davanstrien/model_cards_with_readmes · Datasets at Hugging Face

anas-awadalla/bert-large-uncased-prefix-tuning-squad

anas-awadalla

null

21

0

null

0

null

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,048

false

# bert-large-uncased-prefix-tuning-squad This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the squad 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

e342af630726d4762a4a625962545cae

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_rte

gokuls

mobilebert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,710

false

# mobilebert_sa_GLUE_Experiment_logit_kd_rte This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - Accuracy: 0.5271 ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4088 | 1.0 | 20 | 0.3931 | 0.5271 | | 0.4081 | 2.0 | 40 | 0.3922 | 0.5271 | | 0.4076 | 3.0 | 60 | 0.3910 | 0.5271 | | 0.4068 | 4.0 | 80 | 0.3941 | 0.5343 | | 0.4069 | 5.0 | 100 | 0.3924 | 0.5343 | | 0.4022 | 6.0 | 120 | 0.3975 | 0.5343 | | 0.3801 | 7.0 | 140 | 0.4060 | 0.5415 | | 0.3447 | 8.0 | 160 | 0.5080 | 0.4982 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

3d0d06da45d0bf7c51a1d8375556e17b

sgangireddy/whisper-medium-cv-fleurs-tr-3k

sgangireddy

whisper

22

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['whisper-event', 'generated_from_trainer']

true

1,410

false

# openai/whisper-medium 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: 0.2406 - Wer: 10.0333 ## 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: 64 - eval_batch_size: 32 - 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: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0241 | 1.06 | 1000 | 0.1996 | 10.4543 | | 0.009 | 2.12 | 2000 | 0.2156 | 10.1152 | | 0.0045 | 3.19 | 3000 | 0.2406 | 10.0333 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

9e43b0f6d283c21f4754e2a0ff0ade04

Alred/t5-small-finetuned-summarization-cnn-ver2

Alred

t5

23

3

transformers

0

summarization

true

false

apache-2.0

null

['cnn_dailymail']

null

0

['summarization', 'generated_from_trainer']

true

2,193

false

# t5-small-finetuned-summarization-cnn-ver2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.0084 - Bertscore-mean-precision: 0.8859 - Bertscore-mean-recall: 0.8592 - Bertscore-mean-f1: 0.8721 - Bertscore-median-precision: 0.8855 - Bertscore-median-recall: 0.8578 - Bertscore-median-f1: 0.8718 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 | |:-------------:|:-----:|:----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:| | 2.0422 | 1.0 | 718 | 2.0139 | 0.8853 | 0.8589 | 0.8717 | 0.8857 | 0.8564 | 0.8715 | | 1.9481 | 2.0 | 1436 | 2.0085 | 0.8863 | 0.8591 | 0.8723 | 0.8858 | 0.8577 | 0.8718 | | 1.9231 | 3.0 | 2154 | 2.0084 | 0.8859 | 0.8592 | 0.8721 | 0.8855 | 0.8578 | 0.8718 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

92343c5b8bc4d92da908fe01561334c6

sentence-transformers/paraphrase-albert-small-v2

sentence-transformers

albert

14

100,938

sentence-transformers

3

sentence-similarity

true

false

apache-2.0

null

['flax-sentence-embeddings/stackexchange_xml', 's2orc', 'ms_marco', 'wiki_atomic_edits', 'snli', 'multi_nli', 'embedding-data/altlex', 'embedding-data/simple-wiki', 'embedding-data/flickr30k-captions', 'embedding-data/coco_captions', 'embedding-data/sentence-compression', 'embedding-data/QQP', 'yahoo_answers_topics']

null

1

0

1

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']

false

true

3,560

false

# sentence-transformers/paraphrase-albert-small-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-albert-small-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-albert-small-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-albert-small-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-albert-small-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

26c4452604428d047681ddd0748f88ed

ZJUzpy/mt5-small-finetuned-amazon-en-es

ZJUzpy

mt5

10

1

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,995

false

# mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0346 - Rouge1: 16.8527 - Rouge2: 8.331 - Rougel: 16.4475 - Rougelsum: 16.6421 ## 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: 5.6e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.7536 | 1.0 | 1209 | 3.2881 | 13.6319 | 5.4635 | 13.0552 | 13.1093 | | 3.9312 | 2.0 | 2418 | 3.1490 | 16.8402 | 8.3559 | 16.1876 | 16.2869 | | 3.5987 | 3.0 | 3627 | 3.1043 | 17.9887 | 9.3136 | 17.3034 | 17.4313 | | 3.4261 | 4.0 | 4836 | 3.0573 | 17.0089 | 8.7389 | 16.5351 | 16.5023 | | 3.3221 | 5.0 | 6045 | 3.0569 | 16.8461 | 8.0988 | 16.4898 | 16.4927 | | 3.2549 | 6.0 | 7254 | 3.0511 | 17.3428 | 8.2234 | 16.7312 | 16.8749 | | 3.2067 | 7.0 | 8463 | 3.0334 | 16.268 | 7.9729 | 15.9342 | 16.0065 | | 3.1842 | 8.0 | 9672 | 3.0346 | 16.8527 | 8.331 | 16.4475 | 16.6421 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

4129b1202b3d1edc400c071bdd96f0c3

haesun/xlm-roberta-base-finetuned-panx-de-fr

haesun

xlm-roberta

10

7

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1654 - F1: 0.8590 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2845 | 1.0 | 715 | 0.1831 | 0.8249 | | 0.1449 | 2.0 | 1430 | 0.1643 | 0.8479 | | 0.0929 | 3.0 | 2145 | 0.1654 | 0.8590 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

5baea09f342fbf1390f4d3c00310a9f2

rpv/distilbert-base-uncased-finetuned-squad

rpv

distilbert

10

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

929

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad 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: 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: 6 ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

db8179ee4e670c9f263140f5175fb228

Helsinki-NLP/opus-mt-tc-base-gmw-gmw

Helsinki-NLP

marian

13

6

transformers

0

translation

true

false

cc-by-4.0

['af', 'de', 'en', 'fy', 'gmw', 'gos', 'hrx', 'lb', 'nds', 'nl', 'pdc', 'yi']

null

2

1

0

['translation', 'opus-mt-tc']

true

10,601

false

# opus-mt-tc-base-gmw-gmw Neural machine translation model for translating from West Germanic languages (gmw) to West Germanic languages (gmw). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2021-02-23 * source language(s): afr deu eng fry gos hrx ltz nds nld pdc yid * target language(s): afr deu eng fry nds nld * valid target language labels: >>afr<< >>ang_Latn<< >>deu<< >>eng<< >>fry<< >>ltz<< >>nds<< >>nld<< >>sco<< >>yid<< * model: transformer (base) * data: opus ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opus-2021-02-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.zip) * more information released models: [OPUS-MT gmw-gmw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmw-gmw/README.md) * more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>afr<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>nld<< You need help.", ">>afr<< I love your son." ] model_name = "pytorch-models/opus-mt-tc-base-gmw-gmw" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Je hebt hulp nodig. # Ek is lief vir jou seun. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-gmw-gmw") print(pipe(>>nld<< You need help.)) # expected output: Je hebt hulp nodig. ``` ## Benchmarks * test set translations: [opus-2021-02-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.test.txt) * test set scores: [opus-2021-02-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | afr-deu | tatoeba-test-v2021-08-07 | 0.674 | 48.1 | 1583 | 9105 | | afr-eng | tatoeba-test-v2021-08-07 | 0.728 | 58.8 | 1374 | 9622 | | afr-nld | tatoeba-test-v2021-08-07 | 0.711 | 54.5 | 1056 | 6710 | | deu-afr | tatoeba-test-v2021-08-07 | 0.696 | 52.4 | 1583 | 9507 | | deu-eng | tatoeba-test-v2021-08-07 | 0.609 | 42.1 | 17565 | 149462 | | deu-nds | tatoeba-test-v2021-08-07 | 0.442 | 18.6 | 9999 | 76137 | | deu-nld | tatoeba-test-v2021-08-07 | 0.672 | 48.7 | 10218 | 75235 | | eng-afr | tatoeba-test-v2021-08-07 | 0.735 | 56.5 | 1374 | 10317 | | eng-deu | tatoeba-test-v2021-08-07 | 0.580 | 35.9 | 17565 | 151568 | | eng-nds | tatoeba-test-v2021-08-07 | 0.412 | 16.6 | 2500 | 18264 | | eng-nld | tatoeba-test-v2021-08-07 | 0.663 | 48.3 | 12696 | 91796 | | fry-eng | tatoeba-test-v2021-08-07 | 0.500 | 32.5 | 220 | 1573 | | fry-nld | tatoeba-test-v2021-08-07 | 0.633 | 43.1 | 260 | 1854 | | gos-nld | tatoeba-test-v2021-08-07 | 0.405 | 15.6 | 1852 | 9903 | | hrx-deu | tatoeba-test-v2021-08-07 | 0.484 | 24.7 | 471 | 2805 | | hrx-eng | tatoeba-test-v2021-08-07 | 0.362 | 20.4 | 221 | 1235 | | ltz-deu | tatoeba-test-v2021-08-07 | 0.556 | 37.2 | 347 | 2208 | | ltz-eng | tatoeba-test-v2021-08-07 | 0.485 | 32.4 | 293 | 1840 | | ltz-nld | tatoeba-test-v2021-08-07 | 0.534 | 39.3 | 292 | 1685 | | nds-deu | tatoeba-test-v2021-08-07 | 0.572 | 34.5 | 9999 | 74564 | | nds-eng | tatoeba-test-v2021-08-07 | 0.493 | 29.9 | 2500 | 17589 | | nds-nld | tatoeba-test-v2021-08-07 | 0.621 | 42.3 | 1657 | 11490 | | nld-afr | tatoeba-test-v2021-08-07 | 0.755 | 58.8 | 1056 | 6823 | | nld-deu | tatoeba-test-v2021-08-07 | 0.686 | 50.4 | 10218 | 74131 | | nld-eng | tatoeba-test-v2021-08-07 | 0.690 | 53.1 | 12696 | 89978 | | nld-fry | tatoeba-test-v2021-08-07 | 0.478 | 25.1 | 260 | 1857 | | nld-nds | tatoeba-test-v2021-08-07 | 0.462 | 21.4 | 1657 | 11711 | | afr-deu | flores101-devtest | 0.524 | 21.6 | 1012 | 25094 | | afr-eng | flores101-devtest | 0.693 | 46.8 | 1012 | 24721 | | afr-nld | flores101-devtest | 0.509 | 18.4 | 1012 | 25467 | | deu-afr | flores101-devtest | 0.534 | 21.4 | 1012 | 25740 | | deu-eng | flores101-devtest | 0.616 | 33.8 | 1012 | 24721 | | deu-nld | flores101-devtest | 0.516 | 19.2 | 1012 | 25467 | | eng-afr | flores101-devtest | 0.628 | 33.8 | 1012 | 25740 | | eng-deu | flores101-devtest | 0.581 | 29.1 | 1012 | 25094 | | eng-nld | flores101-devtest | 0.533 | 21.0 | 1012 | 25467 | | ltz-afr | flores101-devtest | 0.430 | 12.9 | 1012 | 25740 | | ltz-deu | flores101-devtest | 0.482 | 17.1 | 1012 | 25094 | | ltz-eng | flores101-devtest | 0.468 | 18.8 | 1012 | 24721 | | ltz-nld | flores101-devtest | 0.409 | 10.7 | 1012 | 25467 | | nld-afr | flores101-devtest | 0.494 | 16.8 | 1012 | 25740 | | nld-deu | flores101-devtest | 0.501 | 17.9 | 1012 | 25094 | | nld-eng | flores101-devtest | 0.551 | 25.6 | 1012 | 24721 | | deu-eng | multi30k_test_2016_flickr | 0.546 | 32.2 | 1000 | 12955 | | eng-deu | multi30k_test_2016_flickr | 0.582 | 28.8 | 1000 | 12106 | | deu-eng | multi30k_test_2017_flickr | 0.561 | 32.7 | 1000 | 11374 | | eng-deu | multi30k_test_2017_flickr | 0.573 | 27.6 | 1000 | 10755 | | deu-eng | multi30k_test_2017_mscoco | 0.499 | 25.5 | 461 | 5231 | | eng-deu | multi30k_test_2017_mscoco | 0.514 | 22.0 | 461 | 5158 | | deu-eng | multi30k_test_2018_flickr | 0.535 | 30.0 | 1071 | 14689 | | eng-deu | multi30k_test_2018_flickr | 0.547 | 25.3 | 1071 | 13703 | | deu-eng | newssyscomb2009 | 0.527 | 25.4 | 502 | 11818 | | eng-deu | newssyscomb2009 | 0.504 | 19.3 | 502 | 11271 | | deu-eng | news-test2008 | 0.518 | 23.8 | 2051 | 49380 | | eng-deu | news-test2008 | 0.492 | 19.3 | 2051 | 47447 | | deu-eng | newstest2009 | 0.516 | 23.4 | 2525 | 65399 | | eng-deu | newstest2009 | 0.498 | 18.8 | 2525 | 62816 | | deu-eng | newstest2010 | 0.546 | 25.8 | 2489 | 61711 | | eng-deu | newstest2010 | 0.508 | 20.7 | 2489 | 61503 | | deu-eng | newstest2011 | 0.524 | 23.7 | 3003 | 74681 | | eng-deu | newstest2011 | 0.493 | 19.2 | 3003 | 72981 | | deu-eng | newstest2012 | 0.532 | 24.8 | 3003 | 72812 | | eng-deu | newstest2012 | 0.493 | 19.5 | 3003 | 72886 | | deu-eng | newstest2013 | 0.548 | 27.7 | 3000 | 64505 | | eng-deu | newstest2013 | 0.517 | 22.5 | 3000 | 63737 | | deu-eng | newstest2014-deen | 0.548 | 27.3 | 3003 | 67337 | | eng-deu | newstest2014-deen | 0.532 | 22.0 | 3003 | 62688 | | deu-eng | newstest2015-deen | 0.553 | 28.6 | 2169 | 46443 | | eng-deu | newstest2015-ende | 0.544 | 25.7 | 2169 | 44260 | | deu-eng | newstest2016-deen | 0.596 | 33.3 | 2999 | 64119 | | eng-deu | newstest2016-ende | 0.580 | 30.0 | 2999 | 62669 | | deu-eng | newstest2017-deen | 0.561 | 29.5 | 3004 | 64399 | | eng-deu | newstest2017-ende | 0.535 | 24.1 | 3004 | 61287 | | deu-eng | newstest2018-deen | 0.610 | 36.1 | 2998 | 67012 | | eng-deu | newstest2018-ende | 0.613 | 35.4 | 2998 | 64276 | | deu-eng | newstest2019-deen | 0.582 | 32.3 | 2000 | 39227 | | eng-deu | newstest2019-ende | 0.583 | 31.2 | 1997 | 48746 | | deu-eng | newstest2020-deen | 0.604 | 32.0 | 785 | 38220 | | eng-deu | newstest2020-ende | 0.542 | 23.9 | 1418 | 52383 | | deu-eng | newstestB2020-deen | 0.598 | 31.2 | 785 | 37696 | | eng-deu | newstestB2020-ende | 0.532 | 23.3 | 1418 | 53092 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.12.3 * OPUS-MT git hash: e56a06b * port time: Sun Feb 13 14:42:10 EET 2022 * port machine: LM0-400-22516.local

