license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Article_100v1_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3140 - Precision: 0.4708 - Recall: 0.4550 - F1: 0.4628 - Accuracy: 0.... | c2fff65d356d6e3f48f63f690c8809a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 40 | 0.4092 | 0.1933 | 0.1445 | 0.1654 | 0.8398 | | No log | 2.0 |... | 9bc23eaeeeedcb8c69f9a114d99236a6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-telugu_150 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [openslr](https://openslr.org/66) dataset. It achieves the following results on the evaluation set: - Loss: 0.3312 - Wer: 0.2213 | 02f7e0ca6c1a0774c4f0fab00f08cd9e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 1817f10895956b3a7c7d20bd2914bd34 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6.096 | 3.84 | 400 | 0.5762 | 0.7029 | | 0.427 | 7.69 | 800 | 0.3124 | 0.5148 | | 0.208 | 11.54 | 1200 | 0.2994 | ... | 842b8a798e0830946680c422a563a344 |
mit | ['generated_from_keras_callback'] | false | KenP/codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 10.3900 - Validation Loss: 9.6171 - Epoch: 0 | cba2cce7854cad512e909b5edfeca1ff |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | b1b971ba9bb0d41c31d53df45e841364 |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1 This model is a fine-tuned version of [gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING2 dataset. It achieves the following res... | f1c2fcf03958db7c6d02e20a5ea857df |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 1bc85549d7a069d649c85b1b8630ebd6 |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.656 | 1.0 | 112 | 1.7625 | 0.9265 | | 1.3693 | 2.0 | 224 | 1.5135 | 0.9243 | | 1.2172 | 3.0 | 336 | 1.2657 | 0.8533 | |... | 600b9befe12ed10a2d2d6f60afaea499 |
apache-2.0 | [] | false | Model description **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also pro... | 13ff4ef306c593cd5d7c8c30056c41bb |
apache-2.0 | [] | false | Word| |-|-|:-:|-:|-:| ||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B| ||`bert-base-arabic-camelbert-ca`|CA|6GB|847M| ||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B| ||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B| ||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B| ||`bert-base-arabic-camelbert-msa-quar... | 80d6e2d9361afaedc333f1a2f5cffc58 |
apache-2.0 | [] | false | Intended uses You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code [here](https://... | 24e0b995388780cfef33e2adc007d213 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') >>> unmasker("الهدف من الحياة هو [MASK] .") [{'sequence': '[CLS] الهدف من الحياة هو ا... | 3b1e969cf9fb060c06c2155e1f5c467c |
apache-2.0 | [] | false | Training data - MSA (Modern Standard Arabic) - [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11) - [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus) - [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osi... | 637ec5acbe2c0c04e0ea5bcef061764f |
apache-2.0 | [] | false | Training procedure We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. | 6bd455923e06cf2693449cc94772ae58 |
apache-2.0 | [] | false | Preprocessing - After extracting the raw text from each corpus, we apply the following pre-processing. - We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61... | da87704a8a8ce71d8df5ee41ba617b42 |
apache-2.0 | [] | false | L286-L297). - We also remove lines without any Arabic characters. - We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools). - Finally, we split each line into sentences with a heuristics-based sentence segmenter. - We train a WordPiece tokenizer on the entire dataset (167 GB... | e33a9a7804192e096bedfb9744c501dd |
apache-2.0 | [] | false | Pre-training - The model was trained on a single cloud TPU (`v3-8`) for one million steps in total. - The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. - The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. - W... | 6716f2cef07bf428524fc1a7ea66293e |
apache-2.0 | [] | false | Evaluation results - We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. - We fine-tune and evaluate the models using 12 dataset. - We used Hugging Face's transformers to fine-tune our CAMeLBERT models. - We used transf... | 51da6da499d94a770d49d685b3a41405 |
apache-2.0 | [] | false | Results | Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | NER | ANERcorp | MSA | 80.8%... | 35e545ca5613763e6f1b63ba06753d5f |
