license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 1 | bfbe5b5bae95bcf65688cda43b90e322 |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-distilbert-base-uncased-label_sexist 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: 0.4852 - F1: 0.7874 | a7016c1dd9b22f3439aa073d195d2bfd |
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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 8 - mixed_precision_train... | b9130b014407e8d6631324eb6ad3f8f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4199 | 1.14 | 400 | 0.3911 | 0.7571 | | 0.293 | 2.29 | 800 | 0.3778 | 0.7899 | | 0.2348 | 3.43 | 1200 | 0.4102 | 0.7894 | |... | 06eee3d957d442cee831a2d85176cb34 |
apache-2.0 | ['generated_from_trainer'] | false | model_syllable_onSet1 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: 0.1815 - 0 Precision: 1.0 - 0 Recall: 0.9677 - 0 F1-score: 0.9836 - 0 Support: 31 ... | 2fd348d18bc0e3eaf0431a99178b5413 |
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... | 5d440deb2b73680e1e06c9c3f48bdd60 |
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 ... | 7acf056f7d45cc74ef83923ee3a4791a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper-base-uk This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 1.3201 - eval_wer: 10.2869 | 6d5f789d97e5322d5b58f3663cc872b6 |
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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 190bf89651be4795c55ca31709151b0e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-5_england-5_s334 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 7eb3a35720500e41998c078faa9befe5 |
other | ['generated_from_keras_callback'] | false | TheNateTCY/fulltrain_optmodel This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.8560 - Validation Loss: 1.2171 - Epoch: 0 | 036cc92afd3191597846eeb680218574 |
other | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | 14e408ae7b30a6a3442118629e857ae4 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on irisg444_4c0 to apply classification on Species **Metrics of the best model:** accuracy 0.953333 recall_macro 0.953333 precision_macro 0.956229 f1_macro 0.953216 Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64 **See model ... | 527a4ae4344dfc9d8e342f55061d9e4c |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.co... | d3ed564c952ec8960b4337166b2512fb |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float ... free_string useless SepalLengthCm True False ... False False SepalWidthCm True False ... False False PetalLengthCm True False ... False False PetalWidthCm True ... | 0ae40a15e737d25be07b1a098a1ebde8 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wra... | 10a5838bc43a55812f31de59ecb52fad |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyP... | 70fd586d778a7927731e4415f5c0ab36 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;, max_iter=1000)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt | add4638b3f16a699897cbc9fe8cf0202 |
apache-2.0 | ['translation'] | false | opus-mt-fr-bi * source languages: fr * target languages: bi * OPUS readme: [fr-bi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 314113b76182d036e94d42113e8d644f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-32-4 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: 0.7384 - Accuracy: 0.724 | 907c2ef0f4a96c59105a10b7f1d5f464 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1013 | 1.0 | 19 | 1.0733 | 0.55 | | 1.0226 | 2.0 | 38 | 1.0064 | 0.65 | | 0.8539 | 3.0 | 57 | 0.8758 | 0.... | 70f673b91cb2ee8e7aa030969366b5fb |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_no-pretraining_s807 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | cd21c65865e8fd83bdd46b1342431e2b |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script d... | 957e1ff94ac90c92c81d9b404a8ca068 |
mit | ['conversational'] | false | generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, tempera... | be4745473152e9c4abed922d91529a6a |
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: 0.1608 - F1: 0.8593 | b66dd2f6aee0fd8751da8ca68e2fe6b8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ... | bff39ae636dd966f0c4ae244d77c4dfb |
