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 | ['speechbrain', 'embeddings', 'Commands', 'Keywords', 'Keyword Spotting', 'pytorch', 'xvectors', 'TDNN', 'Command Recognition', 'audio-classification'] | false | Perform Command Recognition ```python import torchaudio from speechbrain.pretrained import EncoderClassifier classifier = EncoderClassifier.from_hparams(source="speechbrain/google_speech_command_xvector", savedir="pretrained_models/google_speech_command_xvector") out_prob, score, index, text_lab = classifier.classify... | 4b490b6c92df16de9cf2a0c4c2edc47f |
apache-2.0 | ['speechbrain', 'embeddings', 'Commands', 'Keywords', 'Keyword Spotting', 'pytorch', 'xvectors', 'TDNN', 'Command Recognition', 'audio-classification'] | false | Training The model was trained with SpeechBrain (b7ff9dc4). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/Googl... | 37a7177dedd6b2586dd00a98f81b8ffd |
apache-2.0 | ['speechbrain', 'embeddings', 'Commands', 'Keywords', 'Keyword Spotting', 'pytorch', 'xvectors', 'TDNN', 'Command Recognition', 'audio-classification'] | false | Referencing xvectors ```@inproceedings{DBLP:conf/odyssey/SnyderGMSPK18, author = {David Snyder and Daniel Garcia{-}Romero and Alan McCree and Gregory Sell and Daniel Povey and Sanjeev Khudanpur}, title = {Spoken Language Recognition ... | 5b6e6705afbdddc5b5349ac588fe91a0 |
apache-2.0 | ['speechbrain', 'embeddings', 'Commands', 'Keywords', 'Keyword Spotting', 'pytorch', 'xvectors', 'TDNN', 'Command Recognition', 'audio-classification'] | false | Referencing Google Speech Commands ```@article{speechcommands, author = { {Warden}, P.}, title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1804.03209}, primaryClass = "cs.CL", keywords = {Computer Science - ... | aab04ab5c23d13b6df69a01ae9ed4c06 |
apache-2.0 | ['generated_from_trainer'] | false | fatimah_fake_news_bert This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on [Fake and real dataset on kaggle ]([distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-... | 2a242f586851b3e2aa1f5e80f8ecd2a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 10 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 | 8732729e5a5f6298a83d0fe67c0e37d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3298 | 0.06 | 200 | 0.0094 | 0.9987 | | 0.0087 | 0.11 | 400 | 0.0091 | 0.9988 | | 0.0126 | 0.17 | 600 | 0.0132 | 0.... | 855ffc60221ebcaa7456d166d07d7ed6 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-18jan-3 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: - Loss: 2.6115 - Rouge1: 7.259 - Rouge2: 0.3667 - Rougel: 7.1595 - Rougelsum: 7.156 | 1bce3addda4ac9c0cba8a9d841af2d0f |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 7.1947 | 1.0 | 60 | 3.1045 | 5.91 | 0.8583 | 5.8687 | 5.8123 | | 3.8567 | 2.0 | 120 ... | ed1dd299ad03c801040c9c2fd76741d5 |
mit | ['generated_from_trainer'] | false | This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the Webis-Clickbait-17 dataset. It achieves the following results on the evaluation set: Loss: 0.0261 The following list presents the current performances achieved by the participants. As primary evaluation measure, Mean Squared Error... | 01900ed6d4681bb578ada7f667ba3c2c |
mit | ['text-generation', 'gpt2', 'gpt'] | false | pszemraj/gpt2-medium-vaguely-human-dialogue This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on a parsed version of Wizard of Wikipedia. Because the batch size was so large, it learned a general understanding of words that makes sense together but does not specifically respond t... | 913c570b96218b01c45aeb75cf185aa6 |
mit | ['text-generation', 'gpt2', 'gpt'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | 6548c8bd1ff8acc08edde534cf7503cf |
mit | ['text-generation', 'gpt2', 'gpt'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 34.991 | 1.0 | 837 | 14.8359 | | 12.2881 | 2.0 | 1674 | 9.375 | | 8.5071 | 3.0 | 2511 | 7.2148 | | 7.6031 | 4.0 | 3348 | 6.1758 ... | bf33e4a401ef16e420751ca1c0fcef11 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for levit_256.fb_dist_in1k A LeViT image classification model using convolutional mode (using nn.Conv2d and nn.BatchNorm2d). Pretrained on ImageNet-1k using distillation by paper authors. | 65c978549e0c0f13afd2fd9d119ae76a |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 18.9 - GMACs: 1.1 - Activations (M): 4.2 - Image size: 224 x 224 - **Papers:** - LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference: https://arxiv.org/abs/2104.01136 - **Original:** ht... | ed77da21322667c2a9b7b9f434f3df9d |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('levit_256.fb_dist_in1k', pretrained=True) model = mode... | 63b392a4c62bd2384191165b209c7a03 