license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2896 - Bleu: 28.1031 - Gen Len: 34.082 | e17a7ccff315196b41cefb357a15282c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.744 | 1.0 | 38145 | 1.2896 | 28.1031 | 34.082 | | b7c1730e84de455891708bffbef41d69 |
other | ['legal', 'privacy', 'intent', 'privacy policies'] | false | privacy_intent for privacy policy intent classification This model is fine-tuned version of [mukund/privbert](https://huggingface.co/mukund/privbert) model on [PolicyIE dataset ](https://github.com/wasiahmad/PolicyIE/blob/main/data/sanitized_split.zip). - Reference Paper: [Intent Classification and Slot Filling for ... | 9b77431ceb8dd397c3ada28dcd416910 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2299 - Precision: 0.8327 - Recall: 0.8515 - F1: 0.8420 - Accur... | 39bfd33a38f1909db42d42fba8e3bf77 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.6617 | 0.16 | 100 | 0.3490 | 0.7259 | 0.7730 | 0.7487 | 0.8983 | | 0.341 | 0.32 |... | fc2f6e803b6c09c43293a44fd9e8be58 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | **Spider-Verse Diffusion** This is the fine-tuned Stable Diffusion model trained on movie stills from Sony's Into the Spider-Verse. Use the tokens **_spiderverse style_** in your prompts for the effect. **If you enjoy my work, please consider supporting me** [![Become A Patreon](https://badgen.net/badge/become/a%20p... | 9827e49b096990bdaeb350dd2a731e80 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/spider-verse-diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a magical princess with golden hair, spidervers... | c6b27dd883b6dc98cade196f53021260 |
apache-2.0 | ['vision', 'image-classification'] | false | Swin Transformer (large-sized model) Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first release... | 640c62090c43a439fc90808727b1b09c |
apache-2.0 | ['vision', 'image-classification'] | false | How to use 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, SwinForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" imag... | de96a314dd1f14fcebc8ba586023bf22 |
apache-2.0 | ['translation'] | false | opus-mt-en-gil * source languages: en * target languages: gil * OPUS readme: [en-gil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-gil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 48d2f42e40ce22d3d7224aae59d038c1 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | 830605c48038117dd71f5db24c5b7b0e |
mit | ['generated_from_trainer'] | false | spelling-correction-english-base-location This model is a fine-tuned version of [oliverguhr/spelling-correction-english-base](https://huggingface.co/oliverguhr/spelling-correction-english-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0450 - Cer: 0.0109 | fed2f9029f90c5c4041db365eb3d6d0a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0433 | 1.0 | 5875 | 0.0474 | 0.0104 | | 0.0348 | 2.0 | 11750 | 0.0450 | 0.0109 | | ea928c8cd86af44894ae16e8147577c4 |
apache-2.0 | ['generated_from_trainer'] | false | HateXplain-3rd-anno-labeled This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2662 - Accuracy: 0.5478 | 7225be9bfc8c9cb6915245bcf2fd7383 |
mit | ['generated_from_trainer'] | false | indobert-classification This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.3707 - Accuracy: 0.9397 - F1: 0.9393 | 3e2eecb81ab72174593b82c95d819443 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2458 | 1.0 | 688 | 0.2229 | 0.9325 | 0.9323 | | 0.1258 | 2.0 | 1376 | 0.2332 | 0.9373 | 0.9369 | | 0.059 |... | 987893bae04362ebd8d712525862f042 |
apache-2.0 | ['generated_from_trainer'] | false | glue_sst_classifier_2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2359 - F1: 0.9034 - Accuracy: 0.9014 | ce0086ae8048494a29686d7785968903 |
mit | ['generated_from_trainer'] | false | deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa-1epoch](https://huggingface.co/stevemobs/deberta-base-combined-squad1-aqa-1epoch) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7521 | f433481f6ac639b04d93750dfc5db610 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.6693 | 1.0 | 17307 | 0.7171 | | 0.4723 | 2.0 | 34614 | 0.7521 | | bb220bea4d0987043666c84f298399ee |
