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 | [] | false | export for pretrained.pt ``` python pruned_transducer_stateless4/export.py \ --exp-dir ./pruned_transducer_stateless4/exp \ --epoch 29 \ --avg 6 \ --streaming-model 1 \ --causal-convolution 1 ``` for cpu_jit.pt ``` python pruned_transducer_stateless4/export.py \ --exp-dir ./pruned_transducer_s... | 4272b5a348edabc74886eb7444b604fa |
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: 0.7539 - Matthews Correlation: 0.5553 | 20e3bf6def70abd22794ebad31c835ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5245 | 1.0 | 535 | 0.5223 | 0.4063 | | 0.3574 | 2.0 | 1070 | 0.4856 | 0.5079 | | 0.2... | 82c40b9963e94770b47f7ada51d1dcd1 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 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: 2.7515 | 36baf251c4050acc7c4a851a14ca3745 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1203 | 1.0 | 766 | 2.8510 | | 2.9255 | 2.0 | 1532 | 2.8106 | | 2.8669 | 3.0 | 2298 | 2.7515 | | 4cd18c19d3162e13d056325ea66711f7 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for convnext_nano.in12k A ConvNeXt image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | 744dd13024f439ff397f33c5ddcd53b6 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 22.5 - GMACs: 2.5 - Activations (M): 8.4 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/rwightman/pytorch-image-models... | a214750dc2211a5d3ec927f2987f53f1 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_nano.in12k', pretrained=True) model = model.e... | 02bf8a1861fa053dc9983a41eeb68441 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_nano.in12k', pretrained=True, ... | ebd8f0135654c360bb2860160cdee87a |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_nano.in12k', pretrained=True, num_cl... | 44313c0159fb565c314662c5fece9cac |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_xls-r_s246 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | e1b404975d2936bbc0ac844b8701a2bb |
mit | [] | false | Road to Ruin on Stable Diffusion This is the `<RtoR>` 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 tra... | 7de389bc629f43f1f1b29fe36b42f76a |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-TRAC-DS This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8271 - Accuracy: 0.6315 - Precision: 0.6206 - Recall: 0.6201 - F1: 0.6147 | 385e17a1c221d960b4eace192cd1f0b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.919508251872584e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | f02028beb7a189fd3a8b219929662e9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0373 | 1.0 | 612 | 1.1241 | 0.3627 | 0.5914 | 0.3618 | 0.2414 | | 1.0617 | 2.0 |... | 5d3987abb05e325ebfdd484a7992749b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-coscan-region This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech dataset. It achieves the following results on the evaluation set: - Loss: 0.1020 - Accuracy: 0.9735 | 8e4bbc2460ae256fbefe03cb68350141 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2089 | 1.0 | 6644 | 0.1020 | 0.9735 | | 94e7c07608b709ef3e8f8e34f6230206 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts, derived from [wangchanberta-base-wiki-syllable](https://huggingface.co/airesearch/wangchanberta-base-wiki-syllable). Character-embeddings are modified to use BertTokenizerFast. You can fine-tune `roberta-base-thai-syllable` for downstream ... | b6cd873524dfe4301e81d77c547b1810 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-syllable") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-thai-syllable") ``` | 6e842091128c977ea3210d6dd5c54ceb |
mit | [] | false | gpt2-wechsel-malagasy Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ | 3ab0ccfcb4ac1beae30434bfba296b47 |
mit | [] | false | Performance | Model | PPL | |---|---| | `gpt2-wechsel-sundanese` | **111.72** | | `gpt2` (retrained from scratch) | 149.46 | | Model | PPL | |---|---| | `gpt2-wechsel-scottish-gaelic` | **16.43** | | `gpt2` (retrained from scratch) | 19.53 | | Model | PPL | |---|---| | `gpt2-wechsel-uyghur` | **34.33** | | `gpt2`... | 9af5790a4cf3361a009df18d88cce479 |
