license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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creativeml-openrail-m | ['Asian', 'China', 'Character', 'stable-diffusion', 'aiart'] | false | <center><img src="https://huggingface.co/DGSpitzer/Guan-Yu-Diffusion/resolve/main/img/0.jpg" width="768" height="768"/></center>  | 93a943012a3061186b6cd1485a44f0de |
creativeml-openrail-m | ['Asian', 'China', 'Character', 'stable-diffusion', 'aiart'] | false | Guan Yu Diffusion There is few model that focus on elements of Asian culture and heritage, so I manage to make my own one! Hope you like it! XD Guan Yu Diffusion is an AI model that generates traditional oriental hero Guan Yu, from Three Kindoms Era in China during 220 to 280 AD! As an ancient hero "Saint of War" ... | 39d8baa4eae0677443d49a030104da49 |
creativeml-openrail-m | ['Asian', 'China', 'Character', 'stable-diffusion', 'aiart'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "DGSpitzer/Guan-Yu-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "Portrait of guanyu walking in ancient battlefield, cl... | 8e034f343253c2822ff6ef0ca6c21935 |
creativeml-openrail-m | ['Asian', 'China', 'Character', 'stable-diffusion', 'aiart'] | false | Online Demo You can try the Online Web UI demo build with [Gradio](https://github.com/gradio-app/gradio), or use Colab Notebook at here: *My Online Space Demo* [, CFG Scale 7, steps 20 should be fine **Example 1:** ``` Portrait of guanyu walking in ancient battlefield, close up shot ``` You can merge the Guan Yu's outfit with other f... | 862d3f81eeeb406d013533d953263bc1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors'] | false | Had these for a while, made just as an experiment. I'm only uploading them as an online backup, I don't claim anything except that I'm surprised they output anything coherent at all. Recommend using High res fix at higher resolutions, so the second pass fixes and adds extra detail. | be2e7f1194a858dd5053e30829ff3d1e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors'] | false | ABSOLUTE MADMAN mix A mix of too many models, some which were already mixes on their own. Most were merged on a 0.3 to 0.4 ratio, it was just a dumb experiment tbh, no real science at all. The only notable quirk is that it does traditional media weirdly well. Models merged include samdoesbimbosmix + anything + eim... | bec749fbf855d32cc553119e8dd9e2de |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors'] | false | FIXER mix Only called that because it worked well at fixing my own art, it's not only meant for fixing, that's just a dumb name. Regular outputs are ok enough. It has about half the models used + HD + EB  | 5bc8b695a538f4b00defc3985a463f1c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors'] | false | wheatpopman mix A mix of istolemyownwheat, popn and an experimental model trained on bleedman (lol). It's alright for thick lineart.  ... | c3f480bc2a4e4ce5b354152b715202ed |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4032224/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | c0446f87665cdd0e70bc93c5d2076935 |
apache-2.0 | ['generated_from_trainer'] | false | gpt2-small-spanish-finetuned-tweetsv2clean This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.6769 | 487d760e27586a00ce398c0217ea432b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 5.8453 | | No log | 2.0 | 18 | 5.7150 | | No log | 3.0 | 27 | 5.6769 | | a3822300219e0c80c38bf0ea03c5ff7b |
mit | ['roberta-base', 'roberta-base-epoch_72'] | false | RoBERTa, Intermediate Checkpoint - Epoch 72 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 83e56466d87678002f1204741c89ca34 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Ur - Bakht Ullah This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 7.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8930 - Wer: 47.3409 | dca956afb451d0548340b9e6fb0cdc17 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - 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: 100 - training_steps: 300 - mixed_precisio... | 0173d6ed83d66c167c58ba61317e3598 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6104 | 4.17 | 100 | 1.1037 | 163.3827 | | 0.0242 | 8.33 | 200 | 0.8656 | 47.6024 | | 0.0042 | 12.5 | 300 | 0.8930 | 47... | da362e1504d1b6e6a90e6521b1b43b8e |
mit | [] | false | 학습 환경 및 하이퍼파라미터 - TPU V2-8 - Learning Rate: 5e-4, Batch Size: 512(=64 accum x 8 devices), Scheduler: Linear, WarmUp: 1000 step - Optimizer: AdamW(adam_beta1=0.9 adam_beta2=0.98, weight_decay=0.01) - Training Steps: 43247 (3 epoch) - 학습 토큰 수: 21.11B (43247 * 512 * 1024seq / 1024^3) - 학습 기간: 2023/1/25 ~ 2023/1/29 - 학습 코... | 0a52351f709f93606b0e0f4ce788b4a3 |