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_256.fb_dist_in1k', pretrained=True, num... | 6c9da9cc5756045d6d3d2c32ab55a95d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-uncased-finetuned-masress This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0946 - Accuracy: 0.5782 - F1: 0.5769 | f741605e072b7c429cb2bca85376b295 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1646 | 1.0 | 151 | 1.0626 | 0.5588 | 0.5566 | | 0.9281 | 2.0 | 302 | 0.9800 | 0.5869 | 0.5792 | | 0.8269 |... | 40cc0b5f9e83da3d379b5e03d12ff88a |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import IPython.display as ipd import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("MarcNg/fastspee... | 19d98f149a28a41e2aed5eaaf082d360 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Bonus: Convert Mel Spectrogram to Speech ```python mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en") audio_before = mb_melgan.inference(mel_before)[0, :, 0] audio_after = mb_melgan.inference(mel_after)[0, :, 0] sf.write("audio_before.wav", audio_before, 22050, "PCM_16") sf.write("audi... | 5a382b2e420d99977b9b26180b653d3c |
mit | [] | false | Fake News Classification Distilbert 🤗 This model was trained on 32,326 news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news. 0 : Fake News, 1 : Real News | 460cbd1641b30cd37e5451749bc05017 |
mit | ['generated_from_trainer'] | false | distilcamembert-cae-no-territory This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6885 - Precision: 0.7873 - Recall: 0.7848 - F1: 0.7855 | d7719d614c9ed81f53c83bcef2892526 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.1796 | 1.0 | 40 | 0.9743 | 0.5640 | 0.4937 | 0.3731 | | 0.8788 | 2.0 | 80 | 0.8037 | 0.7438 ... | dff6b8c306020c89626b03e4c669bbbf |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1200k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1200k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | 6ede59a37755b15742439acc62955c2f |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1200k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1200k') model = TFBertModel.from_pretrained("google/multib... | 173c0ecb8cf33e204022b8b052167ae6 |
apache-2.0 | ['translation'] | false | opus-mt-mk-en * source languages: mk * target languages: en * OPUS readme: [mk-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mk-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | a18bbbf4bd35ce2d58b0e080beff292c |
cc-by-sa-4.0 | ['japanese', 'masked-lm', 'wikipedia'] | false | Model Description This is a BERT model pre-trained on Japanese Wikipedia texts, derived from [bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2). Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast. You can fine-tune `bert-base-japanese-... | fe54441b5e82ae5db210807b1a754926 |
cc-by-sa-4.0 | ['japanese', 'masked-lm', 'wikipedia'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-char-extended") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/bert-base-japanese-char-extended") ``` | 6e93399c030b285518e739797c8aabfe |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7362 | 50d62236a17eeee58a805b9de069a70f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7793 | 1.0 | 554 | 1.9337 | | 1.4469 | 2.0 | 1108 | 1.7193 | | 1.1585 | 3.0 | 1662 | 1.7362 | | 93068fa7b9c46c033ebfaf9f3458161e |
apache-2.0 | ['generated_from_trainer'] | false | NL-RX-Synth-t5-small-finetuned-en-to-regex This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0131 - Semantic-accuracy: 0.36 - Gen Len: 18.24 | eb0088e98dcc57b55bc6e7d2f7d28636 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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 - training_steps: 10000 - mixed_precision_training: Native AMP | 164820ae0754d9695484bffdc3ecba4a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Semantic-accuracy | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:-------:| | 0.2382 | 1.0 | 563 | 0.0431 | 0.322 | 18.224 | | 0.0477 | 2.0 | 1126 | 0.0229 | 0.3... | c04fb76566689e922c7be4f828871ad7 |
apache-2.0 | ['biomedical', 'lexical semantics', 'bionlp', 'biology', 'science', 'embedding', 'entity linking'] | false | --- datasets: - UMLS **[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br> **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! | a5a792364bf25258a9ed7dfc0b72e45f |
apache-2.0 | ['biomedical', 'lexical semantics', 'bionlp', 'biology', 'science', 'embedding', 'entity linking'] | false | SapBERT-PubMedBERT SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/Bio... | 2127ff92d38e5b349f9850f0346f302d |
