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 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Wav2Vec2-Large-XLSR-53-Fon Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data). When using this model, make sure that your spe... | 411b7cd468ae176edebfef8b8b2a33a4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Usage The model can be used directly (without a language model) as follows: ```python import json import random import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 40b8db4702473b4025dafb89538104f0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Remove unnecessary chars chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]' def remove_special_characters(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch test_dataset = test_dataset.map(remove_special_characters) processor = Wav2Vec2Pro... | 4555298fc337ce66dd1d44d845db71d5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"]=speech_array.squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_... | 0b23ed577a4481e7f4cd007ab841a2f7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation The model can be evaluated as follows on our unique Fon test data. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re for root, dirs, files in os.walk(test/): test_dataset = load_dataset("json", d... | d351d76d5c49268a92307a128406e8a0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = speech_array[0].numpy() batch["sampling_rate"] = sampling_rate batch["target_text"] = batch["sentence"] return batch test_dataset = test_data... | 0bea7b6bee9bd91dd0cc906141674ac6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation on test dataset def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1... | d92bace959a0ec08d9a1257f40ec5060 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Training The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples). The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing) | 5bd40c42d7032c956f6b20ac21332147 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Collaborators on this project - Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))|(chris.emezue@gmail.com) - Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|(femipancrace.dossou@gmail.com) | 0e504a8f8c3a08b025653b9e491edef4 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-it_s965 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 791673b78edd900d41b451f1a9957759 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-finetuned-DA-Zero-shot This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1318 | 0ab13861dca84687bb8d130f5c67c9a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 10.0 - mixed_precision_training: Native AMP | 9e82d8ebd1b83200a642100c27b64f1d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1282 | 1.0 | 435 | 1.3862 | | 1.1307 | 2.0 | 870 | 1.3362 | | 1.2243 | 3.0 | 1305 | 1.2791 | | 1.274 | 4.0 | 1740 | 1.2143 ... | fa874e8fce69af64e288edb7563aada3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1906 - F1: 0.8687 | c589ae060ea72d288f23a7739115d8fc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2884 | 1.0 | 3145 | 0.2390 | 0.8242 | | 0.1639 | 2.0 | 6290 | 0.1944 | 0.8488 | | 0.0952 | 3.0 | 9435 | 0.1906 | 0.8687 | ... | 63e340195bac04ad2577e03978d347b4 |
mit | ['generated_from_keras_callback'] | false | FineTune_Vit5_LR0_00001_time3 This model is a fine-tuned version of [thesunshine36/FineTune_Vit5_LR0_00001_time2](https://huggingface.co/thesunshine36/FineTune_Vit5_LR0_00001_time2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6297 - Validation Loss: 0.5655 - Train R... | 3b67af1de7a188cb1185fe42200bb163 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 | 66dfa29cda5fd784fdec458f8216b02b |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.6297 | 0.5655 | 52.5683 | 31.3753 ... | 346f57c77684358b4e52f28af313e2f0 |
mit | [] | false | vcr classique on Stable Diffusion This is the `<vcr_c>` 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 t... | e4261ce392d368dfdae18e5ac079e8de |
apache-2.0 | ['translation'] | false | opus-mt-pon-en * source languages: pon * target languages: en * OPUS readme: [pon-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pon-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 3a9dd08d6420092d5ec4f170977fcc78 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-squad This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0491 | d1c39c11818acb762c8fa2ab1c06aaa4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 27 | 0.1224 | | No log | 2.0 | 54 | 0.0491 | | 710d6aa42174890dbfedca8877674d44 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{E... | 3d841c8e54d5b40c8701401d8a41bd1d |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-wnli This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6880 - Accuracy: 0.5634 | 9618c76bd1fd42b0b231dfc090c2c351 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 0.6880 | 0.5634 | | No log | 2.0 | 80 | 0.6851 | 0.5634 | | No log | 3.0 | 120 | 0.6961 | 0.... | a319c7751126878d37c0c51c7de7b6ef |
