davanstrien/pytorchmodelmetadata · Datasets at Hugging Face

modelId stringlengths 4 112	sha stringlengths 40 40	lastModified stringlengths 24 24	tags sequence	pipeline_tag stringclasses 29 values	private bool 1 class	author stringlengths 2 38 ⌀	config null	id stringlengths 4 112	likes float64 0 712 ⌀	library_name stringclasses 17 values	__index_level_0__ int64 0 38.5k	readme stringlengths 0 186k
kevinzyz/chinese_roberta_L-12_H-768-finetuned-MC-hyper	d9fbeaab01bb46ec60542e42fc1149e8e39bdbc9	2021-12-09T04:48:15.000Z	[ "pytorch", "bert", "multiple-choice", "transformers" ]	multiple-choice	false	kevinzyz	null	kevinzyz/chinese_roberta_L-12_H-768-finetuned-MC-hyper	null	transformers	35,500	Entry not found
kevinzyz/chinese_roberta_L-2_H-128-finetuned-MC-hyper	7c5490211a3b69e6297937f2ace5fbf4b9b87b17	2021-12-09T13:11:53.000Z	[ "pytorch", "bert", "multiple-choice", "transformers" ]	multiple-choice	false	kevinzyz	null	kevinzyz/chinese_roberta_L-2_H-128-finetuned-MC-hyper	null	transformers	35,501	Entry not found
khizon/greek-speech-emotion-classifier-demo	f214ac6debe63f935a812ef62603481410772912	2022-01-09T12:49:04.000Z	[ "pytorch", "wav2vec2", "transformers" ]	null	false	khizon	null	khizon/greek-speech-emotion-classifier-demo	null	transformers	35,502	Entry not found
kika2000/wav2vec2-large-xls-r-300m-kika3_my-colab	dec99539624deeaebb9f2315b40ed8bd38221549	2022-01-25T23:11:06.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	kika2000	null	kika2000/wav2vec2-large-xls-r-300m-kika3_my-colab	null	transformers	35,503	Entry not found
kika2000/wav2vec2-large-xls-r-300m-kika_my-colab	03143862fc5e7eb24e2198dbf32be7a57f47c5d6	2022-01-25T04:10:14.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	kika2000	null	kika2000/wav2vec2-large-xls-r-300m-kika_my-colab	null	transformers	35,504	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-kika_my-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-kika_my-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.3300 - Wer: 0.5804 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.8067 \| 4.82 \| 400 \| 1.2892 \| 0.8886 \| \| 0.3048 \| 9.64 \| 800 \| 1.2285 \| 0.6797 \| \| 0.1413 \| 14.46 \| 1200 \| 1.1970 \| 0.6509 \| \| 0.1047 \| 19.28 \| 1600 \| 1.3628 \| 0.6166 \| \| 0.0799 \| 24.1 \| 2000 \| 1.3345 \| 0.6014 \| \| 0.0638 \| 28.92 \| 2400 \| 1.3300 \| 0.5804 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
kika2000/wav2vec2-large-xls-r-300m-test80_my-colab	b7a39f206ad59f0c37a86450b8e30c6707987647	2022-01-31T12:19:35.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	kika2000	null	kika2000/wav2vec2-large-xls-r-300m-test80_my-colab	null	transformers	35,505	Entry not found
kika2000/wav2vec2-large-xls-r-300m-test81_my-colab	4fd6a06af3ae843deccacc9b8308903eb2214381	2022-02-04T11:14:16.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	kika2000	null	kika2000/wav2vec2-large-xls-r-300m-test81_my-colab	null	transformers	35,506	Entry not found
kikumaru818/easy_algebra	c2e5c667c8e9d527213c1fa443a9bfdc2345c446	2021-11-29T00:37:27.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	kikumaru818	null	kikumaru818/easy_algebra	null	transformers	35,507	Entry not found
kiyoung2/dpr_p-encoder_roberta-small	fe440fea034a4085323c18ee5eef5558763096d5	2021-10-29T02:38:33.000Z	[ "pytorch", "roberta", "transformers" ]	null	false	kiyoung2	null	kiyoung2/dpr_p-encoder_roberta-small	null	transformers	35,508	Entry not found
kiyoung2/dpr_q-encoder_roberta-small	66437541310a6e3793a9328bcbab521f8439d507	2021-10-29T02:38:21.000Z	[ "pytorch", "roberta", "transformers" ]	null	false	kiyoung2	null	kiyoung2/dpr_q-encoder_roberta-small	null	transformers	35,509	Entry not found
kizunasunhy/fnet-base-finetuned-ner	8adc76990574cc21f9bbc12e8d4254f827de6ad2	2021-10-15T09:33:49.000Z	[ "pytorch", "fnet", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	kizunasunhy	null	kizunasunhy/fnet-base-finetuned-ner	null	transformers	35,510	Entry not found
kmfoda/output_dir	cb4ec67680c0b3f78f114901695306a31a7e563f	2022-02-01T11:06:56.000Z	[ "pytorch", "pegasus", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	kmfoda	null	kmfoda/output_dir	null	transformers	35,511	Entry not found
koala/bert-large-uncased-ko	0cb0428a1f08779ce778fe535d4ce91bea912270	2021-12-10T08:29:40.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	koala	null	koala/bert-large-uncased-ko	null	transformers	35,512	Entry not found
koala/xlm-roberta-large-bn	f7e8ea3f443589a5f5af64c9736ecab943f466ec	2022-01-05T13:05:16.000Z	[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	koala	null	koala/xlm-roberta-large-bn	null	transformers	35,513	Entry not found
koala/xlm-roberta-large-de	08dcb3ce3813f966a5ef6a290d03c1e828b1f078	2021-12-06T18:16:37.000Z	[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	koala	null	koala/xlm-roberta-large-de	null	transformers	35,514	Entry not found
koala/xlm-roberta-large-hi	018b51c821cddccd515436221a7432d3db0f8882	2021-12-21T12:58:05.000Z	[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	koala	null	koala/xlm-roberta-large-hi	null	transformers	35,515	Entry not found
koala/xlm-roberta-large-zh	cbb7d05a8c7243a658ce51dfe4bd28da19feb775	2021-12-06T18:26:29.000Z	[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	koala	null	koala/xlm-roberta-large-zh	null	transformers	35,516	Entry not found
korca/bert-base-mm-cased	6c72fa51242a8a17a4a9e94024f7699f2858e3a6	2021-09-15T07:41:23.000Z	[ "pytorch", "bert", "feature-extraction", "transformers" ]	feature-extraction	false	korca	null	korca/bert-base-mm-cased	null	transformers	35,517	Entry not found
korca/meaning-match-bert-base	8410e651f39d3ea55a401d75474e83cd401de3ba	2021-11-23T08:35:37.000Z	[ "pytorch", "bert", "feature-extraction", "transformers" ]	feature-extraction	false	korca	null	korca/meaning-match-bert-base	null	transformers	35,518	Entry not found
korca/meaning-match-bert-large	8a3eaeb1ba7708e0dcd7a954f35b9445b767d0ea	2021-11-18T17:52:44.000Z	[ "pytorch", "bert", "feature-extraction", "transformers" ]	feature-extraction	false	korca	null	korca/meaning-match-bert-large	null	transformers	35,519	Entry not found
korca/meaning-match-electra-large	81b94142c53bb4a3b4d0f8e24592f346aa1e3f91	2021-11-29T08:40:30.000Z	[ "pytorch", "electra", "feature-extraction", "transformers" ]	feature-extraction	false	korca	null	korca/meaning-match-electra-large	null	transformers	35,520	Entry not found
korca/roberta-base-mm	c04ee9a2161fd67746f2e269250c1ac9b35cf4ca	2021-09-14T07:46:00.000Z	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	false	korca	null	korca/roberta-base-mm	null	transformers	35,521	Entry not found
kp17/DialoGPT-small-tonystark	9107fc295d77cb38eeca18513c88d3be8ad0e1af	2021-08-27T06:44:11.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "Conversational" ]	text-generation	false	kp17	null	kp17/DialoGPT-small-tonystark	null	transformers	35,522	--- tags: - Conversational --- # Tony Stark DialoGPT Model
kr0n0s/AssameseBert	fdebb1792a5f876cf41f4955fb0327ede381bb9b	2021-07-29T20:24:48.000Z	[ "pytorch", "bert", "transformers" ]	null	false	kr0n0s	null	kr0n0s/AssameseBert	null	transformers	35,523	Entry not found
krevas/finance-electra-small-discriminator	139aedb657a7a7d8fc9024d4bc93346fbb8302cc	2020-07-09T05:46:38.000Z	[ "pytorch", "electra", "pretraining", "transformers" ]	null	false	krevas	null	krevas/finance-electra-small-discriminator	null	transformers	35,524	Entry not found
kris/DialoGPT-small-spock4	eb11e5daa35ffee8b279a529c0b0b9cefe46c0c3	2021-09-23T14:16:55.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	kris	null	kris/DialoGPT-small-spock4	null	transformers	35,525	--- tags: - conversational --- #Spock model
kris/DialoGPT-small-spock5	bf3db76f28871513f4b28ceeb22000d81eeb8802	2021-09-23T15:12:30.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	kris	null	kris/DialoGPT-small-spock5	null	transformers	35,526	--- tags: - conversational --- #Spock model
krupine/telectra-discriminator	6900ceca63aeb79f9cecd817ce4c2568d6ab45ef	2021-01-22T08:41:00.000Z	[ "pytorch", "electra", "pretraining", "transformers" ]	null	false	krupine	null	krupine/telectra-discriminator	null	transformers	35,527	Entry not found
kshitiz/testing-bot-repo	7ffcf35a9318f6c9767547481afcf3bb1a545509	2021-11-09T06:58:33.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	kshitiz	null	kshitiz/testing-bot-repo	null	transformers	35,528	--- tags: - conversational --- #testing bot Model
ksinar/DialoGPT-small-morty	b784f81dda6602e541b82686245eff8e810dc9da	2021-08-28T14:40:58.000Z	[ "pytorch" ]	null	false	ksinar	null	ksinar/DialoGPT-small-morty	null	null	35,529	Entry not found
kumakino/fairy-tale-gpt2-small	5b672e6d77bbf42ce54be3790acd7b620428c8b3	2021-12-13T23:07:26.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	kumakino	null	kumakino/fairy-tale-gpt2-small	null	transformers	35,530	Entry not found
kunalbhargava/DialoGPT-small-housebot	20299e2aa368dbb004427976aaffa6829877de9d	2021-11-11T09:00:23.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	kunalbhargava	null	kunalbhargava/DialoGPT-small-housebot	null	transformers	35,531	--- tags: - conversational --- #House BOT
kvothe28/DiabloGPT-small-Rick	50535a92cb645b553224cacfa17c2fc1f124eed4	2021-09-03T21:16:54.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	kvothe28	null	kvothe28/DiabloGPT-small-Rick	null	transformers	35,532	--- tags: - conversational --- # Rick DiabloGPT Model
kwang2049/SBERT-base-nli-stsb-v2	b97422efcaadd383da9a33f423e33271aa8d2047	2021-08-30T13:30:32.000Z	[ "pytorch" ]	null	false	kwang2049	null	kwang2049/SBERT-base-nli-stsb-v2	null	null	35,533	Entry not found
kwang2049/SBERT-base-nli-v2	f38abd2c15aacf7a3993754e72d4f1d0bb6b7843	2021-08-30T13:29:35.000Z	[ "pytorch" ]	null	false	kwang2049	null	kwang2049/SBERT-base-nli-v2	null	null	35,534	Entry not found
kwang2049/TSDAE-askubuntu	a7c794a288383693c964479710f519b30ae8321e	2021-10-25T16:17:47.000Z	[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]	feature-extraction	false	kwang2049	null	kwang2049/TSDAE-askubuntu	null	transformers	35,535	# kwang2049/TSDAE-askubuntu2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on AskUbuntu with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'askubuntu' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'askubuntu' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```
kwang2049/TSDAE-cqadupstack	9e5000f269f165a79392a858ee60653bc2cb634f	2021-10-25T16:18:29.000Z	[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]	feature-extraction	false	kwang2049	null	kwang2049/TSDAE-cqadupstack	null	transformers	35,536	# kwang2049/TSDAE-cqadupstack2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on cqadupstack with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'cqadupstack' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'cqadupstack' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```
kwang2049/TSDAE-scidocs	d2f136c580be5d551232344d1f62b3ccec264d02	2021-10-25T16:19:04.000Z	[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]	feature-extraction	false	kwang2049	null	kwang2049/TSDAE-scidocs	null	transformers	35,537	# kwang2049/TSDAE-scidocs2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on scidocs with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```
kwang2049/TSDAE-scidocs2nli_stsb	425ea67713d31c033f16c5e5a17a80de1ba7cc5a	2021-10-25T16:15:23.000Z	[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]	feature-extraction	false	kwang2049	null	kwang2049/TSDAE-scidocs2nli_stsb	null	transformers	35,538	# kwang2049/TSDAE-scidocs2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on scidocs with the TSDAE objective; 3. Supervised training on the NLI data with cross-entropy loss; 4. Supervised training on the STSb data with MSE loss. The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```
kwang2049/TSDAE-twitterpara	ae49bd6bb98ef1d586a6aee2a345b52a526749e8	2021-10-25T16:18:44.000Z	[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]	feature-extraction	false	kwang2049	null	kwang2049/TSDAE-twitterpara	null	transformers	35,539	# kwang2049/TSDAE-twitterpara2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on twitterpara with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'twitterpara' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'twitterpara' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```
l53513955/False_Entity_Identifier	a259b8994fdb142623f147b2ca6733ac76082492	2021-12-20T05:48:31.000Z	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	l53513955	null	l53513955/False_Entity_Identifier	null	transformers	35,540	Entry not found
lagodw/plotly_gpt2_large	66d42bca940e070d2d846457bbf784d101dc8dd9	2021-10-06T22:33:45.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	lagodw	null	lagodw/plotly_gpt2_large	null	transformers	35,541	Entry not found
lagodw/reddit_bert	1a706487954953e865e6652a5030051d57105bab	2021-09-04T19:12:32.000Z	[ "pytorch", "bert", "next-sentence-prediction", "transformers" ]	null	false	lagodw	null	lagodw/reddit_bert	1	transformers	35,542	Entry not found
lalopey/benn_eifert	a7f2d28e0b18b8fb490ff783e77ad9e557577bfd	2021-05-23T06:25:18.000Z	[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]	text-generation	false	lalopey	null	lalopey/benn_eifert	null	transformers	35,543	Entry not found
lapacc33/DialoGPT-medium-rick	c358130c33a6a9d51ecbcabe43ace1a64bd6bb98	2021-10-29T05:39:42.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	lapacc33	null	lapacc33/DialoGPT-medium-rick	null	transformers	35,544	--- tags: - conversational --- # Rick DialoGPT Model
larcane/kogpt2-cat-diary	d4dbb048874c85408313922dd78e7c9b867312ed	2021-12-18T15:45:25.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	larcane	null	larcane/kogpt2-cat-diary	null	transformers	35,545	Entry not found
laugustyniak/roberta-polish-web-embedding-v1	ea9478a6bd3a99efb45cea4cd9adb635b2f7df3f	2021-05-20T17:37:19.000Z	[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	laugustyniak	null	laugustyniak/roberta-polish-web-embedding-v1	null	transformers	35,546	Entry not found
laxya007/gpt2_BRM	fd100258c6e8867b5d81efa8befbc1933ab48d78	2021-10-23T08:23:11.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	laxya007	null	laxya007/gpt2_BRM	null	transformers	35,547	Entry not found
laxya007/gpt2_BSA_Leg_ipr_OE_OS	8cae9788ca2d14ed1a183231970bacde4fadfe4f	2021-06-18T08:40:06.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	laxya007	null	laxya007/gpt2_BSA_Leg_ipr_OE_OS	null	transformers	35,548	Entry not found
lbh020300/mymodel007	0d94893f4efee21622a8a660129f802193507171	2021-11-02T16:06:03.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	lbh020300	null	lbh020300/mymodel007	null	transformers	35,549	Entry not found
