license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.4068 - F1: 0.6977 | fe975ab925e2b90cf7f37d382a7a7b7f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9585 | 1.0 | 99 | 0.5474 | 0.5651 | | 0.4522 | 2.0 | 198 | 0.3921 | 0.6903 | | 0.3243 | 3.0 | 297 | 0.4068 | 0.6977 | ... | 032979bbbcd8dd7c45c501713cd6ea64 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-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: 0.7730 - Accuracy: 0.9116 | fa1e53bdf2504e5f331cf3a9f4ca048b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.3075 | 0.7416 | | 3.8069 | 2.0 | 636 | 1.8792 | 0.8384 | | 3.8069 | 3.0 | 954 | 1.1514 | 0.... | 372b41d3677fa081faa91893bc2e2e62 |
mit | [] | false | M2M100 418M M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can d... | c72a5be0d10b797c28d7eed261dc6b91 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | sst2-custom-setfit-model 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 contrastive learning. 2. Trai... | c30d6300ac00c8b8aa42e6b6f88a312d |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_xlsr-53_s356 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 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | 910dad1aba1197e407536cf837fa0fc6 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-cifar10-quality-drift This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the cifar10_quality_drift dataset. It achieves the following results on the evaluation set: - Loss: 0.8235 - Accuracy: 0.724 - F1: 0.7222 | f852f0a9a54e865584c0e5449a82a17f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 3 - mixed_precision_training: Native AMP | 231c542bbc3b5fb1fbd01e84a22e24d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.7311 | 1.0 | 750 | 1.1310 | 0.6333 | 0.6300 | | 1.1728 | 2.0 | 1500 | 0.8495 | 0.7153 | 0.7155 | | 1.0322 |... | 30787fce22df09feb938f7acb7851e12 |
apache-2.0 | ['generated_from_trainer'] | false | presentation_irony_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9493 - F1: 0.6746 | eac992c84c76116297a210c245c262ea |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.1637764704815665e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 7c3e63968167669fcdaa92e4930717b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5514 | 1.0 | 90 | 0.5917 | 0.6767 | | 0.6107 | 2.0 | 180 | 0.6123 | 0.6730 | | 0.1327 | 3.0 | 270 | 0.7463 | 0.6970 | |... | bbf6e5af62d30777c25378fc819d51b2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | OU3M is an Stable Diffusion [ V1.5 ] fine tuned model from Mantra Ardhana Digital Art ~ by Organic Mind ~ 2022 ~ Made with sd-dreambooth ~ Use prompt: 'ou3m' If use this model you can say thank and follow my instagram here : [MANTRA ARDHANA INSTAGRAM ](https://www.instagram.com/mantradigital/) ~ Sample pictures of... | 701b9ec8d59681571455559334aeee4b |
mit | ['tn', 'fill-mask', 'pytorch', 'roberta', 'masked-lm'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_tsn_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_tsn_roberta") ``` | e33505deef715cc3a1f1c9620ff9f97c |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6189 - eval_accuracy: 0.7647 - eval_runtime: 274.5502 - eval_samples_per_second: 72.868 - ev... | 08c1392e5bfb74ddc5d682eb8b49f3e9 |
cc-by-sa-4.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | 日本語T5事前学習済みモデル This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus. 次の日本語コーパス（約890GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。 * [Wikipedia](https://ja.wikipedia.org)の日本語ダンプデータ (2020年7月6日時点のもの) * [mC4](https://github.com/allenai/allennlp/discussions/5056)の日本語コーパス（正... | 147d6314af422f9a0098854c1df54f4b |
cc-by-sa-4.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | ベンチマーク livedoorニュースコーパスを用いたニュース記事のジャンル予測タスクの精度は次の通りです。 mC4/ja + Wikipediaを用いて事前学習した日本語T5 ([t5-base-japanese-mC4-Wikipedia](https://huggingface.co/sonoisa/t5-base-japanese-mC4-Wikipedia), パラメータ数は222M) | label | precision | recall | f1-score | support | | ----------- | ----------- | ------- | -------- | ----... | 5f09d3eb87d4a32327840f8fa449c5fa |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper large Odia - Sukanta Nanda with tips from Sanchit language None This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7360 - Wer: 98.6835 | cde16f250d18cbbe0a34acba7148a792 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0048 | 20.0 | 1000 | 0.3668 | 107.1551 | | 0.0001 | 40.0 | 2000 | 0.5503 | 97.1952 | | 0.0001 | 60.0 | 3000 | 0.6984 | 98... | 86f268b667e024d528a6012e15c7cbac |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers'] | false | **Portrait+**  [*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/portraitplus/resolve/main/portrait%2B1.0.ckpt) - this is a dreambooth model trained on a diverse set of close to medium range portraits of people. Use `portra... | b7c3f51b31e4da6d2fc8d0f28d166dfc |
