Datasets:
davanstrien
/
pytorchmodelmetadata

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moussaKam/tiny_bert-base_bert-score
28875f2532114328634580eb1ccfd76e74ed877b
2021-11-26T14:53:19.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
moussaKam
null
moussaKam/tiny_bert-base_bert-score
2
null
transformers
24,500
Entry not found
mptrigo/run1
af248e51b7a75a2891cf561cc9999fcdfd4df258
2022-01-20T10:37:49.000Z
[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
mptrigo
null
mptrigo/run1
2
null
transformers
24,501
--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model_index: - name: run1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation metric: name: Bleu type: bleu value: 8.4217 --- <!-- 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. --> # run1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 3.1740 - Bleu: 8.4217 - Gen Len: 15.9457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 250 | 4.2342 | 0.8889 | 83.4022 | | 4.6818 | 2.0 | 500 | 3.7009 | 4.1671 | 35.587 | | 4.6818 | 3.0 | 750 | 3.4737 | 7.6414 | 23.9674 | | 3.4911 | 4.0 | 1000 | 3.3713 | 7.7512 | 18.6957 | | 3.4911 | 5.0 | 1250 | 3.2689 | 8.0901 | 19.4674 | | 3.0164 | 6.0 | 1500 | 3.2194 | 8.5708 | 25.0543 | | 3.0164 | 7.0 | 1750 | 3.1853 | 9.5275 | 23.9239 | | 2.6954 | 8.0 | 2000 | 3.1562 | 8.5635 | 18.9674 | | 2.6954 | 9.0 | 2250 | 3.1564 | 8.2031 | 17.5978 | | 2.4503 | 10.0 | 2500 | 3.1314 | 8.5638 | 18.1522 | | 2.4503 | 11.0 | 2750 | 3.1511 | 8.8428 | 17.913 | | 2.2554 | 12.0 | 3000 | 3.1513 | 8.1244 | 17.0 | | 2.2554 | 13.0 | 3250 | 3.1664 | 8.0157 | 16.2717 | | 2.1202 | 14.0 | 3500 | 3.1656 | 8.7758 | 16.6087 | | 2.1202 | 15.0 | 3750 | 3.1550 | 8.4637 | 16.4565 | | 2.0082 | 16.0 | 4000 | 3.1702 | 8.2488 | 15.8587 | | 2.0082 | 17.0 | 4250 | 3.1725 | 8.609 | 16.3043 | | 1.9274 | 18.0 | 4500 | 3.1750 | 8.4476 | 15.8043 | | 1.9274 | 19.0 | 4750 | 3.1734 | 8.4753 | 16.5543 | | 1.888 | 20.0 | 5000 | 3.1740 | 8.4217 | 15.9457 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.1.dev0 - Tokenizers 0.10.3
mrm8488/deberta-v3-small-finetuned-squad
46b3f081f0213991682a70a54f08a04e9900b576
2021-11-21T21:14:48.000Z
[ "pytorch", "deberta-v2", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/deberta-v3-small-finetuned-squad
2
1
transformers
24,502
Entry not found
mrm8488/electra-base-finetuned-squadv1
573e287d1cb0bb8ffc70008975154131a11aba0c
2020-12-11T21:53:55.000Z
[ "pytorch", "electra", "question-answering", "en", "transformers", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/electra-base-finetuned-squadv1
2
null
transformers
24,503
--- language: en --- # Electra base ⚡ + SQuAD v1 ❓ [Electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task. ## Details of the downstream task (Q&A) - Model 🧠 **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. ## Details of the downstream task (Q&A) - Dataset 📚 **S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles. ## Model training 🏋️‍ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: ```bash python transformers/examples/question-answering/run_squad.py \ --model_type electra \ --model_name_or_path 'google/electra-base-discriminator' \ --do_eval \ --do_train \ --do_lower_case \ --train_file '/content/dataset/train-v1.1.json' \ --predict_file '/content/dataset/dev-v1.1.json' \ --per_gpu_train_batch_size 16 \ --learning_rate 3e-5 \ --num_train_epochs 10 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir '/content/output' \ --overwrite_output_dir \ --save_steps 1000 ``` ## Test set Results 🧾 | Metric | # Value | | ------ | --------- | | **EM** | **83.03** | | **F1** | **90.77** | | **Size**| **+ 400 MB** | Very good metrics for such a "small" model! ```json { 'exact': 83.03689687795648, 'f1': 90.77486052446231, 'total': 10570, 'HasAns_exact': 83.03689687795648, 'HasAns_f1': 90.77486052446231, 'HasAns_total': 10570, 'best_exact': 83.03689687795648, 'best_exact_thresh': 0.0, 'best_f1': 90.77486052446231, 'best_f1_thresh': 0.0 } ``` ### Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline QnA_pipeline = pipeline('question-answering', model='mrm8488/electra-base-finetuned-squadv1') QnA_pipeline({ 'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.', 'question': 'What has been discovered by scientists from China ?' }) # Output: {'answer': 'A new strain of flu', 'end': 19, 'score': 0.9995211430099182, 'start': 0} ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
mrm8488/electricidad-small-finetuned-muchocine
0440889050df65424f766b01841b35f4f53b3468
2021-01-09T04:46:14.000Z
[ "pytorch", "electra", "text-classification", "es", "dataset:muchocine", "transformers", "sentiment", "analysis", "spanish" ]
text-classification
false
mrm8488
null
mrm8488/electricidad-small-finetuned-muchocine
2
2
transformers
24,504
--- language: es datasets: - muchocine widget: - text: "Una buena película, sin más." tags: - sentiment - analysis - spanish --- # Electricidad-small fine-tuned for (Spanish) Sentiment Anlalysis 🎞️👍👎 [Electricidad](https://huggingface.co/mrm8488/electricidad-small-discriminator) small fine-tuned on [muchocine](https://huggingface.co/datasets/muchocine) dataset for Spanish **Sentiment Analysis** downstream task. ## Fast usage with `pipelines` 🚀 ```python # pip install -q transformers from transformers import AutoModelForSequenceClassification, AutoTokenizer CHKPT = 'mrm8488/electricidad-small-finetuned-muchocine' model = AutoModelForSequenceClassification.from_pretrained(CHKPT) tokenizer = AutoTokenizer.from_pretrained(CHKPT) from transformers import pipeline classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) # It ranks your comments between 1 and 5 (stars) classifier('Es una obra mestra. Brillante.') classifier('Es una película muy buena.') classifier('Una buena película, sin más.') classifier('Esperaba mucho más.') classifier('He tirado el dinero. Una basura. Vergonzoso.') ```
mrm8488/roberta-base-bne-finetuned-sqac
19436e6c5157b575ce1741d58d9fc6cd349ab2c9
2021-10-05T15:03:21.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "es", "dataset:sqac", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/roberta-base-bne-finetuned-sqac
2
1
transformers
24,505
--- language: es license: apache-2.0 tags: - generated_from_trainer datasets: - sqac metrics: - f1 model-index: - name: roberta-base-bne-finetuned-sqac results: - task: name: Question Answering type: Question-Answering dataset: name: sqac type: sqac args: metrics: - name: f1 type: f1 value: 0.7903 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-sqac This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the sqac dataset. It achieves the following results on the evaluation set: - Loss: 1.2111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9971 | 1.0 | 1196 | 0.8646 | | 0.482 | 2.0 | 2392 | 0.9334 | | 0.1652 | 3.0 | 3588 | 1.2111 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
mrm8488/squeezebert-finetuned-squadv1
a0f93afaaf3809fbb2a4bec319567025209094e7
2020-12-11T21:55:22.000Z
[ "pytorch", "squeezebert", "question-answering", "en", "dataset:squad", "arxiv:2006.11316", "arxiv:2004.02984", "transformers", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/squeezebert-finetuned-squadv1
2
null
transformers
24,506