apache-2.0 | ['generated_from_trainer'] | false | bert-nlp-project-ft-imdb-ds-google This model is a fine-tuned version of [jestemleon/bert-nlp-project-imdb](https://huggingface.co/jestemleon/bert-nlp-project-imdb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3097 - Accuracy: 0.9124 - F1: 0.9197 | 2dc520b399b695abcc52bf5c45d07bea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3517 | 0.37 | 196 | 0.2556 | 0.9105 | 0.9187 | | 0.27 | 0.75 | 392 | 0.2369 | 0.9038 | 0.9105 | | 0.2246 |... | 341215b58c88d985957de28f75b1e6bf |
apache-2.0 | ['text2sql'] | false | tscholak/2jrayxos Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [t5.1.1.lm100k.large](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md | 818d15e8946a31faaea13bc6d57478fe |
apache-2.0 | ['text2sql'] | false | Training Data The model has been fine-tuned on the 2,164 training dialogues in the [CoSQL SQL-grounded dialogue state tracking dataset](https://yale-lily.github.io/cosql) and the 7,000 training examples in the [Spider text-to-SQL dataset](https://yale-lily.github.io/spider). The model solves both, CoSQL's zero-shot t... | 5b1d76e8302ffaa47809e644908d1d0b |
apache-2.0 | ['text2sql'] | false | lm-adapted-t511lm100k) and fine-tuned with the text-to-text generation objective. A question is always grounded in both, a database schema and the preceiding questions in the dialogue. The model is trained to predict the SQL query that would be used to answer the user's current natural language question. The input to ... | 409695e9883b35f4fd3f14ac41f745f1 |
apache-2.0 | ['text2sql'] | false | Performance Out of the box, this model achieves 52.5 % question match accuracy on the CoSQL development set. Using the PICARD constrained decoding method (see [the official PICARD implementation](https://github.com/ElementAI/picard)), the model's performance can be improved to **54.2 %** question match accuracy on t... | 7d13ba2e9e42077b1a4de5559070317a |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0483 - Accuracy: 0.9811 | 690a3b61da2fd15ae4a8b7a709667d0e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2623 | 1.0 | 379 | 0.1006 | 0.9674 | | 0.1712 | 2.0 | 758 | 0.0620 | 0.9804 | | 0.1206 | 3.0 | 1137 | 0.0483 | 0.... | 9a678bb6d34009ff46c975141673ada3 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_calendar-roberta-large-v1-5-93 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 con... | 34a001eb328efed68c7f1979fb2323f4 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-nfl This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the bittensor tiny.json dataset. It achieves the following results on the evaluation set: - Loss: 6.4602 - Accuracy: 0.1556 | 5b4efc3d8588c066872e9943e147c708 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-italian-robust This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Italian splits of the following datasets: - Mozilla Foundation Common Voice V7 dataset - [LibriSpeech multilingual](http://www.openslr.org/94) - [TED mult... | 681435480a8d9cceb5feece38932c29e |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_... | 3c3c5af58a2a9ec82db335a43923dbe9 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.06 | 400 | 0.7508 | 0.7354 | | 2.3127 | 0.11 | 800 | 0.5888 | 0.5882 | | 0.7256 | 0.17 | 1200 | 0.5121 | 0.524... | b4a5236715f727caf78727a857359e7f |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Uncanny Valley [Download](https://huggingface.co/deadman44/Miscellaneous_test_models/resolve/main/Uncanny%20Valley.safetensors) [<img src=https://i.imgur.com/UAISybf.png style="max-width: 128px;" width="25%" />](https://i.imgur.com/UAISybf)[<img src=https://i.imgur.com/B3qaYsQ.png style="max-width: 128px;" width="25... | 251c580a2377d8c343472feed233cad7 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | example [<img src=https://i.imgur.com/DNzIfYJ.png>](https://i.imgur.com/DNzIfYJ) ```jsx 1girl, nurse costume, blonde hair, blue eyes, grin, hospital, dynamic action, perfect lighting, looking viewer Negative prompt: (low quality, worst quality:1.6) Steps: 12, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 778402210, ... | b2fc2856a1c280ba569353adc847677c |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Too_old_PC-GameGirls [Download](https://huggingface.co/deadman44/Miscellaneous_test_models/resolve/main/Too_old_PC-GameGirls.safetensors) [<img src=https://i.imgur.com/4DUTMTI.png style="max-width: 128px;" width="25%" />](https://i.imgur.com/4DUTMTI)[<img src=https://i.imgur.com/33K3ptA.png style="max-width: 128px;"... | 05362d47e843df6849d4a1ae506b6c66 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | example <img src=https://i.imgur.com/9M04ect.png> ```jsx best