license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 74 | 1.7148 | | No log | 2.0 | 148 | 1.6994 | | No log | 3.0 | 222 | 1.7922 | | No log | 4.0 | 296 | 1.9947 | | No log | 5.0 | 370 | 2.0753 | | No log | 6.0 | 444 | 2.2096 | | 0.9547 | 7.0 | 518 | 2.3070 | | 0.9547 | 8.0 | 592 | 2.6947 | | 0.9547 | 9.0 | 666 | 2.7169 | | 0.9547 | 10.0 | 740 | 2.8503 | | 0.9547 | 11.0 | 814 | 3.1990 | | 0.9547 | 12.0 | 888 | 3.4931 | | 0.9547 | 13.0 | 962 | 3.6575 | | 0.3191 | 14.0 | 1036 | 3.1863 | | 0.3191 | 15.0 | 1110 | 3.7922 | | 0.3191 | 16.0 | 1184 | 3.6336 | | 0.3191 | 17.0 | 1258 | 4.1156 | | 0.3191 | 18.0 | 1332 | 4.1353 | | 0.3191 | 19.0 | 1406 | 3.9888 | | 0.3191 | 20.0 | 1480 | 4.4290 | | 0.1904 | 21.0 | 1554 | 4.0473 | | 0.1904 | 22.0 | 1628 | 4.5048 | | 0.1904 | 23.0 | 1702 | 4.4026 | | 0.1904 | 24.0 | 1776 | 4.2864 | | 0.1904 | 25.0 | 1850 | 4.3941 | | 0.1904 | 26.0 | 1924 | 4.4921 | | 0.1904 | 27.0 | 1998 | 4.9139 | | 0.1342 | 28.0 | 2072 | 4.8914 | | 0.1342 | 29.0 | 2146 | 5.0148 | | 0.1342 | 30.0 | 2220 | 5.0220 | | 12e687fde59497f1087101a43be2d57b |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | HATE-ITA Base HATE-ITA is a binary hate speech classification model for Italian social media text. <img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/hateita.png?token=GHSAT0AAAAAABTEBAJ4PNDWAMU3KKIGUOCSYWG4IBA" width="200"> | a1ea11f522fe50849cd9208dbd7f2649 |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | Model This model is the fine-tuned version of the [XLM-T](https://arxiv.org/abs/2104.12250) model. | Model | Download | | ------ | -------------------------| | `hate-ita` | [Link](https://huggingface.co/MilaNLProc/hate-ita) | | `hate-ita-xlm-r-base` | [Link](https://huggingface.co/MilaNLProc/hate-ita-xlm-r-base) | | `hate-ita-xlm-r-large` | [Link](https://huggingface.co/MilaNLProc/hate-ita-xlm-r-large) | | 5ee870d233cec334a2cfcb0eb7647abd |
mit | [] | false | mycat on Stable Diffusion This is the `<mycat>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:       | 19552ff139b30d6ffd796e7d2d642db3 |
mit | [] | false | F-22 on Stable Diffusion This is the `<f-22>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | 6ddddec677ea76bafa6a6bdd74f420c0 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'spanish', 'xlrs-53-spanish', 'ciempiess', 'cimpiess-unam'] | false | wav2vec2-large-xlsr-53-spanish-ep5-944h The "wav2vec2-large-xlsr-53-spanish-ep5-944h" is an acoustic model suitable for Automatic Speech Recognition in Spanish. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" for 5 epochs with around 944 hours of Spanish data gathered or developed by the [CIEMPIESS-UNAM Project](https://huggingface.co/ciempiess) since 2012. Most of the data is available at the the CIEMPIESS-UNAM Project homepage http://www.ciempiess.org/. The rest can be found in public repositories such as [LDC](https://www.ldc.upenn.edu/) or [OpenSLR](https://openslr.org/) The specific list of corpora used to fine-tune the model is: - [CIEMPIESS-LIGHT (18h25m)](https://catalog.ldc.upenn.edu/LDC2017S23) - [CIEMPIESS-BALANCE (18h20m)](https://catalog.ldc.upenn.edu/LDC2018S11) - [CIEMPIESS-FEM (13h54m)](https://catalog.ldc.upenn.edu/LDC2019S07) - [CHM150 (1h38m)](https://catalog.ldc.upenn.edu/LDC2016S04) - [TEDX_SPANISH (24h29m)](https://openslr.org/67/) - [LIBRIVOX_SPANISH (73h01m)](https://catalog.ldc.upenn.edu/LDC2020S01) - [WIKIPEDIA_SPANISH (25h37m)](https://catalog.ldc.upenn.edu/LDC2021S07) - [VOXFORGE_SPANISH (49h42m)](http://www.voxforge.org/es) - [MOZILLA COMMON VOICE 10.0 (320h22m)](https://commonvoice.mozilla.org/es) - [HEROICO (16h33m)](https://catalog.ldc.upenn.edu/LDC2006S37) - [LATINO-40 (6h48m)](https://catalog.ldc.upenn.edu/LDC95S28) - [CALLHOME_SPANISH (13h22m)](https://catalog.ldc.upenn.edu/LDC96S35) - [HUB4NE_SPANISH (31h41m)](https://catalog.ldc.upenn.edu/LDC98S74) - [FISHER_SPANISH (127h22m)](https://catalog.ldc.upenn.edu/LDC2010S01) - [Chilean Spanish speech data set (7h08m)](https://openslr.org/71/) - [Colombian Spanish speech data set (7h34m)](https://openslr.org/72/) - [Peruvian Spanish speech data set (9h13m)](https://openslr.org/73/) - [Argentinian Spanish speech data set (8h01m)](https://openslr.org/61/) - [Puerto Rico Spanish speech data set (1h00m)](https://openslr.org/74/) - [MediaSpeech Spanish (10h00m)](https://openslr.org/108/) - [DIMEX100-LIGHT (6h09m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html) - [DIMEX100-NIÑOS (08h09m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html) - [GOLEM-UNIVERSUM (00h10m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html) - [GLISSANDO (6h40m)](https://glissando.labfon.uned.es/es) - TELE_con_CIENCIA (28h16m) **Unplished Material** - UNSHAREABLE MATERIAL (118h22m) **Not available for sharing** The fine-tuning process was performed during November (2022) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena. | bc1897dda24fcb9356975dbee58c6772 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'spanish', 'xlrs-53-spanish', 'ciempiess', 'cimpiess-unam'] | false | Load the processor and model. MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h" processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME) | cae92a433e133f9908f7b59419fff0db |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'spanish', 'xlrs-53-spanish', 'ciempiess', 'cimpiess-unam'] | false | BibTeX entry and citation info *When publishing results based on these models please refer to:* ```bibtex @misc{mena2022xlrs53spanish, title={Acoustic Model in Spanish: wav2vec2-large-xlsr-53-spanish-ep5-944h.