--- language: en datasets: - squad --- # SqueezeBERT + SQuAD (v1.1) [squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task. ## Details of SqueezeBERT This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective. SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/). The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone. More about the model [here](https://arxiv.org/abs/2004.02984) ## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓ **S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles. ## Model training 🏋️‍ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: ```bash python /content/transformers/examples/question-answering/run_squad.py \ --model_type bert \ --model_name_or_path squeezebert/squeezebert-uncased \ --do_eval \ --do_train \ --do_lower_case \ --train_file /content/dataset/train-v1.1.json \ --predict_file /content/dataset/dev-v1.1.json \ --per_gpu_train_batch_size 16 \ --learning_rate 3e-5 \ --num_train_epochs 15 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /content/output_dir \ --overwrite_output_dir \ --save_steps 2000 ``` ## Test set Results 🧾 | Metric | # Value | | ------ | --------- | | **EM** | **76.66** | | **F1** | **85.83** | Model Size: **195 MB** ### Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline QnA_pipeline = pipeline('question-answering', model='mrm8488/squeezebert-finetuned-squadv1') QnA_pipeline({ 'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.', 'question': 'Who did identified it ?' }) # Output: {'answer': 'scientists.', 'end': 106, 'score': 0.6988425850868225, 'start': 96} ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
mrm8488/t5-base-finetuned-math-linear-algebra-1d
b7823a22b3b04166a2f3fd3c63d8dac3e9161250
2020-08-18T17:40:51.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/t5-base-finetuned-math-linear-algebra-1d
2
1
transformers
24,507
Entry not found
mrm8488/t5-base-finetuned-quarel
314ba74577d9e1f1bc37e798b11ff59a4e9d04ab
2021-06-23T12:55:25.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "en", "dataset:quarel", "arxiv:1910.10683", "transformers", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/t5-base-finetuned-quarel
2
null
transformers
24,508
--- language: en datasets: - quarel --- # T5-base fine-tuned on QuaRel [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRel](https://allenai.org/data/quarel) for **QA** downstream task. ## Details of T5 The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract: Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. ![model image](https://i.imgur.com/jVFMMWR.png) ## Details of the dataset 📚 **QuaRel**: *[A Dataset and Models for Answering Questions about Qualitative Relationships](https://www.semanticscholar.org/paper/QuaRel%3A-A-Dataset-and-Models-for-Answering-about-Tafjord-Clark/51004bc6461a572e1189a0e3b32b441155d760ce)* Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges. We present QuaRel, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them. The dataset has 2771 questions relating 19 different types of quantities. For example, "Jenny observes that the robot vacuum cleaner moves slower on the living room carpet than on the bedroom carpet. Which carpet has more friction?" We contribute (1) a simple and flexible conceptual framework for representing these kinds of questions; (2) the QuaRel dataset, including logical forms, exemplifying the parsing challenges; and (3) two novel models for this task, built as extensions of type-constrained semantic parsing. The first of these models (called QuaSP+) significantly outperforms off-the-shelf tools on QuaRel. The second (QuaSP+Zero) demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships without requiring additional training data, something not possible with previous models. This work thus makes inroads into answering complex, qualitative questions that require reasoning, and scaling to new relationships at low cost ## Model fine-tuning 🏋️‍ The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The **context** passed to the *encoder* is the `logical_form_pretty` field (example: `qrel(speed, higher, ice) -> qrel(smoothness, higher, snow) ; qrel(smoothness, higher, ice`) . The **question** is just the `question` field. The **answer** passed to the *decoder* is obtained from `question`using the `answer_index` field. More details about the dataset format/fields [here](https://huggingface.co/nlp/viewer/?dataset=quarel) ## Metrics on validation set 📋 | Metric | Score | |--------|-------| |Accuracy (EM) | **67.98**| ## Model in Action 🚀 ```python from transformers import AutoModelWithLMHead, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-quarel") model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-quarel") def get_response(question, context, max_length=32): input_text = 'question: %s context: %s' % (question, context) features = tokenizer([input_text], return_tensors='pt') output = model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'], max_length=max_length) return tokenizer.decode(output[0]) question = 'As the train left the station it crossed the bridge and being farther away it looked (A) larger (B) smaller' context = 'qrel(distance, higher, Train on a bridge) -> qrel(apparentSize, higher, Train on a bridge) ; qrel(apparentSize, lower, Train on a bridge)' get_response(question, context) # output: 'smaller' ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
mrp/distilbert-base-uncased-finetuned-imdb
69859451907bd8f60fa49138f69148c694bc4cc4
2022-01-19T08:44:09.000Z
[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
mrp
null
mrp/distilbert-base-uncased-finetuned-imdb
2
null
transformers
24,509
--- 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: 2.4718 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.707 | 1.0 | 157 | 2.4883 | | 2.572 | 2.0 | 314 | 2.4240 | | 2.5377 | 3.0 | 471 | 2.4355 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
mrp/marian-finetuned-kde4-en-to-fr
ba8bf152cf80a3a18245ee4b74acf2c11bb8645f
2022-01-20T04:05:30.000Z
[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:kde4", "transformers", "translation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
translation
false
mrp
null
mrp/marian-finetuned-kde4-en-to-fr
2
null
transformers
24,510
--- 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: 50.20410659441166 --- <!-- 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: 0.9643 - Bleu: 50.2041 ## 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 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
mudes/en-large
8cd519f7b74c0e16e57f0667d404401b036186d5
2021-05-20T18:36:06.000Z
[ "pytorch", "jax", "roberta", "token-classification", "en", "arxiv:2102.09665", "arxiv:2104.04630", "transformers", "mudes", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
mudes
null
mudes/en-large
2
null
transformers
24,511
--- language: en tags: - mudes license: apache-2.0 --- # MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in [this paper](https://arxiv.org/abs/2104.04630). ## Usage You can use this model when you have [MUDES](https://github.com/TharinduDR/MUDES) installed: ```bash pip install mudes ``` Then you can use the model like this: ```python from mudes.app.mudes_app import MUDESApp app = MUDESApp("en-large", use_cuda=False) print(app.predict_toxic_spans("You motherfucking cunt", spans=True)) ``` ## System Demonstration An experimental demonstration interface called MUDES-UI has been released on [GitHub](https://github.com/TharinduDR/MUDES-UI) and can be checked out in [here](http://rgcl.wlv.ac.uk/mudes/). ## Citing & Authors If you find this model helpful, feel free to cite our publications ```bibtex @inproceedings{ranasinghemudes, title={{MUDES: Multilingual Detection of Offensive Spans}}, author={Tharindu Ranasinghe and Marcos Zampieri}, booktitle={Proceedings of NAACL}, year={2021} } ``` ```bibtex @inproceedings{ranasinghe2021semeval, title={{WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans}}, author = {Ranasinghe, Tharindu and Sarkar, Diptanu and Zampieri, Marcos and Ororbia, Alex}, booktitle={Proceedings of SemEval}, year={2021} } ```
