license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | [] | false | HindTweetBERT-Scratch A base BERT model trained on Hindi Tweets.<br> More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br> A better version of the model is available here: https://huggingface.co/l3cube-pune/hindi-tweets-bert-v... | 86eaaa203b8fdc880756c32ecf8f683b |
apache-2.0 | ['translation'] | false | opus-mt-tr-fr * source languages: tr * target languages: fr * OPUS readme: [tr-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 35e0b046ace28632dd69b3cf6581af64 |
apache-2.0 | ['generated_from_trainer'] | false | bart-qmsum-meeting-summarization This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the QMSum dataset. It achieves the following results on the evaluation set: - Loss: 4.3354 - Rouge1: 39.5539 - Rouge2: 12.1134 - Rougel: 23.9163 - Rougelsum: ... | 42a13527b4028fcb7776f2853097d3e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 200 - label_smoothing_fac... | 442bf5e1a0205a8dc898608c3974687a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 5.5573 | 2.17 | 100 | 5.4074 | 23.6282 | 4.1122 | 14.584 | 21.2263 |... | e15d7acb6d4e38693d955ba0bdbfe2f6 |
apache-2.0 | ['generated_from_trainer'] | false | electra-base-discriminator-finetuned-filtered-0602 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1685 - Accuracy: 0.9720 - F1: 0.9721 | 6c89b9b7b8c63a2a21db40f1fdc4bc5a |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-1000-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.0992 - Accuracy: 0.48 | 1c50c462ada86ace94a39c0738ebebe5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.9657 | 1.0 | 1 | 4.6928 | 0.2267 | | 4.4151 | 2.0 | 2 | 4.4786 | 0.4289 | | 3.8494 | 3.0 | 3 | 4.2986 | 0.... | 192687e564d4fb53c22b10c13aa227e5 |
apache-2.0 | ['generated_from_keras_callback'] | false | jotero/distilbert-base-uncased-finetuned-cola 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: 0.5164 - Validation Loss: 0.4483 - Train Matthews Correlation: 0.4... | 7b74b03d0c845b70f437149dc363d336 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5164 | 0.4483 | 0.4712 | 0 | | adc8684385dca8664413ba09e75e781c |
mit | [] | false | model by karaage0703 This your the Stable Diffusion model fine-tuned the soraumineko concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks soraumineko** You can also train your own concepts and upload them to the library by using [this notebook](https:... | e56e2da174a7697926bf36b7c3a74c6f |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-Swedish 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: 0.3641 - Wer: 0.2473 - Cer: 0.0758 | cc08144bf33b2196def33fea44d41dc6 |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 618f84e8aa3c723e9597bff7a649a213 |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 6.1097 | 5.49 | 500 | 3.1422 | 1.0 | 1.0 | | 2.985 | 10.98 | 1000 | 1.7357 | 0.9876 | 0.4125 | | 1.0363 | 16.48 |... | 40fcd38d2992e288e37e34a10f62eaba |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Slovenian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Slovenian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 8b9d63a43f80cb44f79b891222d06670 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sl", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | bd9b1ac592cd0b6d8127d335bc3d7011 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Slovenian test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 60f61c370e3837dee47c6d25e5e04b13 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/sl.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.e... | 9bf132b7e620c99e7d07706f21cdef2d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio... | 908bb2f8186dd9881c3458b3ab01bf96 |
mit | [] | false | 80s Anime AI Being on Stable Diffusion This is the `<anime-AI-being>` 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. ... | 58a58b43a7bae45f5d65717fc94eba8b |
apache-2.0 | ['generated_from_trainer'] | false | Copilot_for_poors_v3 This model is a fine-tuned version of [Ahmed007/Copilot_for_poors_v2](https://huggingface.co/Ahmed007/Copilot_for_poors_v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3504 | 7fa9634003bae08fd9f16e7c6c3d4fb5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 | 40e280892a13df9c426f85c54c857b9e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 57 | 1.4567 | | No log | 2.0 | 114 | 1.4510 | | No log | 3.0 | 171 | 1.4376 | | No log | 4.0 | 228 | 1.4255 ... | 6f9fc905c759a2da88d6250483c0962c |