apache-2.0 | ['whisper-event'] | false | Whisper Gujarati Medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Gujarati data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | 7082e2bc7c9ba11e780b9deb077f2cd7 |
apache-2.0 | ['whisper-event'] | false | Training and evaluation data Training Data: ULCA ASR Corpus, OpenSLR, Microsoft Research Telugu Corpus (Train+Dev), Google/Fleurs Train+Dev set. Evaluation Data: Google/Fleurs Test set, Microsoft Research Telugu Corpus Test . | 8fd046eee0ef4b3797bd60d8432604a9 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - training_steps: 21240 - mixed_precision_training: True | dd73fa7c82f271c01cc52933499f3a15 |
mit | ['labse', 'ner'] | false | This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats: 1. Wikiann (LOC, PER, ORG) 2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO) 3. SlavNER 17 (LOC, MISC, ORG, PER) 4. SSJ500k (LOC, MISC, ORG, PER) 5.... | 47587392ad1e85c3c2b6ab5fe30e3d4d |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-da-multiwoz_1000 This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3538 - Accuracy: 41.3747 - Num: 3689 - Gen Len: 15.5115 | 3ea8a17abcbabcce051851001f92e190 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 9eb8d89842deefac20bc5654d15e3a35 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:| | 1.3315 | 0.24 | 200 | 0.5697 | 25.9543 | 3689 | 14.556 | | 0.6418 | 0.48 | 400 | 0.4645 | 30.0503 | 36... | 50be796339e6c33bd3fcbe861fb39a95 |
creativeml-openrail-m | ['text-to-image'] | false | leidirocha Dreambooth model trained by Babivill with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks... | 93eb4331aa505b5907421bd071991006 |
apache-2.0 | [] | false | Model Card for Model ID This model is a finetuned version of [north/t5_large_NCC_modern](https://huggingface.co/north/t5_large_NCC_modern). | | Size |Model|BLEU| |:------------:|:------------:|:------------:|:------------:| |Small |_60M_|[🤗](https://huggingface.co/north/nynorsk_North_small)|93.44| |Base |_220M_|... | a4cc475479fd8a918d92dfa4fedbc239 |
mit | ['sundanese-roberta-base-emotion-classifier'] | false | Sundanese RoBERTa Base Emotion Classifier Sundanese RoBERTa Base Emotion Classifier is an emotion-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Sundanese RoBERTa Base](https://hf.co/w11wo/sundanese-roberta-base) model, which is then... | 4ee824bb4f130be73455eaf7bd7d24d1 |
mit | ['sundanese-roberta-base-emotion-classifier'] | false | params | Arch. | Training/Validation data (text) | | ------------------------------------------- | ------- | ------------ | ------------------------------- | | `sundanese-roberta-base-emotion-classifier` | 124M | RoBERTa Base | Sundanese Twitter dataset | | e34ed16521c3e7961c11078b4490f675 |
mit | ['sundanese-roberta-base-emotion-classifier'] | false | Evaluation Results The model was trained for 10 epochs and the best model was loaded at the end. | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | | ----- | ------------- | --------------- | -------- | -------- | --------- | -------- | | 1 | 0.801800 | 0.293695 ... | 22a7a117a6d70e85532408da52d73681 |
mit | ['sundanese-roberta-base-emotion-classifier'] | false | As Text Classifier ```python from transformers import pipeline pretrained_name = "sundanese-roberta-base-emotion-classifier" nlp = pipeline( "sentiment-analysis", model=pretrained_name, tokenizer=pretrained_name ) nlp("Wah, éta gélo, keren pisan!") ``` | a901fcc6c92251ac70c1905a80132d05 |
mit | ['sundanese-roberta-base-emotion-classifier'] | false | Author Sundanese RoBERTa Base Emotion Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. | 7a7e99803feb89aab483ba6f5f07973e |
apache-2.0 | ['generated_from_trainer'] | false | muril-base-cased-finetuned-non-code-mixed-DS This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2867 - Accuracy: 0.6214 - Precision: 0.6081 - Recall: 0.6009 - F1: 0.60... | ee03983152541df3357020669a9ed309 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 | deb0d7958dedab31ef959ec873547cbd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0861 | 2.0 | 463 | 1.0531 | 0.3506 | 0.1169 | 0.3333 | 0.1731 | | 0.99 | 3.99 |... | 611e2eebc954e148a6547e26034ee7cb |
