595 | 0.6630 | | 0.4117 | 21.0 | 7140 | 0.4796 | 0.5 | | 0.4117 | 22.0 | 7480 | 0.4820 | 0.5 | | 0.4131 | 23.0 | 7820 | 0.5158 | 0.6755 | | 0.406 | 24.0 | 8160 | 0.4801 | 0.5 | | 0.4062 | 25.0 | 8500 | 0.5471 | 0.6646 | | 0.4062 | 26.0 | 8840 | 0.4904 | 0.5 | | 0.4021 | 27.0 | 9180 | 0.4880 | 0.5 | | 0.3971 | 28.0 | 9520 | 0.5019 | 0.6646 | | 0.3971 | 29.0 | 9860 | 0.4825 | 0.5 | | 0.3936 | 30.0 | 10200 | 0.5069 | 0.6693 | | 0.3907 | 31.0 | 10540 | 0.5472 | 0.6693 | | 0.3907 | 32.0 | 10880 | 0.4886 | 0.5 | | 0.3906 | 33.0 | 11220 | 0.5531 | 0.6693 | | 0.3888 | 34.0 | 11560 | 0.5023 | 0.5266 | | 0.3888 | 35.0 | 11900 | 0.4896 | 0.5 | | 0.387 | 36.0 | 12240 | 0.4985 | 0.5 | | 0.3836 | 37.0 | 12580 | 0.5309 | 0.6834 | | 0.3836 | 38.0 | 12920 | 0.5402 | 0.6818 | | 0.3792 | 39.0 | 13260 | 0.4854 | 0.5 | | 0.3789 | 40.0 | 13600 | 0.4971 | 0.5 | | 0.3789 | 41.0 | 13940 | 0.5368 | 0.6803 | | 0.3775 | 42.0 | 14280 | 0.4958 | 0.5047 | | 0.3753 | 43.0 | 14620 | 0.5139 | 0.6897 | | 0.3753 | 44.0 | 14960 | 0.5224 | 0.6834 | | 0.3795 | 45.0 | 15300 | 0.5119 | 0.6865 | | 0.3743 | 46.0 | 15640 | 0.5120 | 0.6740 | | 0.3743 | 47.0 | 15980 | 0.5049 | 0.5204 | | 0.3726 | 48.0 | 16320 | 0.5026 | 0.5 | | 0.3683 | 49.0 | 16660 | 0.5137 | 0.6646 | | 0.3707 | 50.0 | 17000 | 0.5088 | 0.6129 | | 0.3707 | 51.0 | 17340 | 0.5608 | 0.6646 | | 0.3654 | 52.0 | 17680 | 0.5217 | 0.6803 | | 0.3684 | 53.0 | 18020 | 0.5236 | 0.6740 | | 0.3684 | 54.0 | 18360 | 0.5135 | 0.5016 | | 0.3663 | 55.0 | 18700 | 0.5192 | 0.6818 | | 0.3669 | 56.0 | 19040 | 0.5212 | 0.6160 | | 0.3669 | 57.0 | 19380 | 0.5320 | 0.6740 | | 0.3641 | 58.0 | 19720 | 0.5344 | 0.6646 | | 0.3628 | 59.0 | 20060 | 0.4991 | 0.5 | | 0.3628 | 60.0 | 20400 | 0.5341 | 0.6661 | | 0.3612 | 61.0 | 20740 | 0.5039 | 0.5 | | 0.3608 | 62.0 | 21080 | 0.5267 | 0.6379 | | 0.3608 | 63.0 | 21420 | 0.5249 | 0.6364 | | 0.3599 | 64.0 | 21760 | 0.5226 | 0.6599 | | 0.3616 | 65.0 | 22100 | 0.5370 | 0.6834 | | 0.3616 | 66.0 | 22440 | 0.5109 | 0.5 | | 0.3543 | 67.0 | 22780 | 0.5368 | 0.6740 | | 0.3616 | 68.0 | 23120 | 0.5236 | 0.5690 | | 0.3616 | 69.0 | 23460 | 0.5300 | 0.6693 | | 0.3578 | 70.0 | 23800 | 0.5441 | 0.6583 | | 0.3541 | 71.0 | 24140 | 0.5310 | 0.6724 | | 0.3541 | 72.0 | 24480 | 0.5346 | 0.6693 | | 0.354 | 73.0 | 24820 | 0.5338 | 0.6630 | | 0.355 | 74.0 | 25160 | 0.5279 | 0.6599 | | 0.3536 | 75.0 | 25500 | 0.5280 | 0.6552 | | 0.3536 | 76.0 | 25840 | 0.5328 | 0.6693 | | 0.3539 | 77.0 | 26180 | 0.5231 | 0.5376 | | 0.3527 | 78.0 | 26520 | 0.5282 | 0.6646 | | 0.3527 | 79.0 | 26860 | 0.5250 | 0.6364 | | 0.3535 | 80.0 | 27200 | 0.5298 | 0.6771 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