mwesner/layoutlmv2-cord
e1790bc03c38ad9cc697eba0d1b54fa280eb69e5
2022-02-23T19:46:50.000Z
[ "pytorch", "layoutlmv2", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
mwesner
null
mwesner/layoutlmv2-cord
2
null
transformers
24,512
Entry not found
namanrana16/DialoGPT-small-House
9ffff14c4a7c65c0aa6c1aee7a116b08ce1ed773
2021-11-11T08:23:55.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "huggingtweets" ]
text-generation
false
namanrana16
null
namanrana16/DialoGPT-small-House
2
null
transformers
24,513
--- tags: - huggingtweets widget: - text: "" --- #House BOT
napsternxg/scibert_scivocab_uncased_ft_mlm_SDU21_AI
4a1376270e4a74da1b9089be5c2a61c25eecd859
2021-05-20T01:10:55.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
napsternxg
null
napsternxg/scibert_scivocab_uncased_ft_mlm_SDU21_AI
2
null
transformers
24,514
scibert_scivocab_uncased_ft_mlm MLM pretrained on SDU21 Task 1 + 2
naram92/distilgpt2-finetuned-wikitext2
27a9784133b5b81ce487a232e884337fc11525c8
2021-10-01T21:00:11.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
naram92
null
naram92/distilgpt2-finetuned-wikitext2
2
null
transformers
24,515
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 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. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6424 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7608 | 1.0 | 2334 | 3.6655 | | 3.6335 | 2.0 | 4668 | 3.6455 | | 3.6066 | 3.0 | 7002 | 3.6424 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
naram92/distilroberta-base-finetuned-wikitext2
a836137a8032c30f19af8575dbc8ad300cb07be0
2021-10-04T19:49:45.000Z
[ "pytorch", "tensorboard", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
naram92
null
naram92/distilroberta-base-finetuned-wikitext2
2
null
transformers
24,516
Entry not found
nateraw/custom-torch-model
501ae06b1969e4cdf22281fe4ccf387e443fff29
2021-07-06T08:33:17.000Z
[ "pytorch", "transformers" ]
null
false
nateraw
null
nateraw/custom-torch-model
2
null
transformers
24,517
Entry not found
nateraw/my-cool-timm-model
d42d91024b87800f22d62e35d41c922d37a1cb02
2021-11-15T19:55:45.000Z
[ "pytorch", "timm", "image-classification" ]
image-classification
false
nateraw
null
nateraw/my-cool-timm-model
2
null
timm
24,518
--- tags: - image-classification - timm library_tag: timm --- # Model card for my-cool-timm-model
nateraw/resnet152
f1c3635599733f4f7470a578d2fc309495e477ab
2021-04-13T10:00:38.000Z
[ "pytorch", "resnet", "transformers" ]
null
false
nateraw
null
nateraw/resnet152
2
null
transformers
24,519
Entry not found
nates-test-org/cait_xs24_384
25b09eae1f0b081f4b9c25a061cd60e3c6d30ffc
2021-10-29T04:31:21.000Z
[ "pytorch", "timm", "image-classification" ]
image-classification
false
nates-test-org
null
nates-test-org/cait_xs24_384
2
null
timm
24,520
--- tags: - image-classification - timm library_tag: timm --- # Model card for cait_xs24_384
nates-test-org/cait_xxs36_224
0f8f7abc22c35d2b60b36f814e421a4dea3cf6b6
2021-10-29T04:34:40.000Z
[ "pytorch", "timm", "image-classification" ]
image-classification
false
nates-test-org
null
nates-test-org/cait_xxs36_224
2
null
timm
24,521
--- tags: - image-classification - timm library_tag: timm --- # Model card for cait_xxs36_224
nates-test-org/coat_lite_tiny
6a2e8fac0ed1879bc37179823919cfe859c7d361
2021-10-29T04:38:49.000Z
[ "pytorch", "timm", "image-classification" ]
image-classification
false
nates-test-org
null
nates-test-org/coat_lite_tiny
2
null
timm
24,522
--- tags: - image-classification - timm library_tag: timm --- # Model card for coat_lite_tiny
nates-test-org/coat_tiny
2bc6f50e015855cf1b19217f73d2e03847b183f6
2021-10-29T04:40:00.000Z
[ "pytorch", "timm", "image-classification" ]
image-classification
false
nates-test-org
null
nates-test-org/coat_tiny
2
null
timm
24,523
--- tags: - image-classification - timm library_tag: timm --- # Model card for coat_tiny
navteca/qnli-electra-base
807250d39ecddc9c39733c97bb0a9fc34d8f154f
2021-03-25T15:53:55.000Z
[ "pytorch", "electra", "text-classification", "en", "arxiv:1804.07461", "sentence-transformers", "license:mit" ]
text-classification
false
navteca
null
navteca/qnli-electra-base
2
null
sentence-transformers
24,524
--- language: en license: mit pipeline_tag: text-classification tags: - sentence-transformers --- # Cross-Encoder for QNLI This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model uses [electra-base](https://huggingface.co/google/electra-base-discriminator). ## Training Data Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task. ## Usage and Performance Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2')]) print(scores) ```
nazareno/bertimbau-socioambiental
c5d3266f23669acbd267ae18e05256207b51a260
2021-09-16T19:11:54.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
nazareno
null
nazareno/bertimbau-socioambiental
2
null
transformers
24,525
Entry not found
ncduy/distilbert-base-cased-distilled-squad-finetuned-squad-small
49c474d80c6045a4a4092b994850f41dd5e01d2a
2021-12-09T12:41:47.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
ncduy
null
ncduy/distilbert-base-cased-distilled-squad-finetuned-squad-small
2
null
transformers
24,526
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-cased-distilled-squad-finetuned-squad-small results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-cased-distilled-squad-finetuned-squad-small This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
nchervyakov/super-model
6f91717b56e57bf72a0cb3694b795a8e0e345302
2021-05-20T01:28:44.000Z
[ "pytorch", "tf", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
nchervyakov
null
nchervyakov/super-model
2
null
transformers
24,527
hello
ncoop57/codeformer-java
ca3f7fa6c2e9dfc302f8febdd0437d3e8d19a83e
2021-09-30T14:18:00.000Z
[ "pytorch", "roberta", "feature-extraction", "sentence-transformers", "sentence-similarity" ]
sentence-similarity
false
ncoop57
null
ncoop57/codeformer-java
2
null
sentence-transformers
24,528
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 14202 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
negfir/new-wikitext2
b47668655c03e8a64848b88f8094bd126c932c4d
2022-03-09T16:53:41.000Z
[ "pytorch", "tensorboard", "squeezebert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
negfir
null
negfir/new-wikitext2
2
null
transformers
24,529
Entry not found
new5558/wangchan-course
c083c8028bd365d76a91665ff5dd457bbf328445
2021-12-05T20:55:07.000Z
[ "pytorch", "tf", "camembert", "text-classification", "transformers" ]
text-classification
false
new5558
null
new5558/wangchan-course
2
null
transformers
24,530
hello hello
nfliu/roberta_s2orc_books_wiki_bpe_32k
e2c0265230ec34c98f66a8b00d310282add44557
2021-12-08T21:56:00.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nfliu
null
nfliu/roberta_s2orc_books_wiki_bpe_32k
2
null
transformers
24,531
Entry not found
nfliu/roberta_s2orc_bpe_32k
2155bfba2e89ecd8d3b636d876a1ff84bbc9b2c0
2021-12-08T22:05:14.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nfliu
null
nfliu/roberta_s2orc_bpe_32k
2
null
transformers
24,532
Entry not found
niclas/model_sv_4
eff9a40a667916610754f8c51397072408a41442
2021-12-22T23:49:16.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
niclas
null
niclas/model_sv_4
2
null
transformers
24,533
Entry not found
niclas/models_sv_7
222800c14a4c8f28f63bf5de455696881b385cf1
