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 | ['translation'] | false | opus-mt-en-ceb * source languages: en * target languages: ceb * OPUS readme: [en-ceb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ceb/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | acf1b66e0b643366b2457a786a9bbde7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-swedisch-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.6439 - Wer: 0.9678 | a9f771bc714f6fa57569bae7dbb356b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8953 | 1.83 | 400 | 1.6439 | 0.9678 | | 765c44a1efe48131060d4e56259cc576 |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-finetuned-parsed-longer50 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-parsed20](https://huggingface.co/muhtasham/bert-small-finetuned-parsed20) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9278 | 6376d1b57636e6476759cfccc90181c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 153f4cab131a0126322d4f2ff6df6ede |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 2.9807 | | No log | 2.0 | 8 | 2.7267 | | No log | 3.0 | 12 | 3.3484 | | No log | 4.0 | 16 | 2.7573 ... | 609a25d84614be7b51d5f62083184bf9 |
apache-2.0 | ['generated_from_trainer'] | false | door_inner_with_SA-bert-base-uncased 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: 2.1513 | 89d15bbdc6571ea0429e74ee28319ad1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 | 962a78ee5ab1374cbf3f81ecbef4870b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5492 | 1.0 | 96 | 2.3831 | | 2.4031 | 2.0 | 192 | 2.2963 | | 2.3391 | 3.0 | 288 | 2.2000 | | 2.2951 | 4.0 | 384 | 2.2505 ... | b495c309694cd89e10610379d5b1e5c7 |
mit | [] | false | Arona [Arona / アロナ / 아로나 / 阿罗娜](https://huggingface.co/khanon/lora-training/blob/main/arona/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/arona/README.md) | 35c860ae83b74bde5e1584112d1f4065 |
mit | [] | false | Koharu [Shimoe Koharu / 下江コハル / 시모에 코하루 / 下江小春](https://huggingface.co/khanon/lora-training/blob/main/koharu/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/koharu/README.md) | 16afbaff98349606ab0bfae4fc590113 |
mit | [] | false | Kokona [Sunohara Kokona / 春原ココナ / 스노하라 코코나 / 春原心奈](https://huggingface.co/khanon/lora-training/blob/main/kokona/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/kokona/README.md) | fe0d593954cf1e163d7eb1beaba6472c |
mit | [] | false | Mari [Iochi Mari / 伊落マリー / 이오치 마리 / 伊落玛丽](https://huggingface.co/khanon/lora-training/blob/main/mari/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/mari/README.md) | 4af74896f387d57cb8a7cb5b48dfb1da |
mit | [] | false | Reisa [Uzawa Reisa / 宇沢レイサ / 우자와 레이사](https://huggingface.co/khanon/lora-training/blob/main/reisa/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/reisa/README.md) | 2ec0e2bddcb0219942f645cf47d0f0db |
mit | [] | false | Seia [Yurizono Seia / 百合園セイア / 유리조노 세이아 / 百合園圣娅](https://huggingface.co/khanon/lora-training/blob/main/seia/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/seia/README.md) | d569d12cdfa62f16b97d49a45a5c6c4e |
mit | [] | false | Shizuko [Kawawa Shizuko / 河和シズコ / 카와와 시즈코 / 河和静子](https://huggingface.co/khanon/lora-training/blob/main/shizuko/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/shizuko/README.md) | 9b56a75333b218afe4aec978c8161c46 |
mit | [] | false | Sora [Sora / ソラ / 소라 / 空](https://huggingface.co/khanon/lora-training/blob/main/sora/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/sora/README.md) | 5dabca8ab9fa6a39fda9415a8b95208c |
mit | [] | false | Negative embedding I frequently use these negative embeddings in my prompts to improve the output quality. I recommend lowering the attention to ~0.75. - `bad-artist`, `bad-artist-anime` - https://huggingface.co/nick-x-hacker/bad-artist - `badpromptv2` - https://huggingface.co/datasets/Nerfgun3/bad_prompt - `bad-i... | 6336ca5d7599de1a9e7db28a37cf4038 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.05-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2776 - Rmse: 0.5269 - Mse: 0.2776 - Mae: 0.4290 | 903d0fce8c7e752d5e6bc0e90c55d6cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2768 | 1.0 | 1245 | 0.2737 | 0.5232 | 0.2737 | 0.4163 | | 0.274 | 2.0 | 2490 | 0.2779 | 0.5271 | 0.2779 ... | d9cc21c53c18f2ebf0e21070b02a31aa |