aaniket/aniket

aaniket

2023-08-31T05:28:04Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:timit_asr", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-30T17:29:32Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - timit_asr model-index: - name: aniket results: [] ---  # aniket This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the timit_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4216 - eval_wer: 1.0 - eval_runtime: 48.4566 - eval_samples_per_second: 33.576 - eval_steps_per_second: 4.21 - epoch: 19.86 - step: 2800 ## 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 ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

dhmeltzer/llama-7b-SFT-qlora-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16

dhmeltzer

2023-08-31T05:21:39Z

0

null

[ "generated_from_trainer", "base_model:dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged", "base_model:finetune:dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged", "region:us" ]

null

2023-08-31T03:53:40Z

--- base_model: dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged tags: - generated_from_trainer model-index: - name: llama-7b-SFT-qlora-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16 results: [] ---  # llama-7b-SFT-qlora-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16 This model is a fine-tuned version of [dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged](https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6572 - Rewards/chosen: -0.1473 - Rewards/rejected: -0.2755 - Rewards/accuracies: 0.6128 - Rewards/margins: 0.1282 - Logps/rejected: -203.3539 - Logps/chosen: -207.2538 - Logits/rejected: 1.1534 - Logits/chosen: 1.1690 ## 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: 0.0002 - 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: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6925 | 0.1 | 19 | 0.6761 | -0.1021 | -0.1593 | 0.5697 | 0.0573 | -202.1919 | -206.8013 | 1.1506 | 1.1664 | | 0.6754 | 0.21 | 38 | 0.6738 | -0.4156 | -0.5460 | 0.5701 | 0.1303 | -206.0580 | -209.9368 | 1.1257 | 1.1406 | | 0.6799 | 0.31 | 57 | 0.6666 | -0.0458 | -0.1454 | 0.5932 | 0.0996 | -202.0523 | -206.2388 | 1.1176 | 1.1327 | | 0.6618 | 0.42 | 76 | 0.6637 | -0.1458 | -0.2745 | 0.5971 | 0.1286 | -203.3434 | -207.2391 | 1.1195 | 1.1333 | | 0.6706 | 0.52 | 95 | 0.6607 | -0.0386 | -0.1827 | 0.5971 | 0.1440 | -202.4252 | -206.1670 | 1.1334 | 1.1484 | | 0.668 | 0.63 | 114 | 0.6596 | -0.1615 | -0.2945 | 0.6035 | 0.1330 | -203.5434 | -207.3955 | 1.1500 | 1.1661 | | 0.6712 | 0.73 | 133 | 0.6597 | -0.1703 | -0.2905 | 0.5979 | 0.1202 | -203.5037 | -207.4840 | 1.1515 | 1.1672 | | 0.6715 | 0.84 | 152 | 0.6588 | -0.1516 | -0.2745 | 0.6100 | 0.1229 | -203.3436 | -207.2964 | 1.1532 | 1.1691 | | 0.673 | 0.94 | 171 | 0.6572 | -0.1473 | -0.2755 | 0.6128 | 0.1282 | -203.3539 | -207.2538 | 1.1534 | 1.1690 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Saksham1234/helloitsme

Saksham1234

2023-08-31T05:05:59Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2023-08-31T05:05:59Z

--- license: bigscience-openrail-m ---

54data/llama_2_ko_7b_wiki_QA

54data

2023-08-31T05:00:25Z

0

null

[ "generated_from_trainer", "base_model:beomi/llama-2-ko-7b", "base_model:finetune:beomi/llama-2-ko-7b", "region:us" ]

null

2023-08-23T15:46:46Z

--- base_model: beomi/llama-2-ko-7b tags: - generated_from_trainer model-index: - name: llama_2_ko_7b_wiki_QA results: [] ---  # llama_2_ko_7b_wiki_QA This model is a fine-tuned version of [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1703 ## 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: 0.0002 - 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: 100 - training_steps: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8568 | 0.33 | 50 | 1.4294 | | 1.2307 | 0.67 | 100 | 1.2169 | | 1.1788 | 1.0 | 150 | 1.1865 | | 1.0837 | 1.33 | 200 | 1.1810 | | 1.1905 | 1.67 | 250 | 1.1740 | | 1.161 | 2.0 | 300 | 1.1703 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3

Yaxin1992/codellama-13b-multi-1800

Yaxin1992

2023-08-31T04:45:38Z

0

null

[ "generated_from_trainer", "base_model:codellama/CodeLlama-13b-hf", "base_model:finetune:codellama/CodeLlama-13b-hf", "license:llama2", "region:us" ]

null

2023-08-31T01:41:52Z

--- license: llama2 base_model: codellama/CodeLlama-13b-hf tags: - generated_from_trainer model-index: - name: codellama-13b-multi-1800 results: [] ---  # codellama-13b-multi-1800 This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) on an unknown 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1800 ### Training results ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

soohmatthew/reddit-confidence-setfit-model-1

soohmatthew

2023-08-31T04:42:34Z

7

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-08-28T12:20:56Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # soohmatthew/reddit-confidence-setfit-model-1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("soohmatthew/reddit-confidence-setfit-model-1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

whywynn/poca-SoccerTwos

whywynn

2023-08-31T04:29:39Z

37

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2023-08-31T04:19:21Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: whywynn/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