2022-02-21T21:18:40.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
niclas
null
niclas/models_sv_7
2
null
transformers
24,534
Entry not found
nicoladecao/msmarco-word2vec256000-distilbert-base-uncased
b11fda7b499374f6c5423100e5f4b2f350f48c0c
2022-02-18T11:57:55.000Z
[ "pytorch", "distilbert", "feature-extraction", "transformers", "license:mit" ]
feature-extraction
false
nicoladecao
null
nicoladecao/msmarco-word2vec256000-distilbert-base-uncased
2
null
transformers
24,535
--- license: mit ---
nielsr/canine-c
30848eb7ceb4581a2a99e138eb756d69324bebba
2021-06-29T11:37:15.000Z
[ "pytorch", "canine", "feature-extraction", "transformers" ]
feature-extraction
false
nielsr
null
nielsr/canine-c
2
null
transformers
24,536
Entry not found
nielsr/coref-bert-large
26b6e0a353e83236a8cbaf9395cb97e1bdafd0e7
2021-01-21T10:06:48.000Z
[ "pytorch", "en", "dataset:wikipedia", "dataset:quoref", "dataset:docred", "dataset:fever", "dataset:gap", "dataset:winograd_wsc", "dataset:winogender", "dataset:glue", "arxiv:2004.06870", "transformers", "exbert", "license:apache-2.0" ]
null
false
nielsr
null
nielsr/coref-bert-large
2
null
transformers
24,537
--- language: en tags: - exbert license: apache-2.0 datasets: - wikipedia - quoref - docred - fever - gap - winograd_wsc - winogender - glue --- # CorefBERT large model Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in [this paper](https://arxiv.org/abs/2004.06870) and first released in [this repository](https://github.com/thunlp/CorefBERT). Disclaimer: The team releasing CorefBERT did not write a model card for this model so this model card has been written by me. ## Model description CorefBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Mention reference prediction (MRP): this is a novel training task which is proposed to enhance coreferential reasoning ability. MRP utilizes the mention reference masking strategy to mask one of the repeated mentions and then employs a copybased training objective to predict the masked tokens by copying from other tokens in the sequence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks, especially those that involve coreference resolution. If you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the CorefBERT model as inputs. ### BibTeX entry and citation info ```bibtex @misc{ye2020coreferential, title={Coreferential Reasoning Learning for Language Representation}, author={Deming Ye and Yankai Lin and Jiaju Du and Zhenghao Liu and Peng Li and Maosong Sun and Zhiyuan Liu}, year={2020}, eprint={2004.06870}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
nielsr/deformable-detr-single-scale
e271bd29db67d4918195b83ab33d6d86cd97719c
2022-02-01T13:26:54.000Z
[ "pytorch", "deformable_detr", "transformers" ]
null
false
nielsr
null
nielsr/deformable-detr-single-scale
2
null
transformers
24,538
Entry not found
nielsr/deformable-detr-with-box-refine-two-stage
d9f8b025bd654cc5a672499877baf396cf4b40a8
2022-02-01T13:18:39.000Z
[ "pytorch", "deformable_detr", "transformers" ]
null
false
nielsr
null
nielsr/deformable-detr-with-box-refine-two-stage
2
null
transformers
24,539
Entry not found
nielsr/deformable-detr-with-box-refine
1e8a6c40a4e689ea712908f4a64e3c15c9e1d868
2022-02-01T13:21:30.000Z
[ "pytorch", "deformable_detr", "transformers" ]
null
false
nielsr
null
nielsr/deformable-detr-with-box-refine
2
null
transformers
24,540
Entry not found
nielsr/dino_vitb16
7d14921fb6caa80c31d2983a9186054ef85d71e3
2021-08-25T11:57:11.000Z
[ "pytorch", "vit", "feature-extraction", "transformers" ]
feature-extraction
false
nielsr
null
nielsr/dino_vitb16
2
null
transformers
24,541
I've converted the DINO checkpoints from the [official repo](https://github.com/facebookresearch/dino): You can use it as follows: ```python from transformers import ViTModel model = ViTModel.from_pretrained("nielsr/dino_vitb16", add_pooling_layer=False) ```
nielsr/tapex-large
15e33c38efaf7afb49578392b1211ef3235bae13
2022-05-17T07:31:39.000Z
[ "pytorch", "tapex", "text2text-generation", "en", "arxiv:2107.07653", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
nielsr
null
nielsr/tapex-large
2
1
transformers
24,542
--- language: en tags: - tapex license: apache-2.0 inference: false --- TAPEX-large model pre-trained-only model. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Table-Pretraining). To load it and run inference, you can do the following: ``` from transformers import BartTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large") model = BartForConditionalGeneration.from_pretrained("nielsr/tapex-large") # create table data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]} table = pd.DataFrame.from_dict(data) # turn into dict table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]} # turn into format TAPEX expects # define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict) # add query query = "SELECT ... FROM ..." joint_input = query + " " + linear_table # encode encoding = tokenizer(joint_input, return_tensors="pt") # forward pass outputs = model.generate(**encoding) # decode tokenizer.batch_decode(outputs, skip_special_tokens=True) ```
nikitam/mbert-resp-en-it
87da19aa704b89530b55ae90487634b8a3ba8926
2021-10-25T20:32:42.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nikitam
null
nikitam/mbert-resp-en-it
2
null
transformers
24,543
Entry not found
nikitam/mbert-xdm-en-it
15e9699fbeab3a64601b5f16a8f2a73ecbbbd5a1
2021-10-25T21:36:47.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nikitam
null
nikitam/mbert-xdm-en-it
2
null
transformers
24,544
Entry not found
nikkindev/cave
5d8ce6b71d867b764c9e4f22c38e0d05433d8717
2021-06-04T12:11:30.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
nikkindev
null
nikkindev/cave
2
null
transformers
24,545
--- tags: - conversational --- Cave Johnson in town!
ninahrostozova/xlm-roberta-base-finetuned-marc
a99540aded8711da06ad6fd0a990d98414f39bdd
2021-10-16T11:27:29.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "text-classification", "dataset:amazon_reviews_multi", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
ninahrostozova
null
ninahrostozova/xlm-roberta-base-finetuned-marc
2
null
transformers
24,546
--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc 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. --> # xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.1698 - Mae: 0.6090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1662 | 1.0 | 333 | 1.2084 | 0.7068 | | 1.0122 | 2.0 | 666 | 1.1698 | 0.6090 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
nlokam/ada_V.6
2d18a3f650b0cdfd58f324a0bbbb9d7cc790af5a
2022-01-29T18:07:02.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
nlokam
null
nlokam/ada_V.6
2
null
transformers
24,547
--- tags: - conversational --- # Ada model
nlokam/ada_V.7
e0660b7a1b4a10cff0e167204f406404b96d3786
2022-06-11T20:15:39.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
nlokam
null
nlokam/ada_V.7
2
null
transformers
24,548
--- tags: - conversational --- # Ada model
nlp-en-es/bertin-large-finetuned-sqac
3777b086656e4f769c05ab1a45edc6e643d00c0e
2021-10-03T17:23:54.000Z
[ "pytorch", "roberta", "question-answering", "es", "dataset:BSC-TeMU/SQAC", "transformers", "QA", "Q&A", "autotrain_compatible" ]
question-answering
false
nlp-en-es
null
nlp-en-es/bertin-large-finetuned-sqac
2
2
transformers
24,549
--- language: es tags: - QA - Q&A datasets: - BSC-TeMU/SQAC --- # BERTIN (large) fine-tuned on **SQAC** for Spanish **QA** 📖❓ [BERTIN](https://huggingface.co/flax-community/bertin-roberta-large-spanish) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.
