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 | 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: 0.0641 - Precision: 0.9233 - Recall: 0.9322 - F1: 0.9277 - Accuracy: 0.9829 | 4d23df5dda9372238e488425b96ebdc8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2448 | 1.0 | 878 | 0.0718 | 0.9110 | 0.9165 | 0.9138 | 0.9803 | | 0.0547 | 2.0 |... | bb63a1d3d2f6ce7624d1515519973729 |
apache-2.0 | ['automatic-speech-recognition'] | false | Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) + language model This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). It was finetune [wav2vec2-large-xls... | fc38d07c110fa13e671566a00bdf998d |
apache-2.0 | ['automatic-speech-recognition'] | false | Datasets It is increase new data from The Common Voice V8 dataset to Common Voice V7 dataset or remove all data in Common Voice V7 dataset before split Common Voice V8 then add CommonVoice V7 dataset back to dataset. It use [ekapolc/Thai_commonvoice_split](https://github.com/ekapolc/Thai_commonvoice_split) script fo... | 259c45c8348cb05f9a1f71cd6973a4d9 |
apache-2.0 | ['automatic-speech-recognition'] | false | Models This model was finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model with Thai Common Voice V8 dataset and It use pre-tokenize with deepcut.tokenize. | dd17017f66033b8a9c33a345949b998a |
apache-2.0 | ['automatic-speech-recognition'] | false | Evaluation **Test with CommonVoice V8 Testset** | Model | WER by newmm (%) | WER by deepcut (%) | CER | |-----------------------|------------------|--------------------|----------| | AIResearch.in.th and PyThaiNLP | 17.414503 | 11.923089 | 3.854153 | | wav2vec2 w... | dfe64b6115027487c4333cdfd6a46048 |
apache-2.0 | ['automatic-speech-recognition'] | false | BibTeX entry and citation info ``` @misc{phatthiyaphaibun2022thai, title={Thai Wav2Vec2.0 with CommonVoice V8}, author={Wannaphong Phatthiyaphaibun and Chompakorn Chaksangchaichot and Peerat Limkonchotiwat and Ekapol Chuangsuwanich and Sarana Nutanong}, year={2022}, eprint={2208.04799}, ... | 36cd6bad09cc4f7cf161a7ed10763fe0 |
mit | ['generated_from_trainer'] | false | SST2_XLNet_5E This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5502 - Accuracy: 0.9133 | e426d52c67b030570099c2ea9d506c57 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6038 | 0.12 | 50 | 0.2830 | 0.8933 | | 0.3903 | 0.23 | 100 | 0.3346 | 0.9 | | 0.3476 | 0.35 | 150 | 0.4187 | 0.... | 3051576a8fe5f7250b21b1f181636330 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Miyo-Waifu-Diffusion This model is a fine-tuned Waifu-Diffusion v1.3 by dreambooth. that can generate illustrations of Miyo Harada from THE IDOLM@STER CINDERELLA GIRLS. To use at a minimum,Please type "miyoshort" or "miyopony" at the prompt miyoshort sample ![01238-1456063913-(sks miyoshort_1.0),(Driving red car_1... | a40c7b906515f0fadab467addf4c29e1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8575 - Matthews Correlation: 0.5443 | b531cfe066b5af2692bd0d57332d136a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5242 | 1.0 | 535 | 0.5258 | 0.4391 | | 0.346 | 2.0 | 1070 | 0.5264 | 0.5074 | | 0.2... | 80df073d9eb50b4d4b05d4097a953f2d |
apache-2.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: ./conf/train_vits.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/44k/tts_train_vits_raw_char_tacotron ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size... | 768b587e04741afe36e7bacb927b6e7e |
apache-2.0 | [] | false | bert-base-en-fr-lt-no-pl-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exa... | b70afed8fa8833ad0581121ebdd90457 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-lt-no-pl-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-lt-no-pl-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | d6505bafef084090b66fd53b41f8c28c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | e148bd63adb46238cff878d2b36b7094 |
mit | ['summarization'] | false | BART (large-sized model), fine-tuned on CNN Daily Mail BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Com... | f63d7a8344f2aef3859091634454c1de |
mit | ['summarization'] | false | How to use Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html): ```python from transformers import pipeline summarizer = pipeline("summarization", model="facebook/bart-large-cnn") ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, ... | cc336e18b52b918ac6c1db83fd4b2979 |
mit | ['summarization'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov an... | 73870efe772a23d65c8414d6a716a524 |