}, author={Hernandez Mena, Carlos Daniel}, year={2022}, url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h}, } ``` | 41146a0006c16c9f4aa1049854d299e7 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'spanish', 'xlrs-53-spanish', 'ciempiess', 'cimpiess-unam'] | false | Acknowledgements The author wants to thank to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the [Facultad de Ingeniería (FI)](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). He also thanks to the social service students for all the hard work. Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. The author also thanks to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture. | 3e502086d30d4f9cb2ffc29e27e03e45 |
mit | [] | false | Happy_Person12345 on Stable Diffusion This is the `<Happy-Person12345>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:     | 1472029349dda468676956f86f82f8db |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper large v2 vi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5978 - Wer: 18.1509 | 0a1189f08defe19d2b230d72cd70977f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0003 | 32.01 | 1000 | 0.5978 | 18.1509 | | 6d0e7eb3eb81f6f1950d45dac6a89966 |
creativeml-openrail-m | [] | false | model by no3 This your waifu-diffusion v1.3 model fine-tuned ridley taught to waifu-diffusion v1.3 with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_ridley** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts). | 2161a0fcc84d914d9ac1a009c08e29fa |
creativeml-openrail-m | [] | false | note If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download ckpt file here for your convenience. **just click on "ridley-wd-1.3-beta1.ckpt"** [ridley-wd-1.3-beta1.ckpt](https://huggingface.co/no3/ridley-wd-1.3-beta1/resolve/main/ridley-wd-1.3-beta1.ckpt) If you have issues or questions feel free to visit the Community Tab and start discussion about it. Here are images used for training this concept:                | e1274c31c433a997d2cf2d30a0239b00 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-dpv-finetuned-WITH-AUGMENTATION-AUGMENTED-ALL This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6523 - Wer: 35.1345 | c63a30f0587eb481e7b8d02e3a188cf3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 4 - mixed_precision_training: Native AMP | 3c0369476a0e61d47901a65d31589b53 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3432 | 1.25 | 1000 | 0.5472 | 37.2824 | | 0.138 | 2.49 | 2000 | 0.5765 | 37.0563 | | 0.0569 | 3.74 | 3000 | 0.6523 | 35.1345 | | dbc747f61f6cc9d1fffb17233ce07d72 |
apache-2.0 | ['translation'] | false | opus-mt-id-es * source languages: id * target languages: es * OPUS readme: [id-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/id-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/id-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/id-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/id-es/opus-2020-01-16.eval.txt) | 75371d0ef93097a68ec580db30ff2e17 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qnli-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7322 - Matthews Correlation: 0.1353 | 3d549446fbeb0a06a08d67b9bfaee287 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6099 | 1.87 | 500 | 0.6209 | 0.0 | | 0.6009 | 3.73 | 1000 | 0.6169 | 0.0 | | 0.5819 | 5.6 | 1500 | 0.6196 | 0.0545 | | 0.5519 | 7.46 | 2000 | 0.6391 | 0.0997 | | 0.5226 | 9.33 | 2500 | 0.6657 | 0.1182 | | 0.5061 | 11.19 | 3000 | 0.6671 | 0.1357 | | 0.4831 | 13.06 | 3500 | 0.6787 | 0.1205 | | 0.4652 | 14.93 | 4000 | 0.7167 | 0.1264 | | 0.4443 | 16.79 | 4500 | 0.7322 | 0.1353 | | d455bb669e4d44f7c149dd299d2f0d2d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 256 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | 57ce90cc7caf5df4f696792375f709a0 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Classical_Chinese (lzh) 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](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 01:46:05.943 | 675c8ea8a1f3d2e8048eed7a170a1c27 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | Evaluation on Zeroth-Korean ASR corpus [Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb) ``` from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import soundfile as sf import torch from jiwer import wer processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean") model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda') ds = load_dataset("kresnik/zeroth_korean", "clean") test_ds = ds['test'] def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch test_ds = test_ds.map(map_to_array) def map_to_pred(batch): inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") input_values = inputs.input_values.to("cuda") with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` | 2090c6ea96f90a05beb9f508633deef1 |
apache-2.0 | ['voxpopuli', 'google/xtreme_s', 'generated_from_trainer'] | false | xtreme_s_xlsr_300m_voxpopuli_en This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - VOXPOPULI.EN dataset. It achieves the following results on the evaluation set: - Cer: 0.0966 - Loss: 0.3127 - Wer: 0.1549 - Predict Samples: 1842 | 35eb79966f553a6bfe7096e43d6e0ff0 |
apache-2.0 | ['voxpopuli', 'google/xtreme_s', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP | 5978eeb9fede71c5b8a64d3eb923b99c |
