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 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_vp-sv_s347 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) 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 that you... | c7115630deaedc2c1f2585ae3c7c9ac6 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-v3-e1 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset. | 0841a8fbe215efbdd3aef62e5d8ed942 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:|:-------:| | No log | 1.0 | 398 | 1.0222 | 52.722 | 33.3965 | 35.513 | 50.3104 | 142.0 ... | 2b961f9a70762597d0705d96a8dc2e6a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Romanian CV11 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ro dataset. It achieves the following results on the evaluation set: - Loss: 0.2808 - Wer: 17.5713 | cd9eb4c12f44cf9169287efa78487150 |
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: 64 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - train... | bc0600a8fa9f0cbc16f4dd916d4d1fe0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0046 | 8.01 | 1000 | 0.2808 | 17.5713 | | 0.0006 | 17.01 | 2000 | 0.3091 | 17.6916 | | 0.0003 | 25.02 | 3000 | 0.3267 | 17.688... | 3f7fc354fe917210141fd7e1ddf46f83 |
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: 0.9260 - Recall: 0.9384 - F1: 0.9322 - Accuracy: 0.9834 | c407114b8beec44b129d70277e9fc4fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2545 | 1.0 | 878 | 0.0711 | 0.9096 | 0.9214 | 0.9154 | 0.9800 | | 0.0555 | 2.0 |... | 1b2e1dc8ba5387976b67fa72d6c61799 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab3000 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6852 - eval_wer: 0.3845 - eval_runtime: 71.297 - eval_samples_per_second: 9.846 ... | 4a3dabbbeb584ab1a4bc73c703cdadb8 |
apache-2.0 | ['image-to-text'] | false | Model description PARSeq (Permuted Autoregressive Sequence) models unify the prevailing modeling/decoding schemes in Scene Text Recognition (STR). In particular, with a single model, it allows for context-free non-autoregressive inference (like CRNN and ViTSTR), context-aware autoregressive inference (like TRBA), and... | ef230ca02f4e9558907f800867222fe2 |
apache-2.0 | ['image-to-text'] | false | BibTeX entry and citation info ```bibtex @InProceedings{bautista2022parseq, author={Bautista, Darwin and Atienza, Rowel}, title={Scene Text Recognition with Permuted Autoregressive Sequence Models}, booktitle={Proceedings of the 17th European Conference on Computer Vision (ECCV)}, month={10}, year={2022}, ... | 5877bb8c87d8fbdb840cca615374b5a1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Balor-V2は他のモデルとマージすることで描画を向上させることを目標に作成されたマージモデルです。 AIアートにおいて、服装や背景の書き込みが増えるとキャラクターの目の描写があいまいになることが多くあります。 このモデルでは、特にこの目の描写の質を保つことを目標としています。 Balor-V2 is a merged model created with the goal of improving rendering by merging with other models. In AI art, the depiction of a character's eyes often becomes fuzzy as mo... | f5d466d0aac57632787a21422b781b3b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-bbc-news-classification 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: 0.0669 - Accuracy: 0.9880 - F1-score: 0.9880 - Recall: 0.9886 - Precision: 0.9875 | 4646509e0be62de1ada5bdeddec52591 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:| | No log | 1.0 | 98 | 0.1495 | 0.9775 | 0.9775 | 0.9772 | 0.9781 | | No log | 2... | be18f464909f0761db032efe100ec236 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | prada_v2_02 Dreambooth model trained by sunhaha123 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-stabl... | 844cca302e2cc4bf189b0e0ccbeb24b3 |
mit | [] | false | ZINC-t5 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset. It achieves the following results on the evaluation set: - Loss: 0.1202 - Accuracy: 0.9497 | fe49ddc0dae9044e89d80067be016d9d |
