license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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gpl-3.0 | [] | false | 训练过程 使用了[UER-py](https://github.com/dbiir/UER-py/) 进行fine-tuned 加入了包括但不限于摘要、负采样、混淆等数据加强方法 并转换为Huggingface进行上传 | | CMRC 2018 Dev | DRCD Dev | SQuAD-Zen Dev (Answerable) | AVG | | :-------: | :-----------: | :-------: | :------------------------: | :-------: | | PERT-large | 74.4/89.8 | 90.3/94.|... | e4e5719cf0e8e59b977951ac89c76ca6 |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-nt5-small-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) | 876b546c712b55518c3e3d0669d1efb3 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-test-ged-RAW_data_prep_2021_12_26___t1_7.csv_max_target_length_10 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: 2.0338 - Rouge1: 28.7359 - Rouge2: 15.6289 - Rougel: 28.... | c5f1cdfb29fa7ae9255a1829ce78c788 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 6.0554 | 1.0 | 1935 | 2.7346 | 23.7306 | 13.3598 | 23.7172 | 23.7447 | | 2.9111 | 2... | bd7193c79a0986eae3073947515d234d |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-fr_s156 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 00533ea2d59d4f1ef934e354453c1f8e |
apache-2.0 | ['text-generation'] | false | Model description [GPT-2](https://openai.com/blog/better-language-models/) is a large [transformer](https://arxiv.org/abs/1706.03762)-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previo... | efe9057ea5c9e07dfcfe09287bd1a2b6 |
apache-2.0 | ['text-generation'] | false | How to use For best experience and clean outputs, you can use Live Demo mentioned above, also you can use the notebook mentioned in my [GitHub](https://github.com/HamidRezaAttar/GPT2-Home) You can use this model directly with a pipeline for text generation. ```python >>> from transformers import AutoTokenizer, AutoMo... | 55d000e6b40987017749149581086691 |
apache-2.0 | ['text-generation'] | false | Citation info ```bibtex @misc{GPT2-Home, author = {HamidReza Fatollah Zadeh Attar}, title = {GPT2-Home the English home product description generator}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/HamidRezaAttar/GPT2-Home}}, } ``` | a89f338da7dc862c8b6050bc22d57e06 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-sst2-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.2580 | 558d7695811bb96272da8e5147daafeb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.0249 | 0.4 | 500 | 7.2506 | | 7.1076 | 0.8 | 1000 | 7.1057 | | 6.8912 | 1.2 | 1500 | 7.2155 | | 6.8907 | 1.6 | 2000 | 7.3149 ... | 966bd9b03cf274ea88de75f54f5b22ba |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s271 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When usi... | 2e49ad81e39af70619c2884520761231 |
apache-2.0 | ['generated_from_keras_callback'] | false | test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: | f7ae8ddc1bc8aa41165a46f813df7cab |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ru-finetuned-en-to-ru-Legal This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8561 - Bleu: 46.7284 - Gen Len: 23.1317 | dc65ec298ee9fbc705ac908eaa0cdac0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 387 | 1.1719 | 34.0562 | 22.991 | | 1.524 | 2.0 | 774 | 1.0342 | 37.7233 | 23.0052 | | 1.0226 |... | e4e20bcc414593654477ee3569badb96 |
apache-2.0 | ['generated_from_keras_callback'] | false | Gorenzelg/bert-finetuned-squad11 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: 1.0664 - Epoch: 0 | f048baad276e5582a2495040beb9767a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 55450, 'end_learning_r... | 7a80987d639667befc6d85b80abefb3f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 192 - eval_batch_size: 192 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 | 125b9b8d401d48dfbd9e4686fb24f085 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | whisper-small-af-za - Ari This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0002 - eval_wer: 0.0 - eval_runtime: 77.0592 - eval_samples_per_second: 2.569 - ... | 190e284617306b5c6b8f442759482ec9 |
mit | [] | false | Jamiels on Stable Diffusion This is the `<jamiels>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train... | 98dc9b255a3a60c7ab5703682a7db430 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mrpc_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6089 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | ae6bbffbfd4caa01d3b3d1009ab1c73f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6363 | 1.0 | 15 | 0.6257 | 0.6838 | 0.8122 | 0.7480 | | 0.6306 | 2.0 | 30 | 0.62... | 26ba61de9a617343156b5d303a3525d7 |
