license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | load the dataset commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "es", split="validation", streaming=True) commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000)) sample = next(iter(commonvoice_eval))["audio"] | 11c481d52cffaaa889d4be58d29dfbd8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-BERTmodel-A3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3307 - Accuracy: 0.8656 - F1: 0.3576 | f94077ec7694190f0cdfdf0b66a84da1 |
mit | [] | false | Avatar Na'vi model on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 44d6a57fa0f87e20bdece782b96fbf08 |
mit | [] | false | Results: <!-- This section is meant to convey both technical and sociotechnical limitations. -->   on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4624 - Accuracy: 0.3691 | 394f4262542522ff718d4b3a571cef89 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | ef0609fe6bc26b62f66686a9f9ef547b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5643 | 1.0 | 1047 | 1.5474 | 0.3526 | | 0.8147 | 2.0 | 2094 | 2.6498 | 0.3719 | | 0.1618 | 3.0 | 3141 | 3.1061 | 0.... | 6e4626c2a29db20402845893a2da603d |
cc-by-4.0 | [] | false | KannadaBERT KannadaBERT is a Kannada BERT model trained on publicly available Kannada monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBE... | d591e6008d129188dc0eec80f96742af |
apache-2.0 | ['generated_from_keras_callback'] | false | lakshaywadhwa1993/mt5-small-finetuned-hindi-mt5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4909 - Validation Loss: 1.3507 - Epoch: 7 | 60e1ae7cc355929774bc476f3b0e906a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 41000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'deca... | 0305543d1b34f4f07b26a06cfa8f858c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5310 | 1.8341 | 0 | | 2.0735 | 1.6193 | 1 | | 1.7617 | 1.4672 | 2 | | 1.6375 | 1.4271 | 3 | | 1.5712 | 1.3720 | 4 | | 1.5294 |... | 142c4f06f443b9455f9914d2d99a6de6 |
apache-2.0 | ['generated_from_keras_callback'] | false | shaun-e-j/bert-finetuned-testing1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.0242 - Epoch: 4 | 5d8a00f20595c1870b24448bd096fdb3 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': Fals... | 1d3fa6277312a797d35e953d8ebd5a4f |
apache-2.0 | ['translation'] | false | opus-mt-en-ee * source languages: en * target languages: ee * OPUS readme: [en-ee](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ee/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | f779d00a9a18e8a6921de46a69725ccf |
apache-2.0 | ['generated_from_keras_callback'] | false | KenP/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 an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6855 - Validation Loss: 0.8088 - Epoch: 2 | bd34a45fbb36b3e78aa9f61d636e0bfb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0599 | 0.8835 | 0 | | 0.7975 | 0.8254 | 1 | | 0.6855 | 0.8088 | 2 | | a6599952355c845bb08dff0b36e1da67 |
mit | ['generated_from_trainer'] | false | boolq_deberta_model This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue - boolq dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4066 - eval_accuracy: 0.8468 - eval_runtime: 111.0255 - eval_samples_per_se... | 209ce1959ca2f5d76fccafa459a0aa0c |
mit | [] | false | RickyArt on Stable Diffusion This is the `<RickyArt>` 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 tra... | 4189b006ed8e65bb3dcd537713930612 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-wtimit-finetune This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0383 - Wer: 0.0160 | fe6516a9a7227c2588b8e6658cc9088a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_... | ef3a2daf2485d13768c619dd8971ef35 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.3743 | 2.82 | 500 | 2.9567 | 1.0 | | 1.866 | 5.65 | 1000 | 0.2856 | 0.2580 | | 0.2005 | 8.47 | 1500 | 0.0979 | 0.0669 | |... | 3e288397b3a3b4ddfe7a794d9d79365d |
apache-2.0 | ['generated_from_trainer'] | false | (BERT base) NER model in the legal domain in Portuguese **README under construction** **ner-legal-bert-base-cased-ptbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned from the model [dominguesm/legal-bert-base-cased-ptbr](https://huggingface.co/dominguesm/legal-bert-base-... | d03b6dd9e04241cde60d3bcaddcad42f |
apache-2.0 | ['generated_from_trainer'] | false | Training Dataset The dataset of **ner-legal-bert-base-cased-ptbr** include: * 971932 examples of miscellaneous legal documents (train split) * 53996 examples of miscellaneous legal documents (valid split) * 53997 examples of miscellaneous legal documents (test split) The data used was provided by the BRAZILIAN ... | 207dab591aadfb796762a16f83a5cd82 |