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: 0.2913 - Accuracy: 0.88 - F1: 0.8808 | 4c174aca373cff5e335538c006e8c899 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | bert-base-cased-finetuned-stsb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4861 - Pearson: 0.8926 - Spearmanr: 0.8898 - Combined Score: 0.8912 The model was fine-tuned to c... | 8e9d1b89cc2687a35bf34e10f9f15680 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash | 078b71aaaa5b1ad9d4ed315e837689de |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n... | d6a7aa4216f7117a87a2cc901cd9bfc4 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | 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: 3.0 | 63658491ba435f3e49dc512e8638f72d |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:| | 1.1174 | 1.0 | 360 | 0.8816 | 0.5000 | 0.8832 | 0.8800 | | 0.3835 | 2.0 | 720 ... | 6ec0a4a840fb7df8d5a940d9bdc80d0f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-es-zle Neural machine translation model for translating from Spanish (es) to East Slavic languages (zle). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | e57e1597a0e9c9e4dd76fb9e43936d57 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-23 * source language(s): spa * target language(s): bel rus ukr * valid target language labels: >>bel<< >>rus<< >>ukr<< * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original m... | 1d424581220af8f5c81f72b605d376a9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>rus<< Su novela se vendió bien.", ">>ukr<< Quiero ir a Corea del Norte." ] model_name = "pytorch-models/opus-mt-tc-big-es-zle" tokenizer = MarianTokenizer.from_pretrained(model_name) model = Marian... | dd103ca0339371531f0c97fe9944726b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Я хочу поїхати до Північної Кореї. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-es-zle") print(pipe(">>rus<< Su novela se vendió bien.")) | 572dd9b95350c9420dd5f84a88b94b2d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zle/opusTCv20210807_transformer-big_2022-03-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sp... | 54d1355fc6953e53f57c06a486071ae9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | spa-bel | tatoeba-test-v2021-08-07 | 0.54506 | 27.5 | 205 | 1259 | | spa-rus | tatoeba-test-v2021-08-07 | 0.68523 | 49.0 | 10506 | 69242 | | spa-ukr | tatoeba-test-v2021-08-07 | 0.63502 | 42.3 | 10115 | 54544 | | spa-rus | flores101-devtest | 0.49913 | 2... | a7aa96d64593099b597552a8b5d53661 |
apache-2.0 | ['question-answering'] | false | Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-speci... | b61324a447420f363f34209ecca1d742 |
apache-2.0 | ['question-answering'] | false | Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` | 2e72869c5aadad444f4397c76b351fe1 |
apache-2.0 | ['question-answering'] | false | Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. | 70c22dc706382792c026801c82ba1e59 |
apache-2.0 | ['question-answering'] | false | BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv},... | 669eb29c469faf333ac66f139724ead9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becasv3-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv3 dataset. It achieves the following results on the evaluation set: - Loss: 3.1086 | 3afd47fc4bb2060197d4b846a3e55c1f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 8510dfdcd5e2abd7c9662c0feaafd433 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 5.1063 | | No log | 2.0 | 16 | 4.4615 | | No log | 3.0 | 24 | 3.9351 | | No log | 4.0 | 32 | 3.5490 ... | d48fef8daf813fe347dc6d3eca990c12 |
mit | ['generated_from_trainer'] | false | clinical-finetuned-AgitationModel This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9746 - Accuracy: 0.88 - Precision: 0.9178 - Recall: 0.9178 - F1: 0... | ebbd61cf9abd4f54f0c775aca674be3a |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 478785d1e9524ab05e70926183cada2a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.0949 | 1.0 | 50 | 1.0393 | 0.85 | 0.8816 | 0.9178 | 0.8993 | | 0.0475 | 2.0 |... | 23dbb2a1a060d82af9052ffce9f1acb8 |
apache-2.0 | ['generated_from_trainer'] | false | new-test-model2 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: 0.1040 - Precision: 0.9722 - Recall: 0.9757 - F1: 0.9739 - Accuracy: 0.9808 | 8bd5038ecd82d29d827c12deddacc02c |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | f9116a2dc106793943a93a59befb6503 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 151 | 0.1819 | 0.9360 | 0.9405 | 0.9382 | 0.9540 | | No log | 2.0 |... | 8c6e031739344c11ab485d2b725b6120 |