quality, pc-98, 1girl, solo, blonde hair, blue eyes, armpits, long hair, sundress Negative prompt: (worst quality, low quality, blurry:2.0) Steps: 11, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 605166241, Size: 512x768, Model hash: e02f780c05, Clip ski... | 6ebacb81b852ab8f8cfb3b826c62fa8e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Kn - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3057 - Wer: 46.3937 | 2750538c237cff630d88e896c9686b62 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 150 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_preci... | 5185584dbe7a79950f9435e7da949f31 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4091 | 0.1 | 300 | 1.4915 | 101.5026 | | 1.1294 | 0.2 | 600 | 1.2845 | 94.7408 | | 0.5426 | 0.3 | 900 | 0.4621 | 64... | 4737dc95a94ec90037f81841c79d1a7a |
mit | [] | false | grifter on Stable Diffusion This is the `<grifter>` 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... | 09b11c01ea4155118236415147ca3aa1 |
mit | [] | false | trypophobia on Stable Diffusion This is the `trypophobia` 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... | b0f250c2c80175a66a5a2973755accd7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-coffee20230113 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.1553 | 5174343d483b0d9e8d30c61fba74e27d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 88 | 4.3261 | | 5.3354 | 2.0 | 176 | 2.8499 | | 3.0455 | 3.0 | 264 | 2.3299 | | 1.9373 | 4.0 | 352 | 2.5522 ... | 36e3f8c7da9e5e6eec26841c8036c0f0 |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-finetuned-TRAC-DS-new This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2229 - Accuracy: 0.6724 - Precision: 0.6503 - Recall: 0.6556 - F1: 0.6513 | e87f6d1cd66f4fc8bbd800427d9112da |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 1de1eb94f6832110ffcafcaec799f999 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0895 | 0.25 | 612 | 1.0893 | 0.4453 | 0.3220 | 0.4654 | 0.3554 | | 1.0788 | 0.5 ... | 481ddf8c7fcd791dbbb8e3f54d0ebc8e |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4782 - Rouge1: 28.2621 - Rouge2: 7.6583 - Rougel: 22.1971 - Rougelsum: 22.2 - Gen Len: 18.8243 | ed361888ac03fcdb8f0f6fbdf97668e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7138 | 1.0 | 12753 | 2.4782 | 28.2621 | 7.6583 | 22.1971 | 22.2 | 18... | 6851f20eed00d2831bf92b8163f30109 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-32-3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5694 - Accuracy: 0.7073 | 532a7a5d160175b65a8968c2688a9989 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7118 | 1.0 | 13 | 0.6844 | 0.5385 | | 0.6587 | 2.0 | 26 | 0.6707 | 0.6154 | | 0.6067 | 3.0 | 39 | 0.6295 | 0.... | 6781c04ec75a9ea864e8a5f8410e8568 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_age_teens-5_sixties-5_s12 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using thi... | 8b0a04cb4aed40d906c9ec9eb2a8f8eb |
mit | ['generated_from_keras_callback'] | false | FineTune_Vit5_LR0_00001_time4 This model is a fine-tuned version of [thesunshine36/FineTune_Vit5_LR0_00001_time2](https://huggingface.co/thesunshine36/FineTune_Vit5_LR0_00001_time2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5291 - Validation Loss: 0.5800 - Train R... | 13562a253d50919d7eeffa16c69d2319 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.5291 | 0.5800 | 52.3493 | 30.7526 ... | 24f6c096d2103bee90565dc78d600b4e |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_all_16_02_2022-15_46_07 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1750 - Precision: 0.3286... | e5653f0e15e277dff9c68af566c70510 |
creativeml-openrail-m | ['audio', 'text-to-speech'] | false | This is a simple model created using [xVATrainer](https://github.com/DanRuta/xva-trainer) for use with [xVASynth](https://github.com/DanRuta/xVA-Synth) (this can be used with anything that supports this model but the output is designed to work with xVASynth) This model is designed to replicate the voice of the Engine... | 310779071f574a426750bfdb04d16a42 |
creativeml-openrail-m | ['audio', 'text-to-speech'] | false | xVASynth Go to where xVASynth is setup, if it was installed using [the Steam storefront](https://store.steampowered.com/app/1765720/xVASynth_v2/), this will be within the steamapps folder. Put the files from the tf2_engineer folder into > [xVASynth folder]\resources\app\models\teamfortress2 If the teamfortress2 fol... | 5f7067313ce3d8d51e5d22f473550a0c |