0a0ce0c20bf31421179f511fde9c405e

jonatasgrosman/exp_w2v2r_es_xls-r_age_teens-0_sixties-10_s951

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'es']

false

true

476

false

# exp_w2v2r_es_xls-r_age_teens-0_sixties-10_s951 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

482744da6a2507a337b1995bb1113fa9

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp_256

gokuls

distilbert

17

3

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,896

false

# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.7043 - Accuracy: 0.6343 - F1: 0.0148 - Combined Score: 0.3245 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.8369 | 1.0 | 29671 | 0.7043 | 0.6343 | 0.0148 | 0.3245 | | 0.7448 | 2.0 | 59342 | 0.7161 | 0.6355 | 0.0216 | 0.3286 | | 0.7106 | 3.0 | 89013 | 0.7067 | 0.6466 | 0.0843 | 0.3655 | | 0.6924 | 4.0 | 118684 | 0.7200 | 0.6401 | 0.0477 | 0.3439 | | 0.6812 | 5.0 | 148355 | 0.7109 | 0.6424 | 0.0609 | 0.3517 | | 0.6734 | 6.0 | 178026 | 0.7092 | 0.6440 | 0.0696 | 0.3568 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

09d695f938a86e5da407e42e1693251f

ryL/distilbert-base-uncased-finetuned-emotion

ryL

distilbert

14

19

transformers

1

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,356

false

# 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: 0.2175 - Accuracy: 0.9225 - F1: 0.9226 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8152 | 1.0 | 250 | 0.3054 | 0.902 | 0.8992 | | 0.2418 | 2.0 | 500 | 0.2175 | 0.9225 | 0.9226 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0.dev20221006+cu117 - Datasets 2.6.1 - Tokenizers 0.12.1

b7334d3597cddc3e46b0eae2084513fb

jeapaul/wav2vec2-large-xlsr-53-torgo-demo-f01-nolm

jeapaul

wav2vec2

15

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,446

false

# wav2vec2-large-xlsr-53-torgo-demo-f01-nolm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0153 - Wer: 0.4756 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4166 | 0.81 | 500 | 4.5019 | 1.0 | | 3.1088 | 1.62 | 1000 | 3.0459 | 1.0 | | 2.8249 | 2.44 | 1500 | 3.0850 | 1.0 | | 2.625 | 3.25 | 2000 | 2.6827 | 1.3656 | | 1.9816 | 4.06 | 2500 | 1.6636 | 1.3701 | | 1.3036 | 4.87 | 3000 | 0.9710 | 1.2504 | | 0.9862 | 5.68 | 3500 | 0.6023 | 1.0519 | | 0.7012 | 6.49 | 4000 | 0.4404 | 0.9342 | | 0.6102 | 7.31 | 4500 | 0.3297 | 0.8491 | | 0.5463 | 8.12 | 5000 | 0.2403 | 0.7773 | | 0.4897 | 8.93 | 5500 | 0.1907 | 0.7335 | | 0.4687 | 9.74 | 6000 | 0.1721 | 0.7095 | | 0.41 | 10.55 | 6500 | 0.1382 | 0.6851 | | 0.3277 | 11.36 | 7000 | 0.1189 | 0.6598 | | 0.3182 | 12.18 | 7500 | 0.1040 | 0.6372 | | 0.3279 | 12.99 | 8000 | 0.0961 | 0.6274 | | 0.2735 | 13.8 | 8500 | 0.0806 | 0.5880 | | 0.3153 | 14.61 | 9000 | 0.0821 | 0.5748 | | 0.251 | 15.42 | 9500 | 0.0633 | 0.5437 | | 0.2 | 16.23 | 10000 | 0.0534 | 0.5316 | | 0.2134 | 17.05 | 10500 | 0.0475 | 0.5195 | | 0.1727 | 17.86 | 11000 | 0.0435 | 0.5146 | | 0.2143 | 18.67 | 11500 | 0.0406 | 0.5072 | | 0.1679 | 19.48 | 12000 | 0.0386 | 0.5057 | | 0.1836 | 20.29 | 12500 | 0.0359 | 0.4984 | | 0.1542 | 21.1 | 13000 | 0.0284 | 0.4914 | | 0.1672 | 21.92 | 13500 | 0.0289 | 0.4884 | | 0.1526 | 22.73 | 14000 | 0.0256 | 0.4867 | | 0.1263 | 23.54 | 14500 | 0.0247 | 0.4871 | | 0.133 | 24.35 | 15000 | 0.0194 | 0.4816 | | 0.1005 | 25.16 | 15500 | 0.0190 | 0.4798 | | 0.1372 | 25.97 | 16000 | 0.0172 | 0.4786 | | 0.1126 | 26.79 | 16500 | 0.0177 | 0.4773 | | 0.0929 | 27.6 | 17000 | 0.0173 | 0.4775 | | 0.1069 | 28.41 | 17500 | 0.0164 | 0.4773 | | 0.0932 | 29.22 | 18000 | 0.0153 | 0.4756 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.0.0 - Tokenizers 0.13.2

9e6e253a9f5b7a2ab9d86a2938dd6b24

Gergoe/t5-small-booksum-finetuned-booksum-test

Gergoe

t5

15

3

transformers

0

summarization

true

false

mit

null

0

['summarization', 'generated_from_trainer']

true

2,018

false

# t5-small-booksum-finetuned-booksum-test This model is a fine-tuned version of [cnicu/t5-small-booksum](https://huggingface.co/cnicu/t5-small-booksum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2739 - Rouge1: 22.7829 - Rouge2: 4.8349 - Rougel: 18.2465 - Rougelsum: 19.2417 ## 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: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.5123 | 1.0 | 8750 | 3.2816 | 21.7712 | 4.3046 | 17.4053 | 18.4707 | | 3.2347 | 2.0 | 17500 | 3.2915 | 22.2938 | 4.7828 | 17.8567 | 18.9135 | | 3.0892 | 3.0 | 26250 | 3.2568 | 22.4966 | 4.825 | 18.0344 | 19.1306 | | 2.9837 | 4.0 | 35000 | 3.2952 | 22.6913 | 5.0322 | 18.176 | 19.2751 | | 2.9028 | 5.0 | 43750 | 3.2626 | 22.3548 | 4.7521 | 17.8681 | 18.7815 | | 2.8441 | 6.0 | 52500 | 3.2691 | 22.6279 | 4.932 | 18.1051 | 19.0763 | | 2.8006 | 7.0 | 61250 | 3.2753 | 22.8911 | 4.8954 | 18.1204 | 19.1464 | | 2.7742 | 8.0 | 70000 | 3.2739 | 22.7829 | 4.8349 | 18.2465 | 19.2417 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.7.0 - Datasets 2.2.1 - Tokenizers 0.12.1

5a8e062395280bb526c38b23383db5ef

xander71988/t5-base-finetuned-facet-driver-type

xander71988

t5

9

16

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,516

false

# xander71988/t5-base-finetuned-facet-driver-type This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0016 - Validation Loss: 0.0054 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 64768, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0178 | 0.0076 | 0 | | 0.0068 | 0.0057 | 1 | | 0.0042 | 0.0055 | 2 | | 0.0025 | 0.0044 | 3 | | 0.0016 | 0.0054 | 4 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

8f30bfcae39dd438995dbd2bb00169c3

zakria/NLP_Project

zakria

wav2vec2

12

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,972

false

# NLP_Project This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5308 - Wer: 0.3428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5939 | 1.0 | 500 | 2.1356 | 1.0014 | | 0.9126 | 2.01 | 1000 | 0.5469 | 0.5354 | | 0.4491 | 3.01 | 1500 | 0.4636 | 0.4503 | | 0.3008 | 4.02 | 2000 | 0.4269 | 0.4330 | | 0.2229 | 5.02 | 2500 | 0.4164 | 0.4073 | | 0.188 | 6.02 | 3000 | 0.4717 | 0.4107 | | 0.1739 | 7.03 | 3500 | 0.4306 | 0.4031 | | 0.159 | 8.03 | 4000 | 0.4394 | 0.3993 | | 0.1342 | 9.04 | 4500 | 0.4462 | 0.3904 | | 0.1093 | 10.04 | 5000 | 0.4387 | 0.3759 | | 0.1005 | 11.04 | 5500 | 0.5033 | 0.3847 | | 0.0857 | 12.05 | 6000 | 0.4805 | 0.3876 | | 0.0779 | 13.05 | 6500 | 0.5269 | 0.3810 | | 0.072 | 14.06 | 7000 | 0.5109 | 0.3710 | | 0.0641 | 15.06 | 7500 | 0.4865 | 0.3638 | | 0.0584 | 16.06 | 8000 | 0.5041 | 0.3646 | | 0.0552 | 17.07 | 8500 | 0.4987 | 0.3537 | | 0.0535 | 18.07 | 9000 | 0.4947 | 0.3586 | | 0.0475 | 19.08 | 9500 | 0.5237 | 0.3647 | | 0.042 | 20.08 | 10000 | 0.5338 | 0.3561 | | 0.0416 | 21.08 | 10500 | 0.5068 | 0.3483 | | 0.0358 | 22.09 | 11000 | 0.5126 | 0.3532 | | 0.0334 | 23.09 | 11500 | 0.5213 | 0.3536 | | 0.0331 | 24.1 | 12000 | 0.5378 | 0.3496 | | 0.03 | 25.1 | 12500 | 0.5167 | 0.3470 | | 0.0254 | 26.1 | 13000 | 0.5245 | 0.3418 | | 0.0233 | 27.11 | 13500 | 0.5393 | 0.3456 | | 0.0232 | 28.11 | 14000 | 0.5279 | 0.3425 | | 0.022 | 29.12 | 14500 | 0.5308 | 0.3428 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

67a345f38fda06fe9b0db31c7a63bb70

juancopi81/bert-finetuned-ner

juancopi81

bert

8

9

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,428

false

# juancopi81/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0269 - Validation Loss: 0.0528 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1715 | 0.0734 | 0 | | 0.0467 | 0.0535 | 1 | | 0.0269 | 0.0528 | 2 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1

8da68469c46033e81e8341427797ca48

KoichiYasuoka/roberta-small-japanese-luw-upos

KoichiYasuoka

roberta

9

32

transformers

0

token-classification

true

false

cc-by-sa-4.0

['ja']

['universal_dependencies']

null

0

['japanese', 'token-classification', 'pos', 'dependency-parsing']

false

true

1,177

false

# roberta-small-japanese-luw-upos ## Model Description This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-small-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). ## How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-japanese-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-small-japanese-luw-upos") pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple") nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)] print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/roberta-small-japanese-luw-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` ## See Also [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models

02a9defea89f7fc5a56d2e830aa18f1f

Helsinki-NLP/opus-mt-sn-en

Helsinki-NLP

marian

10

198

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-sn-en * source languages: sn * target languages: en * OPUS readme: [sn-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sn-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sn.en | 51.8 | 0.648 |

1831ed0a41ceedece35cd610ef91370e

mijwiz-laboratories/oud_diffusion_unconditional_256

mijwiz-laboratories

null

7

0

diffusers

1

null

false

openrail

null

0

[]

false

true

1,547

false

# Oud (عود) Unconditional Diffusion The Oud is one of the most foundational instruments to all of Arab music. It can be heard in nearly every song, whether the subgenre is rooted in pop or classical music. Its distinguishing sound can be picked out of a crowd of string instruments with little to no training. Our Unconditional Diffusion model ensures that we show respect to the sound and culture it has created. This project could not have been done without [the following audio diffusion tools.](https://github.com/teticio/audio-diffusion) ## Usage Usage of this model is no different from any other audio diffusion model from HuggingFace. ```python import torch from diffusers import DiffusionPipeline # Setup device and create generator device = "cuda" if torch.cuda.is_available() else "cpu" generator = torch.Generator(device=device) # Instantiate model model_id = "mijwiz-laboratories/oud_diffusion_unconditional_256" audio_diffusion = DiffusionPipeline.from_pretrained(model_id).to(device) # Set seed for generator seed = generator.seed() generator.manual_seed(seed) # Run inference output = audio_diffusion(generator=generator) image = output.images[0] # Mel spectrogram generated audio = output.audios[0, 0] # Playable audio file ``` ## Limitations of Model The dataset used was very small, so the diversity of snippets that can be generated is rather limited. Furthermore, with high intensity segments (think a human playing the instrument with high intensity,) the realism/naturalness of the generated oud samples degrades.