apache-2.0 | ['generated_from_trainer'] | false | parameters model_name = "dominguesm/ner-legal-bert-base-cased-ptbr" model = AutoModelForTokenClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) input_text = "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dis... | 62a8692f1a03c4b32018d4bb9c6f5831 |
apache-2.0 | ['generated_from_trainer'] | false | print predictions for token, prediction in zip(tokens, predictions[0].numpy()): print((token, model.config.id2label[prediction])) ``` You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence. ```python from transformers import pipeline model_name = "domingu... | 872976a562d3ca763e7dffcf31bb41b7 |
apache-2.0 | ['generated_from_trainer'] | false | batch, learning rate... - per_device_batch_size = 64 - gradient_accumulation_steps = 2 - learning_rate = 2e-5 - num_train_epochs = 3 - weight_decay = 0.01 - optimizer = torch.optim.AdamW - epsilon = 1e-08 - lr_scheduler_type = linear | e7d6f0625a5c83b5b8a7cddc07debd59 |
apache-2.0 | ['generated_from_trainer'] | false | save model & load best model - save_total_limit = 3 - logging_steps = 1000 - eval_steps = logging_steps - evaluation_strategy = 'steps' - logging_strategy = 'steps' - save_strategy = 'steps' - save_steps = logging_steps - load_best_model_at_end = True - fp16 = True | db8f8ab403947fcd0e85ce7b596eb269 |
apache-2.0 | ['generated_from_trainer'] | false | Training results ``` Num examples = 971932 Num Epochs = 3 Instantaneous batch size per device = 64 Total train batch size (w. parallel, distributed & accumulation) = 128 Gradient Accumulation steps = 2 Total optimization steps = 22779 Evaluation Infos: Num examples = 53996 Batch size = 128 ``` | Step | Training ... | d46f959e33bb2f73c8a362347798235b |
apache-2.0 | ['generated_from_trainer'] | false | Validation metrics by Named Entity (Test Dataset) * **Num examples = 53997** * `overall_precision`: 0.9432396865925381 * `overall_recall`: 0.9614334116769161 * `overall_f1`: 0.9522496545298874 * `overall_accuracy`': 0.9894741602608071 | Label | Precision | Recall | F1 Accuracy | Entity Examples | | ----- | --------... | e1dc5a7b37c6bb1cdc56b515fba96ad1 |
apache-2.0 | ['generated_from_trainer'] | false | Notes * For the production of this `readme`, i used the `readme` written by Pierre Guillou (available [here](https://huggingface.co/pierreguillou/ner-bert-large-cased-pt-lenerbr)) as a basis, reproducing some parts entirely. | eb2ad176e46f06cb59777a0e50d81803 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-profane-final This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2773 - Accuracy: 0.8992 - Precision: 0.8261 - Recall: 0.7987 - F1: 0.81... | 8328acbf5ad752a51db86ca752b42ea4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.2862 | 0.8907 | 0.8230 | 0.7528 | 0.7807 | | 0.3379 | 2.0 |... | a51c0ce254e28a46d681781f3c9510c9 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-MT5-Intento2 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 3.9645 - Rouge2: 0.8023 - Rougel: 3.8615 - Rougelsum: 3.8591 - Gen Len: 13.7379 | d979d4dcc33fa6b0081a89da780c6119 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - 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 - num_epochs: 2 - mixed_precision_training: Native AMP | a58c3dcc860051da31510727f97e650f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 1509 | nan | 3.9645 | 0.8023 | 3.8615 | 3.8591 | 13.7379 | |... | 0e3bed62d08c786d51c16d5c4b6843ab |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3093 - Accuracy: 0.8733 - F1: 0.875 | 4e615af2564161b52134f5bf2cb83540 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_4_ternary 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: 1.2981 - F1: 0.7565 | 191175a22b9882c22f62526a021ec56a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5588 | 0.6984 | | 0.5547 | 2.0 | 578 | 0.5283 | 0.7336 | | 0.5547 | 3.0 | 867 | 0.7038 | 0.7202 | |... | ae247d48f4070083aecb10f251ddfe42 |
apache-2.0 | [] | false | bert-base-en-pt-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the ... | 3775c3c7be6d45c61259a5e4aa874932 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-pt-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](h... | 25818a1198fd1dd43f4f7c22ceb86023 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr 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: 0.1013 - F1: 0.9242 | 48109410a8af31bb791415cb5fb90574 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5667 | 1.0 | 191 | 0.2318 | 0.8415 | | 0.2539 | 2.0 | 382 | 0.1428 | 0.8988 | | 0.1739 | 3.0 | 573 | 0.1013 | 0.9242 | ... | 486cc9b744fc783bcd10791b3c7e9803 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-finetuned-dapt_tapt-lm-music This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8680 - Validation Loss: 2.4306 - Epoch: 0 | e37041353d5cd6a0f7cd6894413e2729 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | 8b37a4ad0fa26d85487f1f795343e708 |