mit | [] | false | Citation Please cite WECHSEL as ``` @inproceedings{minixhofer-etal-2022-wechsel, title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models", author = "Minixhofer, Benjamin and Paischer, Fabian and Rekabsaz, Navid", booktitle... | 4aa98500fea1fcb19df5a2014880a289 |
apache-2.0 | ['onnx', 'exbert'] | false | ONNX export of bert-base-uncased Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a differ... | ba7616021c50b4b7d783b4065505c43e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst-2-english-misogyny-sexism-4tweets-2e-05-0.05 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 se... | de8531176492346d0d2ec2b829081a8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.4807 | 1.0 | 1346 | 0.6685 | ... | a25d63c7e66669dcd3bff74f62fcf7fd |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-mrpc This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2891 - Accuracy: 0.8925 - F1: 0.9228 | 4120a784a13a7de063813002b64ff960 |
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... | 941142d5db4de61f1696976d70ee71ba |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5998 | 1.0 | 57 | 0.5425 | 0.74 | 0.8349 | | 0.5058 | 2.0 | 114 | 0.3020 | 0.875 | 0.9084 | | 0.3316 |... | b81c45ed67365b93820d55a3fb1d6611 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer'] | false | ft-pt-br-local This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) on the None dataset. | 3a8c54b07822dc5533b337f68b6d4a8d |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 | 016b11a3bd6426b5d603a37d5123d9ce |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | t5-base-disfluent-fluent This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1221 - Bleu: 13.8066 | 5fbcb729f3048a88efd4b27d7bc1be1b |
mit | ['generated_from_trainer'] | false | sleepy_panini This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-25000... | 1efccbd48dd1905b2284f5dfeda1c5bd |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | f28533e5eb99fc10363900afa7ca1a09 |
mit | ['generated_from_trainer'] | false | deberta-v3-large__sst2__train-16-9 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: 1.2598 - Accuracy: 0.7809 | e2fb5f124df823e89ae8bc08ff17974d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6887 | 1.0 | 7 | 0.7452 | 0.2857 | | 0.6889 | 2.0 | 14 | 0.7988 | 0.2857 | | 0.6501 | 3.0 | 21 | 0.8987 | 0.... | 6e2357f94cead8612cd363012b79e85a |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments
- date: `Mon Mar 14 22:28:37 UTC 2022`
- python version: `3.8.12 | packaged by conda-forge | (default, Jan 30 2022, 23:42:07) [GCC 9.4.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `d5322b2dc4844dce1d14268b6848607e2a3dee21`
- Commit date: `Mon Mar 14 20:2... | f9d2598bac358d9a9e1b44e145604804 |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/test|49|4134|64.6|23.5|11.8|16.4|51.8|98.0|
|inference_asr_model_valid.acc.ave_5best/valid|47|4178|66.8|20.2|13.0|19.2|52.5|100.0|
| 097011b6f2802bb2c0bf9559abdedd01 |
mit | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/test|49|8690|73.2|18.0|8.8|12.9|39.7|98.0|
|inference_asr_model_valid.acc.ave_5best/valid|47|8751|74.3|15.7|10.0|15.6|41.3|100.0|
| aa0c4e9fcb156fddaccbb9753d7e714d |
mit | ['generated_from_trainer'] | false | bkk-buget-ner 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: 0.0287 - Precision: 0.9796 - Recall: 0.9852 - F1: 0.9824 - Accuracy: 0.9930 | 9b167ccff80e424b879b5813f36ff677 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 1d4dc0ec8d429154c0823fcde8b64966 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 118 | 0.0572 | 0.9238 | 0.9738 | 0.9482 | 0.9849 | | No log | 2.0 |... | 8e1ee42ab9e24af86154a0d19f68880a |