f9bdcbe7fe06ac3a5d73856ce0e243fd

AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana

AndrewMcDowell

wav2vec2

35

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['common_voice']

null

0

['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'ja', 'hf-asr-leaderboard']

true

2,139

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset. It achieves the following results on the evaluation set: - Loss: 0.5500 - Wer: 1.0132 - Cer: 0.1609 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.7019 | 12.65 | 1000 | 1.0510 | 0.9832 | 0.2589 | | 1.6385 | 25.31 | 2000 | 0.6670 | 0.9915 | 0.1851 | | 1.4344 | 37.97 | 3000 | 0.6183 | 1.0213 | 0.1797 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs ``` 2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ```

8101ce068b5902468c49ca316de2a3f3

facebook/convnext-tiny-224

facebook

convnext

6

10,692

transformers

7

image-classification

true

false

apache-2.0

null

['imagenet-1k']

null

0

1

0

1

['vision', 'image-classification']

false

true

2,656

false

# ConvNeXT (tiny-sized model) ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224") inputs = feature_extractor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]), ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2201-03545, author = {Zhuang Liu and Hanzi Mao and Chao{-}Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {CoRR}, volume = {abs/2201.03545}, year = {2022}, url = {https://arxiv.org/abs/2201.03545}, eprinttype = {arXiv}, eprint = {2201.03545}, timestamp = {Thu, 20 Jan 2022 14:21:35 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

2f64e41e0cd4e24a0230ea87d50c6459

Helsinki-NLP/opus-mt-sv-af

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-sv-af * source languages: sv * target languages: af * OPUS readme: [sv-af](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-af/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-af/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-af/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-af/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.af | 44.4 | 0.623 |

9bd0756dbe442a039d11cc83497dca49

mutisya/fine-tune-xlsr-53-wav2vec2-on-swahili-sagemaker-2

mutisya

wav2vec2

27

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice_9_0']

null

0

['generated_from_trainer']

true

4,016

false

# fine-tune-xlsr-53-wav2vec2-on-swahili-sagemaker-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_9_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2089 - Wer: 0.2356 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.3715 | 0.22 | 400 | 3.1337 | 1.0 | | 1.7928 | 0.44 | 800 | 0.7137 | 0.6290 | | 0.5382 | 0.66 | 1200 | 0.5686 | 0.4708 | | 0.4263 | 0.89 | 1600 | 0.3693 | 0.4091 | | 0.3705 | 1.11 | 2000 | 0.3925 | 0.3747 | | 0.3348 | 1.33 | 2400 | 0.2908 | 0.3597 | | 0.3151 | 1.55 | 2800 | 0.3403 | 0.3388 | | 0.2977 | 1.77 | 3200 | 0.2698 | 0.3294 | | 0.2901 | 1.99 | 3600 | 0.6100 | 0.3173 | | 0.2432 | 2.22 | 4000 | 0.2893 | 0.3213 | | 0.256 | 2.44 | 4400 | 0.2604 | 0.3087 | | 0.2453 | 2.66 | 4800 | 0.2448 | 0.3077 | | 0.2427 | 2.88 | 5200 | 0.2391 | 0.2925 | | 0.2235 | 3.1 | 5600 | 0.8570 | 0.2907 | | 0.2078 | 3.32 | 6000 | 0.2289 | 0.2884 | | 0.199 | 3.55 | 6400 | 0.2303 | 0.2852 | | 0.2092 | 3.77 | 6800 | 0.2270 | 0.2769 | | 0.2 | 3.99 | 7200 | 0.2588 | 0.2823 | | 0.1806 | 4.21 | 7600 | 0.2324 | 0.2757 | | 0.1789 | 4.43 | 8000 | 0.2051 | 0.2721 | | 0.1753 | 4.65 | 8400 | 0.2290 | 0.2695 | | 0.1734 | 4.88 | 8800 | 0.2161 | 0.2686 | | 0.1648 | 5.1 | 9200 | 0.2139 | 0.2695 | | 0.158 | 5.32 | 9600 | 0.2218 | 0.2632 | | 0.151 | 5.54 | 10000 | 0.2060 | 0.2594 | | 0.1534 | 5.76 | 10400 | 0.2199 | 0.2638 | | 0.1485 | 5.98 | 10800 | 0.2023 | 0.2584 | | 0.1332 | 6.2 | 11200 | 0.2160 | 0.2547 | | 0.1319 | 6.43 | 11600 | 0.2045 | 0.2547 | | 0.1329 | 6.65 | 12000 | 0.2072 | 0.2545 | | 0.1329 | 6.87 | 12400 | 0.2014 | 0.2502 | | 0.1307 | 7.09 | 12800 | 0.2045 | 0.2487 | | 0.1197 | 7.31 | 13200 | 0.1987 | 0.2491 | | 0.118 | 7.53 | 13600 | 0.1947 | 0.2442 | | 0.1194 | 7.76 | 14000 | 0.1863 | 0.2430 | | 0.1157 | 7.98 | 14400 | 0.3602 | 0.2430 | | 0.1095 | 8.2 | 14800 | 0.2074 | 0.2408 | | 0.1051 | 8.42 | 15200 | 0.2113 | 0.2410 | | 0.1073 | 8.64 | 15600 | 0.2064 | 0.2395 | | 0.1025 | 8.86 | 16000 | 0.2012 | 0.2396 | | 0.1027 | 9.09 | 16400 | 0.2342 | 0.2372 | | 0.0998 | 9.31 | 16800 | 0.2206 | 0.2357 | | 0.0935 | 9.53 | 17200 | 0.2151 | 0.2356 | | 0.0959 | 9.75 | 17600 | 0.2096 | 0.2355 | | 0.095 | 9.97 | 18000 | 0.2089 | 0.2354 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.13.0

08dc2963cc3cdc895837d6737c282860

lmqg/t5-base-subjqa-restaurants-qg

lmqg

t5

34

1

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_subjqa']

null

0

['question generation']

true

4,006

false

# Model Card of `lmqg/t5-base-subjqa-restaurants-qg` This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (restaurants) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/t5-base-subjqa-restaurants-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-base-subjqa-restaurants-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-subjqa-restaurants-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | | Score | Type | Dataset | |:-----------|--------:|:------------|:-----------------------------------------------------------------| | BERTScore | 88.48 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 8.81 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 3.68 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 1.09 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 0 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 14.75 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 56.19 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 11.96 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: restaurants - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-base-squad - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 16 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 32 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-subjqa-restaurants-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

babdeebda8bbbb11b5865884c8ac755b

skr3178/xlm-roberta-base-finetuned-panx-all

skr3178

xlm-roberta

10

5

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,319

false

# xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1752 - F1: 0.8557 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3 | 1.0 | 835 | 0.1862 | 0.8114 | | 0.1552 | 2.0 | 1670 | 0.1758 | 0.8426 | | 0.1002 | 3.0 | 2505 | 0.1752 | 0.8557 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

534de402a4843138897acfd0b08ebcc7

Dinithi/BlueBERT

Dinithi

bert

12

1

transformers

0

text-classification

true

false

cc0-1.0

null

0

['generated_from_trainer']

true

2,251

false

# BlueBERT This model is a fine-tuned version of [bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6525 - Accuracy: 0.83 - Precision: 0.8767 - Recall: 0.8889 - F1: 0.8828 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.6839 | 1.0 | 50 | 0.7208 | 0.39 | 0.9231 | 0.1667 | 0.2824 | | 0.6594 | 2.0 | 100 | 0.5862 | 0.6 | 0.9211 | 0.4861 | 0.6364 | | 0.539 | 3.0 | 150 | 0.5940 | 0.66 | 0.9318 | 0.5694 | 0.7069 | | 0.4765 | 4.0 | 200 | 0.5675 | 0.65 | 0.9512 | 0.5417 | 0.6903 | | 0.3805 | 5.0 | 250 | 0.4494 | 0.79 | 0.9322 | 0.7639 | 0.8397 | | 0.279 | 6.0 | 300 | 0.4760 | 0.84 | 0.8784 | 0.9028 | 0.8904 | | 0.2016 | 7.0 | 350 | 0.5514 | 0.82 | 0.8553 | 0.9028 | 0.8784 | | 0.1706 | 8.0 | 400 | 0.5353 | 0.84 | 0.8889 | 0.8889 | 0.8889 | | 0.1164 | 9.0 | 450 | 0.7676 | 0.82 | 0.8462 | 0.9167 | 0.8800 | | 0.1054 | 10.0 | 500 | 0.6525 | 0.83 | 0.8767 | 0.8889 | 0.8828 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

f723c7cb645941499eb5f2984a240208

Herais/pred_timeperiod

Herais

bert

8

4

transformers

0

text-classification

true

false

apache-2.0

['zh']

['Custom']

null

0

['classification']

false

true

1,487

false

This model predicts the time period given a synopsis of about 200 Chinese characters. The model is trained on TV and Movie datasets and takes simplified Chinese as input. We trained the model from the "hfl/chinese-bert-wwm-ext" checkpoint. #### Sample Usage from transformers import BertTokenizer, BertForSequenceClassification device = torch.device("cuda" if torch.cuda.is_available() else "cpu") checkpoint = "Herais/pred_timeperiod" tokenizer = BertTokenizer.from_pretrained(checkpoint, problem_type="single_label_classification") model = BertForSequenceClassification.from_pretrained(checkpoint).to(device) label2id_timeperiod = {'古代': 0, '当代': 1, '现代': 2, '近代': 3, '重大': 4} id2label_timeperiod = {0: '古代', 1: '当代', 2: '现代', 3: '近代', 4: '重大'} synopsis = """加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\ 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\ 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\ 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\ 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\ 继续为检察事业贡献自己的青春。 """ inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt') model.eval() outputs = model(**input) label_ids_pred = torch.argmax(outputs.logits, dim=1).to('cpu').numpy() labels_pred = [id2label_timeperiod[label] for label in labels_pred] print(labels_pred) # ['当代'] Citation {}

73fd9168466424f9906800ec181e1942

juancopi81/distilbert-finetuned-imdb

juancopi81

distilbert

8

2

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,539

false

# juancopi81/distilbert-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8630 - Validation Loss: 2.5977 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.8630 | 2.5977 | 0 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.8.0 - Datasets 2.2.2 - Tokenizers 0.12.1

880da9e6a0e615bdd7b6be1b81f5faa1

bersanoenrico/movies-ita-classification-bertbased-v2

bersanoenrico

bert

12

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,363

false

# movies-ita-classification-bertbased-v2 This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1995 - Accuracy: 0.6208 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3416 | 1.0 | 1181 | 1.2574 | 0.5897 | | 1.0583 | 2.0 | 2362 | 1.1978 | 0.6091 | | 0.789 | 3.0 | 3543 | 1.1995 | 0.6208 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

f0de53a11c57998d497e14f65f697943

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

Keneston

xlm-roberta

15

11

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,319

false

# 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: 0.1365 - F1: 0.8649 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

fb60876a566443dd73b2de2f0089be79

mastergruffly/temp

mastergruffly

null

18

3

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

612

false

### temp Dreambooth model trained by mastergruffly with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or 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) Sample pictures of this concept:

8a380ad2c02808020084ac6a888ee4d6

DOOGLAK/Tagged_One_500v8_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['tagged_one500v8_wikigold_split']

null

0

['generated_from_trainer']

true

1,565

false

# Tagged_One_500v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2761 - Precision: 0.6785 - Recall: 0.6773 - F1: 0.6779 - Accuracy: 0.9254 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 172 | 0.3004 | 0.5475 | 0.5128 | 0.5296 | 0.9050 | | No log | 2.0 | 344 | 0.2752 | 0.6595 | 0.6422 | 0.6507 | 0.9201 | | 0.112 | 3.0 | 516 | 0.2761 | 0.6785 | 0.6773 | 0.6779 | 0.9254 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

9aeef6d3e88d425d2783082162a341b9

Helsinki-NLP/opus-mt-hu-de

Helsinki-NLP

marian

10

20

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

770

false

### opus-mt-hu-de * source languages: hu * target languages: de * OPUS readme: [hu-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hu-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/hu-de/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-de/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-de/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.hu.de | 44.1 | 0.637 |

207425d4ff9891748db6fae025abef18

nlp04/kobart_64_3e-5_datav2_min30_lp5.0_temperature1.0

nlp04

bart

19

11

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

994

false

# kobart_64_3e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) 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: 3e-05 - train_batch_size: 64 - eval_batch_size: 128 - 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: 5.0 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

ef173c87272d9e752b9b5241dd92ab23

beliv3/albertbezdream

beliv3

null

31

4

diffusers

0

text-to-image

false

creativeml-openrail-m

null

2

0

['text-to-image', 'stable-diffusion']

false

true

1,815

false

### AlbertBezDream Dreambooth model trained by beliv3 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or 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) Sample pictures of this concept: ![0](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(11).jpg) ![1](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(10).jpg) ![2](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(5).jpg) ![3](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(1).jpg) ![4](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(3).jpg) ![5](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(7).jpg) ![6](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(4).jpg) ![7](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert.jpg) ![8](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(6).jpg) ![9](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(8).jpg) ![10](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(9).jpg) ![11](https://huggingface.co/beliv3/albertbezdream/resolve/main/sample_images/realbert_(2).jpg)