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: 0.3124 - Accuracy: 0.8733 - F1: 0.875 | 6a63a072ab7c00dde2e443d32064a827 |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-ours-run-2 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2462 - Accuracy: 0.695 - Precision: 0.6550 - Recall: 0.6529 - F1: 0.6539 | 41cc50db8a82590f766e8794a3f576de |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.999 | 1.0 | 200 | 0.9155 | 0.615 | 0.5590 | 0.5590 | 0.5524 | | 0.7736 | 2.0 |... | acaacb2db88abcf97674f63a8262739a |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0767 - Accuracy: 0.9726 | 17f6e1b1bdb58a05d56783c5327bdd36 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2548 | 1.0 | 190 | 0.1162 | 0.9652 | | 0.1544 | 2.0 | 380 | 0.0894 | 0.9719 | | 0.1182 | 3.0 | 570 | 0.0767 | 0.... | 008564e05d5b5aeb027f428062f05f2b |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | wav2vec2-large-xlsr-53-W2V2-TR-MED This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4467 - Wer: 0.4598 | 8dcd610fc85f08010a97a7f168af5816 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1343 | 4.21 | 400 | 2.3674 | 1.0372 | | 0.8075 | 8.42 | 800 | 0.4583 | 0.6308 | | 0.3209 | 12.63 | 1200 | 0.4291 | 0.5531 | |... | 8bd267792fdf9f0056d73fb7d2583731 |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first rele... | 59ffa560070bf44af0a521f102d18aaf |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Large V2 Cantonese This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 yue dataset. It achieves the following results on the evaluation set: - Loss: 0.2807 - Cer: 6.7274 | 481ab7303c5d1f1824d9e1b803ba2478 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 62df70928c0a676dd3a9b18946619784 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0032 | 13.01 | 1000 | 0.2318 | 6.8569 | | 0.002 | 26.01 | 2000 | 0.2404 | 7.1524 | | 0.0001 | 39.02 | 3000 | 0.2807 | 6.7274 | |... | b351a46ed1ead3ae20b033d417871e54 |
gpl-3.0 | ['generated_from_trainer'] | false | IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0787 - Precision: 0.8948 - Recall: 0.8622 - F1: 0.8782 - Accuracy: 0.9852 | 56284540ab9f2071a3fa1efdbd249110 |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0526 | 1.0 | 2904 | 0.0746 | 0.8802 | 0.8539 | 0.8668 | 0.9836 | | 0.0264 | 2.0 |... | 96dc7324ee476e912c49780a051ed619 |
mit | ['generated_from_trainer'] | false | CodeGeneration This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.5020 - Accuracy: 0.4444 | 9c8e155ac47fadc7110f97813573f6d4 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xlsr-53-urdu This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset. It achieves the following results on the evaluation set: - Wer: 0.5913 - Cer: 0.3310 | 31cd7e02eb4462ef4889ba8c4be6ee3b |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Model description The training and valid dataset is 0.58 hours. It was hard to train any model on lower number of so I decided to take vakyansh-wav2vec2-urdu-urm-60 checkpoint and finetune the wav2vec2 model. | 7fe590d3925983eabdcca9065231f255 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | 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... | 68c1181cdf1895a76098029f75fc0992 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 12.6045 | 8.33 | 100 | 8.4997 | 0.6978 | 0.3923 | | 1.3367 | 16.67 | 200 | 5.0015 | 0.6515 | 0.3556 | | 0.5344 | 25.0 |... | 8400cf13f0be58671bb4c7fc4177e09a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | VIBES-V2 Dreambooth model trained by darkvibes 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-di... | 18f3a209939da76e420ffc8b89029e79 |
apache-2.0 | ['summarization'] | false | mt5-small-prompted-germanquad-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an [philschmid/prompted-germanquad](https://huggingface.co/datasets/philschmid/prompted-germanquad) dataset. A prompt datasets using the [BigScience PromptSource library](https://github... | dee4c03fb663f4ec130bc488561ce33c |
apache-2.0 | ['summarization'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 7 | cb9c12e9ee00873957ad6d4e87a5ef8a |
apache-2.0 | ['summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 3.3795 | 1.0 | 17496 | 2.0693 | 15.8652 | 9.2569 | 15.6237 | 15.6142 | | 2.3582 ... | 5472d1f8c50d74655dbfd7ac0787b886 |
other | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | 49b9a1bca46a117d71d7825e9ed63b1b |
unknown | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | RutkowskiDiffusion_V1 - Trained on SD 2.1 768x768 The model was originally meant to be named FantasyDiffusion, though given recent events I guess you could see why I made the namechange. **Prompts to use:** (SDV1) or (In the style of SDV1) Enjoy! **Disclaimer:** Model was released way earlier than I intended, and... | 01a431f909cd97f4ba86d3b935c9143b |
apache-2.0 | ['t5', 'seq2seq'] | false | t5-base-36L-dutch-english-cased A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned). This **t5 eff** model has **7... | 3e3e9f419d9f02d58d99ac1f2a0fe19a |