lee1jun/wav2vec2-base-100h-finetuned	2b6bcfc36d916596b911d93cd26fc92b28984023	2021-07-06T10:01:40.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	lee1jun	null	lee1jun/wav2vec2-base-100h-finetuned	null	transformers	35,550	Entry not found
leemii18/robustqa-baseline-02	245beb64ac2fe26658f32a30cbca6ebc5118901c	2021-05-05T17:47:41.000Z	[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	false	leemii18	null	leemii18/robustqa-baseline-02	null	transformers	35,551	Entry not found
lewtun/dummy-translation	c2d443e0dccb8040e1f4520aec93db447df7b2d8	2021-07-13T12:43:13.000Z	[ "pytorch", "marian", "text2text-generation", "transformers", "generated_from_trainer", "autotrain_compatible" ]	text2text-generation	false	lewtun	null	lewtun/dummy-translation	null	transformers	35,552	--- tags: - generated_from_trainer model_index: - name: dummy-translation results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dummy-translation This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on an unkown 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: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3
lewtun/marian-finetuned-kde4-en-to-fr	e51aad09934acd4a5d0bb38d0686bedbab7840c4	2021-11-14T16:59:34.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:kde4", "transformers", "translation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	translation	false	lewtun	null	lewtun/marian-finetuned-kde4-en-to-fr	null	transformers	35,553	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 args: en-fr metrics: - name: Bleu type: bleu value: 38.988820814501665 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 1.6772 - Bleu: 38.9888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
lewtun/metnet-test-2	e55e5fd8e175b9679cdcdc21416afff44475d861	2021-09-06T10:42:38.000Z	[ "pytorch", "transformers" ]	null	false	lewtun	null	lewtun/metnet-test-2	null	transformers	35,554	Entry not found
lewtun/metnet-test-3	0ff97bde804713a4ad00899d2862f74586306ba2	2021-09-06T10:53:04.000Z	[ "pytorch", "transformers", "autonlp", "evaluation", "benchmark" ]	null	false	lewtun	null	lewtun/metnet-test-3	null	transformers	35,555	--- tags: - autonlp - evaluation - benchmark --- # Model Card for MetNet
lewtun/metnet-test-5	12fe5921c2c4bef26138ec8d3d34b27c0ebd70bd	2021-09-06T11:01:50.000Z	[ "pytorch", "transformers", "satflow", "license:mit" ]	null	false	lewtun	null	lewtun/metnet-test-5	null	transformers	35,556	--- license: mit tags: - satflow --- # MetNet ## Model description [More information needed] ## Intended uses & limitations [More information needed] ## How to use [More information needed] ## Limitations and bias [More information needed] ## Training data [More information needed] ## Training procedure [More information needed] ## Evaluation results [More information needed]
lewtun/metnet-test	2cb8dbf9b3e001519cb4b6206f93cd62d9ded316	2021-09-06T09:22:37.000Z	[ "pytorch" ]	null	false	lewtun	null	lewtun/metnet-test	null	null	35,557	Entry not found
lewtun/minilm-finetuned-imdb-accelerate	da6f67e725230781b225a986e40c9e283bcb537e	2021-09-29T08:48:14.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	lewtun	null	lewtun/minilm-finetuned-imdb-accelerate	null	transformers	35,558	Entry not found
lewtun/mt5-finetuned-amazon-en-es-accelerate	dfe5bea91037602120869439cb6f63cd259c91e4	2021-11-11T15:12:52.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	lewtun	null	lewtun/mt5-finetuned-amazon-en-es-accelerate	null	transformers	35,559	Entry not found
lg/fexp_1	1ca4f9b9a7238ffa70bc0a46c3a72ea75f81fdf5	2021-05-20T23:37:11.000Z	[ "pytorch", "gpt_neo", "text-generation", "transformers" ]	text-generation	false	lg	null	lg/fexp_1	null	transformers	35,560	# This model is probably not what you're looking for.
lg/fexp_2	ae084e2eb00c3cba0af49853fc3694d321c8a4a6	2021-05-01T17:56:11.000Z	[ "pytorch", "gpt_neo", "text-generation", "transformers" ]	text-generation	false	lg	null	lg/fexp_2	null	transformers	35,561	Entry not found
lg/fexp_7	0f948d3e89789b81f90fbcf1b69aed53afa0269e	2021-05-03T05:27:39.000Z	[ "pytorch", "gpt_neo", "text-generation", "transformers" ]	text-generation	false	lg	null	lg/fexp_7	null	transformers	35,562	Entry not found
lg/fexp_8	e438f00780887eed802bcbf528b4e788760d0aaf	2021-05-02T16:58:34.000Z	[ "pytorch", "gpt_neo", "text-generation", "transformers" ]	text-generation	false	lg	null	lg/fexp_8	null	transformers	35,563	Entry not found
lg/ghpy_2k	14f17bce6bfdfd7e8217d599b125a4ac1dc32c3c	2021-05-14T16:27:41.000Z	[ "pytorch", "gpt_neo", "text-generation", "transformers" ]	text-generation	false	lg	null	lg/ghpy_2k	null	transformers	35,564	Entry not found
lg/ghpy_40k	8994fc0eae81882834ca1c11d7847efd2a9db012	2021-05-20T23:37:47.000Z	[ "pytorch" ]	null	false	lg	null	lg/ghpy_40k	null	null	35,565	# This model is probably not what you're looking for.
lgris/bp-commonvoice10-xlsr	163b0e6c4474f107e62193dae59882cfc73b537c	2021-11-27T21:02:56.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "transformers", "audio", "speech", "portuguese-speech-corpus", "PyTorch", "license:apache-2.0" ]	automatic-speech-recognition	false	lgris	null	lgris/bp-commonvoice10-xlsr	null	transformers	35,566	--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 --- # commonvoice10-xlsr: Wav2vec 2.0 with Common Voice Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Common Voice 7.0](https://commonvoice.mozilla.org/pt) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. \| Dataset \| Train \| Valid \| Test \| \|--------------------------------\|-------:\|------:\|------:\| \| CETUC \| \| -- \| 5.4h \| \| Common Voice \| 37.8h \| -- \| 9.5h \| \| LaPS BM \| \| -- \| 0.1h \| \| MLS \| \| -- \| 3.7h \| \| Multilingual TEDx (Portuguese) \| \| -- \| 1.8h \| \| SID \| \| -- \| 1.0h \| \| VoxForge \| \| -- \| 0.1h \| \| Total \| \| -- \| 21.6h \| #### Summary \| \| CETUC \| CV \| LaPS \| MLS \| SID \| TEDx \| VF \| AVG \| \|----------------------\|---------------\|----------------\|----------------\|----------------\|----------------\|----------------\|----------------\|----------------\| \| commonvoice10 (demonstration below) \| 0.133 \| 0.189 \| 0.165 \| 0.189 \| 0.247 \| 0.474 \| 0.251 \| 0.235 \| \| commonvoice10 + 4-gram (demonstration below) \| 0.060 \| 0.117 \| 0.088 \| 0.136 \| 0.181 \| 0.394 \| 0.227 \| 0.171 \| ## Demonstration ```python MODEL_NAME = "lgris/commonvoice10-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.13291846056190185 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.18909733896486755 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.1655429292929293 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.1894711228284466 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.2471983709551264 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.4739658565194102 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.2510294913419914 ### Tests with LM ```python # !find -type f -name "*.wav" -delete !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.060609303416680915 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.11758415681158373 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.08815340909090909 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.1359966791836458 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.1818429601530829 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.39469326522731385 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.22779897186147183
lgris/bp-lapsbm1-xlsr	905303347f0caffc6a8b13abc00177eedbf9e4ce	2021-11-27T21:07:02.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "transformers", "audio", "speech", "portuguese-speech-corpus", "PyTorch", "license:apache-2.0" ]	automatic-speech-recognition	false	lgris	null	lgris/bp-lapsbm1-xlsr	null	transformers	35,567	--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 --- # lapsbm1-xlsr: Wav2vec 2.0 with LaPSBM Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [LaPS BM](https://github.com/falabrasil/gitlab-resources) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. \| Dataset \| Train \| Valid \| Test \| \|--------------------------------\|-------:\|------:\|------:\| \| CETUC \| \| -- \| 5.4h \| \| Common Voice \| \| -- \| 9.5h \| \| LaPS BM \| 0.8h \| -- \| 0.1h \| \| MLS \| \| -- \| 3.7h \| \| Multilingual TEDx (Portuguese) \| \| -- \| 1.8h \| \| SID \| \| -- \| 1.0h \| \| VoxForge \| \| -- \| 0.1h \| \| Total \| \| -- \| 21.6h \| #### Summary \| \| CETUC \| CV \| LaPS \| MLS \| SID \| TEDx \| VF \| AVG \| \|----------------------\|---------------\|----------------\|----------------\|----------------\|----------------\|----------------\|----------------\|----------------\| \| lapsbm1\_100 (demonstration below) \| 0.111 \| 0.418 \| 0.145 \| 0.299 \| 0.562 \| 0.580 \| 0.469 \| 0.369 \| \| lapsbm1\_100 + 4-gram (demonstration below) \| 0.061 \| 0.305 \| 0.089 \| 0.201 \| 0.452 \| 0.525 \| 0.381 \| 0.287 \| ## Demonstration ```python MODEL_NAME = "lgris/lapsbm1-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.11147816967489037 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.41880890234535906 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.1451893939393939 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.29958960206171104 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.5626767414610376 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.5807549973642049 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.4693479437229436 ### Tests with LM ```python # !find -type f -name "*.wav" -delete !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.06157628194513477 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.3051714756833442 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.0893623737373737 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.20062044237806004 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.4522665618175908 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.5256707813182246 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.38106331168831165
lgris/bp-sid10-xlsr	4284d50d1d0b6561e63615dc1585d9425db2f03d	2021-11-27T21:09:42.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "transformers", "audio", "speech", "portuguese-speech-corpus", "PyTorch", "license:apache-2.0" ]	automatic-speech-recognition	false	lgris	null	lgris/bp-sid10-xlsr	null	transformers	35,568	--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 --- # sid10-xlsr: Wav2vec 2.0 with Sidney Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Sidney](https://igormq.github.io/datasets/) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. \| Dataset \| Train \| Valid \| Test \| \|--------------------------------\|-------:\|------:\|------:\| \| CETUC \| \| -- \| 5.4h \| \| Common Voice \| \| -- \| 9.5h \| \| LaPS BM \| \| -- \| 0.1h \| \| MLS \| \| -- \| 3.7h \| \| Multilingual TEDx (Portuguese) \| \| -- \| 1.8h \| \| SID \| 7.2h \| -- \| 1.0h \| \| VoxForge \| \| -- \| 0.1h \| \| Total \| 7.2h\| -- \| 21.6h \| #### Summary \| \| CETUC \| CV \| LaPS \| MLS \| SID \| TEDx \| VF \| AVG \| \|----------------------\|---------------\|----------------\|----------------\|----------------\|----------------\|----------------\|----------------\|----------------\| \| sid\_10 (demonstration below) \|0.186 \| 0.327 \| 0.207 \| 0.505 \| 0.124 \| 0.835 \| 0.472 \| 0.379\| \| sid\_10 + 4-gram (demonstration below) \|0.096 \| 0.223 \| 0.115 \| 0.432 \| 0.101 \| 0.791 \| 0.348 \| 0.301\| ## Demonstration ```python MODEL_NAME = "lgris/sid10-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.18623689076557778 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.3279775395502392 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.20780303030303032 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.5056711598536057 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.1247776617710105 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.8350609256842175 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.47242153679653687 ### Tests with LM ```python # !find -type f -name "*.wav" -delete !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.09677271347353278 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.22363215674470321 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.1154924242424242 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.4322369152606427 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.10080313085145765 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.7911789829264236 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.34786255411255407
lgris/distilxlsr_bp_16-24	61102aab99832521692f62cdd8a5f9e4ac914047	2021-12-30T00:38:16.000Z	[ "pytorch", "wav2vec2", "feature-extraction", "pt", "arxiv:2110.01900", "transformers", "speech", "license:apache-2.0" ]	feature-extraction	false	lgris	null	lgris/distilxlsr_bp_16-24	null	transformers	35,569	--- language: pt tags: - speech license: apache-2.0 --- # DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee Note 2: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). Abstract Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.
lgris/distilxlsr_bp_4-12	61d69f4659aa8e30bce84daf6f9769f16dfcd68a	2021-12-30T00:38:04.000Z	[ "pytorch", "wav2vec2", "feature-extraction", "pt", "arxiv:2110.01900", "transformers", "speech", "license:apache-2.0" ]	feature-extraction	false	lgris	null	lgris/distilxlsr_bp_4-12	null	transformers	35,570	--- language: pt tags: - speech license: apache-2.0 --- # DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee Note 2: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). Abstract Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.
lgris/distilxlsr_bp_8-12-24	1af2b82d5420a218887d21a032cc37cbadd16842	2021-12-30T00:37:34.000Z	[ "pytorch", "wav2vec2", "feature-extraction", "pt", "arxiv:2110.01900", "transformers", "speech", "license:apache-2.0" ]	feature-extraction	false	lgris	null	lgris/distilxlsr_bp_8-12-24	null	transformers	35,571	--- language: pt tags: - speech license: apache-2.0 --- # DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee Note 2: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). Abstract Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.