mit | ['generated_from_trainer'] | false | Facebook_Mit_HPS_5_Epoch This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4774 - Accuracy: 0.9315 | c125971b504bbc3fa730470178aa40cc |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.546392051994155e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 5 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 09b4c1cf25257fd0972d92f0639198ff |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 292 | 0.2181 | 0.9264 | | 0.2411 | 2.0 | 584 | 0.2571 | 0.9289 | | 0.2411 | 3.0 | 876 | 0.5712 | 0.... | d792a285271baa6d05fee009565be75c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'textual-inversion', 'embedding'] | false | about - 2 embeddings to resemble a popular toy from the 90s - check the [PDF](https://huggingface.co/proxima/foorby/blob/main/foorby_embeddings_handbook.pdf) for comparisons, prompts and settings - v2 seems to trend more towards realism [<img src="https://huggingface.co/proxima/foorby/resolve/main/example_2.jpg">](h... | ae8b2f1872501141c9924ea3ce01695d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'textual-inversion', 'embedding'] | false | how to use - place the .bin files in your embeddings folder - use foorbyv1 or foorbyv2 in your prompt ---- if you enjoy this consider buying me a coffee (ノ◕ヮ◕)ノ*:・゚✧ <a href='https://ko-fi.com/S6S6FUYKY' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi3.png?... | 44ff0f9e026db186a7b0cddd9dcce427 |
cc-by-sa-4.0 | [] | false | ELECTRA base Japanese discriminator This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). | 59f7dcaa28411406408498784bad3f9f |
cc-by-sa-4.0 | [] | false | Model architecture The model architecture is the same as ELECTRA base in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 12 layers, 768 dimensions of hidden states, and 12 attention heads. | 9d29a88e64981c71fa9d1551397741aa |
cc-by-sa-4.0 | [] | false | Training The models are trained with the same configuration as ELECTRA base in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 512 tokens per instance, 256 instances per batch, and 766k training steps. The size of the generator is 1/3 of the size of the discriminator. | cb53086d7147e63ec34f9987347fa1d6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-billsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5953 - Rouge1: 0.1383 - Rouge2: 0.0487 - Rougel: 0.1135 - Rougelsum: 0.1132 - Gen Len: 19.0 | f6acd924d64136143196daa2cf831a4e |
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: 2 - mixed_precision_training: Native AMP | d403cf4bf8674899ac5b1d1a814a5dae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 124 | 2.6810 | 0.1312 | 0.0415 | 0.1076 | 0.1077 | 19.0 | |... | a7814782a6260b1983a3aac8125b2cac |
apache-2.0 | ['automatic-speech-recognition'] | false | wav2vec2-xlsr-korean-senior Futher fine-tuned [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek/wav2vec-large-xlsr-korean) using the [AIhub 자유대화 음성(노인남녀)](https://aihub.or.kr/aidata/30704). - Total train data size: 808,642 - Total vaild data size: 159,970 When using this model, make sure that your spee... | e6cae68e7dec1f1d22e8d66890fc24c4 |
apache-2.0 | ['automatic-speech-recognition'] | false | Inference ``` py import torchaudio from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import re def clean_up(transcription): hangul = re.compile('[^ ㄱ-ㅣ가-힣]+') result = hangul.sub('', transcription) return result model_name "hyyoka/wav2vec2-xlsr-korean-senior" processor = Wav2Vec2Processor.from_... | e9a96a9af0725020feb296d410829909 |
apache-2.0 | [] | false | Introduction This seq-2-seq semantic parsing model is used by [Genie](https://github.com/stanford-oval/genie-toolkit) to compile an assistant in the restaurant domain. This model translates natural language utterances to [ThingTalk](https://github.com/stanford-oval/thingtalk), executed by Genie. | 70d56b2c8c2bb1ec4d2270840cc45148 |