apache-2.0 | ['voxpopuli', 'google/xtreme_s', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 1.4221 | 0.19 | 500 | 1.3325 | 0.8224 | 0.3432 | | 0.8429 | 0.38 | 1000 | 0.7087 | 0.5028 | 0.2023 | | 0.7377 | 0.57 | 1500 | 0.4900 | 0.2778 | 0.1339 | | 0.5641 | 0.77 | 2000 | 0.4460 | 0.2540 | 0.1284 | | 0.5787 | 0.96 | 2500 | 0.4242 | 0.2148 | 0.1167 | | 0.3465 | 1.15 | 3000 | 0.4210 | 0.2087 | 0.1154 | | 0.2787 | 1.34 | 3500 | 0.3954 | 0.2090 | 0.1155 | | 0.2775 | 1.53 | 4000 | 0.3938 | 0.1992 | 0.1133 | | 0.262 | 1.72 | 4500 | 0.3748 | 0.2104 | 0.1151 | | 0.3138 | 1.92 | 5000 | 0.3825 | 0.1993 | 0.1134 | | 0.4331 | 2.11 | 5500 | 0.3648 | 0.1935 | 0.1104 | | 0.3802 | 2.3 | 6000 | 0.3966 | 0.1910 | 0.1109 | | 0.3928 | 2.49 | 6500 | 0.3995 | 0.1898 | 0.1100 | | 0.3441 | 2.68 | 7000 | 0.3764 | 0.1887 | 0.1103 | | 0.3673 | 2.87 | 7500 | 0.3800 | 0.1843 | 0.1086 | | 0.3422 | 3.07 | 8000 | 0.3932 | 0.1830 | 0.1092 | | 0.2933 | 3.26 | 8500 | 0.3672 | 0.1915 | 0.1104 | | 0.1785 | 3.45 | 9000 | 0.3820 | 0.1796 | 0.1072 | | 0.321 | 3.64 | 9500 | 0.3533 | 0.1994 | 0.1126 | | 0.1673 | 3.83 | 10000 | 0.3683 | 0.1856 | 0.1084 | | 0.1757 | 4.02 | 10500 | 0.3365 | 0.1925 | 0.1102 | | 0.1881 | 4.22 | 11000 | 0.3528 | 0.1775 | 0.1066 | | 0.3106 | 4.41 | 11500 | 0.3909 | 0.1754 | 0.1063 | | 0.25 | 4.6 | 12000 | 0.3734 | 0.1723 | 0.1052 | | 0.2005 | 4.79 | 12500 | 0.3358 | 0.1900 | 0.1092 | | 0.2982 | 4.98 | 13000 | 0.3513 | 0.1766 | 0.1060 | | 0.1552 | 5.17 | 13500 | 0.3720 | 0.1729 | 0.1059 | | 0.1645 | 5.37 | 14000 | 0.3569 | 0.1713 | 0.1044 | | 0.2065 | 5.56 | 14500 | 0.3639 | 0.1720 | 0.1048 | | 0.1898 | 5.75 | 15000 | 0.3660 | 0.1726 | 0.1050 | | 0.1397 | 5.94 | 15500 | 0.3731 | 0.1670 | 0.1033 | | 0.2056 | 6.13 | 16000 | 0.3782 | 0.1650 | 0.1030 | | 0.1859 | 6.32 | 16500 | 0.3903 | 0.1667 | 0.1033 | | 0.1374 | 6.52 | 17000 | 0.3721 | 0.1736 | 0.1048 | | 0.2482 | 6.71 | 17500 | 0.3899 | 0.1643 | 0.1023 | | 0.159 | 6.9 | 18000 | 0.3847 | 0.1687 | 0.1032 | | 0.1487 | 7.09 | 18500 | 0.3817 | 0.1671 | 0.1030 | | 0.1942 | 7.28 | 19000 | 0.4120 | 0.1616 | 0.1018 | | 0.1517 | 7.47 | 19500 | 0.3856 | 0.1635 | 0.1020 | | 0.0946 | 7.67 | 20000 | 0.3838 | 0.1621 | 0.1016 | | 0.1455 | 7.86 | 20500 | 0.3749 | 0.1652 | 0.1020 | | 0.1303 | 8.05 | 21000 | 0.4074 | 0.1615 | 0.1011 | | 0.1207 | 8.24 | 21500 | 0.4121 | 0.1606 | 0.1008 | | 0.0727 | 8.43 | 22000 | 0.3948 | 0.1607 | 0.1009 | | 0.1123 | 8.62 | 22500 | 0.4025 | 0.1603 | 0.1009 | | 0.1606 | 8.82 | 23000 | 0.3963 | 0.1580 | 0.1004 | | 0.1458 | 9.01 | 23500 | 0.3991 | 0.1574 | 0.1002 | | 0.2286 | 9.2 | 24000 | 0.4149 | 0.1596 | 0.1009 | | 0.1284 | 9.39 | 24500 | 0.4251 | 0.1572 | 0.1002 | | 0.1141 | 9.58 | 25000 | 0.4264 | 0.1579 | 0.1002 | | 0.1823 | 9.77 | 25500 | 0.4230 | 0.1562 | 0.0999 | | 0.2514 | 9.97 | 26000 | 0.4242 | 0.1564 | 0.0999 | | e177d4f38780a9c73a4f0722ea891956 |
apache-2.0 | ['generated_from_trainer'] | false | Negation_Scope_Detection_NubEs_Spanish_mBERT_fine_tuned This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the nubes dataset. It achieves the following results on the evaluation set: - Loss: 0.1624 - Precision: 0.9012 - Recall: 0.9184 - F1: 0.9098 - Accuracy: 0.9744 | 3082ec371882a3537eda29cbd97ebb74 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1802 | 1.0 | 1726 | 0.1849 | 0.7843 | 0.8526 | 0.8170 | 0.9509 | | 0.1216 | 2.0 | 3452 | 0.1512 | 0.8706 | 0.8352 | 0.8525 | 0.9579 | | 0.0817 | 3.0 | 5178 | 0.1083 | 0.8845 | 0.9038 | 0.8940 | 0.9710 | | 0.0517 | 4.0 | 6904 | 0.1314 | 0.8858 | 0.8960 | 0.8909 | 0.9693 | | 0.0265 | 5.0 | 8630 | 0.1514 | 0.8963 | 0.9079 | 0.9021 | 0.9721 | | 0.0136 | 6.0 | 10356 | 0.1524 | 0.9045 | 0.9092 | 0.9068 | 0.9729 | | 0.0045 | 7.0 | 12082 | 0.1624 | 0.9012 | 0.9184 | 0.9098 | 0.9744 | | 86f216c95cb91cc8f6c2546d6a7e958a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_reg3 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: 0.7954 - Mse: 0.7954 - Mae: 0.6900 - R2: 0.4769 - Accuracy: 0.4459 | 63af25cb62ee062ee024574650016aa2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:| | 1.018 | 1.0 | 3122 | 0.7491 | 0.7491 | 0.6739 | 0.5073 | 0.4555 | | 0.668 | 2.0 | 6244 | 0.7397 | 0.7397 | 0.6687 | 0.5135 | 0.4689 | | 0.4871 | 3.0 | 9366 | 0.7542 | 0.7542 | 0.6730 | 0.5040 | 0.4606 | | 0.3419 | 4.0 | 12488 | 0.7710 | 0.7710 | 0.6802 | 0.4929 | 0.4536 | | 0.2532 | 5.0 | 15610 | 0.7954 | 0.7954 | 0.6900 | 0.4769 | 0.4459 | | 9bc9f40149d5b030dffee9c2b45d26ef |
apache-2.0 | ['generated_from_trainer'] | false | model_broadclass_onSet0 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9207 - 0 Precision: 1.0 - 0 Recall: 1.0 - 0 F1-score: 1.0 - 0 Support: 31 - 1 Precision: 0.9615 - 1 Recall: 1.0 - 1 F1-score: 0.9804 - 1 Support: 25 - 2 Precision: 1.0 - 2 Recall: 0.9630 - 2 F1-score: 0.9811 - 2 Support: 27 - 3 Precision: 1.0 - 3 Recall: 1.0 - 3 F1-score: 1.0 - 3 Support: 15 - Accuracy: 0.9898 - Macro avg Precision: 0.9904 - Macro avg Recall: 0.9907 - Macro avg F1-score: 0.9904 - Macro avg Support: 98 - Weighted avg Precision: 0.9902 - Weighted avg Recall: 0.9898 - Weighted avg F1-score: 0.9898 - Weighted avg Support: 98 - Wer: 0.9344 - Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] | ba650d0b4a021e482dda3132ccbc7be8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 50 - mixed_precision_training: Native AMP | 