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: 0.0607 - Precision: 0.9253 - Recall: 0.9350 - F1: 0.9301 - Accuracy: 0.9836 | b10817275d6dbb6914ed64277c65797e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.237 | 1.0 | 878 | 0.0701 | 0.9131 | 0.9228 | 0.9179 | 0.9809 | | 0.0509 | 2.0 |... | 72ba7d6ef9b88319e4d1941878e0d830 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-yelp-mlm This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the yelp_review_full yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.1743 - Accuracy: 0.7356 | 6b05a51f5353724fcaddb45afd586ce3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.2589 | c4a2860a1f068bbef05721daabb4e0cd |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-manthan-gujarati-digits This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the new_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.5613 - Accuracy: 0.9923 | 4158b039be48a576c8d9a8af2eeef942 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3392 | 0.98 | 12 | 1.1315 | 0.9665 | | 1.2319 | 1.98 | 24 | 0.9487 | 0.9716 | | 1.0824 | 2.98 | 36 | 0.8338 | 0.... | ba02f30f95d497e281799fb64f4f68ca |
apache-2.0 | ['keras', 'tensorflow', 'image-classification'] | false | Image-Classification-using-EANet with Keras This repo contains the model and the notebook on [Image Classification using EANet with Keras](https://keras.io/examples/vision/eanet/). Credits: [ZhiYong Chang](https://github.com/czy00000) - Original Author HF Contribution: [Drishti Sharma](https://huggingface.co/spaces... | 338cdd2d935d193dd689ec8bc6730fb2 |
apache-2.0 | ['keras', 'tensorflow', 'image-classification'] | false | Introduction This example implements the EANet model for image classification, and demonstrates it on the [CIFAR-100](https://huggingface.co/datasets/cifar100) dataset. EANet introduces a novel attention mechanism named external attention, based on two external, small, learnable, and shared memories, which can be imp... | ab1d643ea110cee9cfbe7956cb1743b3 |
apache-2.0 | ['keras', 'tensorflow', 'image-classification'] | false | Implemention of the EANet model The EANet model leverages external attention. The computational complexity of traditional self attention is O(d * N ** 2), where d is the embedding size, and N is the number of patch. The authors find that most pixels are closely related to just a few other pixels, and an N-to-N attent... | 1ee10e4a940e6a3183fc6a5207a3cc58 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_r-wav2vec2_s408 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | 9664420360bddf16e81b9e574dff6fe7 |
apache-2.0 | ['generated_from_trainer'] | false | BART-base Question Generation This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on different questions and answering dataset. It was trained to generation question using two different approaches, <b> Casual-Generation </b> and <b> Context-based-Generation </b>. | 0d0eee502d4d8990c45063c4c84f464e |
apache-2.0 | ['generated_from_trainer'] | false | Model description The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions: - <b> Casual-Generation </b>: where the model is tasked to generate questions answerable by a given passage. The input s... | 04ebb9d30cd826e1f9f05a231199a3e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training and evaluation data The dataset used to train the model comprises the training datasets from: - Reasoning Over Paragraph Effects in Situations (ROPES): https://allenai.org/data/ropes - SQUAD: - DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop - SciQ After preprocessing the data fro... | 6d42ee7eb85d0c13c08018ba82bbfc0e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.25 - num_epochs: 5 At the end of 5 epo... | 6a28808d767b61734c05b7c4d9cb9571 |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | bc8c341a758e543187cd03fa5af8f91d |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-strict-epoch-2' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequence... | 82893403e8aeb667bbbc2f98ba0c5d81 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-torgo-demo-m01-nolm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0161 - Wer: 0.4768 | 0fcfc19edc36422b3d35706b133c044b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3987 | 0.9 | 500 | 4.6666 | 1.0 | | 2.9362 | 1.8 | 1000 | 3.2475 | 1.0 | | 2.7871 | 2.7 | 1500 | 2.9266 | 1.0 ... | 90c74e9b773c0d1a233a109e91251265 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-Telugu_NLP This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9986 | f8129ead64c50bf4e3a46a3e24746db3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4192 | 1.0 | 1250 | 2.1557 | | 2.2859 | 2.0 | 2500 | 2.0632 | | 2.2311 | 3.0 | 3750 | 2.0083 | | df2af3873cb2b2a71c953dfa709c39ad |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1689 - Precision: 0.8091 - Recall: 0.8699 - F1: 0.8384 - Accuracy: 0.9526 | 4564d4d5e92d02949f94fd908630940e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 100 | 0.1917 | 0.7899 | 0.8584 | 0.8227 | 0.9430 | | No log | 2.0 |... | 0d3f2ac8016d243fdeb8340532676a36 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3573 - Precision: 0.5859 - Recall: 0.5074 - F1: 0.5439 - Accura... | 0e51d09dd2ad638161b89ea753bab61a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 93 | 0.3884 | 0.2899 | 0.2006 | 0.2371 | 0.8583 | | No log | 2.0 |... | 01d0c988c773db72c93af07cac665495 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny it 6 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.828768 - Wer: 46.277038 | 03d0cd0bdd11bf58fe9f60ed8f0f3d9d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. As part of the hyperparameter tuning process weight decay set to 0.1, attention dropout, encoder dropout and decoder dropout have been set to 0.1, the learning rate has been set to 1e-4, the number o... | 059e94490b0165236e212a90a0eaf26c |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-04 - train_batch_size: 16 - 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 - training_steps: 4000 - mixed_precisi... | 2555a159dafcee2bdd60baf4e753f90b |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.7168 | 0.95 | 1000 | 1.2107 | 64.8087 | | 1.1073 | 1.91 | 2000 | 0.9891 | 53.0019 | | 1.3410 | 2.86 | 3000 | 0.8742 | 47.7676... | 069dbb141e9549fea11865bf99005f2b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Azerbaijani This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 az dataset. It achieves the following results on the evaluation set: - Loss: 0.7816 - Wer: 47.3373 | 94730b15978fced29188035d1bad331e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.0 | 499.0 | 1000 | 0.7816 | 47.3373 | | 0.0 | 999.0 | 2000 | 0.9050 | 47.3373 | | 0.0 | 1499.0 | 3000 | 0.9688 | 4... | 462d9051bc93c80d6bbab05a0c7c88b8 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.1_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.8789 | 41a28c1748641bf3896c0c1f04a84839 |
creativeml-openrail-m | ['text-to-image'] | false | FOR THE NEW VERSION DOWNLOAD 'D&Diffusion3.0_Protogen.ckpt' The newest version is finetuned from Protogen to great effect. Also works great at resolutions great than 512x512! Species in new version: aarakocra, aasimar, air_genasi, centaur, dragonborn, drow, dwarf, earth_genasi, elf, firbolg, fire_genasi, gith, gnome... | cb8683bdface56ba00ec78136f63a7ae |
mit | [] | false | ESM-2 ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.bio... | f9b10eaf98e3923613b3ac3316dd7002 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | inference The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch import torchaudio | 23c1a40e62af94cb8b33d8bc0408b892 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-es_s692 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 81fb5792eb0169f547064597126f3f6c |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-it_s533 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 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 1669e1223db240e157db36270e357913 |
apache-2.0 | ['afro-digits-speech'] | false | afrospeech-wav2vec-ibo This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface... | c2c51e609761bd217f31beb13ce5fb6b |
apache-2.0 | ['afro-digits-speech'] | false | Training and evaluation data The model was trained on a mixed audio data from Igbo (`ibo`). - Size of training set: 109 - Size of validation set: 28 Below is a distribution of the dataset (training and valdation)  | 85556485242d596ae0ca2c3b639a90fd |