apache-2.0 | ['generated_from_trainer'] | false | gpt2-small-spanish-historias-conflicto-col This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2388 | edbdb2ec53280253d6222f86e45d9aac |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-sp This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4485 - Wer: 20.6842 | 5db6f14a53371ad96ede44bd6835247e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - 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: 25000 - mixed_preci... | 1f7c5e6fadb6602e0bd1f6483d14481f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 2.2671 | 0.13 | 1000 | 2.2108 | 76.2667 | | 1.4465 | 0.26 | 2000 | 1.6057 | 67.8753 | | 1.0997 | 0.39 | 3000 | 1.1928 | 5... | 58a13b29442c3eaaf092753553a7dd01 |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-imdb 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 imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.7710 - Accuracy: 0.8146 - F1: 0.8978 | 95ae7578ea8199527978125ad46eb3c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3417 | 0.64 | 500 | 0.1678 | 0.9286 | 0.9630 | | 0.2401 | 1.28 | 1000 | 0.1262 | 0.9525 | 0.9757 | | 0.1907 |... | ef38cf6939d3f8c046a4e07d021611c6 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_hubert_s596 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech in... | dba1e1b0bf196bb7f697a0e85b4608df |
apache-2.0 | question answering | false | BERT-base-cased for QA
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
| ce2d0a1e4ed190cfb51584b16b1da4e2 |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing, derived from [vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/)(Universal Part-Of-Speech). | c4ddcaad8c42f089c13dd1c21461ef3b |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos") pipeline=TokenClassifi... | 46c41a5a71e7b49d9a1de60f864f51c2 |
apache-2.0 | ['generated_from_trainer'] | false | swin-base-patch4-window7-224-20epochs-finetuned-memes This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7090 - Accuracy: 0.8478 | 10feda7275433c235349999c34eefaab |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 1fd7f8d4d397ddc8d95f52b4369771ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0238 | 0.99 | 40 | 0.9636 | 0.6445 | | 0.777 | 1.99 | 80 | 0.6591 | 0.7666 | | 0.4763 | 2.99 | 120 | 0.5381 | 0.... | df4c0e41d31bba49bb67aeb0aa5900dd |
apache-2.0 | ['t5', 'translation', 'seq2seq'] | false | t5-base-36L-ccmatrix-multi A [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) model finetuned for Dutch to English and English to Dutch translation on the CCMatrix dataset. Evaluation metrics of this model are listed in the **Translation models** section below. You c... | 508c223800f0c3d91524a869e882be37 |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | idT5 for Indonesian Question Generation and Question Answering [idT5](https://huggingface.co/muchad/idt5-base) (Indonesian version of [mT5](https://huggingface.co/google/mt5-base)) is fine-tuned on 30% of [translated SQuAD v2.0](https://github.com/Wikidepia/indonesian_datasets/tree/master/question-answering/squad) fo... | cb65f3db5d268875494e42d9450c587b |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | Question Generation [](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Generation.ipynb) ``` from pipeline_qg import pipeline | 11ff04644ac5a697ba453b89b675094f |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | Question Answering [](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Answering.ipynb) ``` from pipeline_qa import pipeline | dc7b76ea378499bdeefa181073036997 |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | Citation Paper: [idT5: Indonesian Version of Multilingual T5 Transformer](https://arxiv.org/abs/2302.00856) ``` @misc{https://doi.org/10.48550/arxiv.2302.00856, doi = {10.48550/ARXIV.2302.00856}, url = {https://arxiv.org/abs/2302.00856}, author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya}... | 27e6853dfeb348ac39ef3007fd5631b1 |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Usage You can load the CodeParrot model and tokenizer directly in `transformers`: ```Python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small") model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small") inputs = toke... | ec271c695f0a99da624faa8f1bdf7532 |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Training The model was trained on the cleaned [CodeParrot 🦜 dataset](https://huggingface.co/datasets/codeparrot/codeparrot-clean) with the following settings: |Config|Value| |-------|-----| |Batch size| 192 | |Context size| 1024 | |Training steps| 150'000| |Gradient accumulation| 1| |Gradient checkpointing| False| ... | 53765cba1db51abcda12101e53214d82 |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Performance We evaluated the model on OpenAI's [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark which consists of programming challenges: | Metric | Value | |-------|-----| |pass@1 | 3.80% | |pass@10 | 6.57% | |pass@100 | 12.78% | The [pass@k metric](https://huggingface.co/metrics/code_eval)... | 1df4429afb08f3657cfb42d41c6b88ac |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Resources - Dataset: [full](https://huggingface.co/datasets/codeparrot/codeparrot-clean), [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train), [valid](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) - Code: [repository](https://github.com/huggingface/transformers/tree/master/... | 1603fbb824df181af7fe06b413c900b4 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4787 - Accuracy:... | dcb9ad2960fd269bd2f18b771df2be3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.4335 | 0.7732 | 0.8533 | 0.8209 | 0.8883 | | 0.5141 | 2.0 |... | b8e913de8461403156b55c98694e6fb6 |
apache-2.0 | ['vision', 'image-classification'] | false | densenet121-res224-nih A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all p... | bdd6ab553ac21266e95cb70de98caff4 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of xray: Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs ... | 04dfac17b918e855e1c0bbc10b27c2e2 |
apache-2.0 | ['generated_from_trainer'] | false | binary-skills-classifier 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: 0.1373 - Accuracy: 0.9702 | 4a4ee9c0d78e76dd6aafb4b90786b99e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.098 | 1.0 | 1557 | 0.0917 | 0.9663 | | 0.0678 | 2.0 | 3114 | 0.0982 | 0.9712 | | 0.0344 | 3.0 | 4671 | 0.1140 | 0.... | f8a3c30010372214ac71b74bb136b5d2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'rm-vallader', 'robust-speech-event'] | false | sammy786/wav2vec2-xlsr-romansh_vallader This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - rm-vallader dataset. It achieves the following results on evaluation set (which is 10 percent of train data set mer... | 4b66c93cf83fc954bc4d73134c87ffb6 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'rm-vallader', 'robust-speech-event'] | false | Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 200 | 5.895100 | 3.136624 | 0.999713 | | 400 | 1.545700 | 0.445069 | 0.471584 | | 600 | 0.693900 | 0.340700 | 0.363088 | | 800 | 0.510600 | 0.295... | aba7d40fa7b2d126eeed2d9a535a5451 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'rm-vallader', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-romansh_vallader --dataset mozilla-foundation/common_voice_8_0 --config rm-vallader --split test ``` | 43c29378946490f55fdfc3da223aff52 |
apache-2.0 | ['generated_from_trainer'] | false | test-model-lg-data This model is a fine-tuned version of [Monsia/test-model-lg-data](https://huggingface.co/Monsia/test-model-lg-data) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3354 - Wer: 0.4150 | d564d206e21d50f3ac2403532cb9f08f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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_sch... | 2b888c9b3524c794b9664d736974b681 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0236 | 0.67 | 100 | 0.4048 | 0.4222 | | 0.0304 | 1.35 | 200 | 0.4266 | 0.4809 | | 0.0545 | 2.03 | 300 | 0.4309 | 0.4735 | |... | 887ab48c861f3cddd308b2954f187376 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Latvian-LVTB | Feature | Description | | --- | --- | | **Name** | `lv_udv25_latvianlvtb_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | 1e9fb267ce85341fcf340cd4b0f6d6a2 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (6012 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `X`, `affpanc`, `affpanp`, `affpayc`, `affpayp`, `affpays`, `affpdnc`, `affpdnp`, `affpdyc`... | 6a2d09423af5d2314fa0b5308b542e1d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.80 | | `TOKEN_P` | 99.79 | | `TOKEN_R` | 99.81 | | `TOKEN_ACC` | 99.97 | | `SENTS_F` | 97.77 | | `SENTS_P` | 98.24 | | `SENTS_R` | 97.30 | | `TAG_ACC` | 91.59 | | `POS_ACC` | 97.94 | | `MORPH_ACC` | 95.69 | | `DEP_UAS` | 91.30 | | `DEP_LAS` | 87.75 | | `LEMMA_A... | c8a76c134e96be5c3723eee25f9e38c8 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_social-roberta-large-v1-5-7 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://ww... | 95b77acf3e4023b7f6fd89e0dba1acbc |