0479de45968299a026f0f02e4632f432

muhammaddjunas/cvt-13-finetuned-waste

muhammaddjunas

cvt

14

2

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,421

false

# cvt-13-finetuned-waste This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1715 | 0.99 | 117 | 0.0000 | 1.0 | | 0.1194 | 1.99 | 234 | 0.0000 | 1.0 | | 0.1496 | 2.99 | 351 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

e40ee70e6f9ea3f6a56357b33fc50990

omar47/wav2vec2-large-xls-r-300m-urdu-cv-10

omar47

wav2vec2

17

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice_10_0']

null

0

['generated_from_trainer']

true

4,912

false

# wav2vec2-large-xls-r-300m-urdu-cv-10 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_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5959 - Wer: 0.3946 ## 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: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 20.8724 | 0.25 | 32 | 18.0006 | 1.0 | | 10.984 | 0.5 | 64 | 6.8001 | 1.0 | | 5.7792 | 0.74 | 96 | 4.9273 | 1.0 | | 4.2891 | 0.99 | 128 | 3.8379 | 1.0 | | 3.4937 | 1.24 | 160 | 3.2877 | 1.0 | | 3.1605 | 1.49 | 192 | 3.1198 | 1.0 | | 3.0874 | 1.74 | 224 | 3.0542 | 1.0 | | 3.0363 | 1.98 | 256 | 3.0063 | 0.9999 | | 2.9776 | 2.23 | 288 | 2.9677 | 1.0 | | 2.8168 | 2.48 | 320 | 2.4189 | 1.0000 | | 2.0575 | 2.73 | 352 | 1.5330 | 0.8520 | | 1.4248 | 2.98 | 384 | 1.1747 | 0.7519 | | 1.1354 | 3.22 | 416 | 0.9837 | 0.7047 | | 1.0049 | 3.47 | 448 | 0.9414 | 0.6631 | | 0.956 | 3.72 | 480 | 0.8948 | 0.6606 | | 0.8906 | 3.97 | 512 | 0.8381 | 0.6291 | | 0.7587 | 4.22 | 544 | 0.7714 | 0.5898 | | 0.7534 | 4.47 | 576 | 0.8237 | 0.5908 | | 0.7203 | 4.71 | 608 | 0.7731 | 0.5758 | | 0.6876 | 4.96 | 640 | 0.7467 | 0.5390 | | 0.5825 | 5.21 | 672 | 0.6940 | 0.5401 | | 0.5565 | 5.46 | 704 | 0.6826 | 0.5248 | | 0.5598 | 5.71 | 736 | 0.6387 | 0.5204 | | 0.5289 | 5.95 | 768 | 0.6432 | 0.4956 | | 0.4565 | 6.2 | 800 | 0.6643 | 0.4876 | | 0.4576 | 6.45 | 832 | 0.6295 | 0.4758 | | 0.4265 | 6.7 | 864 | 0.6227 | 0.4673 | | 0.4359 | 6.95 | 896 | 0.6077 | 0.4598 | | 0.3576 | 7.19 | 928 | 0.5800 | 0.4477 | | 0.3612 | 7.44 | 960 | 0.5837 | 0.4500 | | 0.345 | 7.69 | 992 | 0.5892 | 0.4466 | | 0.3707 | 7.94 | 1024 | 0.6217 | 0.4380 | | 0.3269 | 8.19 | 1056 | 0.5964 | 0.4412 | | 0.2974 | 8.43 | 1088 | 0.6116 | 0.4394 | | 0.2932 | 8.68 | 1120 | 0.5764 | 0.4235 | | 0.2854 | 8.93 | 1152 | 0.5757 | 0.4239 | | 0.2651 | 9.18 | 1184 | 0.5798 | 0.4253 | | 0.2508 | 9.43 | 1216 | 0.5750 | 0.4316 | | 0.238 | 9.67 | 1248 | 0.6038 | 0.4232 | | 0.2454 | 9.92 | 1280 | 0.5781 | 0.4078 | | 0.2196 | 10.17 | 1312 | 0.5931 | 0.4178 | | 0.2036 | 10.42 | 1344 | 0.6134 | 0.4116 | | 0.2087 | 10.67 | 1376 | 0.5831 | 0.4146 | | 0.1908 | 10.91 | 1408 | 0.5987 | 0.4159 | | 0.1751 | 11.16 | 1440 | 0.5968 | 0.4065 | | 0.1726 | 11.41 | 1472 | 0.6037 | 0.4119 | | 0.1728 | 11.66 | 1504 | 0.5961 | 0.4011 | | 0.1772 | 11.91 | 1536 | 0.5903 | 0.3972 | | 0.1647 | 12.16 | 1568 | 0.5960 | 0.4024 | | 0.1506 | 12.4 | 1600 | 0.5986 | 0.3933 | | 0.1383 | 12.65 | 1632 | 0.5893 | 0.3938 | | 0.1433 | 12.9 | 1664 | 0.5999 | 0.3975 | | 0.1356 | 13.15 | 1696 | 0.6035 | 0.3982 | | 0.1431 | 13.4 | 1728 | 0.5997 | 0.4042 | | 0.1346 | 13.64 | 1760 | 0.6018 | 0.4003 | | 0.1363 | 13.89 | 1792 | 0.5891 | 0.3969 | | 0.1323 | 14.14 | 1824 | 0.5983 | 0.3925 | | 0.1196 | 14.39 | 1856 | 0.6003 | 0.3939 | | 0.1266 | 14.64 | 1888 | 0.5997 | 0.3941 | | 0.1269 | 14.88 | 1920 | 0.5959 | 0.3946 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

864626db792e56b4bf45b4669f411691

sd-concepts-library/pokemon-modern-artwork

sd-concepts-library

null

1,176

0

null

5

null

false

mit

null

0

[]

false

true

147,371

false

### Pokemon modern artwork on Stable Diffusion Pokémon modern artwork up to Hisui concept (re-scaled to max width and height 512 px) Includes mega-evolutions, gigamax, regional and alternate forms. Unown variants are excluded, as well as Arceus/Silvally recolours (to avoid same-species overrepresentation) This is the `<pkmn-modern>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<pkmn-modern> 0](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/0.jpeg) ![<pkmn-modern> 1](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1.jpeg) ![<pkmn-modern> 2](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/2.jpeg) ![<pkmn-modern> 3](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/3.jpeg) ![<pkmn-modern> 4](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/4.jpeg) ![<pkmn-modern> 5](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/5.jpeg) ![<pkmn-modern> 6](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/6.jpeg) ![<pkmn-modern> 7](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/7.jpeg) ![<pkmn-modern> 8](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/8.jpeg) ![<pkmn-modern> 9](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/9.jpeg) ![<pkmn-modern> 10](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/10.jpeg) ![<pkmn-modern> 11](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/11.jpeg) ![<pkmn-modern> 12](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/12.jpeg) ![<pkmn-modern> 13](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/13.jpeg) ![<pkmn-modern> 14](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/14.jpeg) ![<pkmn-modern> 15](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/15.jpeg) ![<pkmn-modern> 16](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/16.jpeg) ![<pkmn-modern> 17](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/17.jpeg) ![<pkmn-modern> 18](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/18.jpeg) ![<pkmn-modern> 19](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/19.jpeg) ![<pkmn-modern> 20](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/20.jpeg) ![<pkmn-modern> 21](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/21.jpeg) ![<pkmn-modern> 22](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/22.jpeg) ![<pkmn-modern> 23](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/23.jpeg) ![<pkmn-modern> 24](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/24.jpeg) ![<pkmn-modern> 25](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/25.jpeg) ![<pkmn-modern> 26](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/26.jpeg) ![<pkmn-modern> 27](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/27.jpeg) ![<pkmn-modern> 28](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/28.jpeg) ![<pkmn-modern> 29](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/29.jpeg) ![<pkmn-modern> 30](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/30.jpeg) ![<pkmn-modern> 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1089](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1089.jpeg) ![<pkmn-modern> 1090](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1090.jpeg) ![<pkmn-modern> 1091](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1091.jpeg) ![<pkmn-modern> 1092](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1092.jpeg) ![<pkmn-modern> 1093](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1093.jpeg) ![<pkmn-modern> 1094](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1094.jpeg) ![<pkmn-modern> 1095](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1095.jpeg) ![<pkmn-modern> 1096](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1096.jpeg) 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1119](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1119.jpeg) ![<pkmn-modern> 1120](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1120.jpeg) ![<pkmn-modern> 1121](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1121.jpeg) ![<pkmn-modern> 1122](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1122.jpeg) ![<pkmn-modern> 1123](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1123.jpeg) ![<pkmn-modern> 1124](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1124.jpeg) ![<pkmn-modern> 1125](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1125.jpeg) ![<pkmn-modern> 1126](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1126.jpeg) 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1149](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1149.jpeg) ![<pkmn-modern> 1150](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1150.jpeg) ![<pkmn-modern> 1151](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1151.jpeg) ![<pkmn-modern> 1152](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1152.jpeg) ![<pkmn-modern> 1153](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1153.jpeg) ![<pkmn-modern> 1154](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1154.jpeg) ![<pkmn-modern> 1155](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1155.jpeg) ![<pkmn-modern> 1156](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1156.jpeg) 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1164](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1164.jpeg) ![<pkmn-modern> 1165](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1165.jpeg) ![<pkmn-modern> 1166](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1166.jpeg) ![<pkmn-modern> 1167](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1167.jpeg) ![<pkmn-modern> 1168](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1168.jpeg) ![<pkmn-modern> 1169](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1169.jpeg) ![<pkmn-modern> 1170](https://huggingface.co/sd-concepts-library/pokemon-modern-artwork/resolve/main/concept_images/1170.jpeg)

67cf5351961d267ddbdeb307d00a48fc

henryscheible/sst2_bert-base-uncased_81

henryscheible

null

13

0

null

0

null

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,016

false

# sst2_bert-base-uncased_81 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3565 - Accuracy: 0.9151 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

7d0d8029900a2c88359478bc0c115eeb

marcel/wav2vec2-large-xlsr-german-demo

marcel

wav2vec2

20

8

transformers

0

automatic-speech-recognition

true

false

true

apache-2.0

['de']

['common_voice', 'wer']

null

0

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

true

4,102

false

# Wav2Vec2-Large-XLSR-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using 3% of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "de", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "de", split="test[:10%]") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]' substitutions = { 'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]', 'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]', 'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]', 'c' : '[\č\ć\ç\с]', 'l' : '[\ł]', 'u' : '[\ú\ū\ứ\ů]', 'und' : '[\&]', 'r' : '[\ř]', 'y' : '[\ý]', 's' : '[\ś\š\ș\ş]', 'i' : '[\ī\ǐ\í\ï\î\ï]', 'z' : '[\ź\ž\ź\ż]', 'n' : '[\ñ\ń\ņ]', 'g' : '[\ğ]', 'ss' : '[\ß]', 't' : '[\ț\ť]', 'd' : '[\ď\đ]', "'": '[\ʿ\་\’\`\´\ʻ\`\‘]', 'p': '\р' } resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() for x in substitutions: batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 29.35 % ## Training The first 3% of the Common Voice `train`, `validation` datasets were used for training. The script used for training can be found TODO

c8362e2e558c5181cd610d370238a9c3

gokuls/mobilebert_sa_GLUE_Experiment_data_aug_stsb

gokuls

mobilebert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

2,046

false

# mobilebert_sa_GLUE_Experiment_data_aug_stsb This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.8342 - Pearson: 0.1765 - Spearmanr: 0.1800 - Combined Score: 0.1782 ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 1.0254 | 1.0 | 2518 | 2.8776 | 0.1575 | 0.1742 | 0.1659 | | 0.5854 | 2.0 | 5036 | 3.1464 | 0.1591 | 0.1679 | 0.1635 | | 0.4255 | 3.0 | 7554 | 2.8342 | 0.1765 | 0.1800 | 0.1782 | | 0.2765 | 4.0 | 10072 | 2.8524 | 0.1815 | 0.1838 | 0.1827 | | 0.1862 | 5.0 | 12590 | 2.9184 | 0.1736 | 0.1768 | 0.1752 | | 0.1339 | 6.0 | 15108 | 2.9817 | 0.1688 | 0.1728 | 0.1708 | | 0.1029 | 7.0 | 17626 | 2.9702 | 0.1618 | 0.1643 | 0.1631 | | 0.0806 | 8.0 | 20144 | 3.0033 | 0.1588 | 0.1624 | 0.1606 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

9c72576f75dfc3c7f6e746911c9b786d

Salesforce/codegen-6B-multi

Salesforce

codegen

10

1,216

transformers

4

text-generation

true

false

bsd-3-clause

null

0

[]

false

true

3,029

false

# CodeGen (CodeGen-Multi 6B) ## Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Multi 6B** in the paper, where "Multi" means the model is initialized with *CodeGen-NL 6B* and further pre-trained on a dataset of multiple programming languages, and "6B" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Multi 6B) was firstly initialized with *CodeGen-NL 6B*, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-multi") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-multi") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```

a76eae558073ce8da010561eaa4746db

abinternet143/t5-small-finetuned-xsum

abinternet143

t5

11

2

transformers

0

text2text-generation

true

false

apache-2.0

null

['xsum']

null

0

['generated_from_trainer']

true

924

false

# t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0a0+bfe5ad2 - Datasets 2.0.0 - Tokenizers 0.11.6

a2637894d1fbfab3c43e03f7b1554a06

Tahsin/distilbert-base-uncased-finetuned-emotion

Tahsin

bert

15

5

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,342

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1561 - Accuracy: 0.9285 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 250 | 0.1635 | 0.9295 | | 0.111 | 2.0 | 500 | 0.1515 | 0.936 | | 0.111 | 3.0 | 750 | 0.1561 | 0.9285 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

0c5dad389ab24e3c229b9d2f02487b64

Davlan/afro-xlmr-small

Davlan

xlm-roberta

9

567

transformers

0

fill-mask

true

false

afl-3.0

null

0

[]

false

true

3,220

false

# afro-xlmr-small AfroXLMR-small was created by [first reducing the vocabulary token size](https://aclanthology.org/2020.sustainlp-1.16/) of XLM-R-base from 250K to 70k, followed by MLM adaptation on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high resource languages (Arabic, French, and English). ## Eval results on MasakhaNER (F-score) language| XLM-R-miniLM| XLM-R-base |XLM-R-large| afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|- amh |69.5|70.6|76.2|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.4|86.6|83.5 kin |68.6|73.3|73.8|78.0|77.5|74.1 lug |64.7|79.7|81.6|82.9|83.2|77.4 luo |11.7|74.9|73.6|75.1|75.4|17.5 pcm |83.2|87.3|89.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|67.4|65.9|59.0 yor |72.0|78.3|78.9|82.1|81.3|75.1 ### BibTeX entry and citation info ``` @inproceedings{alabi-etal-2022-adapting, title = "Adapting Pre-trained Language Models to {A}frican Languages via Multilingual Adaptive Fine-Tuning", author = "Alabi, Jesujoba O. and Adelani, David Ifeoluwa and Mosbach, Marius and Klakow, Dietrich", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.382", pages = "4336--4349", abstract = "Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is language adaptive fine-tuning (LAFT) {---} fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to target language individually takes large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50{\%}. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.", } ```

db061726eea9dddaaeb4a6599b5ce379

Slavka/bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace_large

Slavka

bert

8

4

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,375

false

# bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace_large This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 15321, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 15321, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-06, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1