apache-2.0 | ['translation'] | false | epo-ell * source group: Esperanto * target group: Modern Greek (1453-) * OPUS readme: [epo-ell](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ell/README.md) * model: transformer-align * source language(s): epo * target language(s): ell * model: transformer-align * pre-processing: norma... | 29d6b824769bea3008d796f91221ba79 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: epo-ell - source_languages: epo - target_languages: ell - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ell/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'el'] - src_constituents: {'epo'} - tgt_const... | 65addbf753d9ff63efac3f9ea3632ed9 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.07-0.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8593 - Bleu: 7.0665 - Gen Len: 43.5793 | e0a137297ac774c626b249c4f53ca2ca |
apache-2.0 | ['generated_from_trainer'] | false | DistilGPT2-Beatles-Lyrics-finetuned This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [Huggingartists - beatles](https://huggingface.co/datasets/huggingartists/the-beatles) dataset. It will complete an input prompt with Beatles-like text. | 93b8ea185ee362d554c2d9fd5e2afae7 |
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 - lr_scheduler_warmup_steps: 100 - num_epochs: 5 | c45d772309a55fa78bf38d2c467f75ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.748 | 1.0 | 165 | 2.3732 | | 2.4395 | 2.0 | 330 | 2.1938 | | 2.2968 | 3.0 | 495 | 2.1118 | | 2.2075 | 4.0 | 660 | 2.0721 ... | 4f2f555afdaf45d6062c0a14f5cc4f36 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-dutch-cased-finetuned-mBERT This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0898 - Precision: 0.7255 - Recall: 0.7255 - F1: 0.7255 -... | 718424ff9fe18c7f5839b52b036aafc0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1603 | 1.0 | 533 | 0.0928 | 0.6896 | 0.6962 | 0.6929 | 0.9742 | | 0.0832 | 2.0 |... | 96699c6c2aad0b5f399060c795a46b6f |
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 None dataset. It achieves the following results on the evaluation set: - Loss: 1.0202 - Accuracy: 0.8235 - F1: 0.8223 | 3c144d5d12b553a88f142dbe0431bc2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7099 | 1.0 | 17 | 0.6695 | 0.5294 | 0.3665 | | 0.686 | 2.0 | 34 | 0.6288 | 0.5294 | 0.3665 | | 0.5945 |... | d812666512ab3f48eebb83487a4ef242 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_44 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4608 - Accuracy: 0.8270 - Macro-f1: 0.8253 - Weighted-macro-f1:... | 86ae57da8d28c2485ce9b2bfca9ceebe |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-modelo-becas0 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: 3.1182 | b93a55e3466c5ee64bfdeab9725bb74f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.5381 | | No log | 2.0 | 10 | 4.9493 | | No log | 3.0 | 15 | 4.4985 | | No log | 4.0 | 20 | 4.1063 ... | e4fd3b769f924633877b3285960d2580 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-010099-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8127 - Bleu: 7.735 - Gen Len: 44.5453 | 68eea03b90b2dc98ff1111b5a0d2aba3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1216 - F1: 0.8749 | f0089a2f529b32bfeea03873af596033 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2247 | 1.0 | 834 | 0.1429 | 0.8432 | | 0.1127 | 2.0 | 1668 | 0.1270 | 0.8653 | | 0.0712 | 3.0 | 2502 | 0.1216 | 0.8749 | ... | 0466e7b315a4dffd43469455ae613ae1 |
apache-2.0 | ['generated_from_trainer'] | false | model-1-reverse-bart This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3347 - Rouge1: 95.4467 - Rouge2: 91.7522 - Rougel: 95.448 - Rougelsum: 95.4377 - Gen Len: 1... | 4ede4dbd0c2766955e53bc3193877ac7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | 0.0744 | 1.0 | 28039 | 0.3347 | 95.4467 | 91.7522 | 95.448 | 95.4377 | 15... | dffcc4d31c0b6420789913a269ab510b |
mit | [] | false | Andrej-sternen on Stable Diffusion This is the `<andrej-sternen>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You ... | fd6fabda922994ccc5babf6da80becc4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3830 - Wer: 19.5173 | 520af1647738b24d3fca510de1029a38 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.011 | 4.01 | 1000 | 0.3234 | 20.5978 | | 0.0011 | 8.03 | 2000 | 0.3650 | 19.4070 | | 0.0006 | 12.04 | 3000 | 0.3830 | 19.517... | 6e7bd1d3a7efabfdd94f5697be056e19 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-peyma-fa 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: 0.0937 - F1: 0.9249 | 0f8bee8d5f831b9c7c873b6e8ec161fd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1562 | 1.0 | 998 | 0.0691 | 0.8777 | | 0.0638 | 2.0 | 1996 | 0.0703 | 0.8908 | | 0.0457 | 3.0 | 2994 | 0.0645 | 0.8975 | |... | 4b6c295ca2949e15a521b560449b90e2 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-marathi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5656 - Wer: 0.2156 | fa6fc2b9415c001cf73d803faa90ae70 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-squad-qg-no-answer` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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-gen... | 2c5533e7ae1bf34aeb3025192c1845dc |