6417d24f542023249ae949565cf57c0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:| | 2.3791 | 4.16 | 100 | 2.2297 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 2.276 | 8.33 | 200 | 2.1645 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 1.9646 | 12.49 | 300 | 1.9022 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 1.7089 | 16.65 | 400 | 1.6727 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 1.5546 | 20.82 | 500 | 1.5776 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 1.5671 | 24.98 | 600 | 1.5759 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 1.5548 | 29.16 | 700 | 1.5419 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] | | 1.5148 | 33.33 | 800 | 1.4847 | 0.3263 | 1.0 | 0.4921 | 31 | 0.0 | 0.0 | 0.0 | 25 | 1.0 | 0.1111 | 0.2000 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3469 | 0.3316 | 0.2778 | 0.1730 | 98 | 0.3787 | 0.3469 | 0.2108 | 98 | 0.9837 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 24, 0, 3, 0], [3, 15, 0, 0, 0]] | | 1.4234 | 37.49 | 900 | 1.4497 | 0.4429 | 1.0 | 0.6139 | 31 | 1.0 | 0.28 | 0.4375 | 25 | 1.0 | 0.5556 | 0.7143 | 27 | 1.0 | 0.4 | 0.5714 | 15 | 0.6020 | 0.8607 | 0.5589 | 0.5843 | 98 | 0.8238 | 0.6020 | 0.5900 | 98 | 0.9975 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 18, 7, 0, 0], [2, 12, 0, 15, 0], [3, 9, 0, 0, 6]] | | 1.3619 | 41.65 | 1000 | 1.3438 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9796 | 0.9815 | 0.9827 | 0.9816 | 98 | 0.9811 | 0.9796 | 0.9798 | 98 | 0.9832 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] | | 0.9703 | 45.82 | 1100 | 0.9444 | 1.0 | 1.0 | 1.0 | 31 | 0.9615 | 1.0 | 0.9804 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9898 | 0.9904 | 0.9907 | 0.9904 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9289 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] | | 0.9299 | 49.98 | 1200 | 0.9207 | 1.0 | 1.0 | 1.0 | 31 | 0.9615 | 1.0 | 0.9804 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9898 | 0.9904 | 0.9907 | 0.9904 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9344 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] | | c366b41531a79fccdbbfa4ce28541f82 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Training config: ```yaml data: channels: 1 n_src: 2 root_path: data sample_rate: 16000 samples_per_track: 10 segment: 3.0 task: enh_both filterbank: kernel_size: 20 n_filters: 256 stride: 10 main_args: exp_dir: exp/train_convtasnet help: None masknet: bn_chan: 256 conv_kernel_size: 3 hid_chan: 512 mask_act: relu n_blocks: 8 n_repeats: 4 n_src: 2 norm_type: gLN skip_chan: 256 optim: lr: 0.0003 optimizer: adam weight_decay: 0.0 positional arguments: training: batch_size: 12 early_stop: True epochs: 50 half_lr: True num_workers: 12 ``` | a7e075d631fa9d1b4e012a4611811365 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Results: ```yaml si_sdr: 14.018196157142519 si_sdr_imp: 14.017103133809577 sdr: 14.498517291333885 sdr_imp: 14.463389151567865 sir: 24.149634529133372 sir_imp: 24.11450638936735 sar: 15.338597389045935 sar_imp: -137.30634122401517 stoi: 0.7639416744417206 stoi_imp: 0.1843383526963759 ``` | 2aba23e3b7604f0ae8a14baeff4dcc6b |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | License notice: This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike. | ae1b9f1abd001ee352d7b81deee41967 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | maika Dreambooth model trained by birdaz with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | a27ad891dc312fd9b7a4b41293767a18 |
apache-2.0 | ['text2text-generation'] | false | Hungarian morphological generator model with mT5 For further models, scripts and details, see [our demo site](https://juniper.nytud.hu/demo/nlp). - Pretrained model used: mT5 - Prefix: "morph: " - UD-based generation | 1ca39ea3ea941d683ca02641471fd88b |
apache-2.0 | ['text2text-generation'] | false | Usage with pipeline ```python from transformers import pipeline text2text_generator = pipeline(task="text2text-generation", model="NYTK/morphological-generator-ud-mt5-hungarian") print(text2text_generator("morph: munka NOUN Case=Acc|Number=Sin")[0]["generated_text"]) ``` | 41574c64f6d3e009f28da2ff31007274 |
apache-2.0 | ['text2text-generation'] | false | Citation If you use this model, please cite the following paper: ``` @inproceedings {morph-generator, title = {Neural Morphological Generators for Hungarian}, booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)}, year = {2023}, publisher = {Szegedi Tudományegyetem, Informatikai Intézet}, address = {Szeged, Hungary}, author = {Laki, László János and Ligeti-Nagy, Noémi and Vadász, Noémi and Yang, Zijian Győző}, pages = {331--340} } ``` | 934f4dcf0ebc4a49a5097ef14d1c6959 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 180k (uncased) Seed 3 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-3](https://hf.co/multberts-seed-3). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 8b6f2c192ec62e5cb643e95b13ec4f06 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | 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 BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-3-180k') model = BertModel.from_pretrained("multiberts-seed-3-180k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 07d3b61c9287cbe64ffb6282c7716b76 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4214 | cd74f28b4d49e51ad76713f7526f07c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1814 | 1.0 | 8235 | 1.2488 | | 0.9078 | 2.0 | 16470 | 1.3127 | | 0.7439 | 3.0 | 24705 | 1.4214 | | 10437b58141bf7bd83a0540f0defc031 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Bengali This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 bn dataset. It achieves the following results on the evaluation set: - Loss: 0.1638 - Wer: 18.22 | b97b4659eac2aa2a634fdec5f8c90050 |