nlp-en-es/roberta-base-bne-finetuned-sqac
2cdaab873b4f624a8adb627a1b4dd47babda90bc
2021-10-05T15:03:51.000Z
[ "pytorch", "roberta", "question-answering", "es", "dataset:sqac", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
nlp-en-es
null
nlp-en-es/roberta-base-bne-finetuned-sqac
2
1
transformers
24,550
--- language: es license: apache-2.0 tags: - generated_from_trainer datasets: - sqac metrics: - f1 model-index: - name: roberta-base-bne-finetuned-sqac results: - task: name: Question Answering type: Question-Answering dataset: name: sqac type: sqac args: metrics: - name: f1 type: f1 value: 0.7903 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-sqac This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the sqac dataset. It achieves the following results on the evaluation set: - Loss: 1.2111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9971 | 1.0 | 1196 | 0.8646 | | 0.482 | 2.0 | 2392 | 0.9334 | | 0.1652 | 3.0 | 3588 | 1.2111 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
nlpconnect/dpr-nq-reader-roberta-base
6917660838ccbfa2deea31fbdfbd3c71975d3e4f
2022-01-02T09:50:08.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
nlpconnect
null
nlpconnect/dpr-nq-reader-roberta-base
2
null
transformers
24,551
Entry not found
nlpunibo/distilbert_base_config1
284eefd81a2656d5e73716382cc7d39304b36852
2021-02-19T14:31:23.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
nlpunibo
null
nlpunibo/distilbert_base_config1
2
null
transformers
24,552
Entry not found
nlpunibo/distilbert_base_config2
957d5d798d1db96fbe63331e8114234b69924904
2021-02-19T14:36:05.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
nlpunibo
null
nlpunibo/distilbert_base_config2
2
null
transformers
24,553
Entry not found
nlpunibo/distilbert_classifier2
d198d639c6880f98f9910b533f0577f4b9059547
2021-02-20T15:04:51.000Z
[ "pytorch", "distilbert", "transformers" ]
null
false
nlpunibo
null
nlpunibo/distilbert_classifier2
2
null
transformers
24,554
Entry not found
nlpunibo/distilbert_convolutional_classifier
7ebe541e2dd0e9b39a325cbd493492048569a2ef
2021-03-21T14:59:17.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
nlpunibo
null
nlpunibo/distilbert_convolutional_classifier
2
null
transformers
24,555
Entry not found
nlrgroup/Alice_fine_tuned
27ddd6e43f7630707b3a2ae990bc5b68e6aa879c
2021-12-01T21:30:19.000Z
[ "pytorch", "xlnet", "text-generation", "transformers" ]
text-generation
false
nlrgroup
null
nlrgroup/Alice_fine_tuned
2
null
transformers
24,556
Entry not found
nostalgebraist/nostalgebraist-autoresponder-2_7b
80b1991dbce13af9fea669b414b5b59c0548832c
2021-05-15T02:36:48.000Z
[ "pytorch", "gpt_neo", "text-generation", "transformers" ]
text-generation
false
nostalgebraist
null
nostalgebraist/nostalgebraist-autoresponder-2_7b
2
null
transformers
24,557
notentered/roberta-base-finetuned-cola
4702b3a33548f56250ae9a4f194c8ee05404b941
2022-02-18T10:27:58.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
notentered
null
notentered/roberta-base-finetuned-cola
2
null
transformers
24,558
Entry not found
ntrnghia/mrpc_vn
a1cd84b343d47e4ef2704c5db01b1f3f456b6ba0
2021-05-20T02:08:11.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
ntrnghia
null
ntrnghia/mrpc_vn
2
null
transformers
24,559
Entry not found
nws/test_model
2ee7c454ba400007c50375eed26c04cd612626f2
2021-11-02T12:48:19.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nws
null
nws/test_model
2
null
transformers
24,560
Entry not found
nytestalkerq/DialoGPT-medium-joshua
38e3ee0db9f80a839c9bb9c0cf595a08774ac6f4
2021-06-04T02:29:58.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational", "license:mit" ]
conversational
false
nytestalkerq
null
nytestalkerq/DialoGPT-medium-joshua
2
null
transformers
24,561
--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit --- # DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script). Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```
nyu-mll/roberta-base-100M-2
33062ce4961fc447815f39b410a5a272a6a4728a
2021-05-20T18:54:59.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nyu-mll
null
nyu-mll/roberta-base-100M-2
2
null
transformers
24,562
# RoBERTa Pretrained on Smaller Datasets We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1. ### Hyperparameters and Validation Perplexity The hyperparameters and validation perplexities corresponding to each model are as follows: | Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity | |--------------------------|---------------|------------|-----------|------------|-----------------------| | [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 | | [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 | | [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 | | [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 | | [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 | | [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 | | [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 | | [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 | | [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 | | [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 | | [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 | | [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 | The hyperparameters corresponding to model sizes mentioned above are as follows: | Model Size | L | AH | HS | FFN | P | |------------|----|----|-----|------|------| | BASE | 12 | 12 | 768 | 3072 | 125M | | MED-SMALL | 6 | 8 | 512 | 2048 | 45M | (AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.) For other hyperparameters, we select: - Peak Learning rate: 5e-4 - Warmup Steps: 6% of max steps - Dropout: 0.1 [link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1 [link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2 [link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3 [link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1 [link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2 [link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3 [link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1 [link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2 [link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3 [link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1 [link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2 [link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
nyu-mll/roberta-base-10M-2
3155af7150884a514e02fd57a579a8edcdb0154e
2021-05-20T18:58:09.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nyu-mll
null
nyu-mll/roberta-base-10M-2
2
null
transformers
24,563
# RoBERTa Pretrained on Smaller Datasets We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1. ### Hyperparameters and Validation Perplexity The hyperparameters and validation perplexities corresponding to each model are as follows: | Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity | |--------------------------|---------------|------------|-----------|------------|-----------------------| | [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 | | [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 | | [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 | | [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 | | [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 | | [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 | | [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 | | [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 | | [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 | | [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 | | [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 | | [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 | The hyperparameters corresponding to model sizes mentioned above are as follows: | Model Size | L | AH | HS | FFN | P | |------------|----|----|-----|------|------| | BASE | 12 | 12 | 768 | 3072 | 125M | | MED-SMALL | 6 | 8 | 512 | 2048 | 45M | (AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.) For other hyperparameters, we select: - Peak Learning rate: 5e-4 - Warmup Steps: 6% of max steps - Dropout: 0.1 [link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1 [link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2 [link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3 [link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1 [link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2 [link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3 [link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1 [link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2 [link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3 [link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1 [link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2 [link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
nyu-mll/roberta-base-1B-2
569f553e71d3c43260f81a7b79389b7bdc96a9ca
2021-05-20T19:04:39.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nyu-mll
null
nyu-mll/roberta-base-1B-2
2
null
transformers
24,564
# RoBERTa Pretrained on Smaller Datasets We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1. ### Hyperparameters and Validation Perplexity The hyperparameters and validation perplexities corresponding to each model are as follows: | Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity | |--------------------------|---------------|------------|-----------|------------|-----------------------| | [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 | | [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 | | [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 | | [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 | | [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 | | [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 | | [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 | | [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 | | [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 | | [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 | | [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 | | [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 | The hyperparameters corresponding to model sizes mentioned above are as follows: | Model Size | L | AH | HS | FFN | P | |------------|----|----|-----|------|------| | BASE | 12 | 12 | 768 | 3072 | 125M | | MED-SMALL | 6 | 8 | 512 | 2048 | 45M | (AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.) For other hyperparameters, we select: - Peak Learning rate: 5e-4 - Warmup Steps: 6% of max steps - Dropout: 0.1 [link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1 [link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2 [link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3 [link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1 [link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2 [link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3 [link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1 [link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2 [link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3 [link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1 [link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2 [link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
o2poi/sst2-eda-albert