apache-2.0 | ['generated_from_keras_callback'] | false | Jadiker/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8518 - Validation Loss: 2.6184 - Epoch: 0 | 4bb9fecf778e1ab94b30dfeb5ea99969 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 27 | 3.9313 | 67.6687 | 57.1881 | 66.7507 | 66.2643 | 20... | b9e076df85f25b4bb963ba3311684dce |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-pipeline', 'audio', 'voice', 'speech', 'speaker', 'speaker-diarization', 'speaker-change-detection', 'voice-activity-detection', 'overlapped-speech-detection', 'automatic-speech-recognition'] | false | Advanced usage In case the number of speakers is known in advance, one can use the `num_speakers` option: ```python diarization = pipeline("audio.wav", num_speakers=2) ``` One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options: ```python diarization... | f8e90f02d6428f8cf19c1608a6007c57 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-pipeline', 'audio', 'voice', 'speech', 'speaker', 'speaker-diarization', 'speaker-change-detection', 'voice-activity-detection', 'overlapped-speech-detection', 'automatic-speech-recognition'] | false | Real-time factor Real-time factor is around 5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part). In other words, it takes approximately 3 minutes to process a one hour conversation. | 09f2c85a5a838c663e23569fbd390019 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-pipeline', 'audio', 'voice', 'speech', 'speaker', 'speaker-diarization', 'speaker-change-detection', 'voice-activity-detection', 'overlapped-speech-detection', 'automatic-speech-recognition'] | false | Accuracy This pipeline is benchmarked on a growing collection of datasets. Processing is fully automatic: * no manual voice activity detection (as is sometimes the case in the literature) * no manual number of speakers (though it is possible to provide it to the pipeline) * no fine-tuning of the internal models n... | 68928ab36defddd308169f3dd9b10f4b |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-pipeline', 'audio', 'voice', 'speech', 'speaker', 'speaker-diarization', 'speaker-change-detection', 'voice-activity-detection', 'overlapped-speech-detection', 'automatic-speech-recognition'] | false | Support For commercial enquiries and scientific consulting, please contact [me](mailto:herve@niderb.fr). For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/p... | 8f9e5be991e27485ccff341b4703ddee |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-pipeline', 'audio', 'voice', 'speech', 'speaker', 'speaker-diarization', 'speaker-change-detection', 'voice-activity-detection', 'overlapped-speech-detection', 'automatic-speech-recognition'] | false | Citations ```bibtex @inproceedings{Bredin2021, Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}}, Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine}, Booktitle = {Proc. Interspeech 2021}, Address = {Brno, Czech Republic}, Month = {August}, Year = {2021}, } ``` ```bibtex @inp... | 53680102de9e41adc49dde8fa8ab4d07 |
apache-2.0 | ['speech'] | false | Wav2Vec2-Large-LV60 Parameter changed: "mask_time_prob": 0.05 changed to "mask_time_prob": 0.5. Based on this link:https://github.com/huggingface/transformers/issues/16962 to make pre-training this model work. [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-ra... | db0e9cfb19d8dd536156d1a1a28f6363 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_pretrain_wnli This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3499 - Accuracy: 0.549... | 1ad2d05a2373c25c0ea9263890a2f408 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3552 | 1.0 | 3 | 0.3512 | 0.4085 | | 0.3495 | 2.0 | 6 | 0.3540 | 0.2817 | | 0.3471 | 3.0 | 9 | 0.3499 | 0.... | b9831086db7dd550bed73e70954690da |
apache-2.0 | ['generated_from_trainer'] | false | paper-summary This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scitldr dataset. It achieves the following results on the evaluation set: - Loss: 2.8631 - Rouge1: 0.3484 - Rouge2: 0.1596 - Rougel: 0.2971 - Rougelsum: 0.3047 | 1478b2df3faeb719883314670d6811b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.0545 | 1.0 | 63 | 2.9939 | 0.3387 | 0.1538 | 0.2887 | 0.2957 | | 2.7871 | 2.0 | 126 ... | 7962942a95ef7c58d1e262a97ae22676 |