apache-2.0 | [] | false | Results (Average) | | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | Variant-wise-average<sup>[[1]]( | d3194f3bc761515475cc9cc7369fbbbc |
apache-2.0 | [] | false | footnote-1)</sup> | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% | | | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% | | | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | |... | b10c59ec3431d6b11b3bceb2d597d092 |
apache-2.0 | [] | false | Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proce... | 3dd770f41b01a16704a1d89f9c338e0c |
mit | [] | false | Aflac duck on Stable Diffusion This is the `<aflac duck>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also... | 13a7b896cadbbb4a5ef8a5bc49f0a6e3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-legal-chunk This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0699 - Precision: 0.8994 - Recall: 0.8721 - Macro F1: 0.8855 - Micro F1: 0.8855 - Accuracy: 0.9789 ... | cc1401b197f2172d75f09bfecc6734a1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 | 870a8230069a2340b7c4c573c71ac3eb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Macro F1 | Micro F1 | Accuracy | Marker F1 | Marker Precision | Marker Recall | Reference F1 | Reference Precision | Reference Recall | Term F1 | Term Precision | Term Recall | |:-------------:|:-----:|:-----:|:---------------:|... | 9d04265dd59fb75a3844b9a2a030a404 |
apache-2.0 | ['generated_from_trainer'] | false | bart-samsung-5 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4959 - Rouge1: 48.4734 - Rouge2: 25.3475 - Rougel: 40.9144 - Rougelsum: 44.7797 - Gen Len: 18.22 | 76f1c6c93f21b11bc384819ebabeab33 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6107 | 1.0 | 1841 | 1.5390 | 47.1407 | 24.384 | 40.4826 | 43.4437 | 17... | da634dd7e2f265b31d28fe1e230e7068 |
apache-2.0 | ['translation'] | false | opus-mt-sv-guw * source languages: sv * target languages: guw * OPUS readme: [sv-guw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-guw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 632a7c03e4339c6dc2835ba47c57110e |
apache-2.0 | ['generated_from_trainer'] | false | MIX1_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on a combination of Visual Novel, Light Novel, and Subtitle data. A total of ~10MM lines of training data were used. It achieves the following results on the evaluation set: - Loss... | 181f2075845e5dbf853c8eae58866d44 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | 14f07a618a9e6723095d130b9a3cd08b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.7495 | 0.01 | 2000 | 2.5989 | | 2.5415 | 0.03 | 4000 | 2.4746 | | 2.4409 | 0.04 | 6000 | 2.4731 | | 2.3743 | 0.05 | 8000 | 2... | 2bbea74cd052e0c4e95eb597e80695d1 |
apache-2.0 | ['translation'] | false | opus-mt-de-el * source languages: de * target languages: el * OPUS readme: [de-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-el/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | ed0bff423e5a6444c5462b8acbbb3e71 |
mit | [] | false | doener_red_line_art on Stable Diffusion This is the `<dnr>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can al... | e0d55b3b90464e995bd36659bef63d56 |
mit | ['diffusion', 'netsvetaev', 'dreambooth', 'stable-diffusion', 'text-to-image'] | false | Hello! This is the model based on my paintings and SD 1.5. I did it as an experiment. The token is «in style of netsvetaev abstract paintings». Best suited for: abstract seamless patterns, simple prompts like «orange, fruit», and large objects like «cat face» or «girl face». It works well with landscape orientati... | ecc048d52c81e84b9da5097769f550a3 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Icelandic This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 25f505f22f9dcda8170771fd27c9345e |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-is") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-is") ``` | 3984257ca3e8aa493872a01e21bea866 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | model1_test This model is a fine-tuned version of [DaNLP/da-bert-hatespeech-detection](https://huggingface.co/DaNLP/da-bert-hatespeech-detection) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1816 - Accuracy: 0.9667 - F1: 0.3548 | 5c05d01271fc9171f2b1e69aac5c4352 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 150 | 0.1128 | 0.9667 | 0.2 | | No log | 2.0 | 300 | 0.1666 | 0.9684 | 0.2963 | | No log |... | 97fcdd27160c216edaf7d63ee57ec313 |