22cfb692bd2def9a13f0918cd158e4abb4981704
2021-06-11T12:57:56.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
false
o2poi
null
o2poi/sst2-eda-albert
2
null
transformers
24,565
Entry not found
o2poi/sst2-eda-bert-uncased
96ee8a85f0f4be4fc83b0db2feee4e0e2e873791
2021-06-11T15:44:21.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
o2poi
null
o2poi/sst2-eda-bert-uncased
2
null
transformers
24,566
Entry not found
o2poi/sst2-eda-roberta
6ba62de4e8e6f7ceacb1d0af01fb6519277a4afe
2021-06-11T13:03:15.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
o2poi
null
o2poi/sst2-eda-roberta
2
null
transformers
24,567
Entry not found
obito69/DialoGPT-small-Doctorstrange
4a47abe56d205fd5f7f6eae9fe0adc9b913786ee
2021-09-30T16:13:04.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
obito69
null
obito69/DialoGPT-small-Doctorstrange
2
null
transformers
24,568
---- tags: - conversational --- # Doctor strange DialGPT model
obss/mt5-small-3task-highlight-combined3
4c99115625b50fc17ea200e606fef7d7487f92b6
2021-12-03T23:49:29.000Z
[ "pytorch", "mt5", "text2text-generation", "tr", "dataset:tquad1", "dataset:tquad2", "dataset:xquad", "arxiv:2111.06476", "transformers", "question-generation", "answer-extraction", "question-answering", "text-generation", "license:cc-by-4.0", "autotrain_compatible" ]
text2text-generation
false
obss
null
obss/mt5-small-3task-highlight-combined3
2
null
transformers
24,569
--- language: tr datasets: - tquad1 - tquad2 - xquad tags: - text2text-generation - question-generation - answer-extraction - question-answering - text-generation pipeline_tag: text2text-generation widget: - text: "generate question: Legendary Entertainment, 2016 yılında bilimkurgu romanı Dune'un <hl> film ve TV haklarını <hl> satın aldı. Geliştirme kısa bir süre sonra başladı. Villeneuve projeye olan ilgisini dile getirdi ve resmi olarak yönetmen olarak imza attı. Roth ve Spaihts ile birlikte çalışarak senaryoyu iki bölüme ayırdı ve 1965 romanının 21. yüzyıla güncellenmiş bir uyarlamasını ekledi." example_title: "Question Generation (Movie)" - text: "generate question: Fatih Sultan Mehmet, Cenevizlilerin önemli üslerinden Amasra’yı aldı. 1479’da <hl> bir antlaşma yaparak <hl> Venedik'le 16 yıllık savaşa son verdi." example_title: "Question Generation (History)" - text: "generate question: Cenevizlilerin önemli üslerinden Amasra’yı aldı. 1479’da bir antlaşma yaparak <hl> Venedik'le <hl> 16 yıllık savaşa sona verdi." example_title: "Question Generation (History 2)" - text: "extract answers: Cenevizlilerin önemli üslerinden Amasra’yı aldı. <hl> 1479’da bir antlaşma yaparak Venedik'le 16 yıllık savaşa sona verdi. <hl>" example_title: "Answer Extraction (History)" - text: "question: Bu model ne ise yarar? context: Çalışmada sunulan yöntemle, Türkçe metinlerden otomatik olarak soru ve cevap üretilebilir. Bu proje ile paylaşılan kaynak kodu ile Türkçe Soru Üretme / Soru Cevaplama konularında yeni akademik çalışmalar yapılabilir. Projenin detaylarına paylaşılan Github ve Arxiv linklerinden ulaşılabilir." example_title: "Answer Extraction (Open Domain)" license: cc-by-4.0 --- # mt5-small for Turkish Question Generation Automated question generation and question answering using text-to-text transformers by OBSS AI. ```python from core.api import GenerationAPI generation_api = GenerationAPI('mt5-small-3task-highlight-combined3') ``` ## Citation 📜 ``` @article{akyon2021automated, title={Automated question generation and question answering from Turkish texts using text-to-text transformers}, author={Akyon, Fatih Cagatay and Cavusoglu, Devrim and Cengiz, Cemil and Altinuc, Sinan Onur and Temizel, Alptekin}, journal={arXiv preprint arXiv:2111.06476}, year={2021} } ``` ## Overview ✔️ **Language model:** mt5-small **Language:** Turkish **Downstream-task:** Extractive QA/QG, Answer Extraction **Training data:** TQuADv2-train, TQuADv2-val, XQuAD.tr **Code:** https://github.com/obss/turkish-question-generation **Paper:** https://arxiv.org/abs/2111.06476 ## Hyperparameters ``` batch_size = 256 n_epochs = 15 base_LM_model = "mt5-small" max_source_length = 512 max_target_length = 64 learning_rate = 1.0e-3 task_lisst = ["qa", "qg", "ans_ext"] qg_format = "highlight" ``` ## Performance Refer to [paper](https://arxiv.org/abs/2111.06476). ## Usage 🔥 ```python from core.api import GenerationAPI generation_api = GenerationAPI('mt5-small-3task-highlight-combined3') context = """ Bu modelin eğitiminde, Türkçe soru cevap verileri kullanılmıştır. Çalışmada sunulan yöntemle, Türkçe metinlerden otomatik olarak soru ve cevap üretilebilir. Bu proje ile paylaşılan kaynak kodu ile Türkçe Soru Üretme / Soru Cevaplama konularında yeni akademik çalışmalar yapılabilir. Projenin detaylarına paylaşılan Github ve Arxiv linklerinden ulaşılabilir. """ # a) Fully Automated Question Generation generation_api(task='question-generation', context=context) # b) Question Answering question = "Bu model ne işe yarar?" generation_api(task='question-answering', context=context, question=question) # b) Answer Extraction generation_api(task='answer-extraction', context=context) ```
odinmay/zackbotmodel
5d93e5b1b0eb3453e1503326949581784f4cfa03
2021-06-03T21:37:46.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
odinmay
null
odinmay/zackbotmodel
2
null
transformers
24,570
--- tags: - conversational ---
ohshimalab/bert-base-minpaku
acf6b651419d0ae645bb5c3bcd7bc58f635c9ff6
2022-02-08T05:22:55.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
ohshimalab
null
ohshimalab/bert-base-minpaku
2
null
transformers
24,571
--- license: mit ---
orendar/en_he_large
2cd4d1135007ede8fdf1e36c9eb8e54918c2ba6e
2022-05-08T13:14:15.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
orendar
null
orendar/en_he_large
2
null
transformers
24,572
Entry not found
osama7/t5-summarization-multinews
826dc38918d1702fb1ebe78739fca9cdcbbaa09d
2022-01-30T20:42:51.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
osama7
null
osama7/t5-summarization-multinews
2
null
transformers
24,573
This is a t5-base model trained on the multi_news dataset for abstraction summarization
osanseviero/asr-with-transformers-wav2vec2
65955e59542a7515b8ff85af136930a380e57c5b
2021-11-04T15:38:38.000Z
[ "pytorch", "tf", "wav2vec2", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2006.11477", "superb", "audio", "license:apache-2.0" ]
automatic-speech-recognition
false
osanseviero
null
osanseviero/asr-with-transformers-wav2vec2
2
null
superb
24,574
--- benchmark: superb library_name: superb language: en datasets: - librispeech_asr tags: - audio - automatic-speech-recognition - superb license: apache-2.0 widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # Fork of Wav2Vec2-Base-960h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC from datasets import load_dataset import soundfile as sf import torch # load model and tokenizer tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") # define function to read in sound file def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.map(map_to_array) # tokenize input_values = tokenizer(ds["speech"][:2], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = tokenizer.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-960h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer import soundfile as sf import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch librispeech_eval = librispeech_eval.map(map_to_array) def map_to_pred(batch): input_values = tokenizer(batch["speech"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = tokenizer.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 3.4 | 8.6 |
osanseviero/distilbert-base-nli-wkpooling
29db279c53b58c2ce96e572c6a5956f60e3d5c81
2021-05-04T12:35:09.000Z
[ "pytorch", "distilbert", "feature-extraction", "transformers" ]
feature-extraction
false
osanseviero
null
osanseviero/distilbert-base-nli-wkpooling
2
null
transformers
24,575
Entry not found
osanseviero/flair-ner-english
eafe20407ee6a0abc614d9d6eda4c46489b05ff5
2021-05-19T14:44:12.000Z
[ "pytorch", "en", "dataset:conll2003", "flair", "token-classification", "sequence-tagger-model" ]
token-classification
false
osanseviero
null
osanseviero/flair-ner-english
2
null
flair
24,576
--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - conll2003 widget: - text: "George Washington went to Washington" --- ## English NER in Flair (default model)
osanseviero/my_new_model
5b6825a74420339c7c014e91ef4c0a284c703f75
2021-06-07T14:27:42.000Z
[ "pytorch", "roberta", "feature-extraction", "sentence-transformers" ]
feature-extraction
false
osanseviero
null
osanseviero/my_new_model
2
null
sentence-transformers
24,577
--- tags: - sentence-transformers - feature-extraction --- # Name of Model <!--- Describe your model here --> ## Model Description The model consists of the following layers: (0) Base Transformer Type: RobertaModel (1) mean Pooling ## Usage (Sentence-Transformers) Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) 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"] model = SentenceTransformer('model_name') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sum_embeddings / sum_mask # Sentences we want sentence embeddings for sentences = ['This is an example sentence'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('model_name') model = AutoModel.from_pretrained('model_name') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training Procedure <!--- Describe how your model was trained --> ## Evaluation Results <!--- Describe how your model was evaluated --> ## Citing & Authors <!--- Describe where people can find more information -->
osunlp/ReasonBERT-RoBERTa-base
6e0e8be71a12b7b017a84933718d924b0204954a
2022-01-23T07:49:47.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
osunlp
null
osunlp/ReasonBERT-RoBERTa-base
2
null
transformers
24,578
Entry not found
owen99630/catexp2
2858b787e87a38c972661ff9a7bfed2af75cef2c
2021-10-26T04:58:10.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
owen99630
null
owen99630/catexp2
2
null
transformers
24,579
{0: 'Anorexia', 1: 'Anxiety', 2: 'Bullying', 3: 'Care', 4: 'Creativity', 5: 'Culture', 6: 'Depression', 7: 'Friends', 8: 'Getting help', 9: 'Happiness', 10: 'Helping others', 11: 'Helping yourself', 12: 'Hope', 13: 'Learning', 14: 'Life Issues', 15: 'Mental Health', 16: 'Mental Health Matters', 17: 'Mental health awareness', 18: 'PTSD', 19: 'Positivity', 20: 'Resilience', 21: 'Self-care', 22: 'Sharing', 23: 'Support', 24: 'University'}
owen99630/experience
d32d2af427e3ada77505d7b8906a963c6ec4dc7b
2021-09-28T12:19:46.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
owen99630
null
owen99630/experience
2
null
transformers
24,580
Entry not found
p208p2002/gpt2-drcd-qg-hl
d053f48e819a18cf829f44a2185a2e20387d0edf
2021-05-23T10:52:50.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
p208p2002
null
p208p2002/gpt2-drcd-qg-hl
2
null
transformers
24,581
## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModelForCausalLM, ) tokenizer = BertTokenizerFast.from_pretrained('p208p2002/gpt2-drcd-qg-hl') model = AutoModelForCausalLM.from_pretrained('p208p2002/gpt2-drcd-qg-hl') ``` ### Input Format ``` C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|] ``` ### Input Example ``` 哈利·波特是英國作家[HL]羅琳[HL]撰寫的七部幻想小說系列。 ``` > 誰撰寫哈利·波特?