apache-2.0 | ['translation'] | false | opus-mt-hy-en * source languages: hy * target languages: en * OPUS readme: [hy-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hy-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | e8789f54bcb5b5971bf90c393996861c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | Demo: How to use in ESPnet2 Follow the [CHiME-7 DASR installation instructions](https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md) if you haven't done that already. ```bash cd espnet git checkout 15646109f254de8b39bbe310827d617da5ac858d | 105260d47726145f372d4c820d87ff93 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | follow installation instruction for CHiME-7 DASR recipe https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md ./run.sh --decode-only 1 --use-pretrained popcornell/chime7_task1_asr1_baseline --ngpu PUT YOURS ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 4b12573d828bd85aa487533de0b89035 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | Environments - date: `Wed Feb 8 23:41:28 UTC 2023` - python version: `3.9.2 (default, Mar 3 2021, 20:02:32) [GCC 7.3.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 1.13.1+cu116` - Git hash: `` - Commit date: `` | 53f45e33c46a5bc958fbb28d805de548 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k_raw_e... | 0d34aaf127123d26ac97710d43943c50 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | ' - ñ - â - é - ü - ']' - q - î - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model non... | fb986e828c070452d82d0fa451c336bf |
apache-2.0 | ['generated_from_trainer'] | false | test1000v2 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: 0.7873 - Wer: 0.6162 | e0b209039caf8825023981e9350044d9 |
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: 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_sche... | 7bdbd55ce5ca8202f4f9eed47b0c6f11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.7913 | 3.22 | 100 | 3.3481 | 1.0 | | 3.3831 | 6.44 | 200 | 3.3229 | 1.0 | | 3.3778 | 9.67 | 300 | 3.3211 | 1.0 | |... | b580b506d01d8bd4ff8570f48b7c8155 |
mit | ['exbert'] | false | Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all da... | b08bdaf5cdc42edd7716f6d701f4f5b0 |
mit | ['exbert'] | false | How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained... | 7ded885fce97538f1d2d766d3ab2f53e |
mit | ['exbert'] | false | Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: | 074e57a45f8add8f0a536eac8c057306 |
mit | ['exbert'] | false | BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01... | 5eb753caa7446ba88997960337b87fd2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | koja Dreambooth model trained by Kurapka 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-diffusio... | 2c793be0a02562cce21d438b96357658 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_r-wav2vec2_s418 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 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | 9a442d52f550a9965a4a56c0736c0fee |
unknown | ['stable-diffusion', 'text-to-image'] | false | .safetensor model for automatic1111 webui. Strange_Dedication_v3 is an improvement to Strange_Dedication_v2 using Anything_v4.5. It's better at the cutesexyrobutts style, without having to use a trigger. Also, it's good at shiny_skin and shiny_clothes and artistical backgrounds. I have only used it with "vae-ft-ms... | 9b107d017a9674c092158e4f7c3571ec |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'gakki'] | false | legal & risk ⚠️⚠ It is prohibited to use this model for commercial purposes and any scenarios of illegal acts and purposes. Sample pictures of this concept:   on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5508 - Train Accuracy: 0.0121 - Train Do Wer: 1.0 - Validation Loss: 4.7620 - Validation Accuracy:... | fd5ad5d70cbc0c17215302d532ce9c0e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | ed0e9bb766998d5716ae1931a9b275df |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Do Wer | Validation Loss | Validation Accuracy | Validation Do Wer | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 9.9191 | 0.0046 | 1.0 | 8.5836 | 0.0067... | 0c9642d039f05a5d979c622c7ab8629c |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Small Uncased Generator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the generator model used to sample synthetic text and pret... | 043d1299ffbb5eb40a02bf4588b9714f |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | Citations All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work: ``` @inproceedings{cruz2021exploiting, title={Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets}, author={Cruz, Jan Chri... | 98c8cd28972e108a2891ee9d35eccb1f |
apache-2.0 | ['generated_from_trainer'] | false | nba_pbp_distilgpt2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season. It achieves the following results on the evaluation set: - Loss: 0... | a2a13d2378039df174d55c18cdb4401d |
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: 3.0 | 37b549f9cb3f9b65b3370395f58f47d7 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qqp-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.0065 | ca1e94ec4d13ff12054f54189267efd7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3631 | 0.4 | 500 | 5.9145 | | 5.6422 | 0.8 | 1000 | 5.8224 | | 5.4368 | 1.2 | 1500 | 5.6172 | | 5.1539 | 1.6 | 2000 | 5.4872 ... | 76623b9a43eafbcc608b7dbd93c33a5d |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s945 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | b8fd0221d92f875aa66e5106d1cb7754 |