469736ca16b3cb1b523f5220d2215bdb

jonatasgrosman/exp_w2v2t_en_xlsr-53_s870

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'en']

false

true

467

false

# exp_w2v2t_en_xlsr-53_s870 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

f5b4d487fb7f20dd92cf4994a6f64277

Heldhy/wav2vec2-base-timit-demo-colab

Heldhy

wav2vec2

12

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,641

false

# wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4568 - Wer: 0.3422 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3896 | 4.0 | 500 | 1.1573 | 0.8886 | | 0.5667 | 8.0 | 1000 | 0.4841 | 0.4470 | | 0.2126 | 12.0 | 1500 | 0.4201 | 0.3852 | | 0.1235 | 16.0 | 2000 | 0.4381 | 0.3623 | | 0.0909 | 20.0 | 2500 | 0.4784 | 0.3748 | | 0.0611 | 24.0 | 3000 | 0.4390 | 0.3577 | | 0.0454 | 28.0 | 3500 | 0.4568 | 0.3422 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

723f9b6cd3c29028e33a1633723ae646

dixipi9178/MyCoolModel

dixipi9178

null

11

0

null

0

null

false

other

null

0

[]

false

true

494

false

https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/corneos7thHeavenMix_v2.safetensors https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/novelai%20f111%20sd1.4%20add%20difference%201.0.ckpt https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/Anything-V3.0-pruned-fp16.ckpt !gdown https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/novelai%20f111%20sd1.4%20add%20difference%201.0.ckpt -O /content/stable-diffusion-webui/models/Stable-diffusion/nai_f111.ckpt

9f65e986a8956fb3368c1d7075a0b438

LeoFelix/bert-finetuned-squad

LeoFelix

bert

8

3

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,522

false

# LeoFelix/bert-finetuned-squad This model is a fine-tuned version of [pierreguillou/bert-base-cased-squad-v1.1-portuguese](https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0193 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 852, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.3702 | 0 | | 0.0471 | 1 | | 0.0193 | 2 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

8b6e9722a57c3c9280420f4e8e36d01c

peter2000/wav2vec2-large-xls-r-300m-kinyarwanda

peter2000

wav2vec2

15

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

5,335

false

# wav2vec2-large-xls-r-300m-kinyarwanda 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: 0.3917 - Wer: 0.3246 ## 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: 7e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 9.0634 | 0.12 | 400 | 3.0554 | 1.0 | | 2.8009 | 0.24 | 800 | 1.5927 | 0.9554 | | 0.9022 | 0.36 | 1200 | 0.7328 | 0.6445 | | 0.6213 | 0.48 | 1600 | 0.6138 | 0.5510 | | 0.5299 | 0.6 | 2000 | 0.6072 | 0.5223 | | 0.4999 | 0.72 | 2400 | 0.5449 | 0.4969 | | 0.4731 | 0.84 | 2800 | 0.5261 | 0.4828 | | 0.458 | 0.96 | 3200 | 0.5058 | 0.4607 | | 0.4158 | 1.09 | 3600 | 0.4892 | 0.4463 | | 0.4037 | 1.21 | 4000 | 0.4759 | 0.4429 | | 0.4021 | 1.33 | 4400 | 0.4615 | 0.4330 | | 0.3934 | 1.45 | 4800 | 0.4593 | 0.4315 | | 0.3808 | 1.57 | 5200 | 0.4736 | 0.4344 | | 0.3838 | 1.69 | 5600 | 0.4569 | 0.4249 | | 0.3726 | 1.81 | 6000 | 0.4473 | 0.4140 | | 0.3623 | 1.93 | 6400 | 0.4403 | 0.4097 | | 0.3517 | 2.05 | 6800 | 0.4389 | 0.4061 | | 0.333 | 2.17 | 7200 | 0.4383 | 0.4104 | | 0.3354 | 2.29 | 7600 | 0.4360 | 0.3955 | | 0.3257 | 2.41 | 8000 | 0.4226 | 0.3942 | | 0.3275 | 2.53 | 8400 | 0.4206 | 0.4040 | | 0.3262 | 2.65 | 8800 | 0.4172 | 0.3875 | | 0.3206 | 2.77 | 9200 | 0.4209 | 0.3877 | | 0.323 | 2.89 | 9600 | 0.4177 | 0.3825 | | 0.3099 | 3.01 | 10000 | 0.4101 | 0.3691 | | 0.3008 | 3.14 | 10400 | 0.4055 | 0.3709 | | 0.2918 | 3.26 | 10800 | 0.4085 | 0.3800 | | 0.292 | 3.38 | 11200 | 0.4089 | 0.3713 | | 0.292 | 3.5 | 11600 | 0.4092 | 0.3730 | | 0.2785 | 3.62 | 12000 | 0.4151 | 0.3687 | | 0.2941 | 3.74 | 12400 | 0.4004 | 0.3639 | | 0.2838 | 3.86 | 12800 | 0.4108 | 0.3703 | | 0.2854 | 3.98 | 13200 | 0.3911 | 0.3596 | | 0.2683 | 4.1 | 13600 | 0.3944 | 0.3575 | | 0.2647 | 4.22 | 14000 | 0.3836 | 0.3538 | | 0.2704 | 4.34 | 14400 | 0.4006 | 0.3540 | | 0.2664 | 4.46 | 14800 | 0.3974 | 0.3553 | | 0.2662 | 4.58 | 15200 | 0.3890 | 0.3470 | | 0.2615 | 4.7 | 15600 | 0.3856 | 0.3507 | | 0.2553 | 4.82 | 16000 | 0.3814 | 0.3497 | | 0.2587 | 4.94 | 16400 | 0.3837 | 0.3440 | | 0.2522 | 5.06 | 16800 | 0.3834 | 0.3486 | | 0.2451 | 5.19 | 17200 | 0.3897 | 0.3414 | | 0.2423 | 5.31 | 17600 | 0.3864 | 0.3481 | | 0.2434 | 5.43 | 18000 | 0.3808 | 0.3416 | | 0.2525 | 5.55 | 18400 | 0.3795 | 0.3408 | | 0.2427 | 5.67 | 18800 | 0.3841 | 0.3411 | | 0.2411 | 5.79 | 19200 | 0.3804 | 0.3366 | | 0.2404 | 5.91 | 19600 | 0.3800 | 0.3328 | | 0.2372 | 6.03 | 20000 | 0.3749 | 0.3335 | | 0.2244 | 6.15 | 20400 | 0.3820 | 0.3327 | | 0.2381 | 6.27 | 20800 | 0.3789 | 0.3325 | | 0.2294 | 6.39 | 21200 | 0.3867 | 0.3298 | | 0.2378 | 6.51 | 21600 | 0.3843 | 0.3281 | | 0.2312 | 6.63 | 22000 | 0.3813 | 0.3277 | | 0.2411 | 6.75 | 22400 | 0.3780 | 0.3268 | | 0.2315 | 6.87 | 22800 | 0.3790 | 0.3280 | | 0.241 | 6.99 | 23200 | 0.3776 | 0.3281 | | 0.2313 | 7.11 | 23600 | 0.3929 | 0.3283 | | 0.2423 | 7.24 | 24000 | 0.3905 | 0.3280 | | 0.2337 | 7.36 | 24400 | 0.3979 | 0.3249 | | 0.2368 | 7.48 | 24800 | 0.3980 | 0.3257 | | 0.2409 | 7.6 | 25200 | 0.3937 | 0.3229 | | 0.2416 | 7.72 | 25600 | 0.3867 | 0.3237 | | 0.2364 | 7.84 | 26000 | 0.3912 | 0.3253 | | 0.234 | 7.96 | 26400 | 0.3917 | 0.3246 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

d14045d5cbb680f8883df76c15a2a49e

tau/bart-base-sled-govreport

tau

tau/sled

5

1

transformers

1

null

true

false

mit

['en']

null

0

[]

false

true

5,008

false

# BART-SLED (SLiding-Encoder and Decoder, base-sized model) SLED models use pretrained, short-range encoder-decoder models, and apply them over long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder ## Model description This SLED model is based on the BART model, which is described in its [model card](https://huggingface.co/facebook/bart-base). BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). When used as a BART-SLED model, it can be applied on long text tasks. This model was finetuned on the [GovReport](https://arxiv.org/abs/2104.02112) ## Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. ### How to use To use the model, you first need to install `py-sled` in your environment (or clone the code from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md)) ``` pip install py-sled ``` For more installation instructions, see [here](https://github.com/Mivg/SLED#Installation). Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel and AutoModelForCausalLM) and can be loaded using the from_pretrained methods ```python import sled # *** required so that SledModels will be registered for the AutoClasses *** model = AutoModel.from_pretrained('tau/bart-base-sled') ``` Here is how to use this model in PyTorch: ```python from sled import SledTokenizer, SledModel tokenizer = SledTokenizer.from_pretrained('tau/bart-base-sled') model = SledModel.from_pretrained('tau/bart-base-sled') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation ```python model = SledModelForConditionalGeneration.from_pretrained('tau/bart-base-sled') ``` In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to every chunk, you can pass the `prefix_length` tensor input as well (A LongTensor in the length of the batch size). ```python import torch import sled # *** required so that SledModels will be registered for the AutoClasses *** tokenizer = AutoTokenizer.from_pretrained('tau/bart-base-sled') model = AutoModel.from_pretrained('tau/bart-base-sled') document_input_ids = tokenizer("Dogs are great for you.", return_tensors="pt").input_ids prefix_input_ids = tokenizer("Are dogs good for you?", return_tensors="pt").input_ids input_ids = torch.cat((prefix_input_ids, document_input_ids), dim=-1) attention_mask = torch.ones_like(input_ids) prefix_length = torch.LongTensor([[prefix_input_ids.size(1)]]) outputs = model(input_ids=input_ids, attention_mask=attention_mask, prefix_length=prefix_length) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the BART [paper](https://arxiv.org/abs/1910.13461) by Lewis et al as well as GovReport by Huang et al ```bibtex @inproceedings{Ivgi2022EfficientLU, title={Efficient Long-Text Understanding with Short-Text Models}, author={Maor Ivgi and Uri Shaham and Jonathan Berant}, year={2022} } ``` ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ```bibtex @inproceedings{huang2021govreport, title = "Efficient Attentions for Long Document Summarization", author = "Huang, Luyang and Cao, Shuyang and Parulian, Nikolaus and Ji, Heng and Wang, Lu", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.112", doi = "10.18653/v1/2021.naacl-main.112", pages = "1419--1436" } ```

0a36a53071d980280c5be03099588988

Aalaa/opt-125m-finetuned-wikitext2

Aalaa

opt

13

15

transformers

1

text-generation

true

false

other

null

0

['generated_from_trainer']

true

1,255

false

# opt-125m-finetuned-wikitext2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3409 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4123 | 1.0 | 2370 | 3.3621 | | 3.2096 | 2.0 | 4740 | 3.3452 | | 3.0822 | 3.0 | 7110 | 3.3409 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

85784c63548542d948597041cb25f58a

the-bee/bert-finetuned-ner

the-bee

bert

10

5

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,512

false

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0594 - Precision: 0.9331 - Recall: 0.9529 - F1: 0.9429 - Accuracy: 0.9872 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0872 | 1.0 | 1756 | 0.0631 | 0.9128 | 0.9359 | 0.9242 | 0.9827 | | 0.0338 | 2.0 | 3512 | 0.0578 | 0.9322 | 0.9510 | 0.9415 | 0.9867 | | 0.0174 | 3.0 | 5268 | 0.0594 | 0.9331 | 0.9529 | 0.9429 | 0.9872 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

42f48f6fa61251ad25dd38212b7474c3

Martin97Bozic/xlm-roberta-base-finetuned-squad

Martin97Bozic

xlm-roberta

13

5

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,264

false

# xlm-roberta-base-finetuned-squad This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1433 ## 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: 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 | |:-------------:|:-----:|:-----:|:---------------:| | 2.4107 | 1.0 | 3693 | 2.2321 | | 2.1359 | 2.0 | 7386 | 2.1499 | | 1.9214 | 3.0 | 11079 | 2.1433 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

352a2d7ff9501d866064c7dd961264e6

pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar2022-09-12-08-14-58

pedramyamini

distilbert

8

1

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,699

false

# pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar2022-09-12-08-14-58 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4986 - Validation Loss: 0.7589 - Epoch: 7 ## 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 21392, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.7544 | 0.7034 | 0 | | 0.6815 | 0.6905 | 1 | | 0.6463 | 0.6960 | 2 | | 0.6135 | 0.6896 | 3 | | 0.5764 | 0.7041 | 4 | | 0.5447 | 0.7340 | 5 | | 0.5170 | 0.7562 | 6 | | 0.4986 | 0.7589 | 7 | ### Framework versions - Transformers 4.21.3 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

ad0b6cda5090841ce5236eb04828a519

cuzeverynameistaken/wav2vec2-base-timit-demo-colab1

cuzeverynameistaken

wav2vec2

16

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,462

false

# wav2vec2-base-timit-demo-colab1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7170 - Wer: 0.4784 ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1915 | 13.89 | 500 | 3.1318 | 1.0 | | 1.4993 | 27.78 | 1000 | 0.6736 | 0.5485 | | 0.3416 | 41.67 | 1500 | 0.7111 | 0.5092 | | 0.1937 | 55.56 | 2000 | 0.7170 | 0.4784 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

85b9c640afb568c750016bbc74f726b8

mrm8488/santacoder-finetuned-the-stack-bash-shell

mrm8488

gpt2

17

10

transformers

2

text-generation

true

false

openrail

['code']