soohmatthew/reddit-care-setfit-model-1

soohmatthew

2023-08-31T04:27:13Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-08-28T11:56:12Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # soohmatthew/reddit-care-setfit-model-1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("soohmatthew/reddit-care-setfit-model-1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

spsither/whisper-small-hi

spsither

2023-08-31T04:16:43Z

3

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-16T06:40:04Z

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-hi results: [] ---  # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3488 - Wer: 18.8573 ## 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: 1e-05 - train_batch_size: 16 - 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: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1899 | 3.02 | 1000 | 0.3311 | 22.3674 | | 0.0179 | 6.04 | 2000 | 0.3252 | 19.8309 | | 0.0026 | 9.06 | 3000 | 0.3382 | 18.8189 | | 0.0009 | 12.08 | 4000 | 0.3488 | 18.8573 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

rossevine/Check_Model_1

rossevine

2023-08-31T03:53:52Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-large", "base_model:finetune:facebook/wav2vec2-large", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-30T20:34:44Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: Check_Model_1 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: id split: test args: id metrics: - name: Wer type: wer value: 0.37479022934924483 ---  # Check_Model_1 This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5522 - Wer: 0.3748 - Cer: 0.1158 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 2.1839 | 3.23 | 400 | 0.8796 | 0.7306 | 0.2332 | | 0.6388 | 6.45 | 800 | 0.8702 | 0.6410 | 0.2200 | | 0.4695 | 9.68 | 1200 | 0.7064 | 0.5360 | 0.1632 | | 0.3659 | 12.9 | 1600 | 0.5814 | 0.5211 | 0.1662 | | 0.285 | 16.13 | 2000 | 0.6394 | 0.5041 | 0.1663 | | 0.2254 | 19.35 | 2400 | 0.5889 | 0.4428 | 0.1405 | | 0.1801 | 22.58 | 2800 | 0.5712 | 0.4013 | 0.1182 | | 0.1392 | 25.81 | 3200 | 0.5914 | 0.3934 | 0.1177 | | 0.1051 | 29.03 | 3600 | 0.5522 | 0.3748 | 0.1158 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 1.18.3 - Tokenizers 0.13.3

MohanaSri/Taxi

MohanaSri

2023-08-31T03:48:45Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T03:48:43Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="MohanaSri/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Serotina/Reinforce-PixelCopter

Serotina

2023-08-31T03:43:59Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T02:03:50Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 34.00 +/- 29.07 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

beatwade/alpaca-bitcoin-tweets-sentiment

beatwade

2023-08-31T03:23:57Z

0

peft

[ "peft", "region:us" ]

null

2023-08-30T23:29:05Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

bwarshaw/dqn-SpaceInvadersNoFrameskip-v4

bwarshaw

2023-08-31T03:12:33Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-31T03:11:59Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 577.50 +/- 126.40 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bwarshaw -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bwarshaw -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bwarshaw ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

pensuke/distilbert-base-uncased-finetuned-emotion

pensuke

2023-08-31T03:10:05Z

103

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

2023-08-31T02:32:21Z

--- 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: split metrics: - name: Accuracy type: accuracy value: 0.9325 - name: F1 type: f1 value: 0.9324937609411934 ---  # 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: 0.1879 - Accuracy: 0.9325 - F1: 0.9325 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4344 | 1.0 | 250 | 0.2242 | 0.9185 | 0.9176 | | 0.1857 | 2.0 | 500 | 0.1879 | 0.9325 | 0.9325 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

batman555/layer_1_classifier

batman555

2023-08-31T03:09:03Z

104

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-31T02:46:49Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: layer_1_classifier results: [] ---  # layer_1_classifier 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: 0.1867 - Accuracy: 0.9457 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 0.1221 | 1.0 | | No log | 2.0 | 8 | 0.0832 | 1.0 | | No log | 3.0 | 12 | 0.0647 | 1.0 | | No log | 4.0 | 16 | 0.0591 | 1.0 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

TigerResearch/tigerbot-7b-chat-8bit

TigerResearch

2023-08-31T02:55:52Z

4

0

transformers

[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-08-30T10:16:15Z

--- license: apache-2.0 --- <div style="width: 100%;"> <img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;"> </div> <p align="center"> <font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font> </p> <p align="center"> 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a> </p> This is a 8-bit GPTQ version of the [Tigerbot 13b chat](https://huggingface.co/TigerResearch/tigerbot-7b-chat). It was quantized to 8bit using: https://github.com/PanQiWei/AutoGPTQ ## How to download and use this model in github: https://github.com/TigerResearch/TigerBot Here are commands to clone the TigerBot and install. ``` conda create --name tigerbot python=3.8 conda activate tigerbot conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia git clone https://github.com/TigerResearch/TigerBot cd TigerBot pip install -r requirements.txt ``` Inference with command line interface ``` # 安装auto-gptq pip install auto-gptq # 启动推理 CUDA_VISIBLE_DEVICES=0 python other_infer/gptq_infer.py --model_path TigerResearch/tigerbot-7b-chat-8bit ```