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", "research-backup/t5-small-squad-qg... | e0ec5ba14b409b99a7a74ed81b40f623 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 753bd522b97d14aebc0253cc32023284 |
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_sentence'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 7 - batch:... | ec2fae0bd6dbb391b9a93d898b802d64 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3209 - Accuracy: 0.9429 | d027605c5771711686ed5efc8ed0e03d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | e7e56a522eb3eaaeab5ebe3f22b3210a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.0228 | 1.0 | 318 | 2.2545 | 0.7548 | | 1.7605 | 2.0 | 636 | 1.2040 | 0.8513 | | 0.959 | 3.0 | 954 | 0.6910 | 0.... | 0667926851dee8ba472d49b486df1b8c |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Training Data This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. | e9833aec70e8adc62b2261614f1ce664 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('dangvantuan/CrossEnco... | 151a7d6691c63bcd4543e556dbd1ad34 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Evaluation The model can be evaluated as follows on the French test data of stsb. ```python from sentence_transformers.readers import InputExample from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator from datasets import load_dataset def convert_dataset(dataset): dataset_samples=[] ... | c4ad19240ffaf4c2f40e248de3733a97 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | For Test set test_samples = convert_dataset(df_test) test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test') test_evaluator(models, output_path="./") ``` **Test Result**: The performance is measured using Pearson and Spearman correlation: - On dev | Model | Pearson correlation | S... | e93a74183ed33697168016ee7f8e3ab0 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | params | | ------------- | ------------- | ------------- |------------- | | [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M | - On test | Model | Pearson correlation | Spearman correlation | | ------------- | ------------- | ----------... | 04f86c6a1a66a28787c7b3a3c0c2457d |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> ['Sino-Tibetan_relations_during_the_Ming_dynasty', 'Human_Development_Index', 'Hunter-gatherer', 'Somalis', 'Black_people', 'Bird_migration', 'Biodiversity', 'Mammal', 'Predation', 'Botany', 'Heian_period', 'On_the_Origin_of_Species', 'Domini... | 93146730d98a0ef6bcfd674d512faaf0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-moral-ctx-action-conseq 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: 0.1111 - Accuracy: 0.9676 - F1: 0.9676 | d55e8e9e5e8f034970aefd067d9b6c59 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.989502318502869e-05 - train_batch_size: 2000 - eval_batch_size: 2000 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | e74370b368c24b31f05f110c50f9dd5d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 10 | 0.1569 | 0.9472 | 0.9472 | | No log | 2.0 | 20 | 0.1171 | 0.9636 | 0.9636 | | No log |... | ff340441ff0c54a1fd20e5b6415437cf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-triviaqa 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: 0.9949 | a07314bd5723cedf8e0694e60cb9c9a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0391 | 1.0 | 11195 | 1.0133 | | 0.8425 | 2.0 | 22390 | 0.9949 | | 9df81c54d35bc7fea7c77975cfb0db4b |
apache-2.0 | ['translation'] | false | opus-mt-pis-en * source languages: pis * target languages: en * OPUS readme: [pis-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pis-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 2aef69403ebc1d9f7e7e82bbbf59b5f0 |