mit | [] | false | Training results | Training Loss | Step | Accuracy | Validation Loss | |:-------------:|:------:|:--------:|:---------------:| | 0.2471 | 25000 | 0.9843 | 0.2226 | | 0.1871 | 50000 | 0.9314 | 0.1783 | | 0.1791 | 75000 | 0.9371 | 0.1619 | | 0.1596 | 100... | 6a56a1de2801affd2535c791630fca5d |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_play-roberta-large-v1-2-71 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contras... | 2f22c4037fdc089f0931d99c0d8ac0c2 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0671 - Accuracy: 0.9815 | 21b0c809ef3f58d8d4aedf97fbafc007 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1452 | 1.0 | 190 | 0.1335 | 0.97 | | 0.0683 | 2.0 | 380 | 0.0825 | 0.9763 | | 0.0584 | 3.0 | 570 | 0.0671 | 0.... | 17108940b481493224f512fe3fb92ad8 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Thai This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 th,None,th_th dataset. It achieves the following results on the evaluation set: - Loss: 0.1841 - Wer: 14.060 | 5a7172236037c0b129b4c2774fe8d95a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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: 500 - training_steps: 5000 - mixed_preci... | dc742a950cf2d09da713b8458b685403 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0909 | 0.2 | 1000 | 0.3373 | 25.5752 | | 0.0426 | 1.1 | 2000 | 0.2540 | 20.9739 | | 0.0267 | 2.0 | 3000 | 0.2210 | 17.408... | eb9decf9fb96994b38e0e0be0e8ae768 |
mit | ['generated_from_trainer'] | false | bart-cnn-science-v3-e4 This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8265 - Rouge1: 53.0296 - Rouge2: 33.4957 - Rougel: 35.8876 - Rougelsum: 50.0786 -... | 98ebb95a07bf956fc133dd381a9fadce |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9965 | 52.4108 | 32.1506 | 35.0281 | 50.0368 | ... | fef51ea81dfd303dfbaf466f0d57e12c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-outdomain-fr-trans 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: 2.2136 - Accuracy: 0.2278 - F1: 0.1824 - Precision: 0.1034 - Recall: 0.7747 - Mae:... | 0416b7a98b9a65947735b5a448ae9be2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:| | 0.3894 | 1.0 | 2233 | 0.9776 ... | 69d6a95d1ee99a4a73989b97bf07c6a2 |
apache-2.0 | ['translation'] | false | urj-eng * source group: Uralic languages * target group: English * OPUS readme: [urj-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/urj-eng/README.md) * model: transformer * source language(s): est fin fkv_Latn hun izh kpv krl liv_Latn mdf mhr myv sma sme udm vro * target language(s): ... | 422eb01a7e32d966cbac584a68b7f5c2 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2015-enfi-fineng.fin.eng | 22.7 | 0.511 | | newsdev2018-enet-esteng.est.eng | 26.6 | 0.545 | | newssyscomb2009-huneng.hun.eng | 21.3 | 0.493 | | newstest2009-huneng.hun.eng | 20.1 | 0.487 | | newstest2015-e... | a39ae7f0f45b1b034ec7e988b077064b |
apache-2.0 | ['translation'] | false | System Info: - hf_name: urj-eng - source_languages: urj - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/urj-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['se', 'fi', 'hu', 'et', 'urj', 'en'] - src_constitue... | 1bffb7a878ba9111d2e7612e838acecb |
mit | ['generated_from_keras_callback'] | false | nouman10/robertabase-finetuned-claim-ltp-full-prompt_ 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: - Train Loss: 0.0334 - Validation Loss: 0.0237 - Epoch: 1 | c4777ba4cfc9f7da5357baad83bdf2a3 |
mit | ['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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 8c63cd39e9cbe0be45890b6039d9a9bb |
apache-2.0 | ['multiberts', 'multiberts-seed_19'] | false | MultiBERTs - Seed 19 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 random seeds, which causes variatio... | f188152e2508bc334e82af3d2e9c8a72 |
apache-2.0 | ['multiberts', 'multiberts-seed_19'] | 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_19') model = TFBertModel.from_pretrained("google/multiberts-seed_... | be00267924213d337db531fb7680c5f9 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-base-squad-qg` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 7cd8a9d6d5fcb4f96d41af06f8555198 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-base-squad-qg") output = ... | 4b8e03445cbc361a6d78ec8f3857699a |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------... | 869e1155b058a701dc68fd2c4ea6085d |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 16... | 20ed512099439aa346569ac981d2613e |
creativeml-openrail-m | [] | false | I'll preface this by saying that I have no idea what I'm doing. Also, this is by no means a complete or perfect model. But after many tries I'm at a point where I'm happy with sharing some pictures and an early version for you to try out. | 308d050857a5e20010e2961b306a9282 |