pablouribe/bertstem-copus-administration
54fd227efd6e7e91c8217a5f8fa6acc2b09d7f00
2021-11-19T21:23:54.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
pablouribe
null
pablouribe/bertstem-copus-administration
2
null
transformers
24,582
Entry not found
pablouribe/bertstem-copus-guiding
d1ab0e077a0113934c7ec77937bf08571e0c3594
2021-11-30T15:10:04.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
pablouribe
null
pablouribe/bertstem-copus-guiding
2
null
transformers
24,583
Entry not found
pablouribe/bertstem-copus-overfitted
381881573fb36ab137a81e3a65fb33ee6eb7b514
2022-01-18T18:51:43.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
pablouribe
null
pablouribe/bertstem-copus-overfitted
2
null
transformers
24,584
Entry not found
pablouribe/bertstem-copus-presenting
ca76e55f740efd93bb735d653143d0f84bfb0229
2021-11-22T21:54:23.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
pablouribe
null
pablouribe/bertstem-copus-presenting
2
null
transformers
24,585
Entry not found
paola-md/recipes_italian
df0acc61b51b5cf34d43f214f02e90593ae4dd90
2022-01-31T23:33:29.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
paola-md
null
paola-md/recipes_italian
2
null
transformers
24,586
Entry not found
parthshukla/quotes_v1
a363978d5a21eee1212586885c1501b03f035102
2021-07-16T07:09:46.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
parthshukla
null
parthshukla/quotes_v1
2
null
transformers
24,587
Entry not found
patrickvonplaten/big-bird-base-trivia-qa
fdb7d5354aa128f0ca260db2dd760da82f58ce45
2021-03-04T12:13:47.000Z
[ "pytorch", "big_bird", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
patrickvonplaten
null
patrickvonplaten/big-bird-base-trivia-qa
2
null
transformers
24,588
Entry not found
patrickvonplaten/bigbird-roberta-base-original-attn
12642ec53e9b2dc69e7b5f379a0ae726e22c5642
2021-03-02T16:11:07.000Z
[ "pytorch", "big_bird", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
patrickvonplaten
null
patrickvonplaten/bigbird-roberta-base-original-attn
2
null
transformers
24,589
Entry not found
patrickvonplaten/hello_2b_3
9c30eec9a9df12049c82ee036c9fb8f37708a265
2021-11-04T15:11:04.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "tr", "dataset:common_voice", "transformers", "common_voice", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
patrickvonplaten
null
patrickvonplaten/hello_2b_3
2
null
transformers
24,590
--- language: - tr tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: hello_2b_3 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. --> # hello_2b_3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 1.5615 - Wer: 0.9808 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6389 | 0.92 | 100 | 3.6218 | 1.0 | | 1.6676 | 1.85 | 200 | 3.2655 | 1.0 | | 0.3067 | 2.77 | 300 | 3.2273 | 1.0 | | 0.1924 | 3.7 | 400 | 3.0238 | 0.9999 | | 0.1777 | 4.63 | 500 | 2.1606 | 0.9991 | | 0.1481 | 5.55 | 600 | 1.8742 | 0.9982 | | 0.1128 | 6.48 | 700 | 2.0114 | 0.9994 | | 0.1806 | 7.4 | 800 | 1.9032 | 0.9984 | | 0.0399 | 8.33 | 900 | 2.0556 | 0.9996 | | 0.0729 | 9.26 | 1000 | 2.0515 | 0.9987 | | 0.0847 | 10.18 | 1100 | 2.2121 | 0.9995 | | 0.0777 | 11.11 | 1200 | 1.7002 | 0.9923 | | 0.0476 | 12.04 | 1300 | 1.5262 | 0.9792 | | 0.0518 | 12.96 | 1400 | 1.5990 | 0.9832 | | 0.071 | 13.88 | 1500 | 1.6326 | 0.9875 | | 0.0333 | 14.81 | 1600 | 1.5955 | 0.9870 | | 0.0369 | 15.74 | 1700 | 1.5577 | 0.9832 | | 0.0689 | 16.66 | 1800 | 1.5415 | 0.9839 | | 0.0227 | 17.59 | 1900 | 1.5450 | 0.9878 | | 0.0472 | 18.51 | 2000 | 1.5642 | 0.9846 | | 0.0214 | 19.44 | 2100 | 1.6103 | 0.9846 | | 0.0289 | 20.37 | 2200 | 1.6467 | 0.9898 | | 0.0182 | 21.29 | 2300 | 1.5268 | 0.9780 | | 0.0439 | 22.22 | 2400 | 1.6001 | 0.9818 | | 0.06 | 23.15 | 2500 | 1.5481 | 0.9813 | | 0.0351 | 24.07 | 2600 | 1.5672 | 0.9820 | | 0.0198 | 24.99 | 2700 | 1.6303 | 0.9856 | | 0.0328 | 25.92 | 2800 | 1.5958 | 0.9831 | | 0.0245 | 26.85 | 2900 | 1.5745 | 0.9809 | | 0.0885 | 27.77 | 3000 | 1.5455 | 0.9809 | | 0.0224 | 28.7 | 3100 | 1.5378 | 0.9824 | | 0.0223 | 29.63 | 3200 | 1.5642 | 0.9810 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.15.2.dev0 - Tokenizers 0.10.3
patrickvonplaten/norwegian-roberta-base
83b0a6d5e9a8b513d68680a1c053ac8c4afb1ced
2021-05-19T10:12:21.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
patrickvonplaten
null
patrickvonplaten/norwegian-roberta-base
2
null
transformers
24,591
## Roberta-Base This repo trains [roberta-base](https://huggingface.co/roberta-base) from scratch on the [Norwegian training subset of Oscar](https://oscar-corpus.com/) containing roughly 4.7 GB of data according to [this](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) example. Training is done on a TPUv3-8 in Flax. More statistics on the training run can be found under [tf.hub](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg).