cc-by-sa-4.0 | ['text-classification', 'bart', 'xsum'] | false | Model description A BART (base) model trained to classify whether a summary is *faithful* to the original article. See our [paper in NAACL'21](https://www.seas.upenn.edu/~sihaoc/static/pdf/CZSR21.pdf) for details. | 754f9f3f5fcf0e07ff3cc9bf271bd5e1 |
cc-by-sa-4.0 | ['text-classification', 'bart', 'xsum'] | false | Usage Concatenate a summary and a source document as input (note that the summary needs to be the **first** sentence). Here's an example usage (with PyTorch) ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CogComp/bart-faithful-summary-detector") model = AutoModelForSequenceClassification.from_pretrained("CogComp/bart-faithful-summary-detector") article = "Ban Ki-Moon was re-elected for a second term by the UN General Assembly, unopposed and unanimously, on 21 June 2011." bad_summary = "Ban Ki-moon was elected for a second term in 2007." good_summary = "Ban Ki-moon was elected for a second term in 2011." bad_pair = tokenizer(text=bad_summary, text_pair=article, return_tensors='pt') good_pair = tokenizer(text=good_summary, text_pair=article, return_tensors='pt') bad_score = model(**bad_pair) good_score = model(**good_pair) print(good_score[0][:, 1] > bad_score[0][:, 1]) | 784f6c1614e175e8cb1adb0e74eb876a |
cc-by-sa-4.0 | ['text-classification', 'bart', 'xsum'] | false | BibTeX entry and citation info ```bibtex @inproceedings{CZSR21, author = {Sihao Chen and Fan Zhang and Kazoo Sone and Dan Roth}, title = {{Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection}}, booktitle = {NAACL}, year = {2021} } ``` | fbf4c9e22005e0d7f3e8d9c1da37f452 |
apache-2.0 | ['translation'] | false | opus-mt-sv-kqn * source languages: sv * target languages: kqn * OPUS readme: [sv-kqn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-kqn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-kqn/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-kqn/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-kqn/opus-2020-01-16.eval.txt) | ed06bbc6ec027b330f7f7234bcaf6cfb |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_gender_male-10_female-0_s825 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 1d7e994e80283acf655347c3ff4270e1 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/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://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. | d5b9d6cffd85f4572436e172af3292b0 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s42 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | fab73bace9eacf2463e216dd64de24d8 |
unknown | [] | false | Samples <img src="https://huggingface.co/cyburn/daisy_labour_yak/resolve/main/1.jpg" alt="Map of positive probabilities per country." width="500"/> <img src="https://huggingface.co/cyburn/daisy_labour_yak/resolve/main/2.jpg" alt="Map of positive probabilities per country." width="500"/> <img src="https://huggingface.co/cyburn/daisy_labour_yak/resolve/main/3.jpg" alt="Map of positive probabilities per country." width="500"/> <img src="https://huggingface.co/cyburn/daisy_labour_yak/resolve/main/4.jpg" alt="Map of positive probabilities per country." width="500"/> <img src="https://huggingface.co/cyburn/daisy_labour_yak/resolve/main/5.jpg" alt="Map of positive probabilities per country." width="500"/> <img src="https://huggingface.co/cyburn/daisy_labour_yak/resolve/main/6.jpg" alt="Map of positive probabilities per country." width="500"/> | 46aca7d25a5095bf0cd894d31a502858 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 386 | 3.3464 | 17.6525 | 9.1043 | 16.6246 | 16.3747 | 12.3556 | | c625488633c2070033a3cfcf5a4cfcac |
apache-2.0 | ['generated_from_keras_callback'] | false | fassahat/distillbert-base-uncased-finetuned-150k-patent-sentences 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: - Train Loss: 0.2887 - Validation Loss: 0.4392 - Train Accuracy: 0.8414 - Epoch: 2 | a4d57091caf9a6d18040da42ae6101e6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 22500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | d094c420335bbe8eaf751748d0085077 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.4810 | 0.4276 | 0.8330 | 0 | | 0.3714 | 0.4163 | 0.8415 | 1 | | 0.2887 | 0.4392 | 0.8414 | 2 | | e444123370fc2c2a7f60d8f8daa548e7 |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | MPNet NLI ***Note**: The same model trained with negatives yields better performance. [Find it here](https://huggingface.co/jamescalam/mpnet-snli-negatives).* This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search. It has been fine-tuned using the **S**tanford **N**atural **L**anguage **I**nference (SNLI) dataset and returns MRR@10 and MAP scores of ~0.92 on the SNLI test set. Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp). | 83f569d5d14eba00de76ff67fe4a7251 |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('jamescalam/mpnet-snli') embeddings = model.encode(sentences) print(embeddings) ``` | 000616bcf9bb585a3078405683a1e49a |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 5731 with parameters: ``` {'batch_size': 32} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 573, "weight_decay": 0.01 } ``` | a614dc1a089a5eb59710c0f04b904d2a |