apache-2.0 | ['biomedical', 'lexical semantics', 'bionlp', 'biology', 'science', 'embedding', 'entity linking'] | false | Citation ```bibtex @inproceedings{liu-etal-2021-self, title = "Self-Alignment Pretraining for Biomedical Entity Representations", author = "Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel", booktitle = "Proceedings of the 2021 Conferenc... | 2a47f9ee97c2f16e9f3ccaa529fbb7f7 |
afl-3.0 | [] | false | Question generation using T5 transformer <h2> <i>Input format: context: "..." answer: "..." </i></h2> Import the pretrained model as well as tokenizer: ``` from transformers import T5ForConditionalGeneration, T5Tokenizer model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-squad-marco') tokeni... | 901ebdfc2cfdd70ef2abe6d85dc8fd81 |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-finetuned-ner-ubb-conll-endava-only-misc-v2 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: 0.0190 - Validation Loss: 0.0310 - Epoch: 2 | 966e4a8a331513bf1db731c025adcdcc |
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': 2e-05, 'decay_steps': 1365, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 44c03b6eedde5dd5bd76198b3a40922a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2091 | 0.0391 | 0 | | 0.0336 | 0.0322 | 1 | | 0.0190 | 0.0310 | 2 | | d70f2dfb1a04b40461fbdce8e82e6e7f |
apache-2.0 | [] | false | 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk ⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk This model has apostrophes and hyphens. The language model is trained on the texts of the Common Voice dataset, which is used during training. Metri... | 52ec22ee610b7297ea33df46e97f1b61 |
cc-by-4.0 | [] | false | MahaTweetBERT A MahaBERT (l3cube-pune/marathi-bert-v2) model finetuned on Marathi Tweets. 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>) Released under project: https://github.com/l3cube-pune/MarathiNLP ``` @article{gokhal... | d6654ddcf341d64a689185ce61689571 |
['cc0-1.0'] | ['seq2seq', 'translation'] | false | Keras Implementation of Character-level recurrent sequence-to-sequence model This repo contains the model and the notebook [to this Keras example on Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/). Full credits to: [fchollet](https://twitter.com/fchollet) | 329ae858b4e0e9e326d33fdc9014a058 |
['cc0-1.0'] | ['seq2seq', 'translation'] | false | Background Information This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-leve... | a07f754bcdbd6b4ee281c09318886ea5 |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> ['Daylight_saving_time', 'Chihuahua_(state)', 'United_States_dollar', 'Gregorian_calendar', 'Circadian_rhythm', 'Department_store', 'Planck_constant'] - **Developed by:** nandysoham - **Shared by [optional]:** [More Information Needed] - **M... | 58f39f428dc6cfb6451613a2a1a30246 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7166 - Matthews Correlation: 0.5422 | 7a4c79238384788e9dc9120558c5367b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5239 | 1.0 | 535 | 0.5124 | 0.4240 | | 0.3472 | 2.0 | 1070 | 0.4966 | 0.5180 | | 0.2... | e65deaa38df5c2747cfdc96ce32e2c90 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | nikeardilla Dreambooth model trained by kukuhtw with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook https://linktr.ee/kukuhtw Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/githu... | eb5a1d41beea38613b486da705346a0e |
mit | ['generated_from_trainer'] | false | xlm-eng-beng-tel This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiqa dataset. It achieves the following results on the evaluation set: - Loss: 0.7303 | db8e03f78505af1b890a6bdc0f141d26 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 691ab1abde8904932a4ba1911faaa64a |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_iwae") ``` | 75bfd984021ffad04202978030beccd8 |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | IWAE (n_samples=5) | Binary MNIST | NLL (5000 IS) | 87.85 (0.01) | 87.6 | | **IWAE (n_samples=50)** | Binary MNIST | NLL (5000 IS) | 86.82 (0.0... | 374407a093ceb879a601405d1db7497e |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | deit-base-mri This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the mriDataSet dataset. It achieves the following results on the evaluation set: - Loss: 0.0657 - Accuracy: 0.9901 | 9cc2f3d2b9212a43ba2bc425e4ab1ffe |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - 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 | 7ab21cbbe0ecc0c9480e724ba2ec906f |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0107 | 0.8 | 500 | 0.0782 | 0.9887 | | 0.0065 | 1.6 | 1000 | 0.0657 | 0.9901 | | 90f9b8b1074d30decc613c12b1e4323c |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_cls_CR 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: 0.3385 - Accuracy: 0.9415 | 158c230a97a5c07de9473432b321b4da |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 213 | 0.3553 | 0.8936 | | No log | 2.0 | 426 | 0.3185 | 0.9069 | | 0.2806 | 3.0 | 639 | 0.2679 | 0.... | a7f5398ccd391a89fe283707d6af6d91 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es 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: 3.0229 - Rouge1: 17.552 - Rouge2: 8.6159 - Rougel: 17.3207 - Rougelsum: 17.1968 | e3bb928052c102c3e5a55bf8200c9350 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.6836 | 1.0 | 1209 | 3.2362 | 17.2827 | 8.6322 | 16.7811 | 16.7223 | | 3.6489 | 2.0 |... | 4340101f33994784d5007546ffa28bd4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-2 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: 0.3608 - Accuracy: 0.8433 - F1: 0.8433 | 6848994257ec785e650a50f94a6e2b18 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4095 | 1.0 | 875 | 0.3667 | 0.8353 | 0.8351 | | 0.3348 | 2.0 | 1750 | 0.3608 | 0.8433 | 0.8433 | | 8c79bbe1e0758e51dafc223eb943dbaa |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset. It achieves the following results on the evaluation set: - Loss: 0.1245 - Rouge2 Precision: 0.8183 - Rouge2 Recall: 0.7262 - Rouge2 Fmeasure: 0.7625 | 4d5de162013e07b58d687cbc415946e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.1954 | 1.0 | 4049 | 0.1575 | 0.7935 | 0.7032 | 0.73... | e81edfaf2c57bfa65f8d0895e8601371 |
apache-2.0 | ['wav2vec2'] | false | LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information about our gender study for SSL moddels, please refer to our pa... | a07451dc8c8642ec790209fcc62f5fd6 |
apache-2.0 | ['wav2vec2'] | false | Model and data descriptions We release four gender-specific models trained on 1K hours of speech. - [wav2vec2-FR-1K-Male-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-large/) - [wav2vec2-FR-1k-Male-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-base/) - [wav2vec2-FR-1K-Female-large](https... | ba042245f250657b4b4ff2a378a8f0d9 |
apache-2.0 | ['wav2vec2'] | false | Referencing our gender-specific models ``` @inproceedings{boito22_interspeech, author={Marcely Zanon Boito and Laurent Besacier and Natalia Tomashenko and Yannick Estève}, title={{A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems}}, year=2022, booktitle={Proc. Interspeech 2022}, p... | 2bb087a94498b992f15ea273fe39e8be |
apache-2.0 | ['wav2vec2'] | false | Referencing LeBenchmark ``` @inproceedings{evain2021task, title={Task agnostic and task specific self-supervised learning from speech with \textit{LeBenchmark}}, author={Evain, Sol{\`e}ne and Nguyen, Ha and Le, Hang and Boito, Marcely Zanon and Mdhaffar, Salima and Alisamir, Sina and Tong, Ziyi and Tomashenko, Na... | 0a744ca04de4df8646132d002b542269 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Kn - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1974 - Wer: 30.8790 | 17c0c6cc6504bdf58ea16d41254451f6 |
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: 96 - eval_batch_size: 64 - 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: 5000 - mixed_precis... | 70866c9382712eb380e68dfc3c6485f5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.572 | 0.1 | 500 | 0.3198 | 50.3005 | | 0.3153 | 0.2 | 1000 | 0.2464 | 37.2652 | | 0.2533 | 0.3 | 1500 | 0.2298 | 36.551... | 0e835c2524125a52965a908ad756d2f9 |
apache-2.0 | ['translation'] | false | ita-ara * source group: Italian * target group: Arabic * OPUS readme: [ita-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-ara/README.md) * model: transformer * source language(s): ita * target language(s): ara * model: transformer * pre-processing: normalization + SentencePiece (sp... | a6d52830f70eae335fd693e7e0c612f7 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ita-ara - source_languages: ita - target_languages: ara - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-ara/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'ar'] - src_constituents: {'ita'} - tgt_const... | 5b6b579f9475dc96a6b42a54e2b9af27 |
mit | ['generated_from_trainer', 'nlu', 'intent-classification', 'text-classification'] | false | xlm-r-base-amazon-massive-intent-label_smoothing This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. It achieves the following results on the evaluation set: - Loss: 2.5148 - Accuracy: 0.8... | a3ef25e76aebf7ccaa9c9207cb4f9cb8 |
mit | ['generated_from_trainer', 'nlu', 'intent-classification', 'text-classification'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - label_smoothing_factor: 0.4 | 1d91500099a6cce3778535e60bed07ef |
mit | ['generated_from_trainer', 'nlu', 'intent-classification', 'text-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.3945 | 1.0 | 720 | 2.7175 | 0.7900 | 0.7900 | | 2.7629 | 2.0 | 1440 | 2.5660 | 0.8549 | 0.8549 | | 2.5143 |... | d76691d5386c1c70d83e7369ad3a3133 |