patrickvonplaten/sew-small-100k-timit
bbec12ddceb80cd347df30f4c496962a8930f041
2021-10-27T10:44:41.000Z
[ "pytorch", "tensorboard", "sew", "automatic-speech-recognition", "dataset:timit_asr", "transformers", "timit_asr", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
patrickvonplaten
null
patrickvonplaten/sew-small-100k-timit
2
null
transformers
24,592
--- license: apache-2.0 tags: - automatic-speech-recognition - timit_asr - generated_from_trainer datasets: - timit_asr model-index: - name: sew-small-100k-timit 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. --> # sew-small-100k-timit This model is a fine-tuned version of [asapp/sew-small-100k](https://huggingface.co/asapp/sew-small-100k) on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.4926 - Wer: 0.2988 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.071 | 0.69 | 100 | 3.0262 | 1.0 | | 2.9304 | 1.38 | 200 | 2.9297 | 1.0 | | 2.8823 | 2.07 | 300 | 2.8367 | 1.0 | | 1.5668 | 2.76 | 400 | 1.2310 | 0.8807 | | 0.7422 | 3.45 | 500 | 0.7080 | 0.5957 | | 0.4121 | 4.14 | 600 | 0.5829 | 0.5073 | | 0.3981 | 4.83 | 700 | 0.5153 | 0.4461 | | 0.5038 | 5.52 | 800 | 0.4908 | 0.4151 | | 0.2899 | 6.21 | 900 | 0.5122 | 0.4111 | | 0.2198 | 6.9 | 1000 | 0.4908 | 0.3803 | | 0.2129 | 7.59 | 1100 | 0.4668 | 0.3789 | | 0.3007 | 8.28 | 1200 | 0.4788 | 0.3562 | | 0.2264 | 8.97 | 1300 | 0.5113 | 0.3635 | | 0.1536 | 9.66 | 1400 | 0.4950 | 0.3441 | | 0.1206 | 10.34 | 1500 | 0.5062 | 0.3421 | | 0.2021 | 11.03 | 1600 | 0.4900 | 0.3283 | | 0.1458 | 11.72 | 1700 | 0.5019 | 0.3307 | | 0.1151 | 12.41 | 1800 | 0.4989 | 0.3270 | | 0.0985 | 13.1 | 1900 | 0.4925 | 0.3173 | | 0.1412 | 13.79 | 2000 | 0.4868 | 0.3125 | | 0.1579 | 14.48 | 2100 | 0.4983 | 0.3147 | | 0.1043 | 15.17 | 2200 | 0.4914 | 0.3091 | | 0.0773 | 15.86 | 2300 | 0.4858 | 0.3102 | | 0.1327 | 16.55 | 2400 | 0.5084 | 0.3064 | | 0.1281 | 17.24 | 2500 | 0.5017 | 0.3025 | | 0.0845 | 17.93 | 2600 | 0.5001 | 0.3012 | | 0.0717 | 18.62 | 2700 | 0.4894 | 0.3004 | | 0.0835 | 19.31 | 2800 | 0.4963 | 0.2998 | | 0.1181 | 20.0 | 2900 | 0.4926 | 0.2988 | ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.8.1 - Datasets 1.14.1.dev0 - Tokenizers 0.10.3
patrickvonplaten/unispeech-sat-base-plus-timit-ft
67b7c36ab967f831ca5987da5402622c843ba760
2021-10-21T10:05:15.000Z
[ "pytorch", "tensorboard", "unispeech-sat", "automatic-speech-recognition", "dataset:timit_asr", "transformers", "timit_asr", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
patrickvonplaten
null
patrickvonplaten/unispeech-sat-base-plus-timit-ft
2
null
transformers
24,593
--- tags: - automatic-speech-recognition - timit_asr - generated_from_trainer datasets: - timit_asr model-index: - name: unispeech-sat-base-plus-timit-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # unispeech-sat-base-plus-timit-ft This model is a fine-tuned version of [microsoft/unispeech-sat-base-plus](https://huggingface.co/microsoft/unispeech-sat-base-plus) on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.6549 - Wer: 0.4051 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3838 | 0.69 | 100 | 3.2528 | 1.0 | | 2.9608 | 1.38 | 200 | 2.9682 | 1.0 | | 2.9574 | 2.07 | 300 | 2.9346 | 1.0 | | 2.8555 | 2.76 | 400 | 2.7612 | 1.0 | | 1.7418 | 3.45 | 500 | 1.5732 | 0.9857 | | 0.9606 | 4.14 | 600 | 1.0014 | 0.7052 | | 0.8334 | 4.83 | 700 | 0.7691 | 0.6161 | | 0.852 | 5.52 | 800 | 0.7169 | 0.5997 | | 0.5707 | 6.21 | 900 | 0.6821 | 0.5527 | | 0.4235 | 6.9 | 1000 | 0.6078 | 0.5140 | | 0.4357 | 7.59 | 1100 | 0.5927 | 0.4982 | | 0.5004 | 8.28 | 1200 | 0.5814 | 0.4826 | | 0.3757 | 8.97 | 1300 | 0.5951 | 0.4643 | | 0.2579 | 9.66 | 1400 | 0.5990 | 0.4581 | | 0.2087 | 10.34 | 1500 | 0.5864 | 0.4488 | | 0.3155 | 11.03 | 1600 | 0.5836 | 0.4464 | | 0.2701 | 11.72 | 1700 | 0.6045 | 0.4348 | | 0.172 | 12.41 | 1800 | 0.6494 | 0.4344 | | 0.1529 | 13.1 | 1900 | 0.5915 | 0.4241 | | 0.2411 | 13.79 | 2000 | 0.6156 | 0.4246 | | 0.2348 | 14.48 | 2100 | 0.6363 | 0.4206 | | 0.1429 | 15.17 | 2200 | 0.6394 | 0.4161 | | 0.1151 | 15.86 | 2300 | 0.6186 | 0.4167 | | 0.1723 | 16.55 | 2400 | 0.6498 | 0.4124 | | 0.1997 | 17.24 | 2500 | 0.6541 | 0.4076 | | 0.1297 | 17.93 | 2600 | 0.6546 | 0.4117 | | 0.101 | 18.62 | 2700 | 0.6471 | 0.4075 | | 0.1272 | 19.31 | 2800 | 0.6586 | 0.4065 | | 0.1901 | 20.0 | 2900 | 0.6549 | 0.4051 | ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.8.1 - Datasets 1.14.1.dev0 - Tokenizers 0.10.3
patrickvonplaten/unispeech-sat-base-plus-timit
9414135d94c74123c07d699aec13dfa2d3a9f4ab
2021-10-20T19:43:27.000Z
[ "pytorch", "tensorboard", "unispeech-sat", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
patrickvonplaten
null
patrickvonplaten/unispeech-sat-base-plus-timit
2
null
transformers
24,594
Entry not found
patrickvonplaten/wav2vec2-common_voice-ab-demo
5e09e51d330d5e2766be106c0e5e98ddd75bab67
2021-09-22T13:57:47.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "ab", "transformers", "speech-recognition", "common_voice", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
patrickvonplaten
null
patrickvonplaten/wav2vec2-common_voice-ab-demo
2
null
transformers
24,595
--- language: - ab license: apache-2.0 tags: - speech-recognition - common_voice - generated_from_trainer model-index: - name: wav2vec2-common_voice-ab-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-common_voice-ab-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - AB dataset. It achieves the following results on the evaluation set: - Loss: 15.1812 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
patrickvonplaten/xls-r-300m-tr-phoneme
fa174f09430cb61281cc8c0d9a948b0fc81b526f
2021-12-21T11:13:30.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "mozilla-foundation/common_voice_3_0", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
patrickvonplaten
null
patrickvonplaten/xls-r-300m-tr-phoneme
2
null
transformers
24,596
--- tags: - automatic-speech-recognition - mozilla-foundation/common_voice_3_0 - generated_from_trainer model-index: - name: xls-r-300m-tr-phoneme results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xls-r-300m-tr-phoneme This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the mozilla-foundation/common_voice_3_0 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.4378 - Wer: 0.09936 ## 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: 0.000075 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 - mixed_precision_training: Native AMP ### Training results See Training Metrics Tab. ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
patrickvonplaten/xprophetnet-decoder-clm-large-uncased
f03909560fba63319d11e1581b05a0396b1d1bc8
2020-10-21T10:25:04.000Z
[ "pytorch", "xlm-prophetnet", "text-generation", "transformers" ]
text-generation
false
patrickvonplaten
null
patrickvonplaten/xprophetnet-decoder-clm-large-uncased
2
null
transformers
24,597
Entry not found
pelican/3cls_equal_len
0736236910e284ebdc3bc983bbb21b56914d1f27
2021-12-08T17:37:55.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
pelican
null
pelican/3cls_equal_len
2
null
transformers
24,598
Entry not found
pelican/test_model
1c5fc482c3e343397d661dd39d932843bd2f666b
2021-12-07T16:01:11.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
pelican
null
pelican/test_model
2
null
transformers
24,599
Entry not found