mit | ['generated_from_trainer'] | false | mdeberta-hate-final This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6223 - Accuracy: 0.7424 - Precision: 0.7410 - Recall: 0.7424 - F1: 0.7363 | d9ba71cf54373c8cd3d54bf9eb2a8ec1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5309 | 0.7519 | 0.7685 | 0.7519 | 0.7357 | | 0.5358 | 2.0 |... | e7148e3f2962a6d0e6b8960109c1cc7d |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-mri 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: 0.0608 - Accuracy: 0.9807 | 2fd10b0899558553df39e340d5d009fe |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 952ed6da5b7afcca0105a5b19d36f272 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0592 | 1.0 | 447 | 0.0823 | 0.9695 | | 0.0196 | 2.0 | 894 | 0.0761 | 0.9739 | | 0.0058 | 3.0 | 1341 | 0.0608 | 0.... | f75b707e762284fd8a56419e7f9fcdbb |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/distilbert-base-uncased-padded_left_allsides_news_e20 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: 1.9438 - Validation Loss: 1.8632 - Epoch: 19 | cc540a1ac9d4ec6b50473197e803e4f4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9974 | 1.9203 | 0 | | 1.9860 | 1.9246 | 1 | | 1.9588 | 1.8601 | 2 | | 1.9598 | 1.8668 | 3 | | 1.9330 | 1.8913 | 4 | | 1.9250 |... | cf23600435622d8b7a6d56b75df9f4bf |
apache-2.0 | ['translation'] | false | spa-glg * source group: Spanish * target group: Galician * OPUS readme: [spa-glg](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-glg/README.md) * model: transformer-align * source language(s): spa * target language(s): glg * model: transformer-align * pre-processing: normalization + Sen... | d65942b911993f019f110738bcf611ab |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-glg - source_languages: spa - target_languages: glg - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-glg/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'gl'] - src_constituents: {'spa'} - tgt_const... | 82656367263fea473e2bb80c0a24e91c |
apache-2.0 | ['generated_from_trainer'] | false | barthez-deft-chimie This model is a fine-tuned version of [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) on an unknown dataset. **Note**: this model is one of the preliminary experiments and it underperforms the models published in the paper (using [MBartHez](https://huggingface.co/moussaKam/mbarthez)... | 5a70c30594e5524f98f8897fc0e02457 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.8022 | 1.0 | 118 | 2.5491 | 16.8208 | 7.0027 | 13.957 | 14.0479 | 19... | 067b102e3d0db39aacabe78c9a76aaf8 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_nosp_0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3829 - Train Accuracy: 0.0216 - Validation Loss: 0.8190 - Validation Accuracy: 0.0202 - Epoch: 14 | 94304584384d71e414c6988298b9d56b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 7.5559 | 0.0010 | 6.3853 | 0.0013 | 0 | | 6.3227 | 0.0021 | 5.7023 | 0.0038 ... | 7c7d974c5098c452372971ad5c2c7f48 |
apache-2.0 | ['generated_from_trainer'] | false | base-mlm-tweet-target-tweet This model is a fine-tuned version of [muhtasham/base-mlm-tweet](https://huggingface.co/muhtasham/base-mlm-tweet) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.9081 - Accuracy: 0.7674 - F1: 0.7679 | ab6e8bfd1c339bc11987a9025af974da |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3371 | 4.9 | 500 | 1.0062 | 0.7888 | 0.7891 | | 0.038 | 9.8 | 1000 | 1.4896 | 0.7754 | 0.7802 | | 0.0165 |... | ac127e8522d89d125d1757d9965d1de4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2421 - F1: 0.8248 | 0565cfe395465416f90a6e8c1063a3e6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.809 | 1.0 | 70 | 0.3380 | 0.7183 | | 0.2939 | 2.0 | 140 | 0.2582 | 0.7977 | | 0.1813 | 3.0 | 210 | 0.2421 | 0.8248 | ... | 67a7cfcf9aa959ec39ba68fe29e6e61a |
apache-2.0 | ['translation'] | false | opus-mt-fr-st * source languages: fr * target languages: st * OPUS readme: [fr-st](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-st/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | ddc7023a1996ad003a35b0a44ced717e |
apache-2.0 | ['generated_from_trainer'] | false | test-mlm This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2729 - Accuracy: 0.7100 | e19d590775792a9145e8b7d4c36be707 |