creativeml-openrail-m | [] | false | Classic Negative (SD 1.5)  With Classic Negative I tried to train a model with DreamBooth which closely mimics my style of photography. Its name comes from a built in camera profile in Fujifilm cameras, "Classic Negative". I use a mo... | 5f263cd47f53d33c3e44d22ab7541e56 |
creativeml-openrail-m | [] | false | Training For training I used 100 of my personal images, consisting mainly of environmental portraits and photos of my dog, some macro and some landscape shots. The model is probably biased towards forests and garden pictures, since that's where I took the majority of my photos. It seems to be on the verge of being ove... | 9e850485b5e4fd6b31acf7bae04089d0 |
creativeml-openrail-m | [] | false | Prompts & Parameters The prompts I tried so far are very simple. The activation token is classicnegative - classicnegative photo of a cute raccoon sitting between bushes in a garden, purple tulip flowers - classicnegative photo of a cute small red panda sitting on a branch in the jungle - classicnegative photo of a wh... | a8739e8bf5a23f877e7523b021e73e82 |
creativeml-openrail-m | [] | false | What's next - more testing is needed, different parameters and subjects - create a SD2.1 768px version - finetuning Please feel free to try the model out, test its limitations and if you have any advice on how I can create a better version of it, please let me know ;) | a5e71b097365aa92e0898cb9488f9361 |
mit | [] | false | This model has been pretrained on BEIR corpus then finetuned on MS MARCO with BM25 warmup only, following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here... | 4fd6a94d96383205bc19b40edcfdd0d5 |
apache-2.0 | ['afro-digits-speech'] | false | afrospeech-wav2vec-yor 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... | 9e712b85299c5d5f190b65c4b6a281d5 |
apache-2.0 | ['afro-digits-speech'] | false | Training and evaluation data The model was trained on a mixed audio data from Yoruba (`yor`). - Size of training set: 22 - Size of validation set: 6 Below is a distribution of the dataset (training and valdation)  | 2a7714261064261338a6239a2a5b1c45 |
apache-2.0 | ['afro-digits-speech'] | false | Evaluation performace It achieves the following results on the [validation set](VALID_yoruba_yor_audio_data.csv): - F1: 0.83 - Accuracy: 0.83 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 oth... | ad76444e2f64b69c988249616a030254 |
apache-2.0 | ['afro-digits-speech'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - num_epochs: 150 | 342250accda3a8ee8bd00d71b49f96d6 |
apache-2.0 | ['afro-digits-speech'] | false | Training results | Training Loss | Epoch | Validation Accuracy | |:-------------:|:-----:|:--------:| |0.596 | 1 | 0.5 | | 0.0220 | 50 | 0.5 | |0.00305 | 100 | 0.667 | |0.0993 | 150 | 0.667 | | 9586d285926325062687dc19ad8c1435 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | rasr_sample This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.3147 - Wer: 0.2676 | e42d950d04cd7ae6b58e3aca696f3287 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3332 | 1.45 | 500 | 3.3031 | 1.0 | | 2.9272 | 2.91 | 1000 | 2.9353 | 0.9970 | | 2.0736 | 4.36 | 1500 | 1.1565 | 0.871... | d2a94c671a5f60148d21ed9a723a828e |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.1256 - Accuracy: 0.9877 - F1: 0.9911 - Combined Score: 0... | 1a5185b4365e66d508949d8ff7c87d38 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.2964 | 1.0 | 1959 | 0.2026 | 0.9608 | 0.9718 | 0.9663 | | 0.2307 | 2.0 | 3918 | ... | 85e7bb02f2772b1441c3b52e8b115172 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | wav2vec2-base-ks-ept4 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 1.5663 - Accuracy: 0.6209 | 64c4a69dedcdf2968d5c9c836ac05a17 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5133 | 1.0 | 50 | 1.5663 | 0.6209 | | 1.4819 | 2.0 | 100 | 1.5675 | 0.6169 | | 1.4082 | 3.0 | 150 | 1.5372 | 0.... | 7dcda5500baecc177a4d2f0d3db476bf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 1.0 - mixed_precision_training: Native AMP | e41d87ea401af6668e0ddb64ffd197d4 |