lgris/wav2vec2-large-xls-r-300m-pt-cv	3906b54f22780a919093296fa94edf627b1926a3	2022-03-24T11:52:39.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "transformers", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	lgris	null	lgris/wav2vec2-large-xls-r-300m-pt-cv	null	transformers	35,572	--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - robust-speech-event - pt - hf-asr-leaderboard datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-pt-cv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 6 type: common_voice args: pt metrics: - name: Test WER type: wer value: 24.29 - name: Test CER type: cer value: 7.51 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - name: Test WER type: wer value: 55.72 - name: Test CER type: cer value: 21.82 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: pt metrics: - name: Test WER type: wer value: 47.88 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: pt metrics: - name: Test WER type: wer value: 50.78 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-pt-cv 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: 0.3418 - Wer: 0.3581 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 10.9035 \| 0.2 \| 100 \| 4.2750 \| 1.0 \| \| 3.3275 \| 0.41 \| 200 \| 3.0334 \| 1.0 \| \| 3.0016 \| 0.61 \| 300 \| 2.9494 \| 1.0 \| \| 2.1874 \| 0.82 \| 400 \| 1.4355 \| 0.8721 \| \| 1.09 \| 1.02 \| 500 \| 0.9987 \| 0.7165 \| \| 0.8251 \| 1.22 \| 600 \| 0.7886 \| 0.6406 \| \| 0.6927 \| 1.43 \| 700 \| 0.6753 \| 0.5801 \| \| 0.6143 \| 1.63 \| 800 \| 0.6300 \| 0.5509 \| \| 0.5451 \| 1.84 \| 900 \| 0.5586 \| 0.5156 \| \| 0.5003 \| 2.04 \| 1000 \| 0.5493 \| 0.5027 \| \| 0.3712 \| 2.24 \| 1100 \| 0.5271 \| 0.4872 \| \| 0.3486 \| 2.45 \| 1200 \| 0.4953 \| 0.4817 \| \| 0.3498 \| 2.65 \| 1300 \| 0.4619 \| 0.4538 \| \| 0.3112 \| 2.86 \| 1400 \| 0.4570 \| 0.4387 \| \| 0.3013 \| 3.06 \| 1500 \| 0.4437 \| 0.4147 \| \| 0.2136 \| 3.27 \| 1600 \| 0.4176 \| 0.4124 \| \| 0.2131 \| 3.47 \| 1700 \| 0.4281 \| 0.4194 \| \| 0.2099 \| 3.67 \| 1800 \| 0.3864 \| 0.3949 \| \| 0.1925 \| 3.88 \| 1900 \| 0.3926 \| 0.3913 \| \| 0.1709 \| 4.08 \| 2000 \| 0.3764 \| 0.3804 \| \| 0.1406 \| 4.29 \| 2100 \| 0.3787 \| 0.3742 \| \| 0.1342 \| 4.49 \| 2200 \| 0.3645 \| 0.3693 \| \| 0.1305 \| 4.69 \| 2300 \| 0.3463 \| 0.3625 \| \| 0.1298 \| 4.9 \| 2400 \| 0.3418 \| 0.3581 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
lgris/wav2vec2-large-xlsr-coraa-portuguese-cv7	b1331c0703c2bc32fbaf46d1e12d00d3e990e8b5	2022-02-10T23:22:48.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:mozilla-foundation/common_voice_7_0", "transformers", "generated_from_trainer", "pt", "robust-speech-event", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	lgris	null	lgris/wav2vec2-large-xlsr-coraa-portuguese-cv7	null	transformers	35,573	--- license: apache-2.0 tags: - generated_from_trainer - pt - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-large-xlsr-coraa-portuguese-cv7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-coraa-portuguese-cv7 This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1777 - Wer: 0.1339 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4779 \| 0.13 \| 100 \| 0.2620 \| 0.2020 \| \| 0.4505 \| 0.26 \| 200 \| 0.2339 \| 0.1998 \| \| 0.4285 \| 0.39 \| 300 \| 0.2507 \| 0.2109 \| \| 0.4148 \| 0.52 \| 400 \| 0.2311 \| 0.2101 \| \| 0.4072 \| 0.65 \| 500 \| 0.2278 \| 0.1899 \| \| 0.388 \| 0.78 \| 600 \| 0.2193 \| 0.1898 \| \| 0.3952 \| 0.91 \| 700 \| 0.2108 \| 0.1901 \| \| 0.3851 \| 1.04 \| 800 \| 0.2121 \| 0.1788 \| \| 0.3496 \| 1.17 \| 900 \| 0.2154 \| 0.1776 \| \| 0.3063 \| 1.3 \| 1000 \| 0.2095 \| 0.1730 \| \| 0.3376 \| 1.43 \| 1100 \| 0.2129 \| 0.1801 \| \| 0.3273 \| 1.56 \| 1200 \| 0.2132 \| 0.1776 \| \| 0.3347 \| 1.69 \| 1300 \| 0.2054 \| 0.1698 \| \| 0.323 \| 1.82 \| 1400 \| 0.1986 \| 0.1724 \| \| 0.3079 \| 1.95 \| 1500 \| 0.2005 \| 0.1701 \| \| 0.3029 \| 2.08 \| 1600 \| 0.2159 \| 0.1644 \| \| 0.2694 \| 2.21 \| 1700 \| 0.1992 \| 0.1678 \| \| 0.2733 \| 2.34 \| 1800 \| 0.2032 \| 0.1657 \| \| 0.269 \| 2.47 \| 1900 \| 0.2056 \| 0.1592 \| \| 0.2869 \| 2.6 \| 2000 \| 0.2058 \| 0.1616 \| \| 0.2813 \| 2.73 \| 2100 \| 0.1868 \| 0.1584 \| \| 0.2616 \| 2.86 \| 2200 \| 0.1841 \| 0.1550 \| \| 0.2809 \| 2.99 \| 2300 \| 0.1902 \| 0.1577 \| \| 0.2598 \| 3.12 \| 2400 \| 0.1910 \| 0.1514 \| \| 0.24 \| 3.25 \| 2500 \| 0.1971 \| 0.1555 \| \| 0.2481 \| 3.38 \| 2600 \| 0.1853 \| 0.1537 \| \| 0.2437 \| 3.51 \| 2700 \| 0.1897 \| 0.1496 \| \| 0.2384 \| 3.64 \| 2800 \| 0.1842 \| 0.1495 \| \| 0.2405 \| 3.77 \| 2900 \| 0.1884 \| 0.1500 \| \| 0.2372 \| 3.9 \| 3000 \| 0.1950 \| 0.1548 \| \| 0.229 \| 4.03 \| 3100 \| 0.1928 \| 0.1477 \| \| 0.2047 \| 4.16 \| 3200 \| 0.1891 \| 0.1472 \| \| 0.2102 \| 4.29 \| 3300 \| 0.1930 \| 0.1473 \| \| 0.199 \| 4.42 \| 3400 \| 0.1914 \| 0.1456 \| \| 0.2121 \| 4.55 \| 3500 \| 0.1840 \| 0.1437 \| \| 0.211 \| 4.67 \| 3600 \| 0.1843 \| 0.1403 \| \| 0.2072 \| 4.8 \| 3700 \| 0.1836 \| 0.1428 \| \| 0.2224 \| 4.93 \| 3800 \| 0.1747 \| 0.1412 \| \| 0.1974 \| 5.06 \| 3900 \| 0.1813 \| 0.1416 \| \| 0.1895 \| 5.19 \| 4000 \| 0.1869 \| 0.1406 \| \| 0.1763 \| 5.32 \| 4100 \| 0.1830 \| 0.1394 \| \| 0.2001 \| 5.45 \| 4200 \| 0.1775 \| 0.1394 \| \| 0.1909 \| 5.58 \| 4300 \| 0.1806 \| 0.1373 \| \| 0.1812 \| 5.71 \| 4400 \| 0.1784 \| 0.1359 \| \| 0.1737 \| 5.84 \| 4500 \| 0.1778 \| 0.1353 \| \| 0.1915 \| 5.97 \| 4600 \| 0.1777 \| 0.1349 \| \| 0.1921 \| 6.1 \| 4700 \| 0.1784 \| 0.1359 \| \| 0.1805 \| 6.23 \| 4800 \| 0.1757 \| 0.1348 \| \| 0.1742 \| 6.36 \| 4900 \| 0.1771 \| 0.1341 \| \| 0.1709 \| 6.49 \| 5000 \| 0.1777 \| 0.1339 \| ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
lgris/wavlm-large-CORAA-pt-cv7	abbdddf9b74b4637df78675b0e3a657c190a77bc	2022-02-10T23:16:09.000Z	[ "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "pt", "dataset:mozilla-foundation/common_voice_7_0", "transformers", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	lgris	null	lgris/wavlm-large-CORAA-pt-cv7	null	transformers	35,574	--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer - pt datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wavlm-large-CORAA-pt-cv7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wavlm-large-CORAA-pt-cv7 This model is a fine-tuned version of [lgris/WavLM-large-CORAA-pt](https://huggingface.co/lgris/WavLM-large-CORAA-pt) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2546 - Wer: 0.2261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.6029 \| 0.13 \| 100 \| 0.3679 \| 0.3347 \| \| 0.5297 \| 0.26 \| 200 \| 0.3516 \| 0.3227 \| \| 0.5134 \| 0.39 \| 300 \| 0.3327 \| 0.3167 \| \| 0.4941 \| 0.52 \| 400 \| 0.3281 \| 0.3122 \| \| 0.4816 \| 0.65 \| 500 \| 0.3154 \| 0.3102 \| \| 0.4649 \| 0.78 \| 600 \| 0.3199 \| 0.3058 \| \| 0.461 \| 0.91 \| 700 \| 0.3047 \| 0.2974 \| \| 0.4613 \| 1.04 \| 800 \| 0.3006 \| 0.2900 \| \| 0.4198 \| 1.17 \| 900 \| 0.2951 \| 0.2891 \| \| 0.3864 \| 1.3 \| 1000 \| 0.2989 \| 0.2862 \| \| 0.3963 \| 1.43 \| 1100 \| 0.2932 \| 0.2830 \| \| 0.3953 \| 1.56 \| 1200 \| 0.2936 \| 0.2829 \| \| 0.3962 \| 1.69 \| 1300 \| 0.2952 \| 0.2773 \| \| 0.3811 \| 1.82 \| 1400 \| 0.2915 \| 0.2748 \| \| 0.3736 \| 1.95 \| 1500 \| 0.2839 \| 0.2684 \| \| 0.3507 \| 2.08 \| 1600 \| 0.2914 \| 0.2678 \| \| 0.3277 \| 2.21 \| 1700 \| 0.2895 \| 0.2652 \| \| 0.3344 \| 2.34 \| 1800 \| 0.2843 \| 0.2673 \| \| 0.335 \| 2.47 \| 1900 \| 0.2821 \| 0.2635 \| \| 0.3559 \| 2.6 \| 2000 \| 0.2830 \| 0.2599 \| \| 0.3254 \| 2.73 \| 2100 \| 0.2711 \| 0.2577 \| \| 0.3263 \| 2.86 \| 2200 \| 0.2685 \| 0.2546 \| \| 0.3266 \| 2.99 \| 2300 \| 0.2679 \| 0.2521 \| \| 0.3066 \| 3.12 \| 2400 \| 0.2727 \| 0.2526 \| \| 0.2998 \| 3.25 \| 2500 \| 0.2648 \| 0.2537 \| \| 0.2961 \| 3.38 \| 2600 \| 0.2630 \| 0.2519 \| \| 0.3046 \| 3.51 \| 2700 \| 0.2684 \| 0.2506 \| \| 0.3006 \| 3.64 \| 2800 \| 0.2604 \| 0.2492 \| \| 0.2992 \| 3.77 \| 2900 \| 0.2682 \| 0.2508 \| \| 0.2775 \| 3.9 \| 3000 \| 0.2732 \| 0.2440 \| \| 0.2903 \| 4.03 \| 3100 \| 0.2659 \| 0.2427 \| \| 0.2535 \| 4.16 \| 3200 \| 0.2650 \| 0.2433 \| \| 0.2714 \| 4.29 \| 3300 \| 0.2588 \| 0.2394 \| \| 0.2636 \| 4.42 \| 3400 \| 0.2652 \| 0.2434 \| \| 0.2647 \| 4.55 \| 3500 \| 0.2624 \| 0.2371 \| \| 0.2796 \| 4.67 \| 3600 \| 0.2611 \| 0.2373 \| \| 0.2644 \| 4.8 \| 3700 \| 0.2604 \| 0.2341 \| \| 0.2657 \| 4.93 \| 3800 \| 0.2567 \| 0.2331 \| \| 0.2423 \| 5.06 \| 3900 \| 0.2594 \| 0.2322 \| \| 0.2556 \| 5.19 \| 4000 \| 0.2587 \| 0.2323 \| \| 0.2327 \| 5.32 \| 4100 \| 0.2639 \| 0.2299 \| \| 0.2613 \| 5.45 \| 4200 \| 0.2569 \| 0.2310 \| \| 0.2382 \| 5.58 \| 4300 \| 0.2585 \| 0.2298 \| \| 0.2404 \| 5.71 \| 4400 \| 0.2543 \| 0.2287 \| \| 0.2368 \| 5.84 \| 4500 \| 0.2553 \| 0.2286 \| \| 0.2514 \| 5.97 \| 4600 \| 0.2517 \| 0.2279 \| \| 0.2415 \| 6.1 \| 4700 \| 0.2524 \| 0.2270 \| \| 0.2338 \| 6.23 \| 4800 \| 0.2540 \| 0.2265 \| \| 0.219 \| 6.36 \| 4900 \| 0.2549 \| 0.2263 \| \| 0.2428 \| 6.49 \| 5000 \| 0.2546 \| 0.2261 \| ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
liaad/srl-enpt_xlmr-base	da8cf09e9d7ec9758e2531387b3003114cf9cd9b	2021-09-22T08:56:20.000Z	[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "pt", "en", "dataset:PropBank.Br", "dataset:CoNLL-2012", "arxiv:2101.01213", "transformers", "xlm-roberta-base", "semantic role labeling", "finetuned", "license:apache-2.0" ]	feature-extraction	false	liaad	null	liaad/srl-enpt_xlmr-base	null	transformers	35,575	--- language: - multilingual - pt - en tags: - xlm-roberta-base - semantic role labeling - finetuned license: apache-2.0 datasets: - PropBank.Br - CoNLL-2012 metrics: - F1 Measure --- # XLM-R base fine-tune in English and Portuguese semantic role labeling ## Model description This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-enpt_xlmr-base") model = AutoModel.from_pretrained("liaad/srl-enpt_xlmr-base") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). #### Limitations and bias - This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow. - The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data. ## Training procedure The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results \| Model Name \| F<sub>1</sub> CV PropBank.Br (in domain) \| F<sub>1</sub> Buscapé (out of domain) \| \| --------------- \| ------ \| ----- \| \| `srl-pt_bertimbau-base` \| 76.30 \| 73.33 \| \| `srl-pt_bertimbau-large` \| 77.42 \| 74.85 \| \| `srl-pt_xlmr-base` \| 75.22 \| 72.82 \| \| `srl-pt_xlmr-large` \| 77.59 \| 73.84 \| \| `srl-pt_mbert-base` \| 72.76 \| 66.89 \| \| `srl-en_xlmr-base` \| 66.59 \| 65.24 \| \| `srl-en_xlmr-large` \| 67.60 \| 64.94 \| \| `srl-en_mbert-base` \| 63.07 \| 58.56 \| \| `srl-enpt_xlmr-base` \| 76.50 \| 73.74 \| \| `srl-enpt_xlmr-large` \| 78.22 \| 74.55 \| \| `srl-enpt_mbert-base` \| 74.88 \| 69.19 \| \| `ud_srl-pt_bertimbau-large` \| 77.53 \| 74.49 \| \| `ud_srl-pt_xlmr-large` \| 77.69 \| 74.91 \| \| `ud_srl-enpt_xlmr-large` \| 77.97 \| 75.05 \| ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
liaad/srl-enpt_xlmr-large	25990972fccbf1783c9ffad016cb7fa19c2f6e73	2021-09-22T08:56:23.000Z	[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "pt", "en", "dataset:PropBank.Br", "dataset:CoNLL-2012", "arxiv:2101.01213", "transformers", "xlm-roberta-large", "semantic role labeling", "finetuned", "license:apache-2.0" ]	feature-extraction	false	liaad	null	liaad/srl-enpt_xlmr-large	null	transformers	35,576	--- language: - multilingual - pt - en tags: - xlm-roberta-large - semantic role labeling - finetuned license: apache-2.0 datasets: - PropBank.Br - CoNLL-2012 metrics: - F1 Measure --- # XLM-R large fine-tuned in English and Portuguese semantic role labeling ## Model description This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-enpt_xlmr-large") model = AutoModel.from_pretrained("liaad/srl-enpt_xlmr-large") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). #### Limitations and bias - This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow. - The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data. ## Training procedure The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results \| Model Name \| F<sub>1</sub> CV PropBank.Br (in domain) \| F<sub>1</sub> Buscapé (out of domain) \| \| --------------- \| ------ \| ----- \| \| `srl-pt_bertimbau-base` \| 76.30 \| 73.33 \| \| `srl-pt_bertimbau-large` \| 77.42 \| 74.85 \| \| `srl-pt_xlmr-base` \| 75.22 \| 72.82 \| \| `srl-pt_xlmr-large` \| 77.59 \| 73.84 \| \| `srl-pt_mbert-base` \| 72.76 \| 66.89 \| \| `srl-en_xlmr-base` \| 66.59 \| 65.24 \| \| `srl-en_xlmr-large` \| 67.60 \| 64.94 \| \| `srl-en_mbert-base` \| 63.07 \| 58.56 \| \| `srl-enpt_xlmr-base` \| 76.50 \| 73.74 \| \| `srl-enpt_xlmr-large` \| 78.22 \| 74.55 \| \| `srl-enpt_mbert-base` \| 74.88 \| 69.19 \| \| `ud_srl-pt_bertimbau-large` \| 77.53 \| 74.49 \| \| `ud_srl-pt_xlmr-large` \| 77.69 \| 74.91 \| \| `ud_srl-enpt_xlmr-large` \| 77.97 \| 75.05 \| ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
life4free96/DialogGPT-med-TeiaMoranta	ff21b49f9ea3598fd3650ea1da98cdb741b1f83b	2021-11-11T12:07:03.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	life4free96	null	life4free96/DialogGPT-med-TeiaMoranta	null	transformers	35,577	--- tags: - conversational --- #Teia Moranta
light/small-rickk	e1989ea7092f9666e7d923e7c85aa20e736c6ecf	2021-09-15T18:38:26.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	light	null	light/small-rickk	null	transformers	35,578	--- tags: - conversational --- #rick sanchez
lilitket/wav2vec2-large-xls-r-300m-turkish-colab	b66d79874e2ea37e805d29b53bb857ee011ef5df	2022-02-24T18:57:13.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	lilitket	null	lilitket/wav2vec2-large-xls-r-300m-turkish-colab	null	transformers	35,579	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7126 - Wer: 0.8198 ## 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 120 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 6.7419 \| 2.38 \| 200 \| 3.1913 \| 1.0 \| \| 3.0446 \| 4.76 \| 400 \| 2.3247 \| 1.0 \| \| 1.3163 \| 7.14 \| 600 \| 1.2629 \| 0.9656 \| \| 0.6058 \| 9.52 \| 800 \| 1.2203 \| 0.9343 \| \| 0.3687 \| 11.9 \| 1000 \| 1.2157 \| 0.8849 \| \| 0.2644 \| 14.29 \| 1200 \| 1.3693 \| 0.8992 \| \| 0.2147 \| 16.67 \| 1400 \| 1.3321 \| 0.8623 \| \| 0.1962 \| 19.05 \| 1600 \| 1.3476 \| 0.8886 \| \| 0.1631 \| 21.43 \| 1800 \| 1.3984 \| 0.8755 \| \| 0.15 \| 23.81 \| 2000 \| 1.4602 \| 0.8798 \| \| 0.1311 \| 26.19 \| 2200 \| 1.4727 \| 0.8836 \| \| 0.1174 \| 28.57 \| 2400 \| 1.5257 \| 0.8805 \| \| 0.1155 \| 30.95 \| 2600 \| 1.4697 \| 0.9337 \| \| 0.1046 \| 33.33 \| 2800 \| 1.6076 \| 0.8667 \| \| 0.1063 \| 35.71 \| 3000 \| 1.5012 \| 0.8861 \| \| 0.0996 \| 38.1 \| 3200 \| 1.6204 \| 0.8605 \| \| 0.088 \| 40.48 \| 3400 \| 1.4788 \| 0.8586 \| \| 0.089 \| 42.86 \| 3600 \| 1.5983 \| 0.8648 \| \| 0.0805 \| 45.24 \| 3800 \| 1.5045 \| 0.8298 \| \| 0.0718 \| 47.62 \| 4000 \| 1.6361 \| 0.8611 \| \| 0.0718 \| 50.0 \| 4200 \| 1.5088 \| 0.8548 \| \| 0.0649 \| 52.38 \| 4400 \| 1.5491 \| 0.8554 \| \| 0.0685 \| 54.76 \| 4600 \| 1.5939 \| 0.8442 \| \| 0.0588 \| 57.14 \| 4800 \| 1.6321 \| 0.8536 \| \| 0.0591 \| 59.52 \| 5000 \| 1.6468 \| 0.8442 \| \| 0.0529 \| 61.9 \| 5200 \| 1.6086 \| 0.8661 \| \| 0.0482 \| 64.29 \| 5400 \| 1.6622 \| 0.8517 \| \| 0.0396 \| 66.67 \| 5600 \| 1.6191 \| 0.8436 \| \| 0.0463 \| 69.05 \| 5800 \| 1.6231 \| 0.8661 \| \| 0.0415 \| 71.43 \| 6000 \| 1.6874 \| 0.8511 \| \| 0.0383 \| 73.81 \| 6200 \| 1.7054 \| 0.8411 \| \| 0.0411 \| 76.19 \| 6400 \| 1.7073 \| 0.8486 \| \| 0.0346 \| 78.57 \| 6600 \| 1.7137 \| 0.8342 \| \| 0.0318 \| 80.95 \| 6800 \| 1.6523 \| 0.8329 \| \| 0.0299 \| 83.33 \| 7000 \| 1.6893 \| 0.8579 \| \| 0.029 \| 85.71 \| 7200 \| 1.7162 \| 0.8429 \| \| 0.025 \| 88.1 \| 7400 \| 1.7589 \| 0.8529 \| \| 0.025 \| 90.48 \| 7600 \| 1.7581 \| 0.8398 \| \| 0.0232 \| 92.86 \| 7800 \| 1.8459 \| 0.8442 \| \| 0.0215 \| 95.24 \| 8000 \| 1.7942 \| 0.8448 \| \| 0.0222 \| 97.62 \| 8200 \| 1.6848 \| 0.8442 \| \| 0.0179 \| 100.0 \| 8400 \| 1.7223 \| 0.8298 \| \| 0.0176 \| 102.38 \| 8600 \| 1.7426 \| 0.8404 \| \| 0.016 \| 104.76 \| 8800 \| 1.7501 \| 0.8411 \| \| 0.0153 \| 107.14 \| 9000 \| 1.7185 \| 0.8235 \| \| 0.0136 \| 109.52 \| 9200 \| 1.7250 \| 0.8292 \| \| 0.0117 \| 111.9 \| 9400 \| 1.7159 \| 0.8185 \| \| 0.0123 \| 114.29 \| 9600 \| 1.7135 \| 0.8248 \| \| 0.0121 \| 116.67 \| 9800 \| 1.7189 \| 0.8210 \| \| 0.0116 \| 119.05 \| 10000 \| 1.7126 \| 0.8198 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