openrail | [] | false | <img src = 'https://images.unsplash.com/photo-1628432136678-43ff9be34064?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=663&q=80'> <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> | ca1f1b078c887279e0a0d83438e57e31 |
openrail | [] | false | Usage DialoGPT **large** version, finetuned on Tony Montana sequences (ScarFace main character). Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-tony-montana') mode... | d91b0bd412f8f23b1749f3e4e9a7b8fa |
creativeml-openrail-m | ['text-to-image'] | false | stream_girl Dreambooth model trained by chebao with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebo... | 337fb6ce631a39cea2a0fc64027723ac |
apache-2.0 | ['Early Modern French', 'Historical', 'POS', 'flair'] | false | CamemBERT Early Modern French POS model This model is fine-tuned version of a [CamemBERT model](https://huggingface.co/camembert-base) on the [FreEMLPM corpus](https://doi.org/10.5281/zenodo.6481300) for Early Modern French. It was introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). | 0ef6212dfca113bd25c7384c70f09509 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4813 - Wer: 0.7207 | 2d665e51cf2f9309b8137ea8683dcb43 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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_sch... | 1274391530bbc33b6950680b7a40ba9a |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2 | 0.53 | 400 | 3.1949 | 0.9964 | | 2.9387 | 1.07 | 800 | 2.5015 | 1.0337 | | 1.5975 | 1.6 | 1200 | 1.0928 | 0.9945 | |... | 52c12efd4bcbe26c592330afb60928ae |
apache-2.0 | ['translation'] | false | opus-mt-loz-de * source languages: loz * target languages: de * OPUS readme: [loz-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/loz-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](http... | 4edd4dc93982125a68b12b0000aeec68 |
apache-2.0 | ['translation'] | false | opus-mt-fi-lv * source languages: fi * target languages: lv * OPUS readme: [fi-lv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-lv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 29359beec5230b918c28bb3b9dce8a6e |
apache-2.0 | [] | false | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | bdd45ceb77160b9022c53cfed9e9b215 |
apache-2.0 | [] | false | Results on Trivia QA - Test Set |Id | link | Exact Match | |---|---|---| |T5-11b|https://huggingface.co/google/t5-large-ssm-tqa|60.5| |**T5-xxl**|**https://huggingface.co/google/t5-xxl-ssm-tqa**|**61.6**| | 084120be4f7589e141a969e7d5c399cf |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-xxl-ssm-tqa") t5_tok = AutoTokenizer.from_pretrained("google/t5-xxl-ssm-tqa") input_ids = t5_tok("Whe... | d77b8c3671265fd88b90f259895e8127 |
cc-by-4.0 | ['generated_from_keras_callback'] | false | amitjohn007/roberta-base-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4173 - Epoch: 2 | 90d331d5e1db4c8ec8e883a4e404d2e4 |
cc-by-4.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16608, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 2ce0facbe12604730709500bfac1328b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False... | 625f5bd3c9ae76c8d40eddf0dc26ce6b |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-tr-en-finetuned-tr-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) on the opus_infopankki dataset. It achieves the following results on the evaluation set: - Loss: 0.6924 - Bleu: 54.7617 - Gen Len: 13.5501 | e2a0a9b22d71206a287a1713fa115baa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - 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 - num_epochs: 16 - mixed_precision_training: Native AMP | f7eda2f7b402ff107882e2a59ace4a9b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 412 | 1.1776 | 43.3104 | 12.9297 | | 1.4032 | 2.0 | 824 | 1.0750 | 45.7912 | 12.9155 | | 1.2268 |... | 4cdc4a71a4b4b83d7c9b4ed0f56752ac |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-sigir-support-no-label-40 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1107 | 74df401e0db80b0a7830abd192a5a21f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 30 - eval_batch_size: 30 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 - mixed_precision_training: Native AMP | a0c0d24dddfdd7fcfb868e5cd2b79cf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7638 | 1.0 | 246 | 2.2805 | | 2.1924 | 2.0 | 492 | 1.9602 | | 1.8921 | 3.0 | 738 | 1.7992 | | 1.7412 | 4.0 | 984 | 1.7229 ... | 2cdcf98b613735a2d6d2bd427bc3b182 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned 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: 0.8229 - Accuracy: 0.54 | cc7bf7b111e5a5cc19d80ff30b025e73 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.7709 | 0.74 | | No log | 2.0 | 14 | 0.7048 | 0.72 | | No log | 3.0 | 21 | 0.8728 | 0.... | 4cb5cd8e4087f96a8deda59dad393b06 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2467 - F1: 0.8206 | e03cd592be32c5dbcaacb7c4b9c7c6c0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7897 | 1.0 | 70 | 0.3096 | 0.7519 | | 0.2819 | 2.0 | 140 | 0.2603 | 0.8093 | | 0.1818 | 3.0 | 210 | 0.2467 | 0.8206 | ... | 889258dd47a6e24a173d1176f398deeb |