apache-2.0 | ['image-classification'] | false | resnet34d Implementation of ResNet proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) ``` python ResNet.resnet18() ResNet.resnet26() ResNet.resnet34() ResNet.resnet50() ResNet.resnet101() ResNet.resnet152() ResNet.resnet200() Variants (d) proposed in `Bag of Tricks ... | 3ee9e2419bf78cd01c3b57d265ef27f2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/voxforge_it_e_branchformer` This model was trained by Yifan Peng using voxforge recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention ... | 352330466250442438303f9e4bb78254 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout bf8c8f00194bdfed8ca388d8b20d14791b7d270e pip install -e . cd egs2/voxforge/asr1 ./run.sh --skip_data_prep false --skip_train tr... | 9e03ef33a318029a49e79f5cb1492826 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Dec 29 01:48:29 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `bf8c8f00194bdfed8ca388d8b20d14791b7d270e` - Commit date: `Wed Dec 28 22:43:13 2022 -0500` | 3ccd6a6e59ccb8fee9ae840c06a7537a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|12587|71.3|24.0|4.7|3.8|32.5|95.5| |decode_asr_asr_model_valid.acc.ave/et_it|1103|13699|72.8|22.7|4.5|3.1|30.2|91.7| | 38ae84e4f47c1254a3ab1930281adf4c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|75494|93.2|3.7|3.1|2.0|8.8|95.5| |decode_asr_asr_model_valid.acc.ave/et_it|1103|81228|93.8|3.5|2.7|1.8|8.0|91.7| | b9086f59dfb6d4c53f48229ae8934458 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_e12_mlp1024_linear1024.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_e12_mlp1024_linear1024_raw_it_char_normalize_confnorm_varsFalse ngpu: 1 se... | 6b4ce5dc25b3a50269b669df3e13808e |
mit | ['generated_from_keras_callback', 'id', 'Indonesian'] | false | gpt2-ft-id-puisi This model is a fine-tuned on an [Indonesian Recipe](https://huggingface.co/datasets/Sultannn/id_recipe). It achieves the following results on the evaluation set: - Train Loss: 5.3628 - Validation Loss: 5.8179 | d6b56321e710d97bdaf6b180f761f3ec |
mit | ['generated_from_keras_callback', 'id', 'Indonesian'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.3561 | 6.5449 | 0 | | 6.2176 | 6.1573 | 1 | | 5.8533 | 6.0014 | 2 | | 5.5955 | 5.8798 | 3 | | 5.3628 | 5.8179 | 4 | | 0f19d95f8bcb17759b38ad23aa1d93ae |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Medium Czech CV11 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set: - Loss: 0.2537 - Wer: 11.6893 | 5288043cba5ede1758a46e2cd013c841 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0384 | 2.02 | 1000 | 0.2167 | 13.5467 | | 0.0061 | 4.03 | 2000 | 0.2373 | 12.9172 | | 0.0018 | 6.05 | 3000 | 0.2407 | 12.040... | 3463c94d83ca6e30b9101ca99f4eea9a |
mit | ['generated_from_trainer'] | false | indic-bert-finetuned-combined-DS This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9783 - Accuracy: 0.5871 - Precision: 0.5527 - Recall: 0.5574 - F1: 0.5537 | 7840bec318a5862b140db16f4f0a2f2c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | aff6cf9511bdef52efdac24c95f85be1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0904 | 1.0 | 711 | 1.0759 | 0.4452 | 0.4368 | 0.4155 | 0.3333 | | 1.0537 | 2.0 ... | c2d004efeddfe7d8b83ef8e3cab16cd8 |
openrail | [] | false | Dreambooth model for a high-tech, detailed concept art style This is a model trained on a mix of real images of fighter aircraft, warships, and spacecraft, and techy, detailed concept art from Aaron Beck, Paul Chadeisson and Rasmus Poulsen. High-tech, industrial sci-fi with a grungy aesthetic. Use prompt: 'combotech... | 3abec288828236baa7e79c6b13ab65e2 |
openrail | [] | false | Example images      ![C... | 0e1295d8a238bd7a1fbf0d44a20e1ddc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Why Epic Diffusion Epîc Diffusion is a general purpose model based on Stable Diffusion 1.x intended to replace the official SD releases as your default model. It is focused on providing high quality output in a wide range of different styles, with support for NFSW content. Epîc Diffusion 1.1 is a heavily calibrate... | 0d3c318d91b039d7ad3d1dce5e4afb5c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | e5e7eb"> <th style="vertical-align:top;padding:.5714286em!important;border: 1px solid"> Prompt </th> <th style="vertical-align:top;padding:.5714286em!important;border: 1px solid"> Parameters </th> <th style="vertical-align:top;padding:.5714286em!important;border: 1px solid;min-width:270p... | 2d7c9b8e93f108679cfb3e7814acd9ee |