limivan/DialoGPT-small-c3po	bcc7b306c371668e90f83440dbbf67f6243b0a13	2021-08-27T12:40:06.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	limivan	null	limivan/DialoGPT-small-c3po	null	transformers	35,580	--- tags: - conversational --- #C3PO DialoGPT Model
lincoln/2021twitchfr-conv-bert-small-mlm-simcse	1e612dcc79b987a80bb69608ad2d2318d93b7042	2022-01-07T18:00:43.000Z	[ "pytorch", "convbert", "feature-extraction", "fr", "sentence-transformers", "sentence-similarity", "transformers", "twitch", "license:mit" ]	sentence-similarity	false	lincoln	null	lincoln/2021twitchfr-conv-bert-small-mlm-simcse	1	sentence-transformers	35,581	--- language: - fr license: mit pipeline_tag: sentence-similarity widget: - source_sentence: "Bonsoir" sentences: - "Salut !" - "Hello" - "Bonsoir!" - "Bonsouar!" - "Bonsouar !" - "De rien" - "LUL LUL" example_title: "Coucou" - source_sentence: "elle s'en sort bien" sentences: - "elle a raison" - "elle a tellement raison" - "Elle a pas tort" - "C'est bien ce qu'elle dit là" - "Hello" example_title: "Raison or not" - source_sentence: "et la question énergétique n'est pas politique ?" sentences: - "C'est le nucléaire militaire qui a entaché le nucléaire pour l'énergie." - "La fusion nucléaire c'est pas pour maintenant malheureusement" - "le pro nucléaire redevient acceptable à gauche j'ai l'impression" - "La mer à Nantes?" - "c'est bien un olivier pour l'upr" - "Moi je vois juste sa lavallière" example_title: "Nucléaire" tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - twitch - convbert --- ## Modèle de représentation d'un message Twitch à l'aide de ConvBERT Modèle [sentence-transformers](https://www.SBERT.net): cela permet de mapper une séquence de texte en un vecteur numérique de dimension 256 et peut être utilisé pour des tâches de clustering ou de recherche sémantique. L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire internet que cela implique (fautes, vocabulaire communautaires, abréviations, anglicisme, emotes, ...). Après avoir entrainé un modèle `ConvBert` puis `MLM` (cf section smodèles), nous avons entrainé un modèle _sentence-transformers_ à l'aide du framework d'apprentissage [SimCSE](https://www.sbert.net/examples/unsupervised_learning/SimCSE/README.html) en non supervisée. L'objectif est de spécialiser la moyenne des tokens _CLS_ de chaque token de la séquence pour représenter un vecteur numérique cohérent avec l'ensemble du corpus. _SimCSE_ crée fictivement des exemples positifs et négatifs supervisées à l'aide du dropout pour revenir à une tâche classique. _Nous garantissons pas la stabilité du modèle sur le long terme. Modèle réalisé dans le cadre d'un POC._ ## 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('2021twitchfr-conv-bert-small-mlm-simcse') embeddings = model.encode(sentences) print(embeddings) ``` ## Semantic Textual Similarity ```python from sentence_transformers import SentenceTransformer, models, util # Two lists of sentences sentences1 = ['zackFCZack', 'Team bons petits plats', 'sa commence a quelle heure de base popcorn ?', 'BibleThump'] sentences2 = ['zack titulaire', 'salade de pates c une dinguerie', 'ça commence à être long la', 'NotLikeThis'] # Compute embedding for both lists embeddings1 = model.encode(sentences1, convert_to_tensor=True) embeddings2 = model.encode(sentences2, convert_to_tensor=True) # Compute cosine-similarits cosine_scores = util.cos_sim(embeddings1, embeddings2) # Output the pairs with their score for i in range(len(sentences1)): print("Score: {:.4f} \| \"{}\" -vs- \"{}\" ".format(cosine_scores[i][i], sentences1[i], sentences2[i])) # Score: 0.5783 \| "zackFCZack" -vs- "zack titulaire" # Score: 0.2881 \| "Team bons petits plats" -vs- "salade de pates c une dinguerie" # Score: 0.4529 \| "sa commence a quelle heure de base popcorn ?" -vs- "ça commence à être long la" # Score: 0.5805 \| "BibleThump" -vs- "NotLikeThis" ``` ## Entrainement * 500 000 messages twitchs échantillonnés (cf description données des modèles de bases) * Batch size: 24 * Epochs: 24 * Loss: MultipleNegativesRankingLoss _A noter:_ * _ConvBert a été entrainé avec un longueur de 128 tokens max, mais est utilisé pour 512 dans ce modèle. Pas de problème._ * _La loss d'apprentissage n'est pas encore disponible: peu de visibilité sur les performances._ L'ensemble du code d'entrainement sur le github public [lincoln/twitchatds](https://github.com/Lincoln-France/twitchatds). ## Application: Nous avons utilisé une approche détournée de [BERTopic](https://maartengr.github.io/BERTopic/) pour réaliser un clustering d'un stream en prenant en compte la dimension temporelle: i.e. le nombre de seconde écoulée depuis le début du stream. ![approche_bertopic_lincoln](assets/approche_lincoln_topic_clustering_twitch.jpg) Globalement, l'approche donnes des résultats satisfaisant pour identifier des messages dit "similaires" récurrents. L'approche en revanche est fortement influencée par la ponctuation et la structure d'un message. Cela est largement explicable par le manque d'entrainement de l'ensemble des modèles et une volumétrie faible. ### Clustering émission "Backseat": Entre 19h30 et 20h00: ![1930_2000](./assets/scale_600_1930_2000.png) 🎞️ en vidéo: [youtu.be/EcjvlE9aTls](https://youtu.be/EcjvlE9aTls) ### Exemple regroupement émission "PopCorn": ```txt -------------------- LABEL 106 -------------------- circus (0.88)/sulli (0.23)/connu (0.19)/jure (0.12)/aime (0.11) silouhette moyenne: 0.04 -------------------- LABEL 106 -------------------- 2021-03-30 20:10:22 0.01: les gosse c est des animaux 2021-03-30 20:12:11 -0.03: oue c connu 2021-03-30 20:14:15 0.03: oh le circus !! <3 2021-03-30 20:14:19 0.12: le circus l'anciennnee 2021-03-30 20:14:22 0.06: jure le circus ! 2021-03-30 20:14:27 -0.03: le sulli 2021-03-30 20:14:31 0.09: le circus??? j'aime po 2021-03-30 20:14:34 0.11: le Circus, hors de prix ! 2021-03-30 20:14:35 -0.09: le Paddock a Rignac en Aveyron 2021-03-30 20:14:39 0.11: le circus >< 2021-03-30 20:14:39 0.04: le Titty Twister de Besançon -------------------- LABEL 17 -------------------- pates (0.12)/riz (0.09)/pâtes (0.09)/salade (0.07)/emission (0.07) silouhette moyenne: -0.05 -------------------- LABEL 17 -------------------- 2021-03-30 20:11:18 -0.03: Des nanimaux trop beaux ! 2021-03-30 20:13:11 -0.01: episode des simpsons ça... 2021-03-30 20:13:41 -0.01: des le debut d'emission ca tue mdrrrrr 2021-03-30 20:13:50 0.03: des "lasagnes" 2021-03-30 20:14:37 -0.18: poubelle la vie 2021-03-30 20:15:13 0.03: Une omelette 2021-03-30 20:15:35 -0.19: salade de bite 2021-03-30 20:15:36 -0.00: hahaha ce gastronome 2021-03-30 20:15:43 -0.08: salade de pates c une dinguerie 2021-03-30 20:17:00 -0.11: Une bonne femme ! 2021-03-30 20:17:06 -0.05: bouffe des graines 2021-03-30 20:17:08 -0.06: des pokeball ? 2021-03-30 20:17:11 -0.12: le choux fleur cru 2021-03-30 20:17:15 0.05: des pockeball ? 2021-03-30 20:17:27 -0.00: du chou fleur crue 2021-03-30 20:17:36 -0.09: un râgout de Meynia !!!! 2021-03-30 20:17:43 -0.07: une line up Sa rd o ch Zack Ponce my dream 2021-03-30 20:17:59 -0.10: Pâtes/10 2021-03-30 20:18:09 -0.05: Team bons petits plats 2021-03-30 20:18:13 -0.10: pate level 2021-03-30 20:18:19 -0.03: que des trucs très basiques 2021-03-30 20:18:24 0.03: des pates et du jambon c'est de la cuisine? 2021-03-30 20:18:30 0.05: Des pates et du riz ouai 2021-03-30 20:18:37 -0.02: des gnocchis à la poele c'est cuisiner ? 2021-03-30 20:18:50 -0.03: Pâtes à pizzas, pulled pork, carbonade flamande, etc.. 2021-03-30 20:19:01 -0.11: Des pâtes ou du riz ça compte ? 2021-03-30 20:19:22 -0.21: le noob 2021-03-30 20:19:47 -0.02: Une bonne escalope de milanaise les gars 2021-03-30 20:20:05 -0.04: faites des gratins et des quiches -------------------- LABEL 67 -------------------- 1 1 (0.25)/1 (0.19)/ (0.0)/ (0.0)/ (0.0) silouhette moyenne: 0.96 -------------------- LABEL 67 -------------------- 2021-03-30 20:24:17 0.94: +1 2021-03-30 20:24:37 0.97: +1 2021-03-30 20:24:37 0.97: +1 2021-03-30 20:24:38 0.97: +1 2021-03-30 20:24:39 0.97: +1 2021-03-30 20:24:43 0.97: +1 2021-03-30 20:24:44 0.97: +1 2021-03-30 20:24:47 0.97: +1 2021-03-30 20:24:49 0.97: +1 2021-03-30 20:25:00 0.97: +1 2021-03-30 20:25:21 0.95: +1 2021-03-30 20:25:25 0.95: +1 2021-03-30 20:25:28 0.94: +1 2021-03-30 20:25:30 0.94: +1 ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ConvBertModel (1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Modèles: * [2021twitchfr-conv-bert-small](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small) * [2021twitchfr-conv-bert-small-mlm](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm) * [2021twitchfr-conv-bert-small-mlm-simcse](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm-simcse)
linyi/dummy-model	d254ef8a8bdb3e86752fc45c0d8ce9995c23fb82	2021-11-07T00:42:27.000Z	[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	linyi	null	linyi/dummy-model	null	transformers	35,582	Entry not found
lkh4317/gpt2_fairy_tale	9072d3366c57083192390f449429a234628a8aee	2022-02-02T23:19:25.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	lkh4317	null	lkh4317/gpt2_fairy_tale	null	transformers	35,583	Entry not found
logicbloke/wav2vec2-large-xlsr-53-arabic	e0ab8005d9072404d8768d16c35c030519acd5e0	2021-07-06T10:09:12.000Z	[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	logicbloke	null	logicbloke/wav2vec2-large-xlsr-53-arabic	null	transformers	35,584	Entry not found
logube/DialogGPT_small_harrypotter	d318f5c4d2cae946034ea8531f43217b48a56c22	2021-08-27T23:18:34.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	logube	null	logube/DialogGPT_small_harrypotter	null	transformers	35,585	--- tags: - conversational --- # harry potter DialogGPT Model
lonewanderer27/KeitaroBot	088feb8381efb70f435363bea297a7c19c7b483e	2022-02-12T16:15:10.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	lonewanderer27	null	lonewanderer27/KeitaroBot	null	transformers	35,586	--- tags: - conversational --- # Camp Buddy - Keitaro - DialoGPTSmall Model
longcld/t5-base-squad-visquad-aqg	503405836758f1f9a44bd1b18ecb81510305f9a5	2021-09-08T01:36:55.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	longcld	null	longcld/t5-base-squad-visquad-aqg	null	transformers	35,587	Entry not found
longcld/t5-small-itranslate-visquad-aqg	c2b786e4f1db69d5a660db67876eb07711653300	2021-08-19T08:55:39.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	longcld	null	longcld/t5-small-itranslate-visquad-aqg	null	transformers	35,588	Entry not found
longcld/t5-small-squad-itranslate-aqg	33df45fbff5d865d417ddb101ea19268d4676d0f	2021-08-17T20:44:49.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	longcld	null	longcld/t5-small-squad-itranslate-aqg	null	transformers	35,589	Entry not found
longge/test	3188372dfc9687561e46291998e2656fff84d9e0	2021-11-02T06:36:03.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	longge	null	longge/test	null	transformers	35,590	Entry not found
longjuanfen/model700	88b475ba828919d78debe0f1b1303c694bc1ef12	2021-11-02T16:24:06.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	longjuanfen	null	longjuanfen/model700	null	transformers	35,591	Entry not found
longjuanfen/model701	aef1b4582976b7151e4a6242f5e81ad8d9213bdf	2021-11-03T17:23:57.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	longjuanfen	null	longjuanfen/model701	null	transformers	35,592	Entry not found
longnhit07/distilbert-base-uncased-finetuned-imdb	6fe0181a2aa074e52528005975958ec249b6613f	2022-01-10T09:02:05.000Z	[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	fill-mask	false	longnhit07	null	longnhit07/distilbert-base-uncased-finetuned-imdb	null	transformers	35,593	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-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: 2.4722 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7117 \| 1.0 \| 157 \| 2.4977 \| \| 2.5783 \| 2.0 \| 314 \| 2.4241 \| \| 2.5375 \| 3.0 \| 471 \| 2.4358 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
lovellyweather/DialoGPT-medium-johnny	9e30b225f829b6dde42881274a8d7c063b251817	2021-08-31T13:58:43.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	lovellyweather	null	lovellyweather/DialoGPT-medium-johnny	null	transformers	35,594	--- tags: - conversational --- # Johnny DialoGPT Model
lsy641/ESC_Blender_Strategy	72c5d30a57217f7b44b7dae6d95230241335ec04	2021-07-05T14:23:34.000Z	[ "pytorch" ]	null	false	lsy641	null	lsy641/ESC_Blender_Strategy	1	null	35,595	Entry not found
lsy641/ESC_Blender_noStrategy	793dfc0d06ce980674b226a745d6dffd09761a4c	2021-07-05T14:22:05.000Z	[ "pytorch" ]	null	false	lsy641	null	lsy641/ESC_Blender_noStrategy	null	null	35,596	Entry not found
ltrctelugu/ltrc-albert	4c5f82a4367c836af23a467350d5caf13cdbe819	2021-11-23T16:49:32.000Z	[ "pytorch", "albert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	ltrctelugu	null	ltrctelugu/ltrc-albert	null	transformers	35,597	hello
ltrctelugu/ltrc-roberta	2e5cff54823703dfe5600f733e9d80d0320cee19	2021-10-17T16:45:03.000Z	[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	ltrctelugu	null	ltrctelugu/ltrc-roberta	null	transformers	35,598	RoBERTa trained on 8.8 Million Telugu Sentences
lucasnobre212/description-test	5795790b439bbf641b2fc538fb1ee70741538f48	2021-12-29T15:17:08.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	lucasnobre212	null	lucasnobre212/description-test	null	transformers	35,599	Entry not found