apache-2.0 | ['translation'] | false | opus-mt-sv-yap * source languages: sv * target languages: yap * OPUS readme: [sv-yap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-yap/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 72ec66ea7e61288cc40e7fde44300b72 |
apache-2.0 | ['generated_from_keras_callback'] | false | Lodo97/GPT-2-finetuned-pubmed This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3865 - Validation Loss: 1.2578 - Epoch: 0 | b39001d1c9e7deb6f0d605831e20f50a |
apache-2.0 | ['generated_from_keras_callback'] | false | BERT_Tweet_Sentiment_50k_2eps This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1131 - Train Accuracy: 0.9596 - Validation Loss: 0.6972 - Validation Accuracy: 0.8229 - Epoc... | 31b8bca7fa1d11ab9ef6afb22a8fb723 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | b92b5f8124d6eb6cdd8a640fed342c98 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3420 | 0.8511 | 0.4293 | 0.8299 | 0 | | 0.1131 | 0.9596 | 0.6972 | 0.8229 ... | 5c129c3bc9c57bb74913e100013e8f3c |
agpl-3.0 | ['roberta', 'icelandic', 'masked-lm', 'pytorch'] | false | IceBERT IceBERT was trained with fairseq using the RoBERTa-base architecture. The training data used is shown in the table below. | Dataset | Size | Tokens | |------------------------------------------------------|---------|--------| | Icelandic Gigaword Corpus v20.05 ... | c5d165f5bb28b0786a7be7db1dbf4f2c |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-sst2-KD-BERT_and_distilBERT This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5530 - Accuracy: 0.8326 | 9170002f45dc333149b071c93fec19f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.7317 | 1.0 | 4210 | 1.5887 | 0.8222 | | 1.0068 | 2.0 | 8420 | 1.5530 | 0.8326 | | 0.7961 | 3.0 | 12630 | 1.7072 ... | 09bde99bf52e7adb74a6bf1d69583509 |
mit | ['generated_from_trainer'] | false | Redaction Classifier: NLP Edition This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on a custom dataset. It achieves the following results on the evaluation set: - Loss: 0.0893 - Pearson: 0.8273 | 17df80f8755e810735926197b9df1254 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 - mixed_precision_trai... | 27522e97bd9ff265be3a47c7ab8f3f7a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2054 | 1.0 | 729 | 0.1382 | 0.6771 | | 0.1386 | 2.0 | 1458 | 0.1099 | 0.7721 | | 0.0782 | 3.0 | 2187 | 0.0950 | 0.8083... | 5e50e27398c8bf000183be3afb5ff0a0 |
mit | ['generated_from_keras_callback'] | false | PromptGenerator_5_topic_finetuned This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarakaya/turkishReviews-ds) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6861 - Train Sparse Categorical Accuracy: 0.8150 - Validation Loss... | 406324de078ef7e46cccb5b7040420c9 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 3.0394 | 0.5171 | 2.7152 ... | 1243d2ede69e7428633bb61dc132dce8 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-jm-finetuned-panx-en_hub This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4209 - F1: 0.6542 | eab9b3f23602a4eb29b62b9148fa5e92 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.2098 | 1.0 | 50 | 0.6167 | 0.4802 | | 0.5605 | 2.0 | 100 | 0.4436 | 0.6184 | | 0.4035 | 3.0 | 150 | 0.4209 | 0.6542 | ... | 270303227c0bedfcddb5c0be3644eff2 |
apache-2.0 | ['generated_from_trainer'] | false | natural-language-inference This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4120 - Accuracy: 0.8284 - F1: 0.8822 | e0505ed6c508b8ef0df588a449b4190a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.4288 | 0.8039 | 0.8644 | | No log | 2.0 | 460 | 0.4120 | 0.8284 | 0.8822 | | fe0a20b2458c10dcc59840641aba9a0b |
mit | [] | false | 日本語誤り訂正 - "吾輩をは猫である。名前えはまだない。"→"吾輩は猫である。名前はまだない。" - "-small" has been trained on 20,000 text pairs only. - dataset: [link](http://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9EWikipedia%E5%85%A5%E5%8A%9B%E8%AA%A4%E3%82%8A%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) *used only first 20,000 text pairs.... | 5d06c75ad31b5729fa587e9ef1fdf8b3 |
mit | [] | false | 参考 - "東北大学でMASKが研究をしています。"→"東北大学でMASKの研究をしています。" ジム・キャリーを主語とした唯一のガ格が消され、ジム・キャリーは研究対象となった。易読化のために用いられる主語と動詞を近づける記法は誤り扱い？ - "東北大学でマスクが研究をしています。"→"東北大学でマスクの研究をしています。" - "東北大学でイーロン・マスクが研究をしています。"→"東北大学でイーロン・マスクが研究をしています。" - "東北大学で「イーロン・マスク」が研究をしています。"→"東北大学で「イーロン・マスク」の研究をしています。" 単語の意味も考慮されている？ - "東北大学でイマスクが研究をしています。"→"東北... | 7e1ed7140beb69529893613a010fcf32 |