creativeml-openrail-m | ['audio', 'text-to-speech'] | false | Creation process I created a basic script to convert every file to the correct format and run every line through the [OpenAI Wisper model](https://github.com/openai/whisper) and output the lines in a format for xVATrainer to use, manually adjusting incorrect lines (xVATrainer has it's own model for this but I found it... | c4b12ebd549464317b16b47b8a0cdf71 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'wits'] | false | IT5 Base for Wikipedia Summarization 📑 🇮🇹 This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on Wikipedia summarization on the [WITS](https://www.semanticscholar.org/paper/WITS%3A-Wikipedia-for-Italian-Text-Summarization-Casola-Lavelli/ad6c83122e721c... | 0b71d0a8d1a432bb9e4ba02e9cef7d87 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'wits'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines hg = pipeline("text2text-generation", model='it5/it5-base-wiki-summarization') hg("Le dimensioni dell'isola sono di 8 km di lunghezza e ... | e936fc6286bf8da9525e9c29b2edcd86 |
MIT | ['keytotext', 'k2t', 'Keywords to Sentences'] | false | keytotext [](https://pypi.org/project/keytotext/) [](https://pepy.tech/project/ke... | f2f6c94115de83fe85f7069b59a39b0d |
MIT | ['keytotext', 'k2t', 'Keywords to Sentences'] | false | api) [](https://hub.docker.com/r/gagan30/keytotext) [](https://huggingface.co/models?filter=keytotext) [ notebook. You can also t... | c5660e885674f98fe5e2a6478f6dd028 |
mit | ['generated_from_trainer'] | false | roberta-large-finetuned-clinc-3141 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1533 - Accuracy: 0.9739 | f729d69d293c32fc363cbabe56d5a61d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6064 | 1.0 | 954 | 0.3041 | 0.9368 | | 0.1392 | 2.0 | 1908 | 0.1590 | 0.9723 | | 0.044 | 3.0 | 2862 | 0.1533 | 0.... | 566db2ad5b56d0d80b8b38cbd6fbc4a8 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------------------------|---------------------------... | 42bcb22c818c52e58470003ae98cb00d |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.co... | f94a19e7d4819c573a0b063ef98d7c9d |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;, max_iter=300))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wra... | c121ac314f7292413ba10b1d6dfcdf1a |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;, max_iter=300))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-tog... | 55a379c618cd7029312b98f1f1b04359 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-tog... | c5a8d436240cd024aaaf9e013285013b |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label sk-t... | a4b481f6d7fa521590779970e9536e69 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggle... | 05542adf78616992f25ea4ee9dfaa0f1 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label... | 09fc1c7782c8162051e7b012656628cb |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-misogyny-sexism2 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: 1.5455 - Accuracy: 0.7628 - F1: 0.7606 - Precision: 0.7384 - Recall: 0.7842 - Mae: 0.2372 ... | 18ca5e327d0e6b778b6cf55921d4b49f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.1887 | 1.0 | 2656 | 1.2287 | 0.7790 | 0.7863 | 0.7342 | 0.8463 | 0.2210 ... | 794225cd7c61dd294582ee3a9c388104 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_tiny_tf_512.in1k An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. | 728319fc4636a43698b5d5576e18bd10 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 31.0 - GMACs: 33.5 - Activations (M): 257.6 - Image size: 512 x 512 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 3b64deb3358c3ecedb6b22a1242f9263 |
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('maxvit_tiny_tf_512.in1k', pretrained=True) model = mod... | bde3841736f18e2d5672243b0386829b |
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( 'maxvit_tiny_tf_512.in1k', pretrained=True, ... | 84c40aba62442a861550898dfbf1ace7 |
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( 'maxvit_tiny_tf_512.in1k', pretrained=True, nu... | 6e3e1e01e340c74d9b7ca5981adec73f |