kevinzyz/chinese_roberta_L-12_H-768-finetuned-MC-hyper

d9fbeaab01bb46ec60542e42fc1149e8e39bdbc9

2021-12-09T04:48:15.000Z

[ "pytorch", "bert", "multiple-choice", "transformers" ]

multiple-choice

false

kevinzyz

null

kevinzyz/chinese_roberta_L-12_H-768-finetuned-MC-hyper

0

null

transformers

35,500

Entry not found

kevinzyz/chinese_roberta_L-2_H-128-finetuned-MC-hyper

7c5490211a3b69e6297937f2ace5fbf4b9b87b17

2021-12-09T13:11:53.000Z

[ "pytorch", "bert", "multiple-choice", "transformers" ]

multiple-choice

false

kevinzyz

null

kevinzyz/chinese_roberta_L-2_H-128-finetuned-MC-hyper

0

null

transformers

35,501

Entry not found

khizon/greek-speech-emotion-classifier-demo

f214ac6debe63f935a812ef62603481410772912

2022-01-09T12:49:04.000Z

[ "pytorch", "wav2vec2", "transformers" ]

null

false

khizon

null

khizon/greek-speech-emotion-classifier-demo

0

null

transformers

35,502

Entry not found

kika2000/wav2vec2-large-xls-r-300m-kika3_my-colab

dec99539624deeaebb9f2315b40ed8bd38221549

2022-01-25T23:11:06.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

kika2000

null

kika2000/wav2vec2-large-xls-r-300m-kika3_my-colab

0

null

transformers

35,503

Entry not found

kika2000/wav2vec2-large-xls-r-300m-kika_my-colab

03143862fc5e7eb24e2198dbf32be7a57f47c5d6

2022-01-25T04:10:14.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

kika2000

null

kika2000/wav2vec2-large-xls-r-300m-kika_my-colab

0

null

transformers

35,504

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-kika_my-colab results: [] ---  # wav2vec2-large-xls-r-300m-kika_my-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.3300 - Wer: 0.5804 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.8067 | 4.82 | 400 | 1.2892 | 0.8886 | | 0.3048 | 9.64 | 800 | 1.2285 | 0.6797 | | 0.1413 | 14.46 | 1200 | 1.1970 | 0.6509 | | 0.1047 | 19.28 | 1600 | 1.3628 | 0.6166 | | 0.0799 | 24.1 | 2000 | 1.3345 | 0.6014 | | 0.0638 | 28.92 | 2400 | 1.3300 | 0.5804 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

kika2000/wav2vec2-large-xls-r-300m-test80_my-colab

b7a39f206ad59f0c37a86450b8e30c6707987647

2022-01-31T12:19:35.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

kika2000

null

kika2000/wav2vec2-large-xls-r-300m-test80_my-colab

0

null

transformers

35,505

Entry not found

kika2000/wav2vec2-large-xls-r-300m-test81_my-colab

4fd6a06af3ae843deccacc9b8308903eb2214381

2022-02-04T11:14:16.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

kika2000

null

kika2000/wav2vec2-large-xls-r-300m-test81_my-colab

0

null

transformers

35,506

Entry not found

kikumaru818/easy_algebra

c2e5c667c8e9d527213c1fa443a9bfdc2345c446

2021-11-29T00:37:27.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

kikumaru818

null

kikumaru818/easy_algebra

0

null

transformers

35,507

Entry not found

kiyoung2/dpr_p-encoder_roberta-small

fe440fea034a4085323c18ee5eef5558763096d5

2021-10-29T02:38:33.000Z

[ "pytorch", "roberta", "transformers" ]

null

false

kiyoung2

null

kiyoung2/dpr_p-encoder_roberta-small

0

null

transformers

35,508

Entry not found

kiyoung2/dpr_q-encoder_roberta-small

66437541310a6e3793a9328bcbab521f8439d507

2021-10-29T02:38:21.000Z

[ "pytorch", "roberta", "transformers" ]

null

false

kiyoung2

null

kiyoung2/dpr_q-encoder_roberta-small

0

null

transformers

35,509

Entry not found

kizunasunhy/fnet-base-finetuned-ner

8adc76990574cc21f9bbc12e8d4254f827de6ad2

2021-10-15T09:33:49.000Z

[ "pytorch", "fnet", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

kizunasunhy

null

kizunasunhy/fnet-base-finetuned-ner

0

null

transformers

35,510

Entry not found

kmfoda/output_dir

cb4ec67680c0b3f78f114901695306a31a7e563f

2022-02-01T11:06:56.000Z

[ "pytorch", "pegasus", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

kmfoda

null

kmfoda/output_dir

0

null

transformers

35,511

Entry not found

koala/bert-large-uncased-ko

0cb0428a1f08779ce778fe535d4ce91bea912270

2021-12-10T08:29:40.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

koala

null

koala/bert-large-uncased-ko

0

null

transformers

35,512

Entry not found

koala/xlm-roberta-large-bn

f7e8ea3f443589a5f5af64c9736ecab943f466ec

2022-01-05T13:05:16.000Z

[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

koala

null

koala/xlm-roberta-large-bn

0

null

transformers

35,513

Entry not found

koala/xlm-roberta-large-de

08dcb3ce3813f966a5ef6a290d03c1e828b1f078

2021-12-06T18:16:37.000Z

[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

koala

null

koala/xlm-roberta-large-de

0

null

transformers

35,514

Entry not found

koala/xlm-roberta-large-hi

018b51c821cddccd515436221a7432d3db0f8882

2021-12-21T12:58:05.000Z

[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

koala

null

koala/xlm-roberta-large-hi

0

null

transformers

35,515

Entry not found

koala/xlm-roberta-large-zh

cbb7d05a8c7243a658ce51dfe4bd28da19feb775

2021-12-06T18:26:29.000Z

[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

koala

null

koala/xlm-roberta-large-zh

0

null

transformers

35,516

Entry not found

korca/bert-base-mm-cased

6c72fa51242a8a17a4a9e94024f7699f2858e3a6

2021-09-15T07:41:23.000Z

[ "pytorch", "bert", "feature-extraction", "transformers" ]

feature-extraction

false

korca

null

korca/bert-base-mm-cased

0

null

transformers

35,517

Entry not found

korca/meaning-match-bert-base

8410e651f39d3ea55a401d75474e83cd401de3ba

2021-11-23T08:35:37.000Z

[ "pytorch", "bert", "feature-extraction", "transformers" ]

feature-extraction

false

korca

null

korca/meaning-match-bert-base

0

null

transformers

35,518

Entry not found

korca/meaning-match-bert-large

8a3eaeb1ba7708e0dcd7a954f35b9445b767d0ea

2021-11-18T17:52:44.000Z

[ "pytorch", "bert", "feature-extraction", "transformers" ]

feature-extraction

false

korca

null

korca/meaning-match-bert-large

0

null

transformers

35,519

Entry not found

korca/meaning-match-electra-large

81b94142c53bb4a3b4d0f8e24592f346aa1e3f91

2021-11-29T08:40:30.000Z

[ "pytorch", "electra", "feature-extraction", "transformers" ]

feature-extraction

false

korca

null

korca/meaning-match-electra-large

0

null

transformers

35,520

Entry not found

korca/roberta-base-mm

c04ee9a2161fd67746f2e269250c1ac9b35cf4ca

2021-09-14T07:46:00.000Z

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

false

korca

null

korca/roberta-base-mm

0

null

transformers

35,521

Entry not found

kp17/DialoGPT-small-tonystark

9107fc295d77cb38eeca18513c88d3be8ad0e1af

2021-08-27T06:44:11.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "Conversational" ]

text-generation

false

kp17

null

kp17/DialoGPT-small-tonystark

0

null

transformers

35,522

--- tags: - Conversational --- # Tony Stark DialoGPT Model

kr0n0s/AssameseBert

fdebb1792a5f876cf41f4955fb0327ede381bb9b

2021-07-29T20:24:48.000Z

[ "pytorch", "bert", "transformers" ]

null

false

kr0n0s

null

kr0n0s/AssameseBert

0

null

transformers

35,523

Entry not found

krevas/finance-electra-small-discriminator

139aedb657a7a7d8fc9024d4bc93346fbb8302cc

2020-07-09T05:46:38.000Z

[ "pytorch", "electra", "pretraining", "transformers" ]

null

false

krevas

null

krevas/finance-electra-small-discriminator

0

null

transformers

35,524

Entry not found

kris/DialoGPT-small-spock4

eb11e5daa35ffee8b279a529c0b0b9cefe46c0c3

2021-09-23T14:16:55.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

kris

null

kris/DialoGPT-small-spock4

0

null

transformers

35,525

--- tags: - conversational --- #Spock model

kris/DialoGPT-small-spock5

bf3db76f28871513f4b28ceeb22000d81eeb8802

2021-09-23T15:12:30.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

kris

null

kris/DialoGPT-small-spock5

0

null

transformers

35,526

--- tags: - conversational --- #Spock model

krupine/telectra-discriminator

6900ceca63aeb79f9cecd817ce4c2568d6ab45ef

2021-01-22T08:41:00.000Z

[ "pytorch", "electra", "pretraining", "transformers" ]

null

false

krupine

null

krupine/telectra-discriminator

0

null

transformers

35,527

Entry not found

kshitiz/testing-bot-repo

7ffcf35a9318f6c9767547481afcf3bb1a545509

2021-11-09T06:58:33.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

kshitiz

null

kshitiz/testing-bot-repo

0

null

transformers

35,528

--- tags: - conversational --- #testing bot Model

ksinar/DialoGPT-small-morty

b784f81dda6602e541b82686245eff8e810dc9da

2021-08-28T14:40:58.000Z

[ "pytorch" ]

null

false

ksinar

null

ksinar/DialoGPT-small-morty

0

null

35,529

Entry not found

kumakino/fairy-tale-gpt2-small

5b672e6d77bbf42ce54be3790acd7b620428c8b3

2021-12-13T23:07:26.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

kumakino

null

kumakino/fairy-tale-gpt2-small

0

null

transformers

35,530

Entry not found

kunalbhargava/DialoGPT-small-housebot

20299e2aa368dbb004427976aaffa6829877de9d

2021-11-11T09:00:23.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

kunalbhargava

null

kunalbhargava/DialoGPT-small-housebot

0

null

transformers

35,531

--- tags: - conversational --- #House BOT

kvothe28/DiabloGPT-small-Rick

50535a92cb645b553224cacfa17c2fc1f124eed4

2021-09-03T21:16:54.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

kvothe28

null

kvothe28/DiabloGPT-small-Rick

0

null

transformers

35,532

--- tags: - conversational --- # Rick DiabloGPT Model

kwang2049/SBERT-base-nli-stsb-v2

b97422efcaadd383da9a33f423e33271aa8d2047

2021-08-30T13:30:32.000Z

[ "pytorch" ]

null

false

kwang2049

null

kwang2049/SBERT-base-nli-stsb-v2

0

null

35,533

Entry not found

kwang2049/SBERT-base-nli-v2

f38abd2c15aacf7a3993754e72d4f1d0bb6b7843

2021-08-30T13:29:35.000Z

[ "pytorch" ]

null

false

kwang2049

null

kwang2049/SBERT-base-nli-v2

0

null

35,534

Entry not found

kwang2049/TSDAE-askubuntu

a7c794a288383693c964479710f519b30ae8321e

2021-10-25T16:17:47.000Z

[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]

feature-extraction

false

kwang2049

null

kwang2049/TSDAE-askubuntu

0

null

transformers

35,535

# kwang2049/TSDAE-askubuntu2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on AskUbuntu with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'askubuntu' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'askubuntu' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```

kwang2049/TSDAE-cqadupstack

9e5000f269f165a79392a858ee60653bc2cb634f

2021-10-25T16:18:29.000Z

[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]

feature-extraction

false

kwang2049

null

kwang2049/TSDAE-cqadupstack

0

null

transformers

35,536

# kwang2049/TSDAE-cqadupstack2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on cqadupstack with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'cqadupstack' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'cqadupstack' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```

kwang2049/TSDAE-scidocs

d2f136c580be5d551232344d1f62b3ccec264d02

2021-10-25T16:19:04.000Z

[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]

feature-extraction

false

kwang2049

null

kwang2049/TSDAE-scidocs

0

null

transformers

35,537

# kwang2049/TSDAE-scidocs2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on scidocs with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```

kwang2049/TSDAE-scidocs2nli_stsb

425ea67713d31c033f16c5e5a17a80de1ba7cc5a

2021-10-25T16:15:23.000Z

[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]

feature-extraction

false

kwang2049

null

kwang2049/TSDAE-scidocs2nli_stsb

0

null

transformers

35,538

# kwang2049/TSDAE-scidocs2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on scidocs with the TSDAE objective; 3. Supervised training on the NLI data with cross-entropy loss; 4. Supervised training on the STSb data with MSE loss. The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'scidocs' model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```

kwang2049/TSDAE-twitterpara

ae49bd6bb98ef1d586a6aee2a345b52a526749e8

2021-10-25T16:18:44.000Z

[ "pytorch", "bert", "feature-extraction", "arxiv:2104.06979", "transformers" ]

feature-extraction

false

kwang2049

null

kwang2049/TSDAE-twitterpara

0

null

transformers

35,539

# kwang2049/TSDAE-twitterpara2nli_stsb This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Training procedure of this model: 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased); 2. Unsupervised training on twitterpara with the TSDAE objective; The pooling method is CLS-pooling. ## Usage To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via: ```bash pip install sentence-transformers ``` And then load the model and use it to encode sentences: ```python from sentence_transformers import SentenceTransformer, models dataset = 'twitterpara' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.']) ``` ## Evaluation To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb): ```bash pip install useb # Or git clone and pip install . python -m useb.downloading all # Download both training and evaluation data ``` And then do the evaluation: ```python from sentence_transformers import SentenceTransformer, models import torch from useb import run_on dataset = 'twitterpara' model_name_or_path = f'kwang2049/TSDAE-{dataset}' model = SentenceTransformer(model_name_or_path) model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls') # Note this model uses CLS-pooling @torch.no_grad() def semb_fn(sentences) -> torch.Tensor: return torch.Tensor(model.encode(sentences, show_progress_bar=False)) result = run_on( dataset, semb_fn=semb_fn, eval_type='test', data_eval_path='data-eval' ) ``` ## Training Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers. ## Cite & Authors If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979): ```bibtex @article{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.06979", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.06979", } ```