apache-2.0 | ['exbert'] | false | BERT base model (uncased) for Teachable NLP - This model forked from [bert-base-uncased](https://huggingface.co/bert-base-uncased) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp). Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](h... | d3b7fba9d53ba928f9d2bf97e0ec78ff |
apache-2.0 | ['exbert'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in th... | e5917bb71ef305b25af7a3ec3e0266aa |
apache-2.0 | ['exbert'] | false | Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta*{1} = 0.9\\) a... | 42ee833a06a8fb22ce3b71a4f3b330d5 |
apache-2.0 | ['exbert'] | false | Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | | :--: | :---------: | :--: | :--: | :---: | :--: | :---: | :--: | :--: | :-----: | | | 84.6/83.4 | 71.2 | ... | 426106cbe343d23eb7e9379ead1ccf5d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Turkish This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned [cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial) model on [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, ma... | b3cf32e159958474d8c68bcfacf9a1e9 |
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", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pre... | 9663473f0de55d20897f9375c6013bcb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer ... | 96a5c7090e625e754f8705508e3e9cbe |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor... | 9841b4d543c135309303763548d93097 |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1200k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1200k 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... | 06dac78110b0abbaf8f143ccf88845ea |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1200k'] | 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_1200k') model = TFBertModel.from_pretrained("google/multib... | 7008b7606e213985b4f25840927b5c48 |
apache-2.0 | ['vision'] | false | ImageGPT (small-sized model) ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first release... | aba037af73645d8934a7a9bff38aa4b6 |
apache-2.0 | ['vision'] | false | ONNX Runtime expects NumPy arrays as input inputs = feature_extractor(image, return_tensors="np") outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs)) ``` Or you can use the model with classification head that returns logits ```python from transformers import AutoFeatureExtractor from onn... | 56bdd5ac3995c2af4c43133882ef9a59 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr1e05-wd0.1-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Rmse: 0.5250 - Mse: 0.2756 - Mae: 0.4181 | 6d35e8874d13be248642202fb331cd76 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2768 | 0.5261 | 0.2768 | 0.4281 | | 0.2743 | 2.0 | 1246 | 0.2739 | 0.5234 | 0.2739 ... | 80f6d79651065124ea81177ae42986d9 |
apache-2.0 | ['summarization', 'arabic', 'ar', 'ur', 'urdu', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | mt5-base-finetuned-ar-fa This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0303 - Rouge-1: 26.73 - Rouge-2: 12.63 - Rouge-l: 23.96 - Gen Len: 18.99 - Bertscore: 71.41 | ee3ca5169c004f740add86ba21b81a8d |
apache-2.0 | ['summarization', 'arabic', 'ar', 'ur', 'urdu', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 3.7736 | 1.0 | 3287 | 3.2308 | 24.22 | 10.11 | 21.46 | 18.99 | 7... | 157303e67a11eb02530fbf1e67ecb538 |
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: 0.2156 - Accuracy: 0.9265 - F1: 0.9262 | 803849e71700ba38e8818f8a8d95dc72 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.814 | 1.0 | 250 | 0.3075 | 0.907 | 0.9048 | | 0.2481 | 2.0 | 500 | 0.2156 | 0.9265 | 0.9262 | | 21fa41886397a3f19d718faba966c901 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2371 | 1.0 | 5533 | 1.1739 | | 0.9597 | 2.0 | 11066 | 1.1133 | | 0.7524 | 3.0 | 16599 | 1.1547 | | 4cd9f212f4b0170bb4076d53cee029d0 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_900k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 900k 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 ... | 94476c995ab33b8fcc8550987330e2a5 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_900k'] | 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_1-step_900k') model = TFBertModel.from_pretrained("google/multibe... | bb78b9b8a632c0f74e0bd9f6f87c34dd |