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 dataset. It achieves the following results on the evaluation set: - Loss: 1.1547 | 01ee8b557cfe53d4ecb706fb3e7090d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2164 | 1.0 | 5533 | 1.1486 | | 0.9546 | 2.0 | 11066 | 1.1251 | | 0.7573 | 3.0 | 16599 | 1.1547 | | d8c99812008c24b049de3dec504be50b |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | PyTorch This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://... | 351d4f81e741dc7bf03f133246dc6846 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification int8_model = IncQuantizedModelForSequenceClassification.from_pretrained( 'Intel/bert-base-uncased-mrpc-int8-dynamic', ) ``` | f1b5117cd91336c26dd98272c208bf14 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc). | 475454570558e60e11688fddd3a59672 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/bert-base-uncased-mrpc-int8-dynamic') ``` | 6d374f58e178b42c441a0cd1a8a067ae |
apache-2.0 | ['generated_from_trainer'] | false | InstructDial Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogu... | 50b4681dc54aa3ec1d1395eb67170505 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 9 - eval_batch_size: 9 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 72 - total_eval_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | 4c857c5700de0fc4c0b638c0041cc1e6 |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/alpacadabraz). Dataset is available [here](https://huggingface.co/datasets/huggingnft/alpacadabraz). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Pro... | 74273bb849a98af0f67d7f52b7b43462 |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | About *Built by Aleksey Korshuk* [](https://github.com/AlekseyKorshuk) [](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [ 0.9323 - F1-score (macro) 0.9272 | | True Posititves | False Positives | False Negatives | Precision | Recall | class-F1 | |------|-----|----|----|---------|--------|----------| | LOC | 164 | 7 | 13 | 0.9591 | 0.9266 | 0.9425 | | MISC | 398 | 22 | 37 | 0.9476 | 0.9149 | 0.9310 ... | 0fddb3c1f870e9512b91fa4adb591331 |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Usage ```python from flair.data import Sentence from flair.models import SequenceTagger import pyarabic.araby as araby from icecream import ic arTagger = SequenceTagger.load('megantosh/flair-arabic-MSA-aqmar') sentence = Sentence('George Washington went to Washington .') arSentence = Sentence('عمرو عادلي أستاذ للاقت... | 9c0059bcbb20861d3b0b8201bf313a5b |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Model Configuration ```python (embeddings): StackedEmbeddings( (list_embedding_0): WordEmbeddings('ar') (list_embedding_1): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(7125, 100) (rnn): LSTM(100, 2048) (decoder): Linea... | 3c3d47b724a8477aaf981812151c69c2 |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Citation *if you use this model, please consider citing [this work](https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects):* ```latex @unpublished{MMHU21 author = "M. Megahed", title = "Sequence Labeling Architectures in Diglossia", ye... | 292dd785708b072d9c5a1b0ce026f18e |
apache-2.0 | ['generated_from_trainer'] | false | CTEBMSP_ANAT_DISO This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0909 - Anat Precision: 0.7522 - Anat Recall: 0.7147 - Anat F1: 0.7330 - Anat Number: 361 -... | 9341482384c90286ac255c361b827da2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-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 - num_epochs: 8 | deb4d4b174a250db5bd6f631abee0b88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-... | fff440d4203fa73dbbf54c137d1b116c |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-20percent 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: 0.6513 - Precision: 0.5252 - Recall: 0.6562 - F1: 0.5834 - Accuracy: 0.8044 | a378918f5353f936aa1c2f57d6d0631b |
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: 8 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 69d2ddcf700eb15a31b59e16efe648a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.9155 | 0.3511 | 0.4264 | 0.3851 | 0.7353 | | No log | 2.0 |... | 956c41091aa0f545c6b70d6bea579bce |
mit | ['generated_from_trainer'] | false | Klassifizierung-Heizung This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0936 - F1: 0.9859 | f69e325d937340169e4c532b033ad3d5 |
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