l53513955/False_Entity_Identifier

a259b8994fdb142623f147b2ca6733ac76082492

2021-12-20T05:48:31.000Z

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

l53513955

null

l53513955/False_Entity_Identifier

0

null

transformers

35,540

Entry not found

lagodw/plotly_gpt2_large

66d42bca940e070d2d846457bbf784d101dc8dd9

2021-10-06T22:33:45.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

lagodw

null

lagodw/plotly_gpt2_large

0

null

transformers

35,541

Entry not found

lagodw/reddit_bert

1a706487954953e865e6652a5030051d57105bab

2021-09-04T19:12:32.000Z

[ "pytorch", "bert", "next-sentence-prediction", "transformers" ]

null

false

lagodw

null

lagodw/reddit_bert

0

1

transformers

35,542

Entry not found

lalopey/benn_eifert

a7f2d28e0b18b8fb490ff783e77ad9e557577bfd

2021-05-23T06:25:18.000Z

[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]

text-generation

false

lalopey

null

lalopey/benn_eifert

0

null

transformers

35,543

Entry not found

lapacc33/DialoGPT-medium-rick

c358130c33a6a9d51ecbcabe43ace1a64bd6bb98

2021-10-29T05:39:42.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

lapacc33

null

lapacc33/DialoGPT-medium-rick

0

null

transformers

35,544

--- tags: - conversational --- # Rick DialoGPT Model

larcane/kogpt2-cat-diary

d4dbb048874c85408313922dd78e7c9b867312ed

2021-12-18T15:45:25.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

larcane

null

larcane/kogpt2-cat-diary

0

null

transformers

35,545

Entry not found

laugustyniak/roberta-polish-web-embedding-v1

ea9478a6bd3a99efb45cea4cd9adb635b2f7df3f

2021-05-20T17:37:19.000Z

[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

laugustyniak

null

laugustyniak/roberta-polish-web-embedding-v1

0

null

transformers

35,546

Entry not found

laxya007/gpt2_BRM

fd100258c6e8867b5d81efa8befbc1933ab48d78

2021-10-23T08:23:11.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

laxya007

null

laxya007/gpt2_BRM

0

null

transformers

35,547

Entry not found

laxya007/gpt2_BSA_Leg_ipr_OE_OS

8cae9788ca2d14ed1a183231970bacde4fadfe4f

2021-06-18T08:40:06.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

laxya007

null

laxya007/gpt2_BSA_Leg_ipr_OE_OS

0

null

transformers

35,548

Entry not found

lbh020300/mymodel007

0d94893f4efee21622a8a660129f802193507171

2021-11-02T16:06:03.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

lbh020300

null

lbh020300/mymodel007

0

null

transformers

35,549

Entry not found

lee1jun/wav2vec2-base-100h-finetuned

2b6bcfc36d916596b911d93cd26fc92b28984023

2021-07-06T10:01:40.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

lee1jun

null

lee1jun/wav2vec2-base-100h-finetuned

0

null

transformers

35,550

Entry not found

leemii18/robustqa-baseline-02

245beb64ac2fe26658f32a30cbca6ebc5118901c

2021-05-05T17:47:41.000Z

[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

false

leemii18

null

leemii18/robustqa-baseline-02

0

null

transformers

35,551

Entry not found

lewtun/dummy-translation

c2d443e0dccb8040e1f4520aec93db447df7b2d8

2021-07-13T12:43:13.000Z

[ "pytorch", "marian", "text2text-generation", "transformers", "generated_from_trainer", "autotrain_compatible" ]

text2text-generation

false

lewtun

null

lewtun/dummy-translation

0

null

transformers

35,552

--- tags: - generated_from_trainer model_index: - name: dummy-translation results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation ---  # dummy-translation This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on an unkown 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: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3

lewtun/marian-finetuned-kde4-en-to-fr

e51aad09934acd4a5d0bb38d0686bedbab7840c4

2021-11-14T16:59:34.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:kde4", "transformers", "translation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

translation

false

lewtun

null

lewtun/marian-finetuned-kde4-en-to-fr

0

null

transformers

35,553

--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 args: en-fr metrics: - name: Bleu type: bleu value: 38.988820814501665 ---  # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 1.6772 - Bleu: 38.9888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

lewtun/metnet-test-2

e55e5fd8e175b9679cdcdc21416afff44475d861

2021-09-06T10:42:38.000Z

[ "pytorch", "transformers" ]

null

false

lewtun

null

lewtun/metnet-test-2

0

null

transformers

35,554

Entry not found

lewtun/metnet-test-3

0ff97bde804713a4ad00899d2862f74586306ba2

2021-09-06T10:53:04.000Z

[ "pytorch", "transformers", "autonlp", "evaluation", "benchmark" ]

null

false

lewtun

null

lewtun/metnet-test-3

0

null

transformers

35,555

--- tags: - autonlp - evaluation - benchmark --- # Model Card for MetNet

lewtun/metnet-test-5

12fe5921c2c4bef26138ec8d3d34b27c0ebd70bd

2021-09-06T11:01:50.000Z

[ "pytorch", "transformers", "satflow", "license:mit" ]

null

false

lewtun

null

lewtun/metnet-test-5

0

null

transformers

35,556

--- license: mit tags: - satflow --- # MetNet ## Model description [More information needed] ## Intended uses & limitations [More information needed] ## How to use [More information needed] ## Limitations and bias [More information needed] ## Training data [More information needed] ## Training procedure [More information needed] ## Evaluation results [More information needed]

lewtun/metnet-test

2cb8dbf9b3e001519cb4b6206f93cd62d9ded316

2021-09-06T09:22:37.000Z

[ "pytorch" ]

null

false

lewtun

null

lewtun/metnet-test

0

null

35,557

Entry not found

lewtun/minilm-finetuned-imdb-accelerate

da6f67e725230781b225a986e40c9e283bcb537e

2021-09-29T08:48:14.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

lewtun

null

lewtun/minilm-finetuned-imdb-accelerate

0

null

transformers

35,558

Entry not found

lewtun/mt5-finetuned-amazon-en-es-accelerate

dfe5bea91037602120869439cb6f63cd259c91e4

2021-11-11T15:12:52.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

lewtun

null

lewtun/mt5-finetuned-amazon-en-es-accelerate

0

null

transformers

35,559

Entry not found

lg/fexp_1

1ca4f9b9a7238ffa70bc0a46c3a72ea75f81fdf5

2021-05-20T23:37:11.000Z

[ "pytorch", "gpt_neo", "text-generation", "transformers" ]

text-generation

false

lg

null

lg/fexp_1

0

null

transformers

35,560

# This model is probably not what you're looking for.

lg/fexp_2

ae084e2eb00c3cba0af49853fc3694d321c8a4a6

2021-05-01T17:56:11.000Z

[ "pytorch", "gpt_neo", "text-generation", "transformers" ]

text-generation

false

lg

null

lg/fexp_2

0

null

transformers

35,561

Entry not found

lg/fexp_7

0f948d3e89789b81f90fbcf1b69aed53afa0269e

2021-05-03T05:27:39.000Z

[ "pytorch", "gpt_neo", "text-generation", "transformers" ]

text-generation

false

lg

null

lg/fexp_7

0

null

transformers

35,562

Entry not found

lg/fexp_8

e438f00780887eed802bcbf528b4e788760d0aaf

2021-05-02T16:58:34.000Z

[ "pytorch", "gpt_neo", "text-generation", "transformers" ]

text-generation

false

lg

null

lg/fexp_8

0

null

transformers

35,563

Entry not found

lg/ghpy_2k

14f17bce6bfdfd7e8217d599b125a4ac1dc32c3c

2021-05-14T16:27:41.000Z

[ "pytorch", "gpt_neo", "text-generation", "transformers" ]

text-generation

false

lg

null

lg/ghpy_2k

0

null

transformers

35,564

Entry not found

lg/ghpy_40k

8994fc0eae81882834ca1c11d7847efd2a9db012

2021-05-20T23:37:47.000Z

[ "pytorch" ]

null

false

lg

null

lg/ghpy_40k

0

null

35,565

# This model is probably not what you're looking for.

lgris/bp-commonvoice10-xlsr

163b0e6c4474f107e62193dae59882cfc73b537c

2021-11-27T21:02:56.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "transformers", "audio", "speech", "portuguese-speech-corpus", "PyTorch", "license:apache-2.0" ]

automatic-speech-recognition

false

lgris

null

lgris/bp-commonvoice10-xlsr

0

null

transformers

35,566

--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 --- # commonvoice10-xlsr: Wav2vec 2.0 with Common Voice Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Common Voice 7.0](https://commonvoice.mozilla.org/pt) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | | -- | 5.4h | | Common Voice | 37.8h | -- | 9.5h | | LaPS BM | | -- | 0.1h | | MLS | | -- | 3.7h | | Multilingual TEDx (Portuguese) | | -- | 1.8h | | SID | | -- | 1.0h | | VoxForge | | -- | 0.1h | | Total | | -- | 21.6h | #### Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | commonvoice10 (demonstration below) | 0.133 | 0.189 | 0.165 | 0.189 | 0.247 | 0.474 | 0.251 | 0.235 | | commonvoice10 + 4-gram (demonstration below) | 0.060 | 0.117 | 0.088 | 0.136 | 0.181 | 0.394 | 0.227 | 0.171 | ## Demonstration ```python MODEL_NAME = "lgris/commonvoice10-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.13291846056190185 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.18909733896486755 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.1655429292929293 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.1894711228284466 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.2471983709551264 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.4739658565194102 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.2510294913419914 ### Tests with LM ```python # !find -type f -name "*.wav" -delete !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.060609303416680915 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.11758415681158373 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.08815340909090909 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.1359966791836458 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.1818429601530829 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.39469326522731385 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.22779897186147183

lgris/bp-lapsbm1-xlsr

905303347f0caffc6a8b13abc00177eedbf9e4ce

2021-11-27T21:07:02.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "transformers", "audio", "speech", "portuguese-speech-corpus", "PyTorch", "license:apache-2.0" ]

automatic-speech-recognition

false

lgris

null

lgris/bp-lapsbm1-xlsr

0

null

transformers

35,567

--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 --- # lapsbm1-xlsr: Wav2vec 2.0 with LaPSBM Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [LaPS BM](https://github.com/falabrasil/gitlab-resources) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | | -- | 5.4h | | Common Voice | | -- | 9.5h | | LaPS BM | 0.8h | -- | 0.1h | | MLS | | -- | 3.7h | | Multilingual TEDx (Portuguese) | | -- | 1.8h | | SID | | -- | 1.0h | | VoxForge | | -- | 0.1h | | Total | | -- | 21.6h | #### Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | lapsbm1\_100 (demonstration below) | 0.111 | 0.418 | 0.145 | 0.299 | 0.562 | 0.580 | 0.469 | 0.369 | | lapsbm1\_100 + 4-gram (demonstration below) | 0.061 | 0.305 | 0.089 | 0.201 | 0.452 | 0.525 | 0.381 | 0.287 | ## Demonstration ```python MODEL_NAME = "lgris/lapsbm1-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.11147816967489037 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.41880890234535906 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.1451893939393939 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.29958960206171104 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.5626767414610376 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.5807549973642049 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.4693479437229436 ### Tests with LM ```python # !find -type f -name "*.wav" -delete !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.06157628194513477 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.3051714756833442 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.0893623737373737 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.20062044237806004 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.4522665618175908 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.5256707813182246 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.38106331168831165

lgris/bp-sid10-xlsr

4284d50d1d0b6561e63615dc1585d9425db2f03d

2021-11-27T21:09:42.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "transformers", "audio", "speech", "portuguese-speech-corpus", "PyTorch", "license:apache-2.0" ]

automatic-speech-recognition

false

lgris

null

lgris/bp-sid10-xlsr

0

null

transformers

35,568

--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 --- # sid10-xlsr: Wav2vec 2.0 with Sidney Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Sidney](https://igormq.github.io/datasets/) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | | -- | 5.4h | | Common Voice | | -- | 9.5h | | LaPS BM | | -- | 0.1h | | MLS | | -- | 3.7h | | Multilingual TEDx (Portuguese) | | -- | 1.8h | | SID | 7.2h | -- | 1.0h | | VoxForge | | -- | 0.1h | | Total | 7.2h| -- | 21.6h | #### Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | sid\_10 (demonstration below) |0.186 | 0.327 | 0.207 | 0.505 | 0.124 | 0.835 | 0.472 | 0.379| | sid\_10 + 4-gram (demonstration below) |0.096 | 0.223 | 0.115 | 0.432 | 0.101 | 0.791 | 0.348 | 0.301| ## Demonstration ```python MODEL_NAME = "lgris/sid10-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.18623689076557778 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.3279775395502392 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.20780303030303032 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.5056711598536057 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.1247776617710105 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.8350609256842175 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.47242153679653687 ### Tests with LM ```python # !find -type f -name "*.wav" -delete !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.09677271347353278 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.22363215674470321 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.1154924242424242 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.4322369152606427 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.10080313085145765 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.7911789829264236 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.34786255411255407

lgris/distilxlsr_bp_16-24

61102aab99832521692f62cdd8a5f9e4ac914047

2021-12-30T00:38:16.000Z

[ "pytorch", "wav2vec2", "feature-extraction", "pt", "arxiv:2110.01900", "transformers", "speech", "license:apache-2.0" ]

feature-extraction

false

lgris

null

lgris/distilxlsr_bp_16-24

0

null

transformers

35,569

--- language: pt tags: - speech license: apache-2.0 --- # DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee **Note 2**: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). **Abstract** Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.

lgris/distilxlsr_bp_4-12

61d69f4659aa8e30bce84daf6f9769f16dfcd68a

2021-12-30T00:38:04.000Z

[ "pytorch", "wav2vec2", "feature-extraction", "pt", "arxiv:2110.01900", "transformers", "speech", "license:apache-2.0" ]

feature-extraction

false

lgris

null

lgris/distilxlsr_bp_4-12

0

null

transformers

35,570

--- language: pt tags: - speech license: apache-2.0 --- # DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee **Note 2**: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). **Abstract** Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.

lgris/distilxlsr_bp_8-12-24

1af2b82d5420a218887d21a032cc37cbadd16842

2021-12-30T00:37:34.000Z

[ "pytorch", "wav2vec2", "feature-extraction", "pt", "arxiv:2110.01900", "transformers", "speech", "license:apache-2.0" ]

feature-extraction

false

lgris

null

lgris/distilxlsr_bp_8-12-24

0

null

transformers

35,571

--- language: pt tags: - speech license: apache-2.0 --- # DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee **Note 2**: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). **Abstract** Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.