mit | ['translation', 'speech', 'audio', 'automatic-speech-recognition'] | false | Model Whisper is a multi-lingual speech-to-text model. It takes in raw audio recordings from many languages and outputs transcriptions in the language of origin or translated to english. The model first converts speech to spectrograms, then uses an auto-regressive transformer to decode the speech to text. Here is an o... | 688f0efd82308d7a4930075cbe0daa67 |
mit | ['translation', 'speech', 'audio', 'automatic-speech-recognition'] | false | Training Data The model was trained on 680 000 hours of audio and associated transcripts trained from the internet. The majority of the audio is in english (~65%) while the remainder is in other languages. A total of 98 different languages were used in the dataset. , they find a direct corelation between performance on a given language and the amount of data available in the dataset. As such, languages that are under-represented in the scraped dataset perform less well in whisper. Because english is m... | 34213ad2a30af3f35e2a3ba4487331f8 |
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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.0244 - Precision: 0.7368 - Recall: 0.4 - F1: 0.5185 - Accuracy: 0.9919 | 3e199dd8971a1f8cbc00935d49f71878 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 14 | 0.0598 | 0.0 | 0.0 | 0.0 | 0.9870 | | No log | 2.0 |... | 7e89f5748697b8388c99b00fa8079f67 |
apache-2.0 | ['generated_from_trainer'] | false | bkk-ner-model This model is a fine-tuned version of [Geotrend/bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0518 - Precision: 0.8850 - Recall: 0.9615 - F1: 0.9217 - Accuracy: 0.9822 | 026a75425473775f9f03cf5ebb8bd29c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 15 | dbdccfe2b46952e312846cbd64694dfa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 8 | 0.5592 | 0.3698 | 0.6827 | 0.4797 | 0.7818 | | No log | 2.0 |... | c387c195eb2c98aa3aaab0389bca87df |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0925 - Wer: 41.4086 | fb56bb5c6ad5dc93c975dfd5e096f64f |
apache-2.0 | ['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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precisi... | 5faf467e04eb82a9ba606aeacaf74229 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.5216 | 1.04 | 1000 | 0.7054 | 58.7611 | | 0.0872 | 3.02 | 2000 | 0.7803 | 60.1400 | | 0.1073 | 4.06 | 3000 | 0.8312 | 6... | 4310da649c948c48cbac74ea00e77357 |
apache-2.0 | ['generated_from_trainer'] | false | model_broadclass_onSet0.1 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: 0.1129 - 0 Precision: 1.0 - 0 Recall: 1.0 - 0 F1-score: 1.0 - 0 Support: 31 - ... | 70f1cdf7edc20f1e10db94318cb4a780 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 7cc33e8123abd234d78d49b44a38a74a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall ... | 2e846490bdc4b99fc0163f9e79e0595b |
apache-2.0 | ['generated_from_trainer'] | false | nbme-electra-large-generator This model is a fine-tuned version of [google/electra-large-generator](https://huggingface.co/google/electra-large-generator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0122 - Accuracy: 0.9977 | fcc45ae4b35ea2827e4db218c66cc5f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 195 | 0.1125 | 0.9789 | | No log | 2.0 | 390 | 0.0141 | 0.9973 | | 0.6233 | 3.0 | 585 | 0.0122 | 0.... | cd75fb11c93bda52828b5c51f99d2bd3 |
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: 0.1623 - F1: 0.8602 | fe815501ca783e29627b4aaa501aab93 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2927 | 1.0 | 715 | 0.1798 | 0.8356 | | 0.1482 | 2.0 | 1430 | 0.1573 | 0.8507 | | 0.095 | 3.0 | 2145 | 0.1623 | 0.8602 | ... | cfccb53cfd6c0e3a1aa36f1418a19ab6 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup-3-4 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1949 - Rouge1: 49.6216 - Rouge2: 29.1874 - Rougel: 32.042 - Rougelsum: 46.3679 ... | 7af968501d67f3c8f3c83e9376af5895 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 258 | 1.2708 | 48.8914 | 29.2868 | 30.6203 | 46.2886 | ... | 6f9f6738961ec4c388c548b6ed74cec4 |
apache-2.0 | ['translation'] | false | opus-mt-fi-sw * source languages: fi * target languages: sw * OPUS readme: [fi-sw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | ccd8bb8b715faf565dd47d9d1deb2a61 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_a_fastspeech2 ``` | 199711ae1643b999d74b87fd8e8fba11 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/a/tts_train_fastspeech2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_... | 943f7714eec3e3bde6923e389f329541 |
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