apache-2.0 | ['translation'] | false | ara-pol * source group: Arabic * target group: Polish * OPUS readme: [ara-pol](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-pol/README.md) * model: transformer * source language(s): ara arz * target language(s): pol * model: transformer * pre-processing: normalization + SentencePiece ... | 614855d46f5e6a7e4e0dab2f5f3b2ad0 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ara-pol - source_languages: ara - target_languages: pol - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-pol/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ar', 'pl'] - src_constituents: {'apc', 'ara', 'arq_... | 21c5174b488ebf16c6a98dcdb8c88b6e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | 50bc8a611c5eb474d95063a1eafc9905 |
mit | ['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: 200 - num_epochs: 3 | 64e2104625cd1d8d5ef6a002c52db9b6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_qqp_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6586 - Accuracy: 0.6554 - F1: 0.1310 - Combined Score: 0.3932 | 2e9a4ac14fb9d1481503aefa607376ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.8355 | 1.0 | 1422 | 0.8004 | 0.6318 | 0.0 | 0.3159 | | 0.7677 | 2.0 | 2844 | ... | d8ab2a8a859101803588aaf8e1205b28 |
creativeml-openrail-m | ['text-to-image'] | false | Zsks Dreambooth model trained by HusseinHE with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob... | fe652a8569ed8fbbdabbc56eb880cf6b |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-WIKI_Epiphyte_Finetuned 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: 0.0506 | 2bc492bf444afb0bee00b5b8c13e64a4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0711 | 1.0 | 2094 | 0.0543 | | 0.0512 | 2.0 | 4188 | 0.0474 | | 0.027 | 3.0 | 6282 | 0.0506 | | 77e14f70da117c0c8e1e480c910e7ba0 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-100k_s103 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 7cbac59a76289279f17870db608cf4bb |
apache-2.0 | ['generated_from_trainer'] | false | BERTModified-finetuned-wikitext-test 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: - Loss: 18.8994 - Precision: 0.25 - Recall: 0.25 - F1: 0.25 - Accuracy: 0.25 | 7dfe449daf194140733f7c92db4fe4c9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 50 | 5e10b2273cfd92c174a5f92bbc99e58c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 19.9877 | 1.0 | 250 | 19.8070 | 0.0385 | 0.0385 | 0.0385 | 0.0385 | | 15.4776 | 2.0 ... | 3a4fc2c09903b8d19ae0646dab66650b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Model description This is the [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model, with extended pretraining on over 2 million scientific papers from [S2ORC](https://github.com/allenai/s2orc/) using the self-supervised training strategy presented in [DeCLUTR: Deep Contras... | bfe92be7b3a6cd76aabba59ad61b5865 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Intended uses & limitations The model is intended to be used as a sentence encoder, similar to [Google's Universal Sentence Encoder](https://tfhub.dev/google/universal-sentence-encoder/4) or [Sentence Transformers](https://github.com/UKPLab/sentence-transformers). It is particularly suitable for scientific text. | ba75a2eeadaee1e838c48ce5e72d3b54 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Prepare some text to embed text = [ "Oncogenic KRAS mutations are common in cancer.", "Notably, c-Raf has recently been found essential for development of K-Ras-driven NSCLCs.", ] inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") | 682212f929f7138cd796a11ab5806fa7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Mean pool the token-level embeddings to get sentence-level embeddings embeddings = torch.sum( sequence_output * inputs["attention_mask"].unsqueeze(-1), dim=1 ) / torch.clamp(torch.sum(inputs["attention_mask"], dim=1, keepdims=True), min=1e-9) | 8077671333a39e185791dbd1ded698fd |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | BibTeX entry and citation info ```bibtex @inproceedings{giorgi-etal-2021-declutr, title = {{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations}, author = {Giorgi, John and Nitski, Osvald and Wang, Bo and Bader, Gary}, year = 2021, month = aug,... | a01394756faeae86fa05b2d6cc025064 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the vada-sambhar concept trained by Ashish08 on the Ashish08/vada-sambhar dataset. This is a Stable Diffusion model fine-tuned on the vada-sambhar concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of vada-sambhar south-indian-dish** This model was created as p... | b41330f641479c0d819b8ee6d3012984 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_base_tf_224.in1k An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. | f0e36713f29b663e75c74de9c537e95a |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 119.5 - GMACs: 24.0 - Activations (M): 95.0 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 033bb85e4e77d9836ccae003a1bd2eb9 |
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