apache-2.0 | ['translation'] | false | opus-mt-fi-he * source languages: fi * target languages: he * OPUS readme: [fi-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-he/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | eaa794560600011de5c1edf9b001f8eb |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_unispeech_s833 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) 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 you... | 8b2b9c42eaac1bb369ecee04d7731bd1 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia). Every long-unit-word is tagged by [UPOS](https://universaldepende... | b22773e646cd5b981011a690e4e3bfa6 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos") s="国境の長... | 85ddf38792a2afd808c21fe6cfa4f9bb |
apache-2.0 | ['generated_from_trainer'] | false | xsun_models 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: 0.1191 - Accuracy: 1.0 | ffa7de8bb4ae90bf064a46fe50fdb0ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2054 | 1.0 | 1 | 0.1407 | 1.0 | | 0.1505 | 2.0 | 2 | 0.1191 | 1.0 | | 9ce4407d709d8ccb9918b7f553cb0afb |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/... | 7de17b2a320f05fe0833d92706179894 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-luw-upos") s="国境の長いトンネルを抜けると雪国であった。" p=[mo... | 5a0f85a850c0cb36b76c09ac60f222d6 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qnli-target-glue-wnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0564 - Accuracy: 0.1268 | 5f75eb052cb4c01d3c15033c66c47d6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6898 | 25.0 | 500 | 0.7650 | 0.2113 | | 0.663 | 50.0 | 1000 | 1.1165 | 0.1268 | | 0.6113 | 75.0 | 1500 | 1.6072 | 0.... | 4c5496cedba430e652e41892df849a56 |
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: 0.2205 - Accuracy: 0.923 - F1: 0.9231 | 0e0138f0965aad2ffa4662c615c67f4e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8625 | 1.0 | 250 | 0.3246 | 0.9075 | 0.9062 | | 0.2522 | 2.0 | 500 | 0.2205 | 0.923 | 0.9231 | | 233bc15bba494600764924cf66269964 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Model description The **roberta-large-bne-capitel-ner** is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the [roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) large model pre-trained using the largest Spa... | d2f0a6213107236cf0fc0fad13189c1a |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Intended uses and limitations **roberta-large-bne-capitel-ner** model can be used to recognize Named Entities (NE). The model is limited by its training dataset and may not generalize well for all use cases. | e1a7b537ebff8d674cd1cbb30cb4914d |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | How to use ```python from transformers import pipeline from pprint import pprint nlp = pipeline("ner", model="PlanTL-GOB-ES/roberta-large-bne-capitel-ner") example = "Me llamo Francisco Javier y vivo en Madrid." ner_results = nlp(example) pprint(ner_results) ``` | 099192c6a92743e3cda9a97618ca63ad |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Training procedure The model was trained with a batch size of 32 and a learning rate of 3e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. | a3070c26a9739f8088848fa9fa467449 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Evaluation results We evaluated the **roberta-large-bne-capitel-ner** on the CAPITEL-NERC test set against standard multilingual and monolingual baselines: | Model | CAPITEL-NERC (F1) | | ------------|:----| | roberta-large-bne-capitel-ner | **90.51** | | roberta-base-bne-capitel-ner | 89.60| | BETO | 8... | 428a68162b276bb169ac5251df320fdc |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny It 2 - Gianluca Ruberto This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.711485 - Wer: 43.392956 | 0ce177be244f204158922519017758c6 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training and evaluation data Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voic... | 9ac6c2c1b9d8f8798bf68b4568995131 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precisi... | 5d843c73cd11b3b01ee87709a3eac1dd |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5837 | 0.95 | 1000 | 0.790046 | 50.6032 | | 0.4186 | 1.91 | 2000 | 0.730115 | 46.0067 | | 0.3154 | 2.86 | 3000 | 0.712776 | 44.114... | 9fc95ef8dce9296a9186cc50086c817d |
apache-2.0 | ['generated_from_trainer'] | false | olm-bert-tiny-december-2022-target-glue-qnli This model is a fine-tuned version of [muhtasham/olm-bert-tiny-december-2022](https://huggingface.co/muhtasham/olm-bert-tiny-december-2022) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6358 - Accuracy: 0.6306 | 90073810e4bed27e5c8a3501bfd59d7c |
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