unknown | [] | false | A Big thank you to everyone in the community who did the work on Training the custom Models I used in this. My Mixes can be found a Civitai as well https://civitai.com/models/1348/ultimatemix https://civitai.com/models/1544/cartoon-mix I lost my Original Reciepe for Ultimate Mix 1&2, So I wont be able to upload t... | ca3459cc4a233fa1010891dd635cb92f |
cc-by-sa-4.0 | [] | false | How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese-wikipedia') >>... | 920950a7977af3738175029703bf51eb |
cc-by-sa-4.0 | [] | false | Preprocessing The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining. The model was trained on 8 NVIDIA A100 GPUs. | ede86d1d51e160c0ee2cdb9de6253b1d |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-travel-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1384 - Accuracy: 0.4289 | a4f89aa0780a5471b777d20183a7e14b |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-12 - train_batch_size: 256 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_... | be883ed36829284c3f9687e9dd3fa314 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-5_nortepeninsular-5_s463 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this ... | 5ebe7d1d527e1fee9cb24e81cf5fa486 |
creativeml-openrail-m | ['text-to-image'] | false | CoalForest Dreambooth model trained by DrEsker with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebo... | f46631017df1357a2cfb6e0b1c35aef6 |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/repvgg_a1").eval() img = Ima... | 2214a2d253364146ac912d12f714b703 |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-small-finetuned-xlsum-india-test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9172 - Validation Loss: 2.5929 - Epoch: 0 | 57f1a21f96365ce4b7f1f0ae82b15a76 |
mit | ['generated_from_trainer'] | false | MiniLM-L12-H384-uncased-finetuned-imdb This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 3.9328 | 4cc6670f8c65cb4059ea49d48329f634 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.2464 | 1.0 | 391 | 4.2951 | | 4.2302 | 2.0 | 782 | 4.0023 | | 4.0726 | 3.0 | 1173 | 3.9328 | | 81cef8b5156b1b25c8308188c81281b3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | anything-berry-30.ckpt [Re-uploaded from](https://huggingface.co/misobarisic/anything-berrymix) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.30 | Anything V3 | ... | 0e0078aaec4f800ef00ab2dfacd9f978 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | anything-f222-15.ckpt [Recipe Source](https://www.reddit.com/r/WaifuDiffusion/comments/zdbs3r/comment/iz0nr48/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- ... | 1f6382543613b133187a851c30f6164e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | anything-f222-15-elysiumv2-10.ckpt [Recipe Source](https://www.reddit.com/r/WaifuDiffusion/comments/zg1d8x/comment/izei93c/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- ... | ebdfd478d23ae761758fe821adeb3640 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | berrymix-v3-535d98a3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.05 | AnythingV3.0 | Stable Diffusion 1.5 | n/a | Anything Fix 2 | Add Difference @ 1 | Anyt... | ded03a249a8aa607f4efd77a7b9f3e19 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | blossom-extract.safetensors [Recipe Source](https://www.reddit.com/r/StableDiffusion/comments/zk8y50/comment/izyhn8w/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- |... | d2b0209091992561c29586b226eee91e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | hentai-elysium-50.safetensors [Recipe Source](https://www.reddit.com/r/WaifuDiffusion/comments/zn6wdb/comment/j0fabe6/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- ... | fcf0977200dc2ead25fff1a2df58c58c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | nutmegmix-aa3e502b) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.05 | NovelAI | Stable Diffusion 1.5 | n/a | nutmegmix-part1 2 | Weighted Sum @ 0.05 | nutmegm... | 1b152555351385bd228a70b4559a2615 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | raspberry-mix-4d202242) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.25 | AnythingV3.0 | Stable Diffusion 1.5 | n/a | AnyV3-SD1.5 2 | Add Difference @ 1 | Any... | c4990ba3f33115ec2c83fbaba047864b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | strawberry-mix-e043dfc5) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.25 | AnythingV3.0 | Stable Diffusion 1.4 | n/a | AnyV3-SD1.4 2 | Add Difference @ 1 | An... | 99ddcd5da058548c7115f6c1dcebddda |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2-shake-wiki 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: 0.0096 - Accuracy: 0.9994 | ee6a2ffee71d34346ae626520d08b032 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.001 | 1.0 | 5029 | 0.0120 | 0.9988 | | 0.0017 | 2.0 | 10058 | 0.0028 | 0.9996 | | 0.0 | 3.0 | 15087 | 0.0094 ... | b916eb12be1dd768ad1cd0aaba0ca5e0 |