mit | [] | false | 参考 extra_idを用い探索 ＜＞は半角に変更してください - "東北大学で ＜extra_id_0＞ の研究をしています。"→"東北大学で化学の研究をしています。" - "東北大学で ＜extra_id_0＞ が研究をしています。"→"東北大学で工学が研究をしています。" 工学さん。 - "吾輩は ＜extra_id_0＞ である。"→"吾輩は吾輩である。" - "答えは猫です。吾輩は ＜extra_id_0＞ である。"→"答えは猫です。吾輩は猫である。" - "答えは猫です。吾輩の ＜extra_id_0＞ である。"→"答えは猫です。吾輩の心は猫である。" - "私は猫です。私は ＜extra_id_0＞"→"私は... | 91b89e7bec4d250a768d9c75ec0112f1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becasv2-6 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.8936 | d54dc4d308336b73c1e8b2cc6bbfa9a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 87fcd583cedcdd2d9fc9e6dd048c718f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.0542 | | No log | 2.0 | 18 | 3.0865 | | No log | 3.0 | 27 | 2.8069 | | No log | 4.0 | 36 | 3.3330 ... | 5228abc50fcfa3ac877a67219f53e8d5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased-hate-speech **Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on [hate speech](... | 41d125f035b414f7704764ab4f1fe050 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_tr... | a938f75704eeafe0d7162937e03c62f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.6857 | 1.0 | 3389 | 0.6471 | 1.9725 | | 0.3645 | 2.0 | 6778 | 0.4359 | 1.9725 | | 0.2266 | 3.0 | 10167 | 0.3664 | 1.972... | ed325286a6fbfcc0a5d4307b734541b2 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_unispeech-ml_s51 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using ... | a76f379eab4a42307a7fa8c00a38ccb0 |
apache-2.0 | ['feature-extraction', 'transformers'] | false | Usage ```python from transformers import DPRContextEncoder, DPRContextEncoderTokenizer tokenizer = DPRContextEncoderTokenizer.from_pretrained('firqaaa/indo-dpr-ctx_encoder-single-squad-base') model = DPRContextEncoder.from_pretrained('firqaaa/indo-dpr-ctx_encoder-single-squad-base') input_ids = tokenizer("Ibukota In... | a03cef53d3474b9a73843fe7bb8eb1d4 |
mit | ['legal-contract-review', 'roberta', 'cuad'] | false | Model Description - **Developed by:** Mohammed Rakib - **Shared by [Optional]:** More information needed - **Model type:** Question Answering - **Language(s) (NLP):** en - **License:** MIT - **Related Models:** - **Parent Model:** RoBERTa - **Resources for more information:** - GitHub Repo: [defactolaw](htt... | 852420d1a2490b0d8094a43f2c4814ac |
mit | ['legal-contract-review', 'roberta', 'cuad'] | false | Training Details Read: [An Open Source Contractual Language Understanding Application Using Machine Learning](https://aclanthology.org/2022.lateraisse-1.6/) for detailed information on training procedure, dataset preprocessing and evaluation. | 0d70d203e8f7c65309f4ab334ef98564 |
mit | ['legal-contract-review', 'roberta', 'cuad'] | false | Model Examination More information needed - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed | ea49fc6cd345661866a0f5b79aedf5e0 |
mit | ['legal-contract-review', 'roberta', 'cuad'] | false | Citation **BibTeX:** ``` @inproceedings{nawar-etal-2022-open, title = "An Open Source Contractual Language Understanding Application Using Machine Learning", author = "Nawar, Afra and Rakib, Mohammed and Hai, Salma Abdul and Haq, Sanaulla", booktitle = "Proceedings of the First Wo... | 00fef2186bdda06aa18f2686bedd45f9 |
mit | ['legal-contract-review', 'roberta', 'cuad'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad") model = AutoModelForQuestio... | b4806863c581d1f71b56702210952e22 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3942 - Wer: 0.3149 | 767ff57ffee8d5caaaa259be9b0fcea8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9921 | 3.67 | 400 | 0.7820 | 0.7857 | | 0.4496 | 7.34 | 800 | 0.4630 | 0.4977 | | 0.2057 | 11.01 | 1200 | 0.4293 | 0.4627 | |... | eb4c2169df80072a61cd8381f88c9f18 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_summarization_reward_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6972 - Accuracy: 0.5271 | d30d21a624f8d2fc34912d065d32afda |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6922 | 1.0 | 11608 | 0.6918 | 0.5237 | | 0.6762 | 2.0 | 23216 | 0.6972 | 0.5271 | | 733e06fe493efe132a50f842c54d8e9d |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_wavlm_s545 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | 0aa016107ddc214e1445bbdf2aac8444 |
apache-2.0 | ['translation'] | false | opus-mt-swc-fr * source languages: swc * target languages: fr * OPUS readme: [swc-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/swc-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 1de401739d4089ffff394d86de0e876c |