apache-2.0 | ['generated_from_keras_callback'] | false | giusepperusso/distilbert-base-uncased-finetuned-The_Donald 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: 2.7889 - Validation Loss: 2.5521 - Epoch: 0 | 2103d3c5b431aee3a272d85fa51dc2e6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 6f59a41b5b725343b254ce43920f6e1c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-urdu-commonvoice-google-colab This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.4217 - eval_wer: 0.7946 - eval_runtime: 53.8128 - eval_samples_per_se... | d358216b24916f7dc263e4b3f87af209 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_tr... | 10ce9f13167127c87946800863a558b3 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-sst2 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9876 | 63902792be7429c9b61e85d3b367a869 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5439 | 0.4 | 500 | 2.9993 | | 3.4175 | 0.8 | 1000 | 2.8910 | | 3.2455 | 1.2 | 1500 | 2.9637 | | 3.247 | 1.6 | 2000 | 2.9003 ... | 1ba80aa62c62630cfdeab1f957bb1741 |
cc-by-4.0 | ['norwegian', 'roberta'] | false | This is just a Test Model. Do NOT use for anything! Continued pretrained from the nb-roberta-base. The domain specific pretraining is done on the 102GB (Scandinavian corpus)[https://huggingface.co/datasets/NbAiLab/scandinavian]. | 31c5fa56dc5ad108dc293f44110a4776 |
cc-by-4.0 | ['norwegian', 'roberta'] | false | Train for 180k steps for 128 sequences: ```bash ./run_mlm_flax_stream.py \ --output_dir="./" \ --model_type="roberta" \ --config_name="./" \ --tokenizer_name="./" \ --model_name_or_path="./" \ --dataset_name="NbAiLab/scandinavian" \ --max_seq_length="128" \ --weight_decay="0.01" \ -... | cb1eddec703451ea07280d6b4d6ac1fb |
cc-by-4.0 | ['norwegian', 'roberta'] | false | Train for 20k steps for 512 sequences: ```bash ./run_mlm_flax_stream.py \ --output_dir="./" \ --model_type="roberta" \ --config_name="./" \ --tokenizer_name="./" \ --model_name_or_path="./" \ --dataset_name="NbAiLab/scandinavian" \ --max_seq_length="512" \ --weight_decay="0.01" \ --... | 4e5c0b9e0b7ed96590357304e89d3e8c |
cc-by-sa-4.0 | [] | false | BERATBOS v0.0.1-prealpha > BERT-based Automated Text Classification Based on POS A model I made for my final year assignment, used a random script to train this on GPU, as a result, the usage is kinda non-standard, as far as transformers-based models go, at least. Model is for detecting AI-generated works via cohere... | 3cec616e505405ea6d09a54ea340fb25 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-finetuned-eurosat This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.3045 - Accuracy: 0.8586 | 9ba7c928e8c16f9c1d2addd3b493f84c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.5181 | 0.7071 | | 0.6727 | 2.0 | 14 | 0.4030 | 0.8182 | | 0.3522 | 3.0 | 21 | 0.3045 | 0.... | 44df1aef7a72b0632f1c9ef92afed301 |
apache-2.0 | ['automatic-speech-recognition', 'timit_asr', 'generated_from_trainer'] | false | sew-d-small-100k-ft-timit This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set: - Loss: 1.7482 - Wer: 0.7987 | 2d9610a27d26d2a8cfef794a19d92c31 |
apache-2.0 | ['automatic-speech-recognition', 'timit_asr', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2068 | 0.69 | 100 | 4.0802 | 1.0 | | 2.9805 | 1.38 | 200 | 2.9792 | 1.0 | | 2.9781 | 2.07 | 300 | 2.9408 | 1.0 | |... | d1c6e01ebac4564b9bf173d2acdd5bd2 |
mit | [] | false | LPHR Style on Stable Diffusion This is the `<lphr-style>` 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... | 2bf1aaa3b287d6e1aae5528f284f7f97 |
apache-2.0 | ['vision', 'image-classification'] | false | ConvNeXT (xlarge-sized model) ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).... | b20af885702d8ba03c8bf72da208c70a |
apache-2.0 | ['vision', 'image-classification'] | false | How to use 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 ConvNextFeatureExtractor, ConvNextForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") i... | 3ab569e435d9c62fb0d62d261599ed5c |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mbart-large-cc25-esquad-qag` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) (dataset_name: default) via [`lmqg`](ht... | b001ba41486a9acd837a84da0ee79db3 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** es - **Training data:** [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https:/... | 1f91326b2f314dc71ce7defe1db4d5f7 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-esquad-qag") output = pipe("del Ministerio de Desarrollo Urbano , Gobi... | a12db1084f1a87ca1579af2db94d2f07 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_esquad.default.json) | | Score | Type | Dataset ... | 6bb4f0eef5f723a26c54fc183b8a0276 |
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