lgris/wav2vec2-large-xls-r-300m-pt-cv

3906b54f22780a919093296fa94edf627b1926a3

2022-03-24T11:52:39.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "pt", "dataset:common_voice", "transformers", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

lgris

null

lgris/wav2vec2-large-xls-r-300m-pt-cv

0

null

transformers

35,572

--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - robust-speech-event - pt - hf-asr-leaderboard datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-pt-cv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 6 type: common_voice args: pt metrics: - name: Test WER type: wer value: 24.29 - name: Test CER type: cer value: 7.51 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - name: Test WER type: wer value: 55.72 - name: Test CER type: cer value: 21.82 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: pt metrics: - name: Test WER type: wer value: 47.88 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: pt metrics: - name: Test WER type: wer value: 50.78 ---  # wav2vec2-large-xls-r-300m-pt-cv 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: 0.3418 - Wer: 0.3581 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.9035 | 0.2 | 100 | 4.2750 | 1.0 | | 3.3275 | 0.41 | 200 | 3.0334 | 1.0 | | 3.0016 | 0.61 | 300 | 2.9494 | 1.0 | | 2.1874 | 0.82 | 400 | 1.4355 | 0.8721 | | 1.09 | 1.02 | 500 | 0.9987 | 0.7165 | | 0.8251 | 1.22 | 600 | 0.7886 | 0.6406 | | 0.6927 | 1.43 | 700 | 0.6753 | 0.5801 | | 0.6143 | 1.63 | 800 | 0.6300 | 0.5509 | | 0.5451 | 1.84 | 900 | 0.5586 | 0.5156 | | 0.5003 | 2.04 | 1000 | 0.5493 | 0.5027 | | 0.3712 | 2.24 | 1100 | 0.5271 | 0.4872 | | 0.3486 | 2.45 | 1200 | 0.4953 | 0.4817 | | 0.3498 | 2.65 | 1300 | 0.4619 | 0.4538 | | 0.3112 | 2.86 | 1400 | 0.4570 | 0.4387 | | 0.3013 | 3.06 | 1500 | 0.4437 | 0.4147 | | 0.2136 | 3.27 | 1600 | 0.4176 | 0.4124 | | 0.2131 | 3.47 | 1700 | 0.4281 | 0.4194 | | 0.2099 | 3.67 | 1800 | 0.3864 | 0.3949 | | 0.1925 | 3.88 | 1900 | 0.3926 | 0.3913 | | 0.1709 | 4.08 | 2000 | 0.3764 | 0.3804 | | 0.1406 | 4.29 | 2100 | 0.3787 | 0.3742 | | 0.1342 | 4.49 | 2200 | 0.3645 | 0.3693 | | 0.1305 | 4.69 | 2300 | 0.3463 | 0.3625 | | 0.1298 | 4.9 | 2400 | 0.3418 | 0.3581 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

lgris/wav2vec2-large-xlsr-coraa-portuguese-cv7

b1331c0703c2bc32fbaf46d1e12d00d3e990e8b5

2022-02-10T23:22:48.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:mozilla-foundation/common_voice_7_0", "transformers", "generated_from_trainer", "pt", "robust-speech-event", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

lgris

null

lgris/wav2vec2-large-xlsr-coraa-portuguese-cv7

0

null

transformers

35,573

--- license: apache-2.0 tags: - generated_from_trainer - pt - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-large-xlsr-coraa-portuguese-cv7 results: [] ---  # wav2vec2-large-xlsr-coraa-portuguese-cv7 This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1777 - Wer: 0.1339 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4779 | 0.13 | 100 | 0.2620 | 0.2020 | | 0.4505 | 0.26 | 200 | 0.2339 | 0.1998 | | 0.4285 | 0.39 | 300 | 0.2507 | 0.2109 | | 0.4148 | 0.52 | 400 | 0.2311 | 0.2101 | | 0.4072 | 0.65 | 500 | 0.2278 | 0.1899 | | 0.388 | 0.78 | 600 | 0.2193 | 0.1898 | | 0.3952 | 0.91 | 700 | 0.2108 | 0.1901 | | 0.3851 | 1.04 | 800 | 0.2121 | 0.1788 | | 0.3496 | 1.17 | 900 | 0.2154 | 0.1776 | | 0.3063 | 1.3 | 1000 | 0.2095 | 0.1730 | | 0.3376 | 1.43 | 1100 | 0.2129 | 0.1801 | | 0.3273 | 1.56 | 1200 | 0.2132 | 0.1776 | | 0.3347 | 1.69 | 1300 | 0.2054 | 0.1698 | | 0.323 | 1.82 | 1400 | 0.1986 | 0.1724 | | 0.3079 | 1.95 | 1500 | 0.2005 | 0.1701 | | 0.3029 | 2.08 | 1600 | 0.2159 | 0.1644 | | 0.2694 | 2.21 | 1700 | 0.1992 | 0.1678 | | 0.2733 | 2.34 | 1800 | 0.2032 | 0.1657 | | 0.269 | 2.47 | 1900 | 0.2056 | 0.1592 | | 0.2869 | 2.6 | 2000 | 0.2058 | 0.1616 | | 0.2813 | 2.73 | 2100 | 0.1868 | 0.1584 | | 0.2616 | 2.86 | 2200 | 0.1841 | 0.1550 | | 0.2809 | 2.99 | 2300 | 0.1902 | 0.1577 | | 0.2598 | 3.12 | 2400 | 0.1910 | 0.1514 | | 0.24 | 3.25 | 2500 | 0.1971 | 0.1555 | | 0.2481 | 3.38 | 2600 | 0.1853 | 0.1537 | | 0.2437 | 3.51 | 2700 | 0.1897 | 0.1496 | | 0.2384 | 3.64 | 2800 | 0.1842 | 0.1495 | | 0.2405 | 3.77 | 2900 | 0.1884 | 0.1500 | | 0.2372 | 3.9 | 3000 | 0.1950 | 0.1548 | | 0.229 | 4.03 | 3100 | 0.1928 | 0.1477 | | 0.2047 | 4.16 | 3200 | 0.1891 | 0.1472 | | 0.2102 | 4.29 | 3300 | 0.1930 | 0.1473 | | 0.199 | 4.42 | 3400 | 0.1914 | 0.1456 | | 0.2121 | 4.55 | 3500 | 0.1840 | 0.1437 | | 0.211 | 4.67 | 3600 | 0.1843 | 0.1403 | | 0.2072 | 4.8 | 3700 | 0.1836 | 0.1428 | | 0.2224 | 4.93 | 3800 | 0.1747 | 0.1412 | | 0.1974 | 5.06 | 3900 | 0.1813 | 0.1416 | | 0.1895 | 5.19 | 4000 | 0.1869 | 0.1406 | | 0.1763 | 5.32 | 4100 | 0.1830 | 0.1394 | | 0.2001 | 5.45 | 4200 | 0.1775 | 0.1394 | | 0.1909 | 5.58 | 4300 | 0.1806 | 0.1373 | | 0.1812 | 5.71 | 4400 | 0.1784 | 0.1359 | | 0.1737 | 5.84 | 4500 | 0.1778 | 0.1353 | | 0.1915 | 5.97 | 4600 | 0.1777 | 0.1349 | | 0.1921 | 6.1 | 4700 | 0.1784 | 0.1359 | | 0.1805 | 6.23 | 4800 | 0.1757 | 0.1348 | | 0.1742 | 6.36 | 4900 | 0.1771 | 0.1341 | | 0.1709 | 6.49 | 5000 | 0.1777 | 0.1339 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

lgris/wavlm-large-CORAA-pt-cv7

abbdddf9b74b4637df78675b0e3a657c190a77bc

2022-02-10T23:16:09.000Z

[ "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "pt", "dataset:mozilla-foundation/common_voice_7_0", "transformers", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

lgris

null

lgris/wavlm-large-CORAA-pt-cv7

0

null

transformers

35,574

--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer - pt datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wavlm-large-CORAA-pt-cv7 results: [] ---  # wavlm-large-CORAA-pt-cv7 This model is a fine-tuned version of [lgris/WavLM-large-CORAA-pt](https://huggingface.co/lgris/WavLM-large-CORAA-pt) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2546 - Wer: 0.2261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6029 | 0.13 | 100 | 0.3679 | 0.3347 | | 0.5297 | 0.26 | 200 | 0.3516 | 0.3227 | | 0.5134 | 0.39 | 300 | 0.3327 | 0.3167 | | 0.4941 | 0.52 | 400 | 0.3281 | 0.3122 | | 0.4816 | 0.65 | 500 | 0.3154 | 0.3102 | | 0.4649 | 0.78 | 600 | 0.3199 | 0.3058 | | 0.461 | 0.91 | 700 | 0.3047 | 0.2974 | | 0.4613 | 1.04 | 800 | 0.3006 | 0.2900 | | 0.4198 | 1.17 | 900 | 0.2951 | 0.2891 | | 0.3864 | 1.3 | 1000 | 0.2989 | 0.2862 | | 0.3963 | 1.43 | 1100 | 0.2932 | 0.2830 | | 0.3953 | 1.56 | 1200 | 0.2936 | 0.2829 | | 0.3962 | 1.69 | 1300 | 0.2952 | 0.2773 | | 0.3811 | 1.82 | 1400 | 0.2915 | 0.2748 | | 0.3736 | 1.95 | 1500 | 0.2839 | 0.2684 | | 0.3507 | 2.08 | 1600 | 0.2914 | 0.2678 | | 0.3277 | 2.21 | 1700 | 0.2895 | 0.2652 | | 0.3344 | 2.34 | 1800 | 0.2843 | 0.2673 | | 0.335 | 2.47 | 1900 | 0.2821 | 0.2635 | | 0.3559 | 2.6 | 2000 | 0.2830 | 0.2599 | | 0.3254 | 2.73 | 2100 | 0.2711 | 0.2577 | | 0.3263 | 2.86 | 2200 | 0.2685 | 0.2546 | | 0.3266 | 2.99 | 2300 | 0.2679 | 0.2521 | | 0.3066 | 3.12 | 2400 | 0.2727 | 0.2526 | | 0.2998 | 3.25 | 2500 | 0.2648 | 0.2537 | | 0.2961 | 3.38 | 2600 | 0.2630 | 0.2519 | | 0.3046 | 3.51 | 2700 | 0.2684 | 0.2506 | | 0.3006 | 3.64 | 2800 | 0.2604 | 0.2492 | | 0.2992 | 3.77 | 2900 | 0.2682 | 0.2508 | | 0.2775 | 3.9 | 3000 | 0.2732 | 0.2440 | | 0.2903 | 4.03 | 3100 | 0.2659 | 0.2427 | | 0.2535 | 4.16 | 3200 | 0.2650 | 0.2433 | | 0.2714 | 4.29 | 3300 | 0.2588 | 0.2394 | | 0.2636 | 4.42 | 3400 | 0.2652 | 0.2434 | | 0.2647 | 4.55 | 3500 | 0.2624 | 0.2371 | | 0.2796 | 4.67 | 3600 | 0.2611 | 0.2373 | | 0.2644 | 4.8 | 3700 | 0.2604 | 0.2341 | | 0.2657 | 4.93 | 3800 | 0.2567 | 0.2331 | | 0.2423 | 5.06 | 3900 | 0.2594 | 0.2322 | | 0.2556 | 5.19 | 4000 | 0.2587 | 0.2323 | | 0.2327 | 5.32 | 4100 | 0.2639 | 0.2299 | | 0.2613 | 5.45 | 4200 | 0.2569 | 0.2310 | | 0.2382 | 5.58 | 4300 | 0.2585 | 0.2298 | | 0.2404 | 5.71 | 4400 | 0.2543 | 0.2287 | | 0.2368 | 5.84 | 4500 | 0.2553 | 0.2286 | | 0.2514 | 5.97 | 4600 | 0.2517 | 0.2279 | | 0.2415 | 6.1 | 4700 | 0.2524 | 0.2270 | | 0.2338 | 6.23 | 4800 | 0.2540 | 0.2265 | | 0.219 | 6.36 | 4900 | 0.2549 | 0.2263 | | 0.2428 | 6.49 | 5000 | 0.2546 | 0.2261 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

liaad/srl-enpt_xlmr-base

da8cf09e9d7ec9758e2531387b3003114cf9cd9b

2021-09-22T08:56:20.000Z

[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "pt", "en", "dataset:PropBank.Br", "dataset:CoNLL-2012", "arxiv:2101.01213", "transformers", "xlm-roberta-base", "semantic role labeling", "finetuned", "license:apache-2.0" ]

feature-extraction

false

liaad

null

liaad/srl-enpt_xlmr-base

0

null

transformers

35,575

--- language: - multilingual - pt - en tags: - xlm-roberta-base - semantic role labeling - finetuned license: apache-2.0 datasets: - PropBank.Br - CoNLL-2012 metrics: - F1 Measure --- # XLM-R base fine-tune in English and Portuguese semantic role labeling ## Model description This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-enpt_xlmr-base") model = AutoModel.from_pretrained("liaad/srl-enpt_xlmr-base") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). #### Limitations and bias - This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow. - The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data. ## Training procedure The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results | Model Name | F1 CV PropBank.Br (in domain) | F1 Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84 | | `srl-pt_mbert-base` | 72.76 | 66.89 | | `srl-en_xlmr-base` | 66.59 | 65.24 | | `srl-en_xlmr-large` | 67.60 | 64.94 | | `srl-en_mbert-base` | 63.07 | 58.56 | | `srl-enpt_xlmr-base` | 76.50 | 73.74 | | `srl-enpt_xlmr-large` | **78.22** | 74.55 | | `srl-enpt_mbert-base` | 74.88 | 69.19 | | `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 | | `ud_srl-pt_xlmr-large` | 77.69 | 74.91 | | `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** | ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

liaad/srl-enpt_xlmr-large

25990972fccbf1783c9ffad016cb7fa19c2f6e73

2021-09-22T08:56:23.000Z

[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "pt", "en", "dataset:PropBank.Br", "dataset:CoNLL-2012", "arxiv:2101.01213", "transformers", "xlm-roberta-large", "semantic role labeling", "finetuned", "license:apache-2.0" ]

feature-extraction

false

liaad

null

liaad/srl-enpt_xlmr-large

0

null

transformers

35,576

--- language: - multilingual - pt - en tags: - xlm-roberta-large - semantic role labeling - finetuned license: apache-2.0 datasets: - PropBank.Br - CoNLL-2012 metrics: - F1 Measure --- # XLM-R large fine-tuned in English and Portuguese semantic role labeling ## Model description This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-enpt_xlmr-large") model = AutoModel.from_pretrained("liaad/srl-enpt_xlmr-large") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). #### Limitations and bias - This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow. - The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data. ## Training procedure The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results | Model Name | F1 CV PropBank.Br (in domain) | F1 Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84 | | `srl-pt_mbert-base` | 72.76 | 66.89 | | `srl-en_xlmr-base` | 66.59 | 65.24 | | `srl-en_xlmr-large` | 67.60 | 64.94 | | `srl-en_mbert-base` | 63.07 | 58.56 | | `srl-enpt_xlmr-base` | 76.50 | 73.74 | | `srl-enpt_xlmr-large` | **78.22** | 74.55 | | `srl-enpt_mbert-base` | 74.88 | 69.19 | | `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 | | `ud_srl-pt_xlmr-large` | 77.69 | 74.91 | | `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** | ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

life4free96/DialogGPT-med-TeiaMoranta

ff21b49f9ea3598fd3650ea1da98cdb741b1f83b

2021-11-11T12:07:03.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

life4free96

null

life4free96/DialogGPT-med-TeiaMoranta

0

null

transformers

35,577

--- tags: - conversational --- #Teia Moranta

light/small-rickk

e1989ea7092f9666e7d923e7c85aa20e736c6ecf

2021-09-15T18:38:26.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

light

null

light/small-rickk

0

null

transformers

35,578

--- tags: - conversational --- #rick sanchez

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

b66d79874e2ea37e805d29b53bb857ee011ef5df

2022-02-24T18:57:13.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

lilitket

null

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

0

null

transformers

35,579

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] ---  # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7126 - Wer: 0.8198 ## 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 120 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6.7419 | 2.38 | 200 | 3.1913 | 1.0 | | 3.0446 | 4.76 | 400 | 2.3247 | 1.0 | | 1.3163 | 7.14 | 600 | 1.2629 | 0.9656 | | 0.6058 | 9.52 | 800 | 1.2203 | 0.9343 | | 0.3687 | 11.9 | 1000 | 1.2157 | 0.8849 | | 0.2644 | 14.29 | 1200 | 1.3693 | 0.8992 | | 0.2147 | 16.67 | 1400 | 1.3321 | 0.8623 | | 0.1962 | 19.05 | 1600 | 1.3476 | 0.8886 | | 0.1631 | 21.43 | 1800 | 1.3984 | 0.8755 | | 0.15 | 23.81 | 2000 | 1.4602 | 0.8798 | | 0.1311 | 26.19 | 2200 | 1.4727 | 0.8836 | | 0.1174 | 28.57 | 2400 | 1.5257 | 0.8805 | | 0.1155 | 30.95 | 2600 | 1.4697 | 0.9337 | | 0.1046 | 33.33 | 2800 | 1.6076 | 0.8667 | | 0.1063 | 35.71 | 3000 | 1.5012 | 0.8861 | | 0.0996 | 38.1 | 3200 | 1.6204 | 0.8605 | | 0.088 | 40.48 | 3400 | 1.4788 | 0.8586 | | 0.089 | 42.86 | 3600 | 1.5983 | 0.8648 | | 0.0805 | 45.24 | 3800 | 1.5045 | 0.8298 | | 0.0718 | 47.62 | 4000 | 1.6361 | 0.8611 | | 0.0718 | 50.0 | 4200 | 1.5088 | 0.8548 | | 0.0649 | 52.38 | 4400 | 1.5491 | 0.8554 | | 0.0685 | 54.76 | 4600 | 1.5939 | 0.8442 | | 0.0588 | 57.14 | 4800 | 1.6321 | 0.8536 | | 0.0591 | 59.52 | 5000 | 1.6468 | 0.8442 | | 0.0529 | 61.9 | 5200 | 1.6086 | 0.8661 | | 0.0482 | 64.29 | 5400 | 1.6622 | 0.8517 | | 0.0396 | 66.67 | 5600 | 1.6191 | 0.8436 | | 0.0463 | 69.05 | 5800 | 1.6231 | 0.8661 | | 0.0415 | 71.43 | 6000 | 1.6874 | 0.8511 | | 0.0383 | 73.81 | 6200 | 1.7054 | 0.8411 | | 0.0411 | 76.19 | 6400 | 1.7073 | 0.8486 | | 0.0346 | 78.57 | 6600 | 1.7137 | 0.8342 | | 0.0318 | 80.95 | 6800 | 1.6523 | 0.8329 | | 0.0299 | 83.33 | 7000 | 1.6893 | 0.8579 | | 0.029 | 85.71 | 7200 | 1.7162 | 0.8429 | | 0.025 | 88.1 | 7400 | 1.7589 | 0.8529 | | 0.025 | 90.48 | 7600 | 1.7581 | 0.8398 | | 0.0232 | 92.86 | 7800 | 1.8459 | 0.8442 | | 0.0215 | 95.24 | 8000 | 1.7942 | 0.8448 | | 0.0222 | 97.62 | 8200 | 1.6848 | 0.8442 | | 0.0179 | 100.0 | 8400 | 1.7223 | 0.8298 | | 0.0176 | 102.38 | 8600 | 1.7426 | 0.8404 | | 0.016 | 104.76 | 8800 | 1.7501 | 0.8411 | | 0.0153 | 107.14 | 9000 | 1.7185 | 0.8235 | | 0.0136 | 109.52 | 9200 | 1.7250 | 0.8292 | | 0.0117 | 111.9 | 9400 | 1.7159 | 0.8185 | | 0.0123 | 114.29 | 9600 | 1.7135 | 0.8248 | | 0.0121 | 116.67 | 9800 | 1.7189 | 0.8210 | | 0.0116 | 119.05 | 10000 | 1.7126 | 0.8198 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