apache-2.0 | ['afro-digits-speech'] | false | Evaluation performance It achieves the following results on the [validation set](VALID_igbo_ibo_audio_data.csv): - F1: 1.0 - Accuracy: 1.0 The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other... | 99c05262f3489c5f3bcb173c781b7856 |
apache-2.0 | ['afro-digits-speech'] | false | Training results | Training Loss | Epoch | Validation Accuracy | |:-------------:|:-----:|:--------:| | 0.1415 | 1 | 1.0 | | 0.0241 | 50 | 1.0 | | 0.0019 | 100 | 0.929 | | 0.0012 | 150 | 0.892 | | 581ebe581791aefa899226d9ec18edb2 |
apache-2.0 | [] | false | [DistilBERT base uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). Note that this model is **not** sensitive to capital letters — "english" is the same as "English". For the case sensitive version, please use [el... | fa284021074d34c9ac3378eaa4670260 |
apache-2.0 | [] | false | Training ``` $ run_ner.py \ --model_name_or_path distilbert-base-uncased \ --label_all_tokens True \ --return_entity_level_metrics True \ --dataset_name conll2003 \ --output_dir /tmp/distilbert-base-uncased-finetuned-conll03-english \ --do_train \ --do_eval ``` After training, we update the labels to m... | 7b40125aa360dd0cb1c2d90c2302ac65 |
mit | ['generated_from_trainer'] | false | BerTurk_Electra_15_epoch This model is a fine-tuned version of [dbmdz/electra-base-turkish-cased-discriminator](https://huggingface.co/dbmdz/electra-base-turkish-cased-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0405 - Accuracy: 0.9931 | 1e8d643db5cbf1d31bb7208602db4716 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 50 | 0.5097 | 0.9722 | | No log | 2.0 | 100 | 0.1894 | 0.9514 | | No log | 3.0 | 150 | 0.0747 | 0.... | e4b383ae95ed07d05e1f444c7c6645b7 |
mit | ['question-answering', 'bert', 'bert-base'] | false | BERT-base uncased model fine-tuned on SQuAD v1 This model is block sparse: the **linear** layers contains **20.2%** of the original weights. The model contains **38.1%** of the original weights **overall**. The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) meth... | 5d19b3f7973d046133ce16008bbf28f5 |
mit | ['question-answering', 'bert', 'bert-base'] | false | Pruning details A side-effect of the block pruning is that some of the attention heads are completely removed: 90 heads were removed on a total of 144 (62.5%). Here is a detailed view on how the remaining heads are distributed in the network after pruning. ![Pruning details](https://huggingface.co/madlag/bert-base-u... | ce34e98a282c9b1fe7e53459f67bc00e |
mit | ['question-answering', 'bert', 'bert-base'] | false | Example Usage ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1", tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1" ) predictions = qa_pipeline({ 'context': "Frédéric François Chopi... | 4db8569796ec0c8fb67bea9cab219104 |
mit | ['generated_from_trainer'] | false | roberta-large-finetuned-clinc This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1545 - Accuracy: 0.9768 | 2d522fa2cb5784880326bf8d0ed037ca |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.9... | 3aa8ef629c3f0559c8e1c7b6cb859a4d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.0548 | 1.0 | 120 | 5.0359 | 0.0071 | | 4.4725 | 2.0 | 240 | 2.9385 | 0.7558 | | 1.8924 | 3.0 | 360 | 0.6456 | 0.... | 7ce08506d0c5f9c2ebbd8d68664472f5 |
mit | ['generated_from_trainer'] | false | BERiT_2000_custom_architecture_3 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: 5.6575 | a81f58e1cd9e29ba56c5fef67849dcfe |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 | 979d9d8f83ac4f25e6d7c02e3c119f1f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 16.5165 | 0.19 | 500 | 8.9072 | | 8.208 | 0.39 | 1000 | 7.5024 | | 7.3849 | 0.58 | 1500 | 7.1180 | | 7.0298 | 0.77 | 2000 | 6... | f290d9a14fda6df1ee0220849d14ea5d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Panjabi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6084 - Wer: 36.1004 | 9beee06996925a3c68933765d3b2257a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 64 - 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... | faeea5c5bde42d0fd76078c0f4d7b457 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.349 | 5.86 | 100 | 0.4664 | 49.1929 | | 0.0175 | 11.74 | 200 | 0.4633 | 39.1494 | | 0.0052 | 17.63 | 300 | 0.5317 | 37.714... | c3c14716af8d9a004ba75f4a8f0d8ad6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0592 - Precision: 0.9352 - Recall: 0.9527 - F1: 0.9439 - Accuracy: 0.9868 | d7575842034f52ae13fee794ad0f8a25 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0867 | 1.0 | 1756 | 0.0632 | 0.9229 | 0.9423 | 0.9325 | 0.9836 | | 0.0327 | 2.0 |... | 86eac724438da1baee715efa1b4d3714 |