apache-2.0 | ['translation'] | false | opus-mt-pon-sv * source languages: pon * target languages: sv * OPUS readme: [pon-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pon-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 2c58ad5c1f533b6921cbacf3efbea9e2 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune `deberta-base-thai` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-thai-upos), [dependency-parsing](https://h... | 100a0da6b330e7777fe91c769541158a |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-thai") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-thai") ``` | 87f6f5a9333151162f5b55ae32797acc |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.6587 - Accuracy: 0.77 - F1: 0.7562 | 9c9aa612678291248e1926ce9f3b2390 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0823 - Accuracy: 0.9819 | c161adbd0e44ccdcf7282f540d7b938d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3582 | 1.0 | 1596 | 0.1846 | 0.9681 | | 0.2013 | 2.0 | 3192 | 0.1051 | 0.9776 | | 0.1656 | 3.0 | 4788 | 0.0823 | 0.... | 79636ca5713409a4727925bffaacfd1a |
apache-2.0 | ['named entity recognition', 'token criticality'] | false | Model description DanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has been trained on more than 2 million sentences and 40 millions, danish words. The training has been conducted as part of a thesis. The model can be found at: * [danbert-da](https://huggingface.co/alexanderfalk/danb... | a761f6e403f910fc788497d7a5b5f497 |
apache-2.0 | ['named entity recognition', 'token criticality'] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("alexanderfalk/danbert-small-cased") model = AutoModel.from_pretrained("alexanderfalk/danbert-small-cased") ``` | 08fede3b87a03afdeef159659f3b4b3e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-korean-speech-emotion-recognition This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15](https://huggingface.co/jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6651... | 606cd9ff7f72ed29399e6254d96e145c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | e22a8f6b04724d4aa1550d5625632eb2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7098 | 0.02 | 20 | 1.6849 | 0.1986 | | 1.6093 | 0.05 | 40 | 1.6102 | 0.2237 | | 1.5673 | 0.07 | 60 | 1.5126 | 0.... | 616b1ab996036f658ade033622a2ee0f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-checkpoint-3 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-2](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-2) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7007 - Wer: 0.5514 | d315002b3279fd5e23dd1ff9eefcdee1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.358 | 14.8 | 400 | 1.4841 | 0.5338 | | 0.1296 | 29.62 | 800 | 1.7007 | 0.5514 | | 1433719c635b1a4df584b9fd00d6975d |
apache-2.0 | ['translation'] | false | cpf-eng * source group: Creoles and pidgins, French‑based * target group: English * OPUS readme: [cpf-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cpf-eng/README.md) * model: transformer * source language(s): gcf_Latn hat mfe * target language(s): eng * model: transformer * pre-proce... | 997d6d22ec875a56dece6354e84332fa |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.gcf-eng.gcf.eng | 8.4 | 0.229 | | Tatoeba-test.hat-eng.hat.eng | 28.0 | 0.421 | | Tatoeba-test.mfe-eng.mfe.eng | 66.0 | 0.808 | | Tatoeba-test.multi.eng | 16.3 | 0.323 | | 727b5fbea8d0bf7b3c0e03b2efa46d80 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cpf-eng - source_languages: cpf - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cpf-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ht', 'cpf', 'en'] - src_constituents: {'gcf_Latn', ... | ca1a7b70de182e745f755ba67e797339 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-BERT-model-samples This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.7999 - Accuracy: 0.86 - F1: 0.8627 | 09f8920b4ead34ea6e9c0e628f92e6ac |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best` ♻️ Imported from https://zenodo.org/record/3986225/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 738dcb2059716730bce9a937fa4b271e |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UK dataset. It achieves the following results on the evaluation set: - Loss: 0.1747 - Wer: 0.2107 - Cer: 0.0408 | 90e047bd64774164ebfb83249aa5a868 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sch... | 7ea882e7f0f7ee140cb696662c5f725d |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.3719 | 4.35 | 500 | 0.3389 | 0.4236 | 0.0833 | | 1.1361 | 8.7 | 1000 | 0.2309 | 0.3162 | 0.0630 | | 1.0517 | 13.04 |... | 76f7c2773c0ca8057102f139808e4721 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | output This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.1822 - Wer: 0.1423 | 092b201169c1aa98de7daac66002c2ee |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-07 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 58f6992bb51c33c8eaff97752a29a507 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilbert-fakenews-detection 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: 0.0000 - Accuracy: 1.0 - F1: 1.0 | ec4a11b60ec35af2a457fc5126d0b94b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:| | 0.0125 | 1.0 | 978 | 0.0000 | 1.0 | 1.0 | | 0.0 | 2.0 | 1956 | 0.0000 | 1.0 | 1.0 | | 0.0 | 3.0 | 293... | 94f8bcea0bd72c8b2bbb3a7978439da7 |
apache-2.0 | ['translation'] | false | mkh-eng * source group: Mon-Khmer languages * target group: English * OPUS readme: [mkh-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mkh-eng/README.md) * model: transformer * source language(s): kha khm khm_Latn mnw vie vie_Hani * target language(s): eng * model: transformer * pre-pr... | 671ba611a95113c88271edd56b0b6081 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.kha-eng.kha.eng | 0.5 | 0.108 | | Tatoeba-test.khm-eng.khm.eng | 8.5 | 0.206 | | Tatoeba-test.mnw-eng.mnw.eng | 0.7 | 0.110 | | Tatoeba-test.multi.eng | 24.5 | 0.407 | | Tatoeba-test.vie-eng.vie.eng ... | 914a67fd798f4b91b89496589dc4596f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: mkh-eng - source_languages: mkh - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mkh-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'km', 'mkh', 'en'] - src_constituents: {'vie_H... | 291b7c1aa29552b8f5c48c17341d2e94 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | emhavrans Dreambooth model trained by wxcvbnw with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-dif... | 32ee0b2863a5b35bc120447805dc86c7 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Old_French-SRCMF | Feature | Description | | --- | --- | | **Name** | `xx_udv25_oldfrenchsrcmf_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experime... | cb1aca93db2ef1b271ddc3d2526dd89a |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (16214 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ADJQUA`, `ADJcar`, `ADJind`, `ADJord`, `ADJpos`, `ADJqua`, `ADVgen`, `ADVgen.PROadv`, `AD... | 9242dbbe604aacd584b9cc9e322cbace |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 81.11 | | `SENTS_P` | 79.75 | | `SENTS_R` | 82.52 | | `TAG_ACC` | 96.41 | | `POS_ACC` | 96.52 | | `MORPH_ACC` | 97.74 | | `DEP_UAS` | 90.21 | | `DEP_LAS` | 85.42 | | `LEM... | 79976775762b844bc3920d733e43107e |
mit | ['generated_from_trainer'] | false | goofy_mirzakhani This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomek... | 80b6940d50842a3181923599d316310a |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 550dc25fa3f4cc799948f6db9ade7d30 |
apache-2.0 | [] | false | GALACTICA (mini) Following [Mitchell et al. (2018)](https://arxiv.org/abs/1810.03993), this model card provides information about the GALACTICA model, how it was trained, and the intended use cases. Full details about how the model was trained and evaluated can be found in the [release paper](https://galactica.org/pa... | 681017f1959d817d33f1716be9aa681f |
apache-2.0 | [] | false | Model Details The GALACTICA models are trained on a large-scale scientific corpus. The models are designed to perform scientific tasks, including but not limited to citation prediction, scientific QA, mathematical reasoning, summarization, document generation, molecular property prediction and entity extraction. The ... | 715ae1cf7b234ef084298efd99488acb |
apache-2.0 | [] | false | Model Use The primary intended users of the GALACTICA models are reserachers studying language models applied to the scientific domain. We also anticipate the model will be useful for developers who wish to build scientific tooling. However, we caution against production use without safeguards given the potential of... | a48dd40aa3298d7891cc4f0d3539c270 |