apache-2.0 | [] | false | DAF:re Results | Top-1 Val Acc | Top-5 Val Acc | Top-1 Test Acc| Top-5 Test Acc| |:-------------:|:-------------:|:-------------:|:-------------:| | 95.26 | 98.38 | 94.84 | 98.30 | | af5299d08435ec62dc71300c47da10a8 |
mit | [] | false | osaka jyo2 on Stable Diffusion This is the `<osaka-jyo2>` 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... | 05bce009d97c89faa7b7e000d59201e0 |
apache-2.0 | [] | false | distilbert-base-no-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | b2950d043becf235c7be6101d8f8251e |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-no-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-no-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | d9d8e378ee04566b107296c5d0521fae |
apache-2.0 | ['generated_from_trainer'] | false | model1_absa_cont This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2382 - Precision: 0.2445 - Recall: 0.3046 - F1: 0.2712 - Accuracy: 0.5420 | dcd2e413e718a2630d905e214a7dfb9f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 7 | 1.2382 | 0.2445 | 0.3046 | 0.2712 | 0.5420 | | No log | 2.0 |... | 40d1d6ac1793b6d650b5a6c678e22ca7 |
apache-2.0 | ['summarization'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv") | ab706af9b8ac9f8bcf9b91bffa06fbf7 |
apache-2.0 | ['summarization'] | false | you can change `attention_type` (encoder only) to full attention like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv", attention_type="original_full") | dd4188383de641070022682649c3b49d |
apache-2.0 | ['summarization'] | false | you can change `block_size` & `num_random_blocks` like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv", block_size=16, num_random_blocks=2) text = "Replace me by any text you'd like." inputs = tokenizer(text, return_tensors='pt') prediction = model.generate(**... | 86e10ed08b9a4c7f7c16aaa3aecf5950 |
apache-2.0 | ['summarization'] | false | Training Procedure This checkpoint is obtained after fine-tuning `BigBirdPegasusForConditionalGeneration` for **summarization** on **arxiv dataset** from [scientific_papers](https://huggingface.co/datasets/scientific_papers). | 4c5859577c6ba5b498acfe536c235187 |
apache-2.0 | ['generated_from_trainer'] | false | distilr2-lr5e05-wd0.08-bs32 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2814 - Rmse: 0.5305 - Mse: 0.2814 - Mae: 0.4398 | 621ccb2cd1f7a85411325df86697849f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2772 | 1.0 | 623 | 0.2735 | 0.5230 | 0.2735 | 0.4158 | | 0.2731 | 2.0 | 1246 | 0.2753 | 0.5247 | 0.2753 ... | 12d81787861ab248e03308e272744a89 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 4f36236ed7c8a25c2f869e518614e1ad4a8b50d6 pip install -e . cd egs2/aishell/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/aishell_conformer_e12_amp ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 2f51a6152df3bfa0984a4714bfd1acc1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri May 27 13:37:48 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0` - Git hash: `4f36236ed7c8a25c2f869e518614e1ad4a8b50d6` - Commit date: `Thu May 26 00:22:45 2022 -0400` | 05e95663309514b538b5a37eb898deac |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam10_ctc0.4/dev|14326|14326|66.9|33.1|0.0|0.0|33.1|33.1| |beam10_ctc0.4/test|7176|7176|65.3|34.7|0.0|0.0|34.7|34.7| | 508497ab86669ea63bdd07ad5d4e9243 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam10_ctc0.4/dev|14326|205341|95.8|4.1|0.1|0.1|4.3|33.1| |beam10_ctc0.4/test|7176|104765|95.4|4.4|0.1|0.1|4.6|34.7| | a330a1247e4f4cbd0e46fe4313c61db3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e12_amp.