apache-2.0 | ['t5', 'contrastive learning', 'ranking', 'decoding', 'metric learning', 'pytorch', 'text generation', 'retrieval'] | false | Method-2: Loading the model with HuggingFace APIs ``` from transformers import T5Tokenizer, AutoModel tokenizer = T5Tokenizer.from_pretrained(f"google/t5-v1_1-base") model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True) ``` | 3b2ef37c693c8101e798f0f6ed8fc525 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | distilroberta-base-mrpc-glue-tadeous This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.6243 - Accuracy: 0.8211 - F1: 0.8726 | be4206d88c0d7af2cb9adb23d31e9159 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3219 | 1.09 | 500 | 0.6243 | 0.8211 | 0.8726 | | 0.3173 | 2.18 | 1000 | 0.6243 | 0.8211 | 0.8726 | | 2630041ebcb31001fa2f0df711ad9d4c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5644 - Pearson: 0.8666 - Spearmanr: 0.8636 | 924da22ad9fc19fa5e4fd60808682f98 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6366 | 0.8537 | 0.8516 | | 1.0464 | 2.0 | 720 | 0.6171 | 0.8632 | 0.8626 | | 0.4002 ... | fd11e31b6c165259de5d4fe1b33ec7a1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | supastazzz Dreambooth model trained by supastazz 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-... | 8d4e4a3595c4861aea81e5f912c7f546 |
mit | [] | false | hebrew-gpt_neo-tiny Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. | 1ebdbd00147218d34ba3f1bafafd9324 |
mit | [] | false | 4INvMes-56m_WUi7jQMbJQ) 2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he) The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classific... | 4ec314cc5393cb741433c0bc8fabe95d |
mit | [] | false | Simple usage sample code ```python !pip install tokenizers==0.10.2 transformers==4.6.0 from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-tiny") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-tiny", pad_token_id=tok... | 7e92afce87a62f5482514a8e33666c32 |
apache-2.0 | ['irish'] | false | BERTreach ([beirtreach](https://www.teanglann.ie/en/fgb/beirtreach) means 'oyster bed') **Model size:** 84M **Training data:** * [PARSEME 1.2](https://gitlab.com/parseme/parseme_corpus_ga/-/blob/master/README.md) * Newscrawl 300k portion of the [Leipzig Corpora](https://wortschatz.uni-leipzig.de/en/download/irish... | 9c2ac4cc92c4121e659c20c0dd7647e8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_pretrain_sst2 This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.7266 - Accuracy: 0.... | ebfec7f46e574456467a482bf68e1553 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9277 | 1.0 | 264 | 0.7266 | 0.8085 | | 0.4581 | 2.0 | 528 | 0.9527 | 0.7844 | | 0.3282 | 3.0 | 792 | 0.8676 | 0.... | 4abd0bc347edbaa246aa33f300359894 |
apache-2.0 | [] | false | Question Answering model for Hindi and Tamil This model is part of the ensemble that ranked 4/943 in the [Hindi and Tamil Question Answering](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition held by Google Research India at Kaggle. ``` from transformers import AutoTokenizer, AutoModelFor... | 292abb5a29157ec10958821a722cb4a7 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_250v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3389 - Precision: 0.5352 - Recall: 0.4795 - F1: 0.5058 - Accura... | e2854803a0e62bda2f7e5b9b0221c0f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 95 | 0.4305 | 0.3497 | 0.1814 | 0.2389 | 0.8488 | | No log | 2.0 |... | adfc76c232b572ce1f943488cff1dd80 |
mit | ['generated_from_keras_callback'] | false | javilonso/classificationPolEsp1 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3728 - Validation Loss: 0.62... | 4ac2b83a8f42677b9fbe3305f23ef2ec |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6282 | 0.6017 | 0 | | 0.5129 | 0.6177 | 1 | | 0.3728 | 0.6217 | 2 | | 9e469785c089f692c932e7d4a81f42fc |