['bigcode/the-stack-dedup']

null

0

['generated_from_trainer', 'bash', 'shell', 'code', 'codegen']

true

4,251

false

# SantaCoder 🎅 fine-tuned on bash/shell 🐚 scripts This model is a fine-tuned version of [BigCode/SantaCoder](https://huggingface.co/bigcode/santacoder) on The Stack [bash/shell scripts](https://huggingface.co/datasets/bigcode/the-stack-dedup). It achieves the following results on the evaluation set: - Loss: 1.2272 ## Model description The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255). In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. ## Intended uses & limitations The model has been trained on source code in Python, Java, and JavaScript and fine-tuned on bash/shell scripts. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. ## Training and evaluation data The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.** ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6101 | 0.05 | 500 | 1.5078 | | 1.6156 | 0.1 | 1000 | 1.4687 | | 1.4916 | 0.15 | 1500 | 1.4728 | | 1.4027 | 0.2 | 2000 | 1.4237 | | 1.499 | 0.25 | 2500 | 1.4067 | | 1.4378 | 0.3 | 3000 | 1.3838 | | 1.3698 | 0.35 | 3500 | 1.3767 | | 1.3021 | 0.4 | 4000 | 1.3562 | | 4.0521 | 0.45 | 4500 | 1.3433 | | 0.9722 | 0.5 | 5000 | 1.3461 | | 1.3836 | 0.55 | 5500 | 1.2955 | | 1.3727 | 0.6 | 6000 | 1.2809 | | 1.3332 | 0.65 | 6500 | 1.2665 | | 1.2232 | 0.7 | 7000 | 1.2573 | | 1.2373 | 0.75 | 7500 | 1.2463 | | 1.3759 | 0.8 | 8000 | 1.2391 | | 1.3021 | 0.85 | 8500 | 1.2325 | | 1.369 | 0.9 | 9000 | 1.2292 | | 1.4911 | 0.95 | 9500 | 1.2275 | | 1.1677 | 1.0 | 10000 | 1.2272 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2 ### Citation ``` @misc {manuel_romero_2023, author = { {Manuel Romero} }, title = { santacoder-finetuned-the-stack-bash-shell (Revision d3e56a7) }, year = 2023, url = { https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-bash-shell }, doi = { 10.57967/hf/0320 }, publisher = { Hugging Face } } ```

c68441cf7841f7b2af5c7cb6e2f77819

patrickvonplaten/sew-d-small-100k-timit

patrickvonplaten

sew-d

47

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['timit_asr']

null

1

0

['automatic-speech-recognition', 'timit_asr', 'generated_from_trainer']

true

2,968

false

# sew-d-small-100k-timit This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set: - Loss: 1.7541 - Wer: 0.8061 ## 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: 32 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2068 | 0.69 | 100 | 4.0802 | 1.0 | | 2.9805 | 1.38 | 200 | 2.9792 | 1.0 | | 2.9781 | 2.07 | 300 | 2.9408 | 1.0 | | 2.9655 | 2.76 | 400 | 2.9143 | 1.0 | | 2.8953 | 3.45 | 500 | 2.8775 | 1.0 | | 2.7718 | 4.14 | 600 | 2.7787 | 1.0 | | 2.6711 | 4.83 | 700 | 2.6401 | 0.9786 | | 2.6403 | 5.52 | 800 | 2.5435 | 1.0392 | | 2.4052 | 6.21 | 900 | 2.4580 | 1.0706 | | 2.1708 | 6.9 | 1000 | 2.2800 | 1.0090 | | 2.2555 | 7.59 | 1100 | 2.1493 | 0.9579 | | 2.3673 | 8.28 | 1200 | 2.0709 | 0.9051 | | 2.091 | 8.97 | 1300 | 2.0258 | 0.8926 | | 1.8433 | 9.66 | 1400 | 1.9645 | 0.8243 | | 1.6824 | 10.34 | 1500 | 1.9211 | 0.8707 | | 2.2282 | 11.03 | 1600 | 1.8914 | 0.8695 | | 1.9027 | 11.72 | 1700 | 1.8718 | 0.8343 | | 1.6303 | 12.41 | 1800 | 1.8646 | 0.8232 | | 1.648 | 13.1 | 1900 | 1.8297 | 0.8177 | | 2.0429 | 13.79 | 2000 | 1.8127 | 0.8642 | | 1.8833 | 14.48 | 2100 | 1.8005 | 0.8307 | | 1.5996 | 15.17 | 2200 | 1.7926 | 0.8467 | | 1.4876 | 15.86 | 2300 | 1.7795 | 0.8341 | | 1.8925 | 16.55 | 2400 | 1.7716 | 0.8199 | | 1.814 | 17.24 | 2500 | 1.7846 | 0.8086 | | 1.536 | 17.93 | 2600 | 1.7655 | 0.8019 | | 1.4476 | 18.62 | 2700 | 1.7599 | 0.8070 | | 1.7629 | 19.31 | 2800 | 1.7589 | 0.8119 | | 1.7646 | 20.0 | 2900 | 1.7541 | 0.8061 | ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.8.1 - Datasets 1.14.1.dev0 - Tokenizers 0.10.3

a41b43bc7b9f6e84bb953280b42cd573

migueladarlo/distilbert-depression-mixed

migueladarlo

distilbert

5

1

transformers

1

text-classification

true

false

mit

['en']

['CLPsych 2015']

null

0

['text', 'Twitter']

true

2,731

false

# distilbert-depression-mixed This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression. It achieves the following results on the evaluation set: - Evaluation Loss: 0.71 - Accuracy: 0.63 - F1: 0.59 - Precision: 0.66 - Recall: 0.53 - AUC: 0.63 ## Intended uses & limitations Feed a corpus of tweets to the model to generate label if input is indicative of a depressed user or not. Label 1 is depressed, Label 0 is not depressed. Limitation: All token sequences longer than 512 are automatically truncated. Also, training and test data may be contaminated with mislabeled users. ### How to use You can use this model directly with a pipeline for sentiment analysis: ```python >>> from transformers import DistilBertTokenizerFast, AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') >>> from transformers import DistilBertForSequenceClassification >>> model = DistilBertForSequenceClassification.from_pretrained(r"distilbert-depression-mixed") >>> from transformers import pipeline >>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) >>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512} >>> result=classifier('pain peko',**tokenizer_kwargs) #For truncation to apply in the pipeline >>> #Should note that the string passed as the input can be a corpus of tweets concatenated together into one document. [{'label': 'LABEL_1', 'score': 0.5048992037773132}] ``` Otherwise, download the files and specify within the pipeline the path to the folder that contains the config.json, pytorch_model.bin, and training_args.bin ## Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.19e-05 - train_batch_size: 16 - eval_batch_size: 16 - weight_decay: 0.06 - num_epochs: 5.0 ## Training results | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | AUC | |:-----:|:-------------:|:---------------:|:--------:|:--------:|:---------:|:--------:|:--------:| | 1.0 | 0.68 | 0.66 | 0.61 | 0.54 | 0.60 | 0.50 | 0.60 | | 2.0 | 0.65 | 0.65 | 0.63 | 0.49 | 0.70 | 0.37 | 0.62 | | 3.0 | 0.53 | 0.63 | 0.66 | 0.58 | 0.69 | 0.50 | 0.65 | | 4.0 | 0.39 | 0.66 | 0.67 | 0.61 | 0.69 | 0.54 | 0.67 | | 5.0 | 0.27 | 0.72 | 0.65 | 0.61 | 0.63 | 0.60 | 0.64 |

434f18c78e00d54225c013a1fbda0b45

gchhablani/wav2vec2-large-xlsr-rm-sursilv

gchhablani

wav2vec2

10

7

transformers

0

automatic-speech-recognition

true

false

true

apache-2.0

['rm-sursilv']

['common_voice']

null

0

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

true

3,511

false

# Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Sursilvan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "rm-sursilv", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "rm-sursilv", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\…\\«\\»\\–]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 25.16 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://colab.research.google.com/drive/1dpZr_GzRowCciUbzM3GnW04TNKnB7vrP?usp=sharing).

68f8762d20f5946505cb8fdbc69b90d4

laituan245/molt5-small-smiles2caption

laituan245

t5

8

24

transformers

1

text2text-generation

true

false

apache-2.0

null

0

[]

false

true

851

false

This model can be used to generate an input caption from a SMILES string. ## Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-smiles2caption", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-smiles2caption') input_text = 'C1=CC2=C(C(=C1)[O-])NC(=CC2=O)C(=O)O' input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids, num_beams=5, max_length=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Paper For more information, please take a look at our paper. Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817) Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*

062b2e15a45d53dfaaac9b66c2d89766

piyusharma/bert-base-uncased-finetuned-lex

piyusharma

bert

10

15

transformers

0

text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,114

false

# piyusharma/bert-base-uncased-finetuned-lex This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2112 - 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': '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 | Epoch | |:----------:|:-----:| | 0.2112 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

2358de434bb834f31427f7a6f8f48292

recklessrecursion/Heresy-clustered

recklessrecursion

distilbert

8

18

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,869

false

# recklessrecursion/Heresy-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1793 - Train End Logits Accuracy: 0.9618 - Train Start Logits Accuracy: 0.9549 - Validation Loss: 0.7725 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 0.3333 - 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1793 | 0.9618 | 0.9549 | 0.7725 | 0.6667 | 0.3333 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

204c60b53ae2eddc6f59b9f2fa2b80db

gokuls/distilbert_add_GLUE_Experiment_qnli

gokuls

distilbert

17

4

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,615

false

# distilbert_add_GLUE_Experiment_qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 - Accuracy: 0.6066 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6886 | 1.0 | 410 | 0.6648 | 0.6066 | | 0.6569 | 2.0 | 820 | 0.6677 | 0.5999 | | 0.6419 | 3.0 | 1230 | 0.6672 | 0.5914 | | 0.6293 | 4.0 | 1640 | 0.6677 | 0.5977 | | 0.6118 | 5.0 | 2050 | 0.6691 | 0.6002 | | 0.5857 | 6.0 | 2460 | 0.6854 | 0.6077 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

3227794d9c795ea84355ac34c5cc3b61

veereshd/Berlinberger-berger

veereshd

null

17

9

diffusers

0

text-to-image

true

false

creativeml-openrail-m

null

1

0

['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food']

false

true

762

false

# DreamBooth model for the Berlinberger concept trained by veereshd on the veereshd/Dreambooth_food_dataset dataset. This is a Stable Diffusion model fine-tuned on the Berlinberger concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Berlinberger berger** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `berger` images for the food theme. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('veereshd/Berlinberger-berger') image = pipeline().images[0] image ```

f051385e92efe2f18d8c34d639fd1475

tomekkorbak/confident_knuth

tomekkorbak

gpt2

36

2

transformers

0

null

true

false

mit

['en']

['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

7,736

false

# confident_knuth This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 0.1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'confident_knuth', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/q3c975dt

6a88a70c15533118fd438b88ad5c5764

jamie613/distilbert-base-uncased-finetuned-emotion

jamie613

distilbert

20

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,344

false

# 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: 0.2240 - Accuracy: 0.9265 - F1: 0.9265 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8488 | 1.0 | 250 | 0.3268 | 0.9055 | 0.9031 | | 0.2532 | 2.0 | 500 | 0.2240 | 0.9265 | 0.9265 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

b6d3c61883639df4c42c29f8b1860d6a

lijingxin/distilbert-base-uncased-finetuned-emotion

lijingxin

distilbert

18

2

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

1

0

['generated_from_trainer']

true

1,339

false

# 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: 0.2161 - Accuracy: 0.9225 - F1: 0.9226 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8009 | 1.0 | 250 | 0.3027 | 0.9045 | 0.9015 | | 0.2402 | 2.0 | 500 | 0.2161 | 0.9225 | 0.9226 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2 - Datasets 1.16.1 - Tokenizers 0.10.3

5459c02bc005fc599a60f3d54d51ddd4

rishabhjain16/whisper_base_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,698

false

# openai/whisper-base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1929 - Wer: 4.3549 ## 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: 64 - eval_batch_size: 32 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0326 | 10.0 | 500 | 0.1670 | 5.0398 | | 0.0019 | 20.0 | 1000 | 0.1728 | 4.5113 | | 0.0008 | 30.01 | 1500 | 0.1820 | 4.4071 | | 0.0005 | 40.01 | 2000 | 0.1847 | 4.3773 | | 0.0004 | 51.0 | 2500 | 0.1886 | 4.3549 | | 0.0003 | 61.0 | 3000 | 0.1910 | 4.3475 | | 0.0003 | 71.01 | 3500 | 0.1925 | 4.3549 | | 0.0002 | 81.01 | 4000 | 0.1929 | 4.3549 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

94df78e12bf9d305df5928b21d67fa5b

tanvirkhan/distilbert-base-uncased-finetuned-imdb

tanvirkhan

distilbert

12

2

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,318

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

71ccf957693d94b0d04257190d54c569

jsnfly/wav2vec2-large-xlsr-53-german-gpt2

jsnfly

speech-encoder-decoder

20

6

transformers

2

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'de', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event']

true

1,143

false

# Wav2Vec2-Large-XLSR-53-German-GPT2 This is an encoder-decoder model for automatic speech recognition trained on on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset. The encoder was initialized from [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) and the decoder from [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). It was trained using a two step process: * fine-tuning only the cross-attention weights and the decoder using the pre-computed outputs of the Wav2Vec-Modell * relatively fast training * also works on small GPU (eg. 8 GB) * but may take a lot of disk space * should already yield decent results * fine-tuning the model end-to-end * much slower * needs a bigger GPU There is also one trick, which seemed to improve performance significantly: adding position embeddings to the encoder outputs and initializing them with the pre-trained position embeddings of the GPT2 model (See `eval.py`). The training notebooks are still early drafts. Also results can probably improved a lot by using for example a learning rate schedule.