cc-by-4.0 | [] | false | Nordic ELECTRA-Small This model was pretrained on the following corpora: * The [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/) (IGC) * The Icelandic Common Crawl Corpus (IC3) * The [Icelandic Crawled Corpus](https://huggingface.co/datasets/jonfd/ICC) (ICC) * The [Multilingual Colossal Clean Crawled Corpus](htt... | a5a48307dc227e974c5e9cc0d170ad71 |
mit | ['generated_from_trainer'] | false | deberta-base-finetuned-rte This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6508 - Accuracy: 0.6101 | 8743a11e505f5000475c02413c77038b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.7013 | 0.4982 | | No log | 2.0 | 312 | 0.6508 | 0.6101 | | 8d394b1d1b99f2f298196f35da4ee9d5 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1638 - F1: 0.8584 | 49f7ef13226026c80b3c98d208cda841 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2938 | 1.0 | 715 | 0.1806 | 0.8238 | | 0.1504 | 2.0 | 1430 | 0.1598 | 0.8469 | | 0.0964 | 3.0 | 2145 | 0.1638 | 0.8584 | ... | f1b43f20ac6022ba4d7669279a4e8149 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_44 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.3977 - Accuracy: 0.8947 - Macro-f1: 0.8... | b9ca067baaf6d657fb23e7d0802d5d33 |
mit | ['question generation'] | false | mT5-base finetuned on the GermanQuAD dataset for answer-agnostic question generation This model is a finetuned [mT5-base](https://arxiv.org/abs/2010.11934) model for the task of answer-agnostic (or end-to-end) question generation. The approach from [Lopez et al.](https://arxiv.org/abs/2005.01107) was used called *All... | f55d1c18f0de95bd474cd60f6574d918 |
mit | ['question generation'] | false | Training, test and evaluation data For training and test the original split from GermanQuAD was used. As evaluation dataset the German split of the [XQuAD](https://github.com/deepmind/xquad) dataset was used. | 7911554f3a6fb2d6c0f88b2f9ab03384 |
mit | ['question generation'] | false | Training hyperparameters The training parameters are provided in JSON and can be used with a training script provided in a [repository](https://github.com/TiloMichel/textgen-for-chatbot-training-german/tree/main/2_training) ```JS { "model_name_or_path": "google/mt5-base", "output_dir": "mt5-base-germanquad-e2... | edf69523e66a2ad00d75b2cab6a028e9 |
mit | ['question generation'] | false | Training results The evaluation is reported on XQuAD. The implementations and configurations can be found in [another repository](https://github.com/TiloMichel/textgen-for-chatbot-training-german/tree/main/3_evaluation). | 319af3ad695007985a7109a9747a72a2 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-deep-ritmo This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5837 | 83f23d6a806c90e16927713368080f18 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.0463 | 1.0 | 1875 | 3.7428 | | 3.3393 | 2.0 | 3750 | 3.0259 | | 2.7435 | 3.0 | 5625 | 2.5837 | | 8389284710e4d56f43f6e95e5242f7ab |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/msmarco-roberta-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 78d9a983b810f7c1a2f1c36ed110bc06 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 194bcf4df50e173929ff7544e376b73c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-roberta-base-v2') model = AutoModel.from_pretrained('sentence-transformers/msmarco-roberta-base-v2') | 1f53f2efc40344679c3f9dcdb5500299 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-roberta-base-v2) | f6c8fc0b60cd509803f9d7d80204f97b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_... | 4cd6aa9c0f6b001bcc6dee67dcfb3a5d |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-large-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-char-luw-upos), [dependency-parsing](https://huggingface.co/Koich... | ec150a0842b1dd879ffcfb828eeaca13 |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora-char") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora-char") ``` | 5aa43ed1fea514437f8a6d9f4badd919 |
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