mit | ['generated_from_trainer'] | false | deberta-v3-large__sst2__train-8-5 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3078 - Accuracy: 0.6930 | 8b414df4795c55724d46eb56a21fcf51 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6813 | 1.0 | 3 | 0.7842 | 0.25 | | 0.6617 | 2.0 | 6 | 0.7968 | 0.25 | | 0.6945 | 3.0 | 9 | 0.7746 | 0.... | 9f5fdebb430fd76a74e0bd23f1a4267c |
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: 0.3124 - Accuracy: 0.87 - F1: 0.8704 | c28b361467319b49f0cb0a0d814f09ed |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | bert-base-finetuned-sts This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3951 - Pearsonr: 0.9116 | 0383862ca7a0e26b6840804528ea447e |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearsonr | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2345 | 1.0 | 2917 | 0.7037 | 0.8757 | | 0.1491 | 2.0 | 5834 | 0.4869 | 0.8846 | | 0.097 | 3.0 | 8751 | 0.4023 ... | b50aa44c430180ece63b577e7f810206 |
mit | ['Cometrain AutoCode', 'Cometrain AlphaML'] | false | stocks-news-t5 This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned [T5](https://huggingface.co/t5-base) allows to analyze financial market news. Automatically trained on [Fina... | 537f91b832803a81865d5db2f6378b1c |
mit | ['generated_from_trainer'] | false | cranky_northcutt 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 tomek... | 730321f800449d959abd7b396cbd50d1 |
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', ... | 0500afd87b8628e7fd95ce669591f303 |
mit | ['generated_from_keras_callback'] | false | madatnlp/gamza-bart-for-kormath This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1418 - Validation Loss: 0.3009 - Epoch: 29 | e612f20931fbd0c46d5fe44a868e8386 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.4155 | 1.9300 | 0 | | 1.4995 | 1.0293 | 1 | | 1.0445 | 0.8365 | 2 | | 0.8775 | 0.7569 | 3 | | 0.8198 | 0.7778 | 4 | | 0.7619 |... | 0e1485182f062b182f7eb1eedf8a832f |
mit | ['generated_from_keras_callback'] | false | deepiit98/Wayback_Machine-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3876 - Train End Logits Accuracy: 0.9271 - Train Start Logits ... | 5edec4b6cf08f5b6c3453e0907dfc6fd |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | ac861d073d816805f6cbf288e815d2ea |
mit | [] | false | Ilo Kunst on Stable Diffusion This is the `<ilo-kunst>` 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 t... | f2ddb542544ea937854e83cd0c8f23c4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | GorynichMix Welcome to GorynichMix - a latent diffusion models mix for realistic/anime styles. The user has complete control over whether or not to generate NSFW content, and the user's decision to enjoy either SFW or NSFW is entirely up to the user. | 27c20fa95abd1f0bead4a8aba082cd5d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Recipe **I used old Checkpoint Merger in AUTOMATIC1111 webui (commit hash: b6e5edd74657e3fd1fbd04f341b7a84625d4aa7a) and Merge Block Weighted plugin.** - Step1: AnythingV4.5-FP32 + Elysium Anime V3 -> (Weighted Sum 0.5) = Tmp1 - Step2: Tmp1 + F222 + SD1.5-pruned-emaonly -> (Add Difference 1.0) = Tmp2 - Step3: (Merge... | ac5101de5fee1bdcf1c0e271147453cf |
cc-by-sa-4.0 | ['spacy', 'floret', 'fasttext', 'feature-extraction', 'token-classification'] | false | Hungarian word vectors for HuSpaCy. The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: `floret cbow -dim 100 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.1 -thread 30 -epoch 5` Vectors are published in fasttext and floret forma... | 6c680eb5b54a534b3a201505ef5ad731 |
apache-2.0 | ['generated_from_trainer'] | false | bart-mlm-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Rouge2 Precision: 0.6572 - Rouge2 Recall: 0.5164 - Rouge2 Fmeasure: 0.5662 | ea49725679f792fcf172d8f5f7e11fa3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.0322 | 1.0 | 663 | 0.7891 | 0.639 | 0.4989 | 0.5491 ... | 81ccf4241b32f7b43726e219be627388 |
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