apache-2.0 | [] | false | Training Data The GALACTICA models are trained on 106 billion tokens of open-access scientific text and data. This includes papers, textbooks, scientific websites, encyclopedias, reference material, knowledge bases, and more. We tokenize different modalities to provide a natural langauge interface for different tasks... | d3330124b146d037d9c872430653e86c |
apache-2.0 | [] | false | Performance and Limitations The model outperforms several existing language models on a range of knowledge probes, reasoning, and knowledge-intensive scientific tasks. This also extends to general NLP tasks, where GALACTICA outperforms other open source general language models. That being said, we note a number of li... | ad6d5adcd5ec6af2eff86aa3cec10898 |
apache-2.0 | [] | false | Broader Implications GALACTICA can potentially be used as a new way to discover academic literature. We also expect a lot of downstream use for application to particular domains, such as mathematics, biology and chemistry. In the paper, we demonstrated several examples of the model acting as alternative to standard s... | 25df84ec8ffcd373dd83cd512c83f65f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5573 | 1.0 | 2249 | 6.4633 | | 6.1893 | 2.0 | 4498 | 6.1993 | | 6.0153 | 3.0 | 6747 | 6.1085 | | 8295e99a4a55487b90febb770ad801ad |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `speechcatcher/speechcatcher_german_espnet_streaming_transformer_13k_train_size_m_raw_de_bpe1024` This model was trained by bmilde using speechcatcher recipe in [espnet](https://github.com/espnet/espnet/). | 725c598f1e294e68c52cfad1f2176539 |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout df10e664a3e1a3cbbe8363b1d93e94ad5d8b147f pip install -e . cd egs2/speechcatcher/asr1 ./run.sh --skip_data_prep false --skip_tra... | 7752af95cf172ef368a04decd26ebf78 |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Feb 5 11:50:19 UTC 2023` - python version: `3.10.8 (main, Nov 4 2022, 13:48:29) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1+cu116` - Git hash: `df10e664a3e1a3cbbe8363b1d93e94ad5d8b147f` - Commit date: `Fri Feb 3 13:38:18 2023 +0000` | 39ccc336df461559dc7c3c3909b8f0a3 |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_streaming_transformer_size_m.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_streaming_transformer_size_m_raw_de_bpe1024 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 0 dist_backend: ... | 0740615293ff73c6cb8b817d538410e0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-sufficiency-dagstuhl 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: - Loss: 0.8318 - Accuracy: 0.6032 | e6b7fa0c697018a62bd26d1ba2445388 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 16 | 0.8674 | 0.5714 | | No log | 2.0 | 32 | 0.8350 | 0.5714 | | No log | 3.0 | 48 | 0.8318 | 0.... | 1266a22192d705c2de8d1161c2e9de67 |
mit | [] | false | tela lenca2 on Stable Diffusion This is the `<tela-lenca>` 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 als... | ea3823ea5d850377489e35448d31d73e |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | This is a direct state_dict transfer from fairseq to huggingface, the weights are identical [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 10min of Libri-Light and Librispeech on 16kHz sampled speech audio. M... | 41c89d75927508654b836be683d07f98 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self") model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self") | 2d7c6446362d13398d5abda5b59857a5 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate facebook's **Splend1dchan/wav2vec2-large-10min-lv60-self** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval =... | 0e8252b3edfd8ceee83333a8cf8b3181 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_rte_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.4228 - Accuracy: 0.4729 | 7f59859cb2ac02f5df4f3da38226d350 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4351 | 1.0 | 10 | 0.4238 | 0.4729 | | 0.4173 | 2.0 | 20 | 0.4246 | 0.4729 | | 0.4173 | 3.0 | 30 | 0.4238 | 0.... | 45ec78a661f46d5fc9f63ef6bda2e65d |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2491 - F1: 0.8213 | 3b14a05bcdac87e0ebf7e0a03b83f901 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8192 | 1.0 | 70 | 0.3300 | 0.7184 | | 0.2949 | 2.0 | 140 | 0.2817 | 0.7959 | | 0.189 | 3.0 | 210 | 0.2491 | 0.8213 | ... | ef9e42e83b7f1cdfffd36ca811894563 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Precision: 0.9245 - Recall: 0.9365 - F1: 0.9304 - Accuracy: 0.9834 | 78914e08399678ccce61146593bd1e5e |
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