65e73c3669e140e5586179e4c9f60168

lsnoo/wav2vec2-large-xlsr-53k-russian

lsnoo

wav2vec2

11

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,851

false

# wav2vec2-large-xlsr-53k-russian 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: 0.2660 - Wer: 0.2052 ## 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: 96 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2873 | 1.09 | 400 | 0.8580 | 0.8982 | | 0.4728 | 2.19 | 800 | 0.3182 | 0.3892 | | 0.1639 | 9.83 | 1200 | 0.2374 | 0.2646 | | 0.1014 | 13.11 | 1600 | 0.2470 | 0.2467 | | 0.0754 | 16.39 | 2000 | 0.2516 | 0.2337 | | 0.0616 | 19.67 | 2400 | 0.2559 | 0.2237 | | 0.0505 | 22.95 | 2800 | 0.2557 | 0.2155 | | 0.0437 | 26.23 | 3200 | 0.2711 | 0.2099 | | 0.0377 | 29.51 | 3600 | 0.2660 | 0.2052 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

849f08035a00ccce85baa15bf4e0b9e0

Helsinki-NLP/opus-mt-fi-ZH

Helsinki-NLP

marian

10

32

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

1,250

false

### opus-mt-fi-ZH * source languages: fi * target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh * OPUS readme: [fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | bible-uedin.fi.zh | 23.4 | 0.326 |

e68ab4a8eb2e12be6ad5e747064ea1df

MultiBertGunjanPatrick/multiberts-seed-0-1000k

MultiBertGunjanPatrick

bert

7

2

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-0']

false

true

6,487

false

# MultiBERTs Seed 0 Checkpoint 1000k (uncased) Seed 0 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-1000k') model = BertModel.from_pretrained("multiberts-seed-0-1000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

39fa715d99c79a3acad38bf134539766

jeniakim/hedgehog

jeniakim

bert

9

10

transformers

1

token-classification

true

false

mit

['en']

null

0

[]

false

true

3,816

false

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

b1097ead7b2384a065ba719fe9f45d30

RuudVelo/wav2vec2-large-xls-r-1b-nl

RuudVelo

wav2vec2

22

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['nl']

['mozilla-foundation/common_voice_8_0']

null

0

['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'nl', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard']

true

7,151

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. This model is also available with a language model which improves these results. This model can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl-lm. The Common Voice 8 Dutch test Wer is 9.73 of that model. It achieves the following results on the evaluation set: - Loss: 0.1479 - Wer: 0.1156 ## Model description Model fine-tuned using the wav2vec-als-r-1b model architecture ## Intended uses & limitations More information needed ## Training and evaluation data Model has been trained on Common Voice 8 Dutch ## Training procedure ### Training hyperparameters Model parameters can be found under Files and versions in the run.sh file. ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.2223 | 0.52 | 500 | 0.3866 | 0.3425 | | 1.0748 | 1.03 | 1000 | 0.2574 | 0.2169 | | 1.0416 | 1.55 | 1500 | 0.2177 | 0.1946 | | 0.9951 | 2.06 | 2000 | 0.2008 | 0.1760 | | 0.975 | 2.58 | 2500 | 0.1961 | 0.1751 | | 0.9461 | 3.1 | 3000 | 0.1989 | 0.1782 | | 0.9381 | 3.61 | 3500 | 0.1928 | 0.1699 | | 0.934 | 4.13 | 4000 | 0.1923 | 0.1633 | | 0.9322 | 4.64 | 4500 | 0.1871 | 0.1634 | | 0.9012 | 5.16 | 5000 | 0.1890 | 0.1702 | | 0.9045 | 5.68 | 5500 | 0.1882 | 0.1740 | | 0.8826 | 6.19 | 6000 | 0.1856 | 0.1575 | | 0.8848 | 6.71 | 6500 | 0.1861 | 0.1617 | | 0.8723 | 7.22 | 7000 | 0.1927 | 0.1646 | | 0.8725 | 7.74 | 7500 | 0.1798 | 0.1531 | | 0.8573 | 8.26 | 8000 | 0.1781 | 0.1587 | | 0.8633 | 8.77 | 8500 | 0.1852 | 0.1628 | | 0.8603 | 9.29 | 9000 | 0.1833 | 0.1601 | | 0.8421 | 9.8 | 9500 | 0.1788 | 0.1543 | | 0.8404 | 10.32 | 10000 | 0.1844 | 0.1556 | | 0.8342 | 10.84 | 10500 | 0.1770 | 0.1538 | | 0.8161 | 11.35 | 11000 | 0.1821 | 0.1567 | | 0.8371 | 11.87 | 11500 | 0.1909 | 0.1629 | | 0.8083 | 12.38 | 12000 | 0.1778 | 0.1498 | | 0.806 | 12.9 | 12500 | 0.1802 | 0.1547 | | 0.8013 | 13.42 | 13000 | 0.1859 | 0.1584 | | 0.7913 | 13.93 | 13500 | 0.1875 | 0.1517 | | 0.8063 | 14.45 | 14000 | 0.1799 | 0.1571 | | 0.7991 | 14.96 | 14500 | 0.1792 | 0.1538 | | 0.7843 | 15.48 | 15000 | 0.1753 | 0.1464 | | 0.7905 | 16.0 | 15500 | 0.1784 | 0.1508 | | 0.7808 | 16.51 | 16000 | 0.1771 | 0.1485 | | 0.7743 | 17.03 | 16500 | 0.1795 | 0.1491 | | 0.7833 | 17.54 | 17000 | 0.1722 | 0.1484 | | 0.7763 | 18.06 | 17500 | 0.1767 | 0.1518 | | 0.7698 | 18.58 | 18000 | 0.1720 | 0.1460 | | 0.7571 | 19.09 | 18500 | 0.1735 | 0.1478 | | 0.7673 | 19.61 | 19000 | 0.1817 | 0.1511 | | 0.7415 | 20.12 | 19500 | 0.1763 | 0.1481 | | 0.751 | 20.64 | 20000 | 0.1742 | 0.1484 | | 0.7563 | 21.16 | 20500 | 0.1810 | 0.1611 | | 0.7423 | 21.67 | 21000 | 0.1817 | 0.1557 | | 0.7242 | 22.19 | 21500 | 0.1690 | 0.1446 | | 0.7251 | 22.7 | 22000 | 0.1684 | 0.1446 | | 0.7302 | 23.22 | 22500 | 0.1735 | 0.1430 | | 0.733 | 23.74 | 23000 | 0.1720 | 0.1454 | | 0.7128 | 24.25 | 23500 | 0.1668 | 0.1383 | | 0.7184 | 24.77 | 24000 | 0.1635 | 0.1377 | | 0.7015 | 25.28 | 24500 | 0.1646 | 0.1389 | | 0.7198 | 25.8 | 25000 | 0.1775 | 0.1462 | | 0.7178 | 26.32 | 25500 | 0.1705 | 0.1419 | | 0.7199 | 26.83 | 26000 | 0.1649 | 0.1416 | | 0.6981 | 27.35 | 26500 | 0.1724 | 0.1418 | | 0.6886 | 27.86 | 27000 | 0.1633 | 0.1382 | | 0.6922 | 28.38 | 27500 | 0.1698 | 0.1420 | | 0.6833 | 28.9 | 28000 | 0.1611 | 0.1351 | | 0.6798 | 29.41 | 28500 | 0.1639 | 0.1365 | | 0.6711 | 29.93 | 29000 | 0.1668 | 0.1358 | | 0.6762 | 30.44 | 29500 | 0.1682 | 0.1355 | | 0.6594 | 30.96 | 30000 | 0.1629 | 0.1345 | | 0.6664 | 31.48 | 30500 | 0.1625 | 0.1321 | | 0.6838 | 31.99 | 31000 | 0.1597 | 0.1372 | | 0.6603 | 32.51 | 31500 | 0.1583 | 0.1302 | | 0.6468 | 33.02 | 32000 | 0.1595 | 0.1322 | | 0.6464 | 33.54 | 32500 | 0.1609 | 0.1315 | | 0.6623 | 34.06 | 33000 | 0.1622 | 0.1366 | | 0.6414 | 34.57 | 33500 | 0.1587 | 0.1330 | | 0.6242 | 35.09 | 34000 | 0.1614 | 0.1337 | | 0.632 | 35.6 | 34500 | 0.1568 | 0.1272 | | 0.6346 | 36.12 | 35000 | 0.1583 | 0.1274 | | 0.6143 | 36.64 | 35500 | 0.1576 | 0.1264 | | 0.6208 | 37.15 | 36000 | 0.1621 | 0.1263 | | 0.6185 | 37.67 | 36500 | 0.1623 | 0.1270 | | 0.6128 | 38.18 | 37000 | 0.1604 | 0.1268 | | 0.6151 | 38.7 | 37500 | 0.1593 | 0.1246 | | 0.6082 | 39.22 | 38000 | 0.1532 | 0.1238 | | 0.6 | 39.73 | 38500 | 0.1524 | 0.1224 | | 0.6032 | 40.25 | 39000 | 0.1521 | 0.1212 | | 0.6016 | 40.76 | 39500 | 0.1551 | 0.1215 | | 0.6009 | 41.28 | 40000 | 0.1523 | 0.1215 | | 0.5875 | 41.8 | 40500 | 0.1541 | 0.1216 | | 0.608 | 42.31 | 41000 | 0.1536 | 0.1209 | | 0.5876 | 42.83 | 41500 | 0.1567 | 0.1211 | | 0.5714 | 43.34 | 42000 | 0.1532 | 0.1217 | | 0.5756 | 43.86 | 42500 | 0.1516 | 0.1196 | | 0.5719 | 44.38 | 43000 | 0.1491 | 0.1191 | | 0.5829 | 44.89 | 43500 | 0.1497 | 0.1193 | | 0.5664 | 45.41 | 44000 | 0.1487 | 0.1173 | | 0.5707 | 45.92 | 44500 | 0.1470 | 0.1164 | | 0.5696 | 46.44 | 45000 | 0.1479 | 0.1161 | | 0.5767 | 46.96 | 45500 | 0.1492 | 0.1175 | | 0.5573 | 47.47 | 46000 | 0.1471 | 0.1165 | | 0.5625 | 47.99 | 46500 | 0.1484 | 0.1168 | | 0.5671 | 48.5 | 47000 | 0.1474 | 0.1162 | | 0.5484 | 49.02 | 47500 | 0.1479 | 0.1158 | | 0.555 | 49.54 | 48000 | 0.1477 | 0.1157 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

dabf84e154c72cda3fe2e3fdf99d63b1

jonatasgrosman/exp_w2v2r_en_xls-r_accent_us-10_england-0_s253

jonatasgrosman

wav2vec2

10

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'en']

false

true

476

false

# exp_w2v2r_en_xls-r_accent_us-10_england-0_s253 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

cd12583a670a4bc3e3c36e6a0569c928

VietAI/envit5-base

VietAI

t5

8

15

transformers

0

question-answering

true

mit

['vi']

['cc100']

null

0

['summarization', 'translation', 'question-answering']

false

true

2,182

false

# EnViT5-base State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese and English used in [MTet's paper](https://arxiv.org/abs/2210.05610). ## How to use For more details, do check out [our Github repo](https://github.com/vietai/mtet). [Finetunning examples can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface). ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("VietAI/envit5-base") model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/envit5-base") model.cuda() # need prefix for en: and vi: sentences inputs = [ "vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam.", "vi: Theo báo cáo mới nhất của Linkedin về danh sách việc làm triển vọng với mức lương hấp dẫn năm 2020, các chức danh công việc liên quan đến AI như Chuyên gia AI (Artificial Intelligence Specialist), Kỹ sư ML (Machine Learning Engineer) đều xếp thứ hạng cao.", "en: Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.", "en: We're on a journey to advance and democratize artificial intelligence through open source and open science." ] outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids.to('cuda'), max_length=512) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ``` ## Citation ``` @misc{mtet, doi = {10.48550/ARXIV.2210.05610}, url = {https://arxiv.org/abs/2210.05610}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {MTet: Multi-domain Translation for English and Vietnamese}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

1046f80a8172b6a856f4f5e0fbbe7779

lunarfish/furrydiffusion

lunarfish

null

18

1,252

diffusers

12

text-to-image

false

creativeml-openrail-m

null

1

0

1

0

1

0

['text-to-image', 'stable-diffusion', 'furry', 'anything-v3.0']

false

true

1,075

false

![images](https://cdn.discordapp.com/attachments/1050047774315532300/1057079481581445230/grid-0005.png) FurryDiffusion is a model made to generate furry art, this model is very much in beta still and will keep improoving! To use this please make sure to include `furry` in your prompt and to make a specific breed add the breed name only. Example Prompts: ``` Positive: highres, furry, fox, orange fur, blue eyes Negative: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, blurry ``` Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or 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) **NOTE**: Its better to run it in Google Colab since you can use google's powerful gpu's for free. Go ahead try it now!