mit | ['russian', 'fill-mask', 'pretraining', 'embeddings', 'masked-lm', 'tiny', 'feature-extraction', 'sentence-similarity'] | false | This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. This [post in Russian](https://habr.com/ru/post/669674/) gives more details. The differences from the previous version include: - a larger... | b61cee9c523542c3cbcfa0fd318f5e6e |
mit | ['russian', 'fill-mask', 'pretraining', 'embeddings', 'masked-lm', 'tiny', 'feature-extraction', 'sentence-similarity'] | false | pip install transformers sentencepiece import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2") model = AutoModel.from_pretrained("cointegrated/rubert-tiny2") | 92ea2953163607ddd161d012d61476e8 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_hubert_s507 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 5666fe1a5ca5745f35b19727e5763663 |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et a... | 9e127671c90f64a17b82f83fcd0947e9 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0661 - Precision: 0.9318 - Recall: 0.9495 - F1: 0.9406 - Accuracy: 0.9854 | 3c1fd26f3b7f84ba938cdf5ddcbc7d0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0885 | 1.0 | 1756 | 0.0664 | 0.9189 | 0.9327 | 0.9257 | 0.9820 | | 0.0346 | 2.0 |... | 5b3b3cf3012681d33df9063a393de059 |
apache-2.0 | ['translation'] | false | opus-mt-fr-fj * source languages: fr * target languages: fj * OPUS readme: [fr-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 57d932a49c5b0b1d5239f6a8abcb065b |
mit | [] | false | transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - [MIYAGINO](https://www.ntj.jac.go.jp/assets/images/member/pertopics/image/per100510_3.jpg) This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences. The input text is tokeni... | ced3f526036ec0b0cb59b5c5be96c9f1 |
mit | [] | false | How to use ```python from transformers import ElectraModel from sudachitra import ElectraSudachipyTokenizer model = ElectraModel.from_pretrained("megagonlabs/transformers-ud-japanese-electra-base-discriminator") tokenizer = ElectraSudachipyTokenizer.from_pretrained("megagonlabs/transformers-ud-japanese-electra-base-d... | 51b489c1111c8dfd02de1aa25f17d31b |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-rte This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 1.5187 - Accuracy: 0.6643 | b64e8096c6df898bda334f1927a42f13 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6969 | 1.0 | 623 | 0.7039 | 0.5343 | | 0.5903 | 2.0 | 1246 | 0.6461 | 0.7184 | | 0.4557 | 3.0 | 1869 | 1.5187 | 0.... | 2c877a5a0d841954078022e21d1b854e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8747 | 1.0 | 1063 | 3.7718 | | 3.7769 | 2.0 | 2126 | 3.7559 | | 3.7321 | 3.0 | 3189 | 3.7535 | | 4c509b8c505ab8dffe13182a3e5076f9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9274 - Recall: 0.9372 - F1: 0.9323 - Accuracy: 0.9840 | 55ee578a9053ecf498fb2b1f69de5aaa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2378 | 1.0 | 878 | 0.0719 | 0.9107 | 0.9200 | 0.9154 | 0.9801 | | 0.0509 | 2.0 |... | e3e49a8079d39ccaa59e2e069d074bcb |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squadshifts-vanilla-nyt-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](htt... | b2e4aca995e9be9cdcc9f755c30be233 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.... | a1066a9b2a414e4bb4fc8edb41415de8 |
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/bart-large-squads... | cb2e6b2c406e45fb3b22f3d409311b07 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset ... | 3a0b5060553849930be7b9612bcc390e |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 5 ... | d39e65f890165de1eef1498cc30daf81 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-uk-fi Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mo... | a8da2249dc2a36e9ebd64565e3802c6b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-17 * source language(s): ukr * target language(s): fin * model: transformer-align * data: opusTCv20210807+pft+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pft+pbt_transformer-align_2022-... | 8fe00fc8b1960ed1cd3a75e4d6286974 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Африка є колискою людства.", "Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять." ] model_name = "pytorch-models/opus-mt-tc-base-uk-fi" tokenizer = MarianTokenizer.from_pretrained(mod... | 300bd7447c847c336da3dcee2b44989c |
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