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e12_amp_raw_zh_char_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_... | 434c5f4e4d1b93a23f95a516298d511c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | First Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/vintage-blip-captions](https://huggingface.co/datasets/Norod78/vintage-blip-captions) then it un... | 582f540b7888524f4fddcffd16a53c69 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | Because the model was first fined-tuned on the whole dataset and only then it was fine-tuned again to learn each individual concept, you can use prompts without Trigger-Words and still get a subtle "Vintage" touch | 21bdae3728bc17823ff32757c3febd70 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | A few sample pictures generated with this mode (more available [here](https://huggingface.co/Norod78/SD15-VinageStyle/tree/main/sample_images)): A photo of Gal Gadot as wonderwoman, Vintage style, very detailed, clean, high quality, sharp image.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.S... | 363afe857882af596b20064b295a5beb |
mit | ['generated_from_trainer'] | false | hungry_austin 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 tomekkor... | c2a0ba68dc19c1c62a41afee6512d232 |
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', ... | c3efea5116efb127f16fbb93dcb054b2 |
mit | ['text-classification'] | false | Hungarian Aspect-based Sentiment Analysis with finetuned XLM-RoBERTa model For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp). - Pretrained model used: XLM-RoBERTa - Finetuned on OpinHuBank (OHB) Corpus ... | 2b3edbb591777f71154644c5539307f5 |
mit | ['text-classification'] | false | Usage with pipeline ```python from transformers import pipeline classification = pipeline(task="sentiment-analysis", model="NYTK/sentiment-ohb3-xlm-roberta-hungarian") input_text = "Kovácsné Nagy Erzsébet </s> A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a Németországból érkezett Kovács Péter n... | ad1f19c25bc063c57982a36a318df918 |
mit | ['text-classification'] | false | Citation If you use this model, please cite the following paper: ``` @inproceedings {yang-asent, title = {Neurális entitásorientált szentimentelemző alkalmazás magyar nyelvre}, booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)}, year = {2023}, publisher = {Szegedi Tudományegyetem, Info... | 22a970e987048369cf777dbd28d98183 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22 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: 0.7600 - Accuracy... | 45f5075ba99a8005475cf243293df2eb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 185bc6be7f9983452f77fb513139ff3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 | | No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 | | 0.3147 |... | 38eb655b30176bd789e591bbdc9c2bbf |
apache-2.0 | ['generated_from_trainer'] | false | funnel-transformer-xlarge_ner_conll2003 This model is a fine-tuned version of [funnel-transformer/xlarge](https://huggingface.co/funnel-transformer/xlarge) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0436 - Precision: 0.9565 - Recall: 0.9593 - F1: 0.9579 - Accuracy: 0.... | 8b1bd36d29acaaafbd91e2f7c2b3edc1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1349 | 1.0 | 878 | 0.0441 | 0.9328 | 0.9438 | 0.9383 | 0.9881 | | 0.0308 | 2.0 |... | 707114197e92caf7da11410e6c556cdb |
apache-2.0 | ['accelerator'] | false | finetuned-vit-base-patch16-224-upside-down-detector This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following re... | 78d879f3c937dae7baa29f9dc9a59633 |
apache-2.0 | ['accelerator'] | false | Training and evaluation data The custom dataset for image orientation adapted from [beans](https://huggingface.co/datasets/beans) dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The da... | 62b3340b6cf031a509dd469f2562b9f8 |
apache-2.0 | ['accelerator'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-04 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32 - num_epo... | 3c938497b73031052de602b2eec0e789 |
apache-2.0 | ['accelerator'] | false | Training results | Epoch | Accuracy | |:----------:|:----------:| | 0 | 0.8609 | | 1 | 0.8835 | | 2 | 0.8571 | | 3 | 0.8941 | | 4 | 0.8941 | | 715e64348ea4206c86449416380352ca |
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: 0.0629 - Precision: 0.9265 - Recall: 0.9357 - F1: 0.9310 - Accuracy: 0.9835 | 481dc37192a663fb4d668b8f5a28c53f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2396 | 1.0 | 878 | 0.0706 | 0.9172 | 0.9186 | 0.9179 | 0.9810 | | 0.0539 | 2.0 |... | aff47e2ac67a250aea87faf813a54c10 |