mit | [] | false | cute cat on Stable Diffusion This is the `<cute-bear>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | 0646ca951e57fd7f3723837bd697a49c |
mit | [] | false | yesdelete on Stable Diffusion This is the `<yesdelete>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:     | 38482b6118877d871fe930e643afa2c1 |
apache-2.0 | ['multiberts', 'multiberts-seed_0'] | false | MultiBERTs - Seed 0 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | c587a7d34884184140a312e62225a7d5 |
apache-2.0 | ['multiberts', 'multiberts-seed_0'] | 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_0') model = TFBertModel.from_pretrained("google/multiberts-seed_0") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0') model = BertModel.from_pretrained("google/multiberts-seed_0") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | a6c8c320eb77036db63fbafaedbad691 |
openrail | [] | false | The mecha model needs low cfg, such as 3.5-7. Because the training set has only the upper body, it can only be partially stable, Forgive me for not doing well, Thanks to QQ friends for their long-term help and teaching. Thank you again Thank Mr. Lin for his training set BY昂扬 Use vae with high saturation Real mechanical texture Realistic Metal details Dirt, dust, damage and wear, battle damage Mecha model           | 94004fcb8240d8a16e4580d445caa7a1 |
apache-2.0 | ['generated_from_trainer'] | false | wnli_bert-base-uncased_144_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7001 - Accuracy: 0.5634 | 2cabea52d1037d46ad162424738cb36c |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Italian T5 Large 🇮🇹 The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer). This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://arxiv.org/abs/2203.03759) (to be released), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process. *The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.* | 34157b62c6415cb66a1310513731e627 |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Model variants This repository contains the checkpoints for the `base` version of the model. The model was trained for one epoch (1.05M steps) on the [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB) using 🤗 Datasets and the `google/t5-v1_1-large` improved configuration. The training procedure is made available [on Github](https://github.com/gsarti/t5-flax-gcp). The following table summarizes the parameters for all available models | |`it5-small` |`it5-base` |`it5-large` (this one) |`it5-base-oscar` | |-----------------------|-----------------------|----------------------|-----------------------|----------------------------------| |`dataset` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`oscar/unshuffled_deduplicated_it`| |`architecture` |`google/t5-v1_1-small` |`google/t5-v1_1-base` |`google/t5-v1_1-large` |`t5-base` | |`learning rate` | 5e-3 | 5e-3 | 5e-3 | 1e-2 | |`steps` | 1'050'000 | 1'050'000 | 2'100'000 | 258'000 | |`training time` | 36 hours | 101 hours | 370 hours | 98 hours | |`ff projection` |`gated-gelu` |`gated-gelu` |`gated-gelu` |`relu` | |`tie embeds` |`false` |`false` |`false` |`true` | |`optimizer` | adafactor | adafactor | adafactor | adafactor | |`max seq. length` | 512 | 512 | 512 | 512 | |`per-device batch size`| 16 | 16 | 8 | 16 | |`tot. batch size` | 128 | 128 | 64 | 128 | |`weigth decay` | 1e-3 | 1e-3 | 1e-2 | 1e-3 | |`validation split size`| 15K examples | 15K examples | 15K examples | 15K examples | The high training time of `it5-base-oscar` was due to [a bug](https://github.com/huggingface/transformers/pull/13012) in the training script. For a list of individual model parameters, refer to the `config.json` file in the respective repositories. | da486cbb4f5caa24d209504e097fc9e7 |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Using the models ```python from transformers import AutoTokenzier, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-large") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-large") ``` *Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example [here](https://huggingface.co/gsarti/it5-base-nli).* Flax and Tensorflow versions of the model are also available: ```python from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-large") model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-large") ``` | d9c18a2584db4569defdb6b306cdeb40 |
['apache-2.0'] | ['vision'] | false | Model description The Vision Transformer (ViT) is a transformer model pretrained on a large collection of images in a supervised fashion, namely [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M), at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer. This ViT model is pretrained with COYO-Labeled-300M at resolution 224x224. Please see details [here](https://github.com/kakaobrain/coyo-vit) | b59b973b3dd22d98d5533306627054c6 |