apache-2.0 | [] | false | bert-base-en-es-it-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 exactly t... | 285b2b0a570b3632635afaf87cdcadee |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-es-it-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-es-it-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 55b5417736c0dca82c564246883f0ac1 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0646 - Precision: 0.9308 - Recall: 0.9485 - F1: 0.9396 - Accuracy: 0.9859 | d195e4c3a45d37c58f77dfb17d0f1111 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0904 | 1.0 | 1756 | 0.0683 | 0.9165 | 0.9344 | 0.9253 | 0.9819 | | 0.0351 | 2.0 |... | 6b3ba5d1b726daa3150b28427ae854e8 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/libritts_gst+xvector_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4418774/ This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | ab105604cacbb9d912391038210704b8 |
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: 0.1386 - F1: 0.8605 | 92acd2994bfc3f47665f98e463fca1d1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2725 | 1.0 | 525 | 0.1557 | 0.8246 | | 0.1306 | 2.0 | 1050 | 0.1438 | 0.8417 | | 0.0825 | 3.0 | 1575 | 0.1386 | 0.8605 | ... | 4b8c93103174ef365d6d9035eb9c5147 |
mit | ['vision', 'image-classification'] | false | DiNAT (mini variant) DiNAT-Mini trained on ImageNet-1K at 224x224 resolution. It was introduced in the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). | 63926daae88aa006560ffe3a0c68d4c5 |
mit | ['vision', 'image-classification'] | false | Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, DinatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image ... | e84970741aa9932e6ae52703b5a01b29 |
apache-2.0 | ['translation'] | false | opus-mt-ht-sv * source languages: ht * target languages: sv * OPUS readme: [ht-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ht-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | fc0c75c5e3f41ba78c6eb81695a96164 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic) and [UD_Japanese-GSDLUW](https://github.com/UniversalDepende... | f845ae8ca670b7913e736cda466e6431 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic-ud-head") qap=Qu... | e1358f2e7b98cf981146d8e1f7ef5cdc |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersSl... | 2979dbf4dfd91e1f49055ec121ff0b44 |
apache-2.0 | ['object-detection', 'vision'] | false | DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage) DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et... | 38fb2095da952682b1597d99135d283a |
apache-2.0 | ['object-detection', 'vision'] | false | Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queri... | c010a6dcab1e8e8161e5ca537254ce91 |
apache-2.0 | ['object-detection', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import DetrFeatureExtractor, DetrForObjectDetection from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = DetrFeatureExtrac... | 4cefdd4d1cdb16b93f26ba12a2f61e63 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-pubmed-finetuned-roundup-e16 This model is a fine-tuned version of [theojolliffe/bart-large-cnn-finetuned-pubmed](https://huggingface.co/theojolliffe/bart-large-cnn-finetuned-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6815 - Rouge1: 48.76... | 4e17a2dbb7a6876b25e42d3aa89052d4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 25 | 1.4287 | 46.5701 | 28.6267 | 34.7827 | 45.0622 | 14... | d2f51c68f384a169ce01a5d3f1443b90 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | sv_core_news_lg Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `sv_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`... | 0d884ac83893ef96011b6e0e21c42988 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.99 | | `TOKEN_P` | 99.95 | | `TOKEN_R` | 99.96 | | `TOKEN_F` | 99.95 | | `TAG_ACC` | 95.09 | | `POS_ACC` | 96.38 | | `MORPH_ACC` | 95.85 | | `MORPH_MICRO_P` | 97.77 | | `MORPH_MICRO_R` | 97.39 | | `MORPH_MICRO_F` | 97.58 | | `SENTS_P` | 92.29 | | `SENTS_R` | ... | 32ba38f4e4a77ae298518f5e1a739083 |
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