mit | [] | false | overprettified on Stable Diffusion This is the `<overprettified>` 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 ... | ed831ccc1f4c0a1f9f250b12b2ca1baa |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-wikitext2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5020 | 91e323285fc929a31deae6780542cfea |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and... | 6de68f17e6cafeca5883da680f11b35a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6689 | 1.0 | 300 | 1.5518 | | 1.7525 | 2.0 | 600 | 1.5078 | | 1.5267 | 3.0 | 900 | 1.4971 | | 8f20c1ce989e3c8d4387afb6e68298ea |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_800k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 800k 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 ... | 83a74f818be4adec1828ece97b773c6d |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_800k'] | 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_4-step_800k') model = TFBertModel.from_pretrained("google/multibe... | 7f1b22396136dd17da456a0548e30126 |
apache-2.0 | ['generated_from_trainer'] | false | bart-samsum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.5877 | 841096fd45bae739d7ad1849234ea9f7 |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-finetuned-HC3-mix This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6998 - F1: 0.0 | a4ac724b7bd109815c6bcba9399a2e2b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:------:|:---------------:|:---:| | 0.6506 | 1.0 | 35824 | 0.6998 | 0.0 | | 0.6481 | 2.0 | 71648 | 0.7662 | 0.0 | | 0.6391 | 3.0 | 107472 | 0.7492 | 0.0 | | 0.63... | b002715a6a69df671e2955aeb884c0be |
apache-2.0 | ['translation'] | false | opus-mt-de-ilo * source languages: de * target languages: ilo * OPUS readme: [de-ilo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ilo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 1fb4ab99864d4fe682324fbc8ff31d2f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sat Oct 22 17:36:51 CDT 2022` - python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.12.1+cu116` - Git hash: `14fcb2d42b2609f766ffaa7a79e9c921cd8398d9` - Commit date: `Tue Sep 27 20:02:22 2022 +0000` | 19ea365f65a28df5cae5b628b364e5b4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|610500|72.9|24.4|2.7|3.1|30.2|95.5| |decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|1592160|72.2|25.0|... | a49be13e829c59db6f5452b8ff6e047b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|3988181|92.6|4.7|2.6|2.2|9.6|95.5| |decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|10235271|92.5|4.7|2... | c70f68f024bde5fdedc33cef20665214 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|3547834|91.4|5.8|2.8|2.5|11.0|95.4| |decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|9622352|91.6|5.6|2... | c1bda11fcb85cda2bd5376d995498d5e |
apache-2.0 | ['generated_from_trainer'] | false | whisper-NST2-unfreeze-constanti-low-lr This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3562 - Wer: 8.5519 | 04b47f3c28d2bc502bc688c752eddb0b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 96 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_prec... | 478dde76f0c6d74c31fa3ceb8232f1aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1901 | 0.05 | 1000 | 0.3069 | 14.8233 | | 0.1323 | 0.1 | 2000 | 0.2687 | 11.2885 | | 0.1137 | 0.15 | 3000 | 0.2620 | 1... | e070a4c5567ef3579140875b4d418ded |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-hk Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Cantonese using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | ac873c577fbfc085ff3ece5b3ab6a40e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Usage [Colab trial](https://colab.research.google.com/drive/1nBRLf4Pwiply_y5rXWoaIB8LxX41tfEI?usp=sharing) ``` import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys model_name = "voidful/wav2vec2-larg... | c71ffaf540374c665c4d29adb72b4956 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\\-.\\:;<=>?@\\[\\]\\\\\\/^_`{|}~]" model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name) resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def load_file_to_data(file): batch = {} speech, _ = torchaudio.... | 02dd6bfa0d1b870af5972dc2c1510969 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Cantonese (Hong Kong) test data of Common Voice. CER calculation refer to https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese ```python !mkdir cer !wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py !pip install ... | 4ba96a11b92ea3d12a7a23203843f691 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\\-.