apache-2.0 | ['generated_from_keras_callback'] | false | shaun-e-j/bert-finetuned-testing2-colab This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.9576 - Epoch: 4 | 6f8d4faaae9bf9dbabd7735445332c19 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-subjqa-vanilla-grocery-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 1a9448f9192c606ff6c964985a8bd627 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (grocery) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | f3dae3fde11e46ece6581fa933272538 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-subjqa-vanilla-grocery-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 541446897d2faeca81dd3839f31481fb |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-grocery-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 80.35 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 3.07 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 0.56 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 0 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 0 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 5.04 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 50.49 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 4.54 | grocery | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | 27b242222361b93a667c096370789bf1 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: grocery - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 3 - batch: 32 - lr: 1e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-grocery-qg/raw/main/trainer_config.json). | 80eab7fe66bdf998829fa420c14ffcbe |
apache-2.0 | ['vision', 'image-classification'] | false | Convolutional Vision Transformer (CvT) CvT-13 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT). Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team. | 96b71485c421571b9a4d5211e985bc14 |
apache-2.0 | ['vision', 'image-classification'] | false | Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, CvtForImageClassification 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 = AutoFeatureExtractor.from_pretrained('microsoft/cvt-13-384') model = CvtForImageClassification.from_pretrained('microsoft/cvt-13-384') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits | d97aef9176509d2bf850712c029a0103 |
apache-2.0 | ['generated_from_keras_callback'] | false | Hardik1313X/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0279 - Validation Loss: 0.0571 - Epoch: 2 | 07a4a447ff0e5e612c39c23fa98902c9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1745 | 0.0630 | 0 | | 0.0468 | 0.0578 | 1 | | 0.0279 | 0.0571 | 2 | | f4f13e29f80b675c26b68f3ae723c3af |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Finnish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 2167a1eefe2045c1bd12aaa676cae598 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fi") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fi") ``` | 084c7bf2919354714b3f72d8c4e9c288 |
apache-2.0 | [] | false | *Text classification model SloBERTa-Trendi-Topics 1.0* The SloBerta-Trendi-Topics model is a text classification model for categorizing news texts with one of 13 topic labels. It was trained on a set of approx. 36,000 Slovene texts from various Slovene news sources included in the Trendi Monitor Corpus of Slovene (http://hdl.handle.net/11356/1590) such as "rtvslo.si", "sta.si", "delo.si", "dnevnik.si", "vecer.com", "24ur.com", "siol.net", "gorenjskiglas.si", etc. The texts were semi-automatically categorized into 13 categories based on the sections under which they were published (i.e. URLs). The set of labels was developed in accordance with related categorization schemas used in other corpora and comprises the following topics: "črna kronika" (crime and accidents), "gospodarstvo, posel, finance" (economy, business, finance), "izobraževanje" (education), "okolje" (environment), "prosti čas" (free time), "šport" (sport), "umetnost, kultura" (art, culture), "vreme" (weather), "zabava" (entertainment), "zdravje" (health), "znanost in tehnologija" (science and technology), "politika" (politics), and "družba" (society). The categorization process is explained in more detail in Kosem et al. (2022): https://nl.ijs.si/jtdh22/pdf/JTDH2022_Kosem-et-al_Spremljevalni-korpus-Trendi.pdf The model was trained on the labeled texts using the SloBERTa 2.0 contextual embeddings model (https://huggingface.co/EMBEDDIA/sloberta, also available at CLARIN.SI: http://hdl.handle.net/11356/1397) and validated on a development set of 1,293 texts using the simpletransformers library and the following hyperparameters: - Train batch size: 8 - Learning rate: 1e-5 - Max. sequence length: 512 - Number of epochs: 2 The model achieves a macro-F1-score of 0.94 on a test set of 1,295 texts (best for "črna kronika", "politika", "šport", and "vreme" at 0.98, worst for "prosti čas" at 0.83). | beb07806afbb40f4f4ae14233f0dca9b |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | **m**utual **i**nformation **C**ontrastive **S**entence **E**mbedding (**miCSE**): [](https://arxiv.org/abs/2211.04928) Language model of the pre-print arXiv paper titled: "_**miCSE**: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings_" | b1690839e1e02ae64a1eebc1bbac8377 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Brief Model Description The **miCSE** language model is trained for sentence similarity computation. Training the model imposes alignment between the attention pattern of different views (embeddings of augmentations) during contrastive learning. Intuitively, learning sentence embeddings with miCSE entails enforcing __syntactic consistency across dropout augmented views__. Practically, this is achieved by regularizing the self-attention distribution. By regularizing