99193babd3ebc26c279378896f50f2ab

theojolliffe/bart-large-cnn-pubmed1o3

theojolliffe

bart

13

1

transformers

0

text2text-generation

true

false

mit

null

['scientific_papers']

null

0

['generated_from_trainer']

true

1,464

false

# bart-large-cnn-pubmed1o3 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 1.9359 - Rouge1: 36.7566 - Rouge2: 14.813 - Rougel: 22.4693 - Rougelsum: 33.4325 - Gen Len: 138.7332 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 2.028 | 1.0 | 19988 | 1.9359 | 36.7566 | 14.813 | 22.4693 | 33.4325 | 138.7332 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

972f29669d4dd7f8d1d3f2847aae7dad

DonatoFrancioso/NLP2122_FranciosoDonato

DonatoFrancioso

distilbert

13

2

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,007

false

# NLP2122_FranciosoDonato This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8885 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.21.3 - Pytorch 1.11.0+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

610b11667d5954ad132f1f5f10bcd9f8

imfiba1991/gpt2-wikitext2

imfiba1991

gpt2

11

4

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,216

false

# gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.2082 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 13 | 8.1476 | | No log | 2.0 | 26 | 7.4435 | | No log | 3.0 | 39 | 7.2082 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

e0437311c5d2265e96ba4ae6f5602989

bitsanlp/roberta-finetuned-DA-100k

bitsanlp

roberta

13

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

954

false

# roberta-finetuned-DA-100k This model is a fine-tuned version of [bitsanlp/roberta-retrained_100k](https://huggingface.co/bitsanlp/roberta-retrained_100k) 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: 32 - eval_batch_size: 8 - seed: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

a535bc03dda9b5ec99b1afa9c0e26c46

jonatasgrosman/exp_w2v2t_ja_vp-100k_s219

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ja']

false

true

475

false

# exp_w2v2t_ja_vp-100k_s219 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

1e3fb32a3f84af763f95225905496890

neelrr/xlm-roberta-base-finetuned-panx-ta

neelrr

xlm-roberta

10

5

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,314

false

# xlm-roberta-base-finetuned-panx-ta 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: 0.2183 - F1: 0.8145 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5477 | 1.0 | 209 | 0.2732 | 0.7305 | | 0.2506 | 2.0 | 418 | 0.2425 | 0.7626 | | 0.168 | 3.0 | 627 | 0.2183 | 0.8145 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

bd37ec4839300a4eabe486689c8d3f06

lighteternal/fact-or-opinion-xlmr-el

lighteternal

xlm-roberta

11

7

transformers

2

text-classification

true

false

apache-2.0

['en', 'el', 'multilingual']

null

0

['text-classification', 'fact-or-opinion', 'transformers']

false

true

1,491

false

# Fact vs. opinion binary classifier, trained on a mixed EN-EL annotated corpus. ### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) This is an XLM-Roberta-base model with a binary classification head. Given a sentence, it can classify it either as a fact or an opinion based on its content. You can use this model in any of the XLM-R supported languages for the same task, taking advantage of its 0-shot learning capabilities. However, the model was trained only using English and Greek sentences. Legend of HuggingFace API labels: * Label 0: Opinion/Subjective sentence * Label 1: Fact/Objective sentence ## Dataset training info The original dataset (available here: https://github.com/1024er/cbert_aug/tree/crayon/datasets/subj) contained aprox. 9000 annotated sentences (classified as subjective or objective). It was translated to Greek using Google Translate. The Greek version was then concatenated with the original English one to create the mixed EN-EL dataset. The model was trained for 5 epochs, using batch size = 8. Detailed metrics and hyperparameters available on the "Metrics" tab. ## Evaluation Results on test set | accuracy | precision | recall | f1 | | ----------- | ----------- | ----------- | ----------- | |0.952 | 0.945 | 0.960 | 0.952 | ## Acknowledgement The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)

71abf5ffa1b1a0d36f333004363282e0

sd-concepts-library/schloss-mosigkau

sd-concepts-library

null

10

0

null

0

null

false

mit

null

0

[]

false

true

1,144

false

### schloss mosigkau on Stable Diffusion This is the `<ralph>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<ralph> 0](https://huggingface.co/sd-concepts-library/schloss-mosigkau/resolve/main/concept_images/0.jpeg) ![<ralph> 1](https://huggingface.co/sd-concepts-library/schloss-mosigkau/resolve/main/concept_images/3.jpeg) ![<ralph> 2](https://huggingface.co/sd-concepts-library/schloss-mosigkau/resolve/main/concept_images/4.jpeg) ![<ralph> 3](https://huggingface.co/sd-concepts-library/schloss-mosigkau/resolve/main/concept_images/1.jpeg) ![<ralph> 4](https://huggingface.co/sd-concepts-library/schloss-mosigkau/resolve/main/concept_images/2.jpeg)

c9b40c7cc16abf2f541091aefdb1c799

suonbo/bert-finetuned-ner

suonbo

bert

12

3

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,518

false

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0637 - Precision: 0.9336 - Recall: 0.9488 - F1: 0.9412 - Accuracy: 0.9854 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0897 | 1.0 | 1756 | 0.0648 | 0.9152 | 0.9408 | 0.9278 | 0.9837 | | 0.0384 | 2.0 | 3512 | 0.0601 | 0.9277 | 0.9507 | 0.9391 | 0.9859 | | 0.0201 | 3.0 | 5268 | 0.0637 | 0.9336 | 0.9488 | 0.9412 | 0.9854 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

48d9a903a9471a7bd4e179d2e731cc7f

facebook/mask2former-swin-tiny-cityscapes-semantic

facebook

mask2former

5

40

transformers

0

image-segmentation

true

false

other

null

['coco']

null

1

0

1

0

['vision', 'image-segmentation']

false

true

2,928

false

# Mask2Former Mask2Former model trained on Cityscapes semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on Cityscapes semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-semantic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).

e622d93122371942b2caf9013e61eb71

andrewburns/clay-icon

andrewburns

null

56

14

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image']

false

true

4,267

false

### clay_icon Dreambooth model trained by andrewburns with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You 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). Don't forget to use the concept prompts! Sample pictures of: clay (use that on your prompt) ![clay 0](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%281%29.jpg)![clay 1](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%282%29.jpg)![clay 2](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%283%29.jpg)![clay 3](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%284%29.jpg)![clay 4](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%285%29.jpg)![clay 5](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%286%29.jpg)![clay 6](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%287%29.jpg)![clay 7](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%288%29.jpg)![clay 8](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%289%29.jpg)![clay 9](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2810%29.jpg)![clay 10](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2811%29.jpg)![clay 11](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2812%29.jpg)![clay 12](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2813%29.jpg)![clay 13](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2814%29.jpg)![clay 14](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2815%29.jpg)![clay 15](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2816%29.jpg)![clay 16](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2817%29.jpg)![clay 17](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2818%29.jpg)![clay 18](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2819%29.jpg)![clay 19](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2820%29.jpg)![clay 20](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2821%29.jpg)![clay 21](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2822%29.jpg)![clay 22](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2823%29.jpg)![clay 23](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2824%29.jpg)![clay 24](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2825%29.jpg)![clay 25](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2826%29.jpg)![clay 26](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2827%29.jpg)![clay 27](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2828%29.jpg)![clay 28](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2829%29.jpg)![clay 29](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2830%29.jpg)![clay 30](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2831%29.jpg)![clay 31](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2832%29.jpg)![clay 32](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2833%29.jpg)![clay 33](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2834%29.jpg)![clay 34](https://huggingface.co/andrewburns/clay-icon/resolve/main/concept_images/clay_sks_%2835%29.jpg)

c71145f09aa42bd853cdbc5359a3596c

gngpostalsrvc/w2v2-ami

gngpostalsrvc

wav2vec2

12

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,770

false

# w2v2-ami This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8686 - Wer: 0.2861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.6021 | 3.07 | 500 | 2.9176 | 0.9997 | | 2.5006 | 6.13 | 1000 | 1.0535 | 0.3617 | | 0.9926 | 9.2 | 1500 | 0.8614 | 0.3036 | | 0.809 | 12.27 | 2000 | 0.8676 | 0.2921 | | 0.73 | 15.34 | 2500 | 0.8190 | 0.2966 | | 0.6658 | 18.4 | 3000 | 0.8707 | 0.2900 | | 0.6295 | 21.47 | 3500 | 0.8660 | 0.2821 | | 0.6089 | 24.54 | 4000 | 0.8767 | 0.2829 | | 0.5974 | 27.61 | 4500 | 0.8686 | 0.2861 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

97016253d217a5591665ba2396466736

matteow/fin_sentiment

matteow

distilbert

12

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,109

false

# fin_sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.4842 | 0.8129 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

4898c0a79ddaa0898d2266d073d50ea3

Helsinki-NLP/opus-mt-pis-fr

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-pis-fr * source languages: pis * target languages: fr * OPUS readme: [pis-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pis-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pis-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pis-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pis-fr/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.pis.fr | 24.9 | 0.421 |

52cf37018c78ede5b7237fa456b3f3ed

timm/eca_nfnet_l0

timm

null

4

532

timm

1

image-classification

true

false

apache-2.0

null

['imagenet']

null

0

['image-classification', 'timm', 'normalization-free', 'efficient-channel-attention']

false

true

4,295

false

# ECA-NFNet-L0 Pretrained model on [ImageNet](http://www.image-net.org/), this is a variant of the [NFNet (Normalization Free)](https://arxiv.org/abs/2102.06171) model family. ## Model description This model variant was slimmed down from the original F0 variant in the paper for improved runtime characteristics (throughput, memory use) in PyTorch, on a GPU accelerator. It utilizes [Efficient Channel Attention (ECA)](https://arxiv.org/abs/1910.03151) instead of Squeeze-Excitation. It also features SiLU activations instead of the usual GELU. Like other models in the NF family, this model contains no normalization layers (batch, group, etc). The models make use of [Weight Standardized](https://arxiv.org/abs/1903.10520) convolutions with additional scaling values in lieu of normalization layers. ## Intended uses & limitations You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head to fine-tune it on a downstream task (another classification task with different labels, image segmentation or object detection, to name a few). ### How to use You can use this model with the usual factory method in [`timm`](https://github.com/rwightman/pytorch-image-models): ```python import PIL import timm import torch model = timm.create_model("hf_hub:timm/eca_nfnet_l0") config = model.default_cfg img_size = config["test_input_size"][-1] if "test_input_size" in config else config["input_size"][-1] transform = timm.data.transforms_factory.transforms_imagenet_eval( img_size=img_size, interpolation=config["interpolation"], mean=config["mean"], std=config["std"], crop_pct=config["crop_pct"], ) img = PIL.Image.open(path_to_an_image) img = img.convert("RGB") input_tensor = transform(cat_img) input_tensor = input_tensor.unsqueeze(0) # ^ batch size = 1 with torch.no_grad(): output = model(input_tensor) probs = output.squeeze(0).softmax(dim=0) ``` ### Limitations and bias The training images in the dataset are usually photos clearly representing one of the 1,000 labels. The model will probably not generalize well on drawings or images containing multiple objects with different labels. The training images in the dataset come mostly from the US (45.4%) and Great Britain (7.6%). As such the model or models created by fine-tuning this model will work better on images picturing scenes from these countries (see [this paper](https://arxiv.org/abs/1906.02659) for examples). More generally, [recent research](https://arxiv.org/abs/2010.15052) has shown that even models trained in an unsupervised fashion on ImageNet (i.e. without using the labels) will pick up racial and gender bias represented in the training images. ## Training data This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 millions of hand-annotated images with 1,000 categories. ## Training procedure For stability during training it is highly recommended to train all NFNet variants with gradient clipping enabled. This model was trained with an Adaptive Gradient Clipping (AGC) factor of 0.015 as described in [the paper](https://arxiv.org/abs/2102.06171). Similar to the paper, a cosine learning rate decay was employed using SGD w/ nesterov. Moderate to heavy augmentation ([RandAugment](https://arxiv.org/abs/1909.13719)) and regularization (dropout, stochastic depth) is recommended for training. ### Preprocessing The images are resized using bicubic interpolation to 288x288 and normalized with the usual ImageNet statistics. ## Evaluation results This model has a top1-accuracy of 82.6% and a top-5 accuracy of 96.5% on the ImageNet evaluation set. ### BibTeX entry and citation info NFNet model architecture: ```bibtex @article{brock2021high, author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan}, title={High-Performance Large-Scale Image Recognition Without Normalization}, journal={arXiv preprint arXiv:2102.06171}, year={2021} } ``` L0 model variant & pretraining: ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ```

544877f14049e05670e9bf3f10fd10da

Helsinki-NLP/opus-mt-bzs-fi

Helsinki-NLP

marian

10

9

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-bzs-fi * source languages: bzs * target languages: fi * OPUS readme: [bzs-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fi/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fi/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fi/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bzs.fi | 24.7 | 0.464 |

aa2c307e6e9c48f2d113e0a41f572c52

Tinsae/beyaynetu

Tinsae

null

23

19

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

822

false

### beyaynetu" Dreambooth model trained by Tinsae with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/Tinsae/beyaynetu/resolve/main/sample_images/beyaynetu_(24).png) ![1](https://huggingface.co/Tinsae/beyaynetu/resolve/main/sample_images/beyaynetu_(12).png) ![2](https://huggingface.co/Tinsae/beyaynetu/resolve/main/sample_images/beyaynetu_(20).png) ![3](https://huggingface.co/Tinsae/beyaynetu/resolve/main/sample_images/beyaynetu_(16).png)

b9b5a5237d4df8050efdf6a37a361b6d

bthomas/tuto-bert-finetuned-ner

bthomas

bert

10

6

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,517

false

# tuto-bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0827 - Precision: 0.9380 - Recall: 0.9525 - F1: 0.9452 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0218 | 1.0 | 1756 | 0.0714 | 0.9372 | 0.9524 | 0.9447 | 0.9862 | | 0.0123 | 2.0 | 3512 | 0.0761 | 0.9347 | 0.9510 | 0.9428 | 0.9859 | | 0.0063 | 3.0 | 5268 | 0.0827 | 0.9380 | 0.9525 | 0.9452 | 0.9867 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.11.0

bac342223c1599c252019dc83e4a93b6

Stricky/JellyCute7-Style-Hypernetwork

Stricky

null

5

0

null

0

null

false

creativeml-openrail-m

null

0

[]

false

true

1,060

false

# JellyCute7 (artist) Style [Hypernetwork] Hypernetwork trained on art by artist [JellyCute7](https://www.pixiv.net/en/users/1053112). [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/stricky) ### Settings ``` Model: NAI Layer structure: (1, 2, 1) Activation function: relu Layer normalization: False Use dropout: False Raw dataset size: 208 images Final dataset size: 832 images Size: 512x512 Create flipped copies: True Split oversized images: True Captions: DeepBooru Learning rate: 0.000005 -> 13000 steps Recommended: 13000 steps ``` ### Steps comparison (recommended: 13000) ![Steps comparison](https://huggingface.co/Stricky/JellyCute7-Style-Hypernetwork/resolve/main/steps.png) ### Sample images ![Sample images](https://huggingface.co/Stricky/JellyCute7-Style-Hypernetwork/resolve/main/samples_in.png) ### Sample output (jellytits7-13000) ![Sample output for 13000 steps](https://huggingface.co/Stricky/JellyCute7-Style-Hypernetwork/resolve/main/samples_out-13000.png)

3e7c766a3050fa6bb8f333b96a47b208