apache-2.0 | [] | false | doc2query/stackexchange-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 q... | b365fa2d49d7c040a4339c8614240738 |
apache-2.0 | [] | false | Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/stackexchange-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpos... | f88cdc9af0899c5a801b47e8f656d935 |
apache-2.0 | [] | false | Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 449k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was train... | 9d9374026f20c48cea8565d6d2bc00bc |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.05-0.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8318 - Bleu: 7.1808 - Gen Len: 44.1986 | c079c8ca91e5f658b47597ef80adefa8 |
apache-2.0 | ['translation'] | false | opus-mt-ts-fi * source languages: ts * target languages: fi * OPUS readme: [ts-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ts-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 9be8620de45601eb6f5fcd5ddbb02c59 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_datetime-roberta-large-v1-2-0.82 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with c... | 1379ebf3bf0954ca388631b57f713a7e |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-lv Neural machine translation model for translating from English (en) to Latvian (lv). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | c729c4d2fc978e666d1dd5f8de2d0b46 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): eng * target language(s): lav * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](htt... | 718a37b441380ff5a8d544954e59972c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>lav<< A day has twenty-four hours.", ">>ltg<< He's a good lawyer." ] model_name = "pytorch-models/opus-mt-tc-big-en-lv" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel... | 71782f98a077e1fbfd3ad93ac2876d8f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Vyss ir labs advokats. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-lv") print(pipe(">>lav<< A day has twenty-four hours.")) | 08309cc0eb6ad067a17d9530b8175d6f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | e61b0b02a7f992b775852704b695630d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-lav | tatoeba-test-v2021-08-07 | 0.66411 | 44.0 | 1631 | 9932 | | eng-lav | flores101-devtest | 0.59397 | 30.1 | 1012 | 22092 | | eng-lav | newsdev2017 | 0.58082 | 28.9 | 2003 | 41503 | | eng-lav | newstest2017 | 0.53202 | 22.1 | 2001 | 39392 | | 72008d99b75a2b0781b815019337b22d |
mit | ['modelcards', 'autogenerated-modelcard'] | false | Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> This isn't really a model, it's just a test repo to see if the [model card creator](https://huggingface.co/spaces/nateraw/modelcard-creator) works! - Developed by: Nathan Raw - Language(s): - License: modelca... | 3889a4c3e307e2ff16b88924e005c0a3 |
mit | ['modelcards', 'autogenerated-modelcard'] | false | Limitations and Biases <!-- Describe limitations and biases of this model or models of it's type. --> **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** [More Information Needed] | 58ecf65cc57e699487a58e632ca92612 |
mit | ['modelcards', 'autogenerated-modelcard'] | false | Environmental Impact <!-- Provide information to document the environmental impact of this model --> You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact | 1137bd43a2d54aea1be8d25ec331de93 |
mit | ['modelcards', 'autogenerated-modelcard'] | false | Citation Information ```bibtex @inproceedings{Mitchell_2019, doi = {10.1145/3287560.3287596}, url = {https://doi.org/10.1145%2F3287560.3287596}, year = 2019, month = {jan}, publisher = {{ACM} }, author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Be... | 9b2cf9c6388353118c79807d88c30b51 |
apache-2.0 | [] | false | distilbert-base-en-el-ru-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original ac... | 35c11500c9b050362ecd7b5915a392a1 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-el-ru-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-el-ru-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | b9fd2426fd355dc242f850e12db0c833 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8488 - Wer: 16.5882 | b61407c0d3f257b714892d7f76367424 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.2963 | 0.1 | 1000 | 0.9115 | 27.3641 | | 0.2676 | 0.2 | 2000 | 0.8796 | 24.1024 | | 0.3166 | 0.3 | 3000 | 0.8467 | 2... | e7efdf332ed5c57630f87c5cb727cfcd |
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