\\:;<=>?@\\[\\]\\\\\\/^_`{|}~]" model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name) ds = load_dataset("common_voice", 'zh-HK', data_dir="./cv-corpus-6.1-2020-12-11", split="test") resampler = torchaudio.transforms.Resample(orig_freq=... | fa14f8269a14c0431749a1e7c7a626e5 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | SD_pikachu Dreambooth model trained by johndeer with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started ... | 3786e63b3eb7aec2844196a4d1ad2340 |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | article2keyword2.2_barthez-orangesum-title_finetuned_for_mlm This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0452 | 300263604bdc20f5473f3f0f45f07895 |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3187 | 1.0 | 1235 | 0.0545 | | 0.0544 | 2.0 | 2470 | 0.0491 | | 0.0461 | 3.0 | 3705 | 0.0463 | | 0.042 | 4.0 | 4940 | 0.0452 ... | fa7fd13de26e27ab58b287c9e072e3e5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Hungarian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 3cca575717ab20b9addc04135dfa0bcb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "hu", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 81872f9fadaae58e8c79ac40f8efd6a2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Hungarian test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | d6291b17cf2fdde9e2ba84ba2b77663d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/hu.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.e... | cf13b0f4003286e1677daf68b71eb705 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio... | bc5078e671b1db9eafe49890cc5fdb01 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for French (fr) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](http... | 10e3ec9d595edf1101a1f8b1e00b318b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 30 - mixed_precision_tra... | a6152e716d668b6a96e21a66dbec4fed |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_hubert_s486 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 412b64b0a4a5f6225ca790bf025fe4f4 |
apache-2.0 | ['generated_from_trainer'] | false | model 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: 1.4554 - Accuracy: 0.6971 - F1: 0.7012 - Precision: 0.7069 - Recall: 0.6971 | 4662c3f948356631bc2a898d0db7b7e7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Horovod_Tweet_Sentiment_100k_2eps This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.35511288 - Train Accuracy: 0.8470289 - Validation Loss: 0.42278787 - Validation Accuracy... | 3d7b404129df98e3dcd5ac7f345a3530 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 7b6c2fa43d601ac094ffd73ea00fca37 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.44729528 | 0.79518235 | 0.39121008 | 0.8254654 | 0 | | 0.35511288 | 0.8470289 | 0.42278787 | 0.8168883 ... | 85b5258f2f24e4e462235ea33e31c681 |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2187 - Precision: 0.7293 - Recall: 0.7575 - F1: 0.7431 - Accuracy: 0.... | 33181b4c3893dbe0fc36676f30d729c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 187 | 0.2080 | 0.6933 | 0.7109 | 0.7020 | 0.9363 | | No log | 2.0 |... | 59a7339d47f2984b8e22f7a634eec3ab |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Swedish This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) trained on NST Swedish ASR and evaluated on Common Voice 11 testset. It achieves the following results on the evaluation set - Loss: 0.2337 - Wer: 9.2206 | 061eb4109fcf38783c868531286913d5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training and evaluation data The training dataset contains 276 000 examples and with a batch size of 64 and training 5000 it is 1.14 epochs. More training data or more epochs would probably improve the result even further. | 833cf79b9db61982f0fea78507eebb1b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0695 | 0.2 | 1000 | 0.2695 | 12.4671 | | 0.0524 | 0.4 | 2000 | 0.2659 | 11.6367 | | 0.046 | 0.6 | 3000 | 0.2402 | 10.655... | 871f259a0f87198947663b6d9a8e48e6 |
mit | [] | false | clip-vit-base-patch32-ko Korean CLIP model trained by [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813) [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)로 학습된 한국어 CLIP 모델입니다. 훈련 코드: ... | 9b90e1d265d04059b948b3f3dd62206e |
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