mit | ['generated_from_trainer'] | false | reco-ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0668 - Precision: 0.8125 - Recall: 0.8790 - F1: 0.8444 - Accuracy: 0.9819 | 3436675b5f3966fe7d4806baaeddb51a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | e1fc8dc53a6338084bed20528e891d4a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4516 | 1.0 | 626 | 0.4047 | 0.4332 | 0.4564 | 0.4445 | 0.8980 | | 0.3677 | 2.0 |... | 380ea6be992ca3214a262198868f88ac |
creativeml-openrail-m | ['text-to-image'] | false | training params ```json { "pretrained_model_name_or_path": "multimodalart/sd-fine-tunable", "instance_data_dir": "./70b5371d-e556-4093-b549-2a8848d59ebd/instance_data", "class_data_dir": "./class_data/class", "output_dir": "./70b5371d-e556-4093-b549-2a8848d59ebd/", "train_text_encoder": true, "... | 99f8be89a905f7ea23b16d50ced2a249 |
apache-2.0 | ['translation'] | false | opus-mt-lv-fr * source languages: lv * target languages: fr * OPUS readme: [lv-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lv-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | b703b5318c1580f96926aec9a1cde8b4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becas-4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.1357 | c81ee011d3d407d4eb0ef741cec4f457 |
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: 10 | e4be8375e7ed3f1b6fd472ce0316dbc9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.9618 | | No log | 2.0 | 18 | 4.1071 | | No log | 3.0 | 27 | 3.5438 | | No log | 4.0 | 36 | 3.2115 ... | 6b26562a162449aaade8c07a9fcab4db |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_xlsr-53_s286 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | 4c5c7e2e49c3461bd30ddc95ac6a3bd6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 120 | 0.0053 | 0.8410 | 0.9372 | 0.8865 | 0.9991 | | 15b1a0cf804b3643a3573777f7bf0866 |
apache-2.0 | [] | false | hyunwoo3235/t5-v1_1-base-ko [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 that trained on korean corpus t5-v1_1-base-ko은 한국어 코퍼스에서 학습된 t5 v1.1 모델입니다. OOV을 막기 위해 BBPE를 사용하였으며, HyperCLOVA에서 형태소 분석이 성능을 높히는데 도움이 되는 것을 보고 토크나이저 학습 과정에서 MeCab을 이용해 형태소가 이상하게 토큰화 되지 ... | 4220ae79471a814b075ed6f71e7b3763 |
apache-2.0 | [] | false | Usage ```python from transformers import AutoTokenizer, T5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained('hyunwoo3235/t5-v1_1-base-ko') model = T5ForConditionalGeneration.from_pretrained('hyunwoo3235/t5-v1_1-base-ko') ``` | 153b26ff726a83391cf79ac3118e1c41 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept any-ely-wd-ira-olympus-4000 được train bởi hr16 bằng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bằng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJV... | 9ee1669bf98b1b7a120e606325965b05 |
apache-2.0 | ['generated_from_trainer'] | false | Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.5062 - Precision: 0.6603 - Recall: 0.4682 - F1: 0.5479 - Accur... | d5d4fec795a9173275fbd2ae7976d1bb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 107 | 0.3396 | 0.6470 | 0.4269 | 0.5144 | 0.9330 | | No log | 2.0 |... | 68e83f0fae30fceca12d8b7130d473df |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best` ♻️ Imported from https://zenodo.org/record/5590384 This model was trained by siddhana using slurp/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | f2f0e6f143b44d1aaa18ac0fc8a76114 |
creativeml-openrail-m | ['text-to-image'] | false | bcddih_1ndrprkr_rvgbnchly Dreambooth model trained by taskmasterpeace with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github... | 17b20a48e5e29650b1fea3cb2905835b |
apache-2.0 | ['generated_from_trainer'] | false | eval_masked_v4_qnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4833 - Accuracy: 0.8944 | 42f0f1cfa0d846853484d02f5fa9ee5a |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | d2ed5d41ae88f16a03c9d8490e436cb9 |
mit | ['generated_from_trainer'] | false | hungry_pasteur This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekko... | 4e5b5aa5e7d25c51fdf3e8ac6b8208db |
mit | ['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: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 33a0af855f8d2a88ef100709b909a161 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 0737fe99072dd68121f5358fe4baefe3 |