limivan/DialoGPT-small-c3po

bcc7b306c371668e90f83440dbbf67f6243b0a13

2021-08-27T12:40:06.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

limivan

null

limivan/DialoGPT-small-c3po

0

null

transformers

35,580

--- tags: - conversational --- #C3PO DialoGPT Model

lincoln/2021twitchfr-conv-bert-small-mlm-simcse

1e612dcc79b987a80bb69608ad2d2318d93b7042

2022-01-07T18:00:43.000Z

[ "pytorch", "convbert", "feature-extraction", "fr", "sentence-transformers", "sentence-similarity", "transformers", "twitch", "license:mit" ]

sentence-similarity

false

lincoln

null

lincoln/2021twitchfr-conv-bert-small-mlm-simcse

0

1

sentence-transformers

35,581

--- language: - fr license: mit pipeline_tag: sentence-similarity widget: - source_sentence: "Bonsoir" sentences: - "Salut !" - "Hello" - "Bonsoir!" - "Bonsouar!" - "Bonsouar !" - "De rien" - "LUL LUL" example_title: "Coucou" - source_sentence: "elle s'en sort bien" sentences: - "elle a raison" - "elle a tellement raison" - "Elle a pas tort" - "C'est bien ce qu'elle dit là" - "Hello" example_title: "Raison or not" - source_sentence: "et la question énergétique n'est pas politique ?" sentences: - "C'est le nucléaire militaire qui a entaché le nucléaire pour l'énergie." - "La fusion nucléaire c'est pas pour maintenant malheureusement" - "le pro nucléaire redevient acceptable à gauche j'ai l'impression" - "La mer à Nantes?" - "c'est bien un olivier pour l'upr" - "Moi je vois juste sa lavallière" example_title: "Nucléaire" tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - twitch - convbert --- ## Modèle de représentation d'un message Twitch à l'aide de ConvBERT Modèle [sentence-transformers](https://www.SBERT.net): cela permet de mapper une séquence de texte en un vecteur numérique de dimension 256 et peut être utilisé pour des tâches de clustering ou de recherche sémantique. L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire internet que cela implique (fautes, vocabulaire communautaires, abréviations, anglicisme, emotes, ...). Après avoir entrainé un modèle `ConvBert` puis `MLM` (cf section smodèles), nous avons entrainé un modèle _sentence-transformers_ à l'aide du framework d'apprentissage [SimCSE](https://www.sbert.net/examples/unsupervised_learning/SimCSE/README.html) en non supervisée. L'objectif est de spécialiser la moyenne des tokens _CLS_ de chaque token de la séquence pour représenter un vecteur numérique cohérent avec l'ensemble du corpus. _SimCSE_ crée fictivement des exemples positifs et négatifs supervisées à l'aide du dropout pour revenir à une tâche classique. _Nous garantissons pas la stabilité du modèle sur le long terme. Modèle réalisé dans le cadre d'un POC._ ## 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('2021twitchfr-conv-bert-small-mlm-simcse') embeddings = model.encode(sentences) print(embeddings) ``` ## Semantic Textual Similarity ```python from sentence_transformers import SentenceTransformer, models, util # Two lists of sentences sentences1 = ['zackFCZack', 'Team bons petits plats', 'sa commence a quelle heure de base popcorn ?', 'BibleThump'] sentences2 = ['zack titulaire', 'salade de pates c une dinguerie', 'ça commence à être long la', 'NotLikeThis'] # Compute embedding for both lists embeddings1 = model.encode(sentences1, convert_to_tensor=True) embeddings2 = model.encode(sentences2, convert_to_tensor=True) # Compute cosine-similarits cosine_scores = util.cos_sim(embeddings1, embeddings2) # Output the pairs with their score for i in range(len(sentences1)): print("Score: {:.4f} | \"{}\" -vs- \"{}\" ".format(cosine_scores[i][i], sentences1[i], sentences2[i])) # Score: 0.5783 | "zackFCZack" -vs- "zack titulaire" # Score: 0.2881 | "Team bons petits plats" -vs- "salade de pates c une dinguerie" # Score: 0.4529 | "sa commence a quelle heure de base popcorn ?" -vs- "ça commence à être long la" # Score: 0.5805 | "BibleThump" -vs- "NotLikeThis" ``` ## Entrainement * 500 000 messages twitchs échantillonnés (cf description données des modèles de bases) * Batch size: 24 * Epochs: 24 * Loss: MultipleNegativesRankingLoss _A noter:_ * _ConvBert a été entrainé avec un longueur de 128 tokens max, mais est utilisé pour 512 dans ce modèle. Pas de problème._ * _La loss d'apprentissage n'est pas encore disponible: peu de visibilité sur les performances._ L'ensemble du code d'entrainement sur le github public [lincoln/twitchatds](https://github.com/Lincoln-France/twitchatds). ## Application: Nous avons utilisé une approche détournée de [BERTopic](https://maartengr.github.io/BERTopic/) pour réaliser un clustering d'un stream en prenant en compte la dimension temporelle: i.e. le nombre de seconde écoulée depuis le début du stream. ![approche_bertopic_lincoln](assets/approche_lincoln_topic_clustering_twitch.jpg) Globalement, l'approche donnes des résultats satisfaisant pour identifier des messages dit "similaires" récurrents. L'approche en revanche est fortement influencée par la ponctuation et la structure d'un message. Cela est largement explicable par le manque d'entrainement de l'ensemble des modèles et une volumétrie faible. ### Clustering émission "Backseat": Entre 19h30 et 20h00: ![1930_2000](./assets/scale_600_1930_2000.png) 🎞️ en vidéo: [youtu.be/EcjvlE9aTls](https://youtu.be/EcjvlE9aTls) ### Exemple regroupement émission "PopCorn": ```txt -------------------- LABEL 106 -------------------- circus (0.88)/sulli (0.23)/connu (0.19)/jure (0.12)/aime (0.11) silouhette moyenne: 0.04 -------------------- LABEL 106 -------------------- 2021-03-30 20:10:22 0.01: les gosse c est des animaux 2021-03-30 20:12:11 -0.03: oue c connu 2021-03-30 20:14:15 0.03: oh le circus !! <3 2021-03-30 20:14:19 0.12: le circus l'anciennnee 2021-03-30 20:14:22 0.06: jure le circus ! 2021-03-30 20:14:27 -0.03: le sulli 2021-03-30 20:14:31 0.09: le circus??? j'aime po 2021-03-30 20:14:34 0.11: le Circus, hors de prix ! 2021-03-30 20:14:35 -0.09: le Paddock a Rignac en Aveyron 2021-03-30 20:14:39 0.11: le circus >< 2021-03-30 20:14:39 0.04: le Titty Twister de Besançon -------------------- LABEL 17 -------------------- pates (0.12)/riz (0.09)/pâtes (0.09)/salade (0.07)/emission (0.07) silouhette moyenne: -0.05 -------------------- LABEL 17 -------------------- 2021-03-30 20:11:18 -0.03: Des nanimaux trop beaux ! 2021-03-30 20:13:11 -0.01: episode des simpsons ça... 2021-03-30 20:13:41 -0.01: des le debut d'emission ca tue mdrrrrr 2021-03-30 20:13:50 0.03: des "lasagnes" 2021-03-30 20:14:37 -0.18: poubelle la vie 2021-03-30 20:15:13 0.03: Une omelette 2021-03-30 20:15:35 -0.19: salade de bite 2021-03-30 20:15:36 -0.00: hahaha ce gastronome 2021-03-30 20:15:43 -0.08: salade de pates c une dinguerie 2021-03-30 20:17:00 -0.11: Une bonne femme ! 2021-03-30 20:17:06 -0.05: bouffe des graines 2021-03-30 20:17:08 -0.06: des pokeball ? 2021-03-30 20:17:11 -0.12: le choux fleur cru 2021-03-30 20:17:15 0.05: des pockeball ? 2021-03-30 20:17:27 -0.00: du chou fleur crue 2021-03-30 20:17:36 -0.09: un râgout de Meynia !!!! 2021-03-30 20:17:43 -0.07: une line up Sa rd o ch Zack Ponce my dream 2021-03-30 20:17:59 -0.10: Pâtes/10 2021-03-30 20:18:09 -0.05: Team bons petits plats 2021-03-30 20:18:13 -0.10: pate level 2021-03-30 20:18:19 -0.03: que des trucs très basiques 2021-03-30 20:18:24 0.03: des pates et du jambon c'est de la cuisine? 2021-03-30 20:18:30 0.05: Des pates et du riz ouai 2021-03-30 20:18:37 -0.02: des gnocchis à la poele c'est cuisiner ? 2021-03-30 20:18:50 -0.03: Pâtes à pizzas, pulled pork, carbonade flamande, etc.. 2021-03-30 20:19:01 -0.11: Des pâtes ou du riz ça compte ? 2021-03-30 20:19:22 -0.21: le noob 2021-03-30 20:19:47 -0.02: Une bonne escalope de milanaise les gars 2021-03-30 20:20:05 -0.04: faites des gratins et des quiches -------------------- LABEL 67 -------------------- 1 1 (0.25)/1 (0.19)/ (0.0)/ (0.0)/ (0.0) silouhette moyenne: 0.96 -------------------- LABEL 67 -------------------- 2021-03-30 20:24:17 0.94: +1 2021-03-30 20:24:37 0.97: +1 2021-03-30 20:24:37 0.97: +1 2021-03-30 20:24:38 0.97: +1 2021-03-30 20:24:39 0.97: +1 2021-03-30 20:24:43 0.97: +1 2021-03-30 20:24:44 0.97: +1 2021-03-30 20:24:47 0.97: +1 2021-03-30 20:24:49 0.97: +1 2021-03-30 20:25:00 0.97: +1 2021-03-30 20:25:21 0.95: +1 2021-03-30 20:25:25 0.95: +1 2021-03-30 20:25:28 0.94: +1 2021-03-30 20:25:30 0.94: +1 ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ConvBertModel (1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Modèles: * [2021twitchfr-conv-bert-small](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small) * [2021twitchfr-conv-bert-small-mlm](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm) * [2021twitchfr-conv-bert-small-mlm-simcse](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm-simcse)

linyi/dummy-model

d254ef8a8bdb3e86752fc45c0d8ce9995c23fb82

2021-11-07T00:42:27.000Z

[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

linyi

null

linyi/dummy-model

0

null

transformers

35,582

Entry not found

lkh4317/gpt2_fairy_tale

9072d3366c57083192390f449429a234628a8aee

2022-02-02T23:19:25.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

lkh4317

null

lkh4317/gpt2_fairy_tale

0

null

transformers

35,583

Entry not found

logicbloke/wav2vec2-large-xlsr-53-arabic

e0ab8005d9072404d8768d16c35c030519acd5e0

2021-07-06T10:09:12.000Z

[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

logicbloke

null

logicbloke/wav2vec2-large-xlsr-53-arabic

0

null

transformers

35,584

Entry not found

logube/DialogGPT_small_harrypotter

d318f5c4d2cae946034ea8531f43217b48a56c22

2021-08-27T23:18:34.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

logube

null

logube/DialogGPT_small_harrypotter

0

null

transformers

35,585

--- tags: - conversational --- # harry potter DialogGPT Model

lonewanderer27/KeitaroBot

088feb8381efb70f435363bea297a7c19c7b483e

2022-02-12T16:15:10.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

lonewanderer27

null

lonewanderer27/KeitaroBot

0

null

transformers

35,586

--- tags: - conversational --- # Camp Buddy - Keitaro - DialoGPTSmall Model

longcld/t5-base-squad-visquad-aqg

503405836758f1f9a44bd1b18ecb81510305f9a5

2021-09-08T01:36:55.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

longcld

null

longcld/t5-base-squad-visquad-aqg

0

null

transformers

35,587

Entry not found

longcld/t5-small-itranslate-visquad-aqg

c2b786e4f1db69d5a660db67876eb07711653300

2021-08-19T08:55:39.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

longcld

null

longcld/t5-small-itranslate-visquad-aqg

0

null

transformers

35,588

Entry not found

longcld/t5-small-squad-itranslate-aqg

33df45fbff5d865d417ddb101ea19268d4676d0f

2021-08-17T20:44:49.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

longcld

null

longcld/t5-small-squad-itranslate-aqg

0

null

transformers

35,589

Entry not found

longge/test

3188372dfc9687561e46291998e2656fff84d9e0

2021-11-02T06:36:03.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

longge

null

longge/test

0

null

transformers

35,590

Entry not found

longjuanfen/model700

88b475ba828919d78debe0f1b1303c694bc1ef12

2021-11-02T16:24:06.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

longjuanfen

null

longjuanfen/model700

0

null

transformers

35,591

Entry not found

longjuanfen/model701

aef1b4582976b7151e4a6242f5e81ad8d9213bdf

2021-11-03T17:23:57.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

longjuanfen

null

longjuanfen/model701

0

null

transformers

35,592

Entry not found

longnhit07/distilbert-base-uncased-finetuned-imdb

6fe0181a2aa074e52528005975958ec249b6613f

2022-01-10T09:02:05.000Z

[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

fill-mask

false

longnhit07

null

longnhit07/distilbert-base-uncased-finetuned-imdb

0

null

transformers

35,593

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # 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: 2.4722 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.7117 | 1.0 | 157 | 2.4977 | | 2.5783 | 2.0 | 314 | 2.4241 | | 2.5375 | 3.0 | 471 | 2.4358 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

lovellyweather/DialoGPT-medium-johnny

9e30b225f829b6dde42881274a8d7c063b251817

2021-08-31T13:58:43.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

lovellyweather

null

lovellyweather/DialoGPT-medium-johnny

0

null

transformers

35,594

--- tags: - conversational --- # Johnny DialoGPT Model

lsy641/ESC_Blender_Strategy

72c5d30a57217f7b44b7dae6d95230241335ec04

2021-07-05T14:23:34.000Z

[ "pytorch" ]

null

false

lsy641

null

lsy641/ESC_Blender_Strategy

0

1

null

35,595

Entry not found

lsy641/ESC_Blender_noStrategy

793dfc0d06ce980674b226a745d6dffd09761a4c

2021-07-05T14:22:05.000Z

[ "pytorch" ]

null

false

lsy641

null

lsy641/ESC_Blender_noStrategy

0

null

35,596

Entry not found

ltrctelugu/ltrc-albert

4c5f82a4367c836af23a467350d5caf13cdbe819

2021-11-23T16:49:32.000Z

[ "pytorch", "albert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

ltrctelugu

null

ltrctelugu/ltrc-albert

0

null

transformers

35,597

hello

ltrctelugu/ltrc-roberta

2e5cff54823703dfe5600f733e9d80d0320cee19

2021-10-17T16:45:03.000Z

[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

ltrctelugu

null

ltrctelugu/ltrc-roberta

0

null

transformers

35,598

RoBERTa trained on 8.8 Million Telugu Sentences

lucasnobre212/description-test

5795790b439bbf641b2fc538fb1ee70741538f48

2021-12-29T15:17:08.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

lucasnobre212

null

lucasnobre212/description-test

0

null

transformers

35,599

Entry not found