self-attention during training, representation learning becomes much more sample efficient. Hence, self-supervised learning becomes tractable even when the training set is limited in size. This property makes miCSE particularly interesting for __real-world applications__, where training data is typically limited. | e2a9e5265e9c31acd05281a00cf14106 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Model Use Cases The model intended to be used for encoding sentences or short paragraphs. Given an input text, the model produces a vector embedding capturing the semantics. Sentence representations correspond to embedding of the _**[CLS]**_ token. The embedding can be used for numerous tasks such as **retrieval**,**sentence similarity** comparison (see example 1) or **clustering** (see example 2). | e7398c9b1bec9335375aa6fc0ae1677d |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Training data The model was trained on a random collection of **English** sentences from Wikipedia: [Training data file](https://huggingface.co/datasets/princeton-nlp/datasets-for-simcse/resolve/main/wiki1m_for_simcse.txt) | 0666d60ed8fb18e69cb6381e9a3e0509 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Model Training <mark>In order to make use of the **few-shot** capability of **miCSE**, the mode needs to be trained on your data. The source code and instructions to do so will be provided shortly. Stay tuned :). </mark> | ca128041a18b678820cd0222ef1573e8 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Example 1) - Sentence Similarity <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModel import torch.nn as nn tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/miCSE") model = AutoModel.from_pretrained("sap-ai-research/miCSE") | 0b8094fad91c29373f6f09f6c6e89f27 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Encoding of sentences in a list with a predefined maximum lengths of tokens (max_length) max_length = 32 sentences = [ "This is a sentence for testing miCSE.", "This is yet another test sentence for the mutual information Contrastive Sentence Embeddings model." ] batch = tokenizer.batch_encode_plus( sentences, return_tensors='pt', padding=True, max_length=max_length, truncation=True ) | 62c3cdbed5cf1a2ad3300994efdebc36 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Compute similarity between the **first** and the **second** sentence cos_sim = sim(embeddings.unsqueeze(1), embeddings.unsqueeze(0)) print(f"Distance: {cos_sim[0,1].detach().item()}") ``` </details> | d3c5796b74e39b414730a8b4c3d28375 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Example 2) - Clustering <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModel import torch.nn as nn import torch import numpy as np import tqdm from datasets import load_dataset import umap import umap.plot as umap_plot | 17224847cd5a72de6979dcea1d65ecf1 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Determine available hardware if torch.backends.mps.is_available(): device = torch.device("mps") elif torch.cuda.is_available(): device = torch.device("cuda") else: device = torch.device("cpu") | 753a2d6626599c2332ed84a36801b2c7 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | set batch size and maxium tweet token length batch_size = 50 max_length = 128 iterations = int(np.floor(len(dataset['train'])/batch_size))*batch_size embedding_stack = [] classes = [] for i in tqdm.notebook.tqdm(range(0,iterations,batch_size)): | 91ed6b12cd072d3e74b9b1ace66c0881 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | create batch batch = tokenizer.batch_encode_plus( dataset['train'][i:i+batch_size]['text'], return_tensors='pt', padding=True, max_length=max_length, truncation=True ).to(device) classes = classes + dataset['train'][i:i+batch_size]['label'] | 01f50cc4abf7f8458c9386a06beea191 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | model inference without gradient with torch.no_grad(): outputs = model(**batch, output_hidden_states=True, return_dict=True) embeddings = outputs.last_hidden_state[:,0] embedding_stack.append( embeddings.cpu().clone() ) embeddings = torch.vstack(embedding_stack) | 6c901ba53139c848e2750794bb52f14d |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Cluster embeddings in 2D with UMAP umap_model = umap.UMAP(n_neighbors=250, n_components=2, min_dist=1.0e-9, low_memory=True, angular_rp_forest=True, metric='cosine') umap_model.fit(embeddings) | df9f938f373c759779b1325026591fd0 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Plot result umap_plot.points(umap_model, labels = np.array(classes),theme='fire') ```  </details> | 2561957cb17c2471ad6957ba7f4b6ed1 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Example 3) - Using [SentenceTransformers](https://www.sbert.net/) <details> <summary> Click to expand </summary> ```python from sentence_transformers import SentenceTransformer, util from sentence_transformers import models import torch.nn as nn | cd836bb4ccc4e9773619a3f6b2cc4992 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Using the model with [CLS] embeddings model_name = 'sap-ai-research/miCSE' word_embedding_model = models.Transformer(model_name, max_seq_length=32) pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension()) model = SentenceTransformer(modules=[word_embedding_model, pooling_model]) | 6866ad14dfed4a4650aa0c8742832128 |
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