apache-2.0 | ['generated_from_trainer'] | false | portuguese-archival-finding-aids This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.1812 - Precision: 0.8624 - Recall: 0.9557 - F1: 0.9067 - Accuracy: 0.9618 ... | 88ed9b99ffa6085a19943429ab4084c8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 192 | 0.1565 | 0.8511 | 0.9327 | 0.8900 | 0.9563 | | 0.1849 | 2.0 |... | 0746be652c8d5e6c4048205b077219a3 |
mit | ['generated_from_trainer'] | false | deberta-base-newsqa This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7628 | 384494670b4bde3b2d4b19f83211dcef |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.6847 | 1.0 | 17307 | 0.7396 | | 0.4916 | 2.0 | 34614 | 0.7628 | | b3b31ae34539be4e8778b0ae574aac3f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_rte_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.4229 - Accuracy: 0.4729 | 66a279c2dc5ae6926a5c82b46c7f69e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4295 | 1.0 | 10 | 0.4238 | 0.4729 | | 0.4192 | 2.0 | 20 | 0.4234 | 0.4729 | | 0.4175 | 3.0 | 30 | 0.4239 | 0.... | a8e189055a5a8767f8a9e9c630f45082 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2237 - Accuracy: 0.929 - F1: 0.9290 | b4ccc5333f4ef19dd924ac891674ef44 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8466 | 1.0 | 250 | 0.3299 | 0.899 | 0.8944 | | 0.2589 | 2.0 | 500 | 0.2237 | 0.929 | 0.9290 | | f93cc90941e9cb3bdc16760b51e4923e |
apache-2.0 | ['generated_from_keras_callback'] | false | Metformin/T5model_medFineTune This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.0442 - Validation Loss: 6.1005 - Epoch: 9 | 10fad73e3211c58052f21ea317e74483 |
apache-2.0 | ['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': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps... | 6f1ffc67d6a9b5f0768252a185ee7e44 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 42.6321 | 28.0647 | 0 | | 31.2672 | 21.0068 | 1 | | 24.8310 | 16.6186 | 2 | | 20.5368 | 13.8025 | 3 | | 17.3796 | 11.7180 | 4 | | 15.0329 |... | c48e93776d8b2fa3c75ca4cba47e4acd |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2747 - Precision: 0.5674 - Recall: 0.2966 - F1: 0.3895 - Accuracy: 0.9411 | b62858ab574bfe45e33cb2aef143b37f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2827 | 0.5605 | 0.2317 | 0.3279 | 0.9380 | | No log | 2.0 |... | dffd7fac015e93dad8671834ffde3b3d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_sst2_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5204 - Accuracy: 0.7913 | d348bc0ce02b698857eb5d17fb2b6f6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.3537 | 1.0 | 4374 | 0.7913 | 0.5204 | | 0.2638 | 2.0 | 8748 | 0.6801 | 0.7649 | | 0.2156 | 3.0 | 13122 | 0.6808 | 0.77... | 4ac458842e8194ee167dc70ab37ca6c2 |
isc | ['music', 't5', 'byt5'] | false | ByT5 Song Lyrics This is a Seq2Seq model trained on a karaoke dataset to predict syllables with pitch and timing from song lyrics. As of writing, the model has only been trained on 1/2 of the full dataset. Expect the quality to improve later. The Huggingface demo seems to produce outputs with a small sequence lengt... | d332c728f75b4d9bb54a8aa2cdceb82c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5816 - Wer: 0.3533 | c4a3fc4debbd4d0d06eaa974ad810818 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - 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: 1000 - num_epochs: 30 - mixed_precision_tr... | ab4bc63349b4db1b4f0aba3d9abdd64a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.243 | 0.5 | 500 | 1.0798 | 0.7752 | | 0.834 | 1.01 | 1000 | 0.6206 | 0.5955 | | 0.5503 | 1.51 | 1500 | 0.5387 | 0.515... | cafb487c5069aaf97846dffedf1b8431 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-8-16-5 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: 2.2920 - Accuracy: 0.3982 | 7ee931957344dd83450fa202d70f7e6d |
['cc-by-sa-4.0'] | ['sentence-transformers', 'causal-lm'] | false | Usage (Sentence-Transformers) Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer... | 5499213d6e0d339e1541dc774ad54948 |
apache-2.0 | [] | false | ParsBERT + Sentence Transformers Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models. ```bibtex @misc{SentenceTransformerWiki, author = {Mehrdad Farahani}, title = {Sentence Embeddings with ParsBERT}, yea... | 370cf17f965c37c62cd8ceb2760b2c35 |
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