mit | [] | false | 학습에 사용한 데이터 - AIHub SNS 대화(730MB) - AIHub 구어체(422MB) - AIHub 도서(1.6MB) - AIHub 대규모 웹데이터 기반 한국어 말뭉치(12GB) - 한국어 위키(867MB) - 나무위키(6.4GB) - 국립국어원 메신저 대화(21MB) - 국립국어원 일상대화 말뭉치(23MB) - 국립국어원 문어 말뭉치(3.2GB) - 국립국어원 구어 말뭉치(1.1GB) - 국립국어원 신문 말뭉치(~2022, 17GB) - 청와대 국민청원(525MB) 데이터셋 크기는 전처리한 jsonl파일을 기준으로 함. 총 토큰 수는 약 7B임 | b1082c39c24cce006628b77d8c43b946 |
mit | [] | false | 사용 예시 ```python from transformers import pipeline model_name = "heegyu/ajoublue-gpt2-base-24L" pipe = pipeline('text-generation', model=model_name) print(pipe("안녕하세요", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128)) print(pipe("오늘 정부 발표에 따르면, ", repetition_penalty=1.... | 295d7313443488d5666541842c240d1f |
mit | ['m2m100-12B'] | false | M2M100 12B (average of last 10 checkpoints) M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) r... | 4083ffb1f3429256290b9c038c902dc5 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the None dataset. It achieves the following results on the evalu... | 328cd52060ec42d7557bca764ae25921 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 4.6112 | 1.0 | 839 | 4.5615 | 0.1425 | 0.0837 | 0.1425 | 0.0646 | | 3.1177 | 2.0 ... | 3e7a07879e9dddb26589256b5cf0c8a6 |
apache-2.0 | ['generated_from_trainer'] | false | v1_speech_processing_project_wav2vec2 This model is a fine-tuned version of [kingabzpro/wav2vec2-large-xls-r-300m-Urdu](https://huggingface.co/kingabzpro/wav2vec2-large-xls-r-300m-Urdu) on the None dataset. | ddd234e98f3a3fe313eddae430cb5870 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 1000 - num_epochs: 60 - mixed_precision_t... | 74776a25d5e347d9fd32b9d90fd79ede |
mit | ['deberta', 'deberta-v3', 'mdeberta', 'fill-mask'] | false | DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of ... | d9647d28b63669a0251f6a80464522ac |
mit | ['deberta', 'deberta-v3', 'mdeberta', 'fill-mask'] | false | Fine-tuning on NLU tasks We present the dev results on XNLI with zero-shot cross-lingual transfer setting, i.e. training with English data only, test on other languages. | Model |avg | en | fr| es | de | el | bg | ru |tr |ar |vi | th | zh | hi | sw | ur | |--------------| ----|----|----|---- ... | c3567f64c1d67e589908e807e6eedb2a |
mit | ['deberta', 'deberta-v3', 'mdeberta', 'fill-mask'] | false | !/bin/bash cd transformers/examples/pytorch/text-classification/ pip install datasets output_dir="ds_results" num_gpus=8 batch_size=4 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \ run_xnli.py \ --model_name_or_path microsoft/mdeberta-v3-base \ --task_name $TASK_NAME \ --do_train \ --... | 540c32209b01941751877ca007eba64b |
apache-2.0 | ['generated_from_trainer'] | false | platzi-vit-model-yeder-lvicente This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0077 - Accuracy: 1.0 | dd13bf2e2925ebfbb16fa8496656b083 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0084 | 3.85 | 500 | 0.0077 | 1.0 | | 028128778b88068f200fc3779aa25e9c |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.2582 | 0aa8f3498b474438c5ec8e0dde5a9c5c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0462 | 1.0 | 8235 | 1.0822 | | 0.7579 | 2.0 | 16470 | 1.1160 | | 0.5734 | 3.0 | 24705 | 1.2582 | | a0537753af4f8b16b7d1a1b450fb0b4a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned_panx_de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1338 - F1: 0.8565 | 2b659fe710dffecef86b3a7f22c5cdd4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 263 | 0.1602 | 0.8158 | | 0.2169 | 2.0 | 526 | 0.1372 | 0.8407 | | 0.2169 | 3.0 | 789 | 0.1338 | 0.8565 | ... | 91e573d149dc1f2a91b1134109a08d3e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-XLSR-ft-10 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on an unknown dataset. | 7390da571d95ca1491e322daac21d8fd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 5a54cb1db2120874cec3c4195b519f64 |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on [Kinetics-600](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... | 309b1c6a49094f0d18cd551e17e599ed |
mit | ['One-Class Image Classification'] | false | Description This series of models are part of an investigation of "One-Class Image Classification with AI Algorithms", and are able to recognize patacones on images. - **Developed by:** https://github.com/frncscp - **Model type:** Convolutional Neural Network, DNN - **License:** mit - **Finetuned from model (the one... | 209d63d49657cdc1f401091a373e4018 |
mit | ['One-Class Image Classification'] | false | Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Resize the images to 224x224, with RGB channels A clear, good definition photo will always be preferable The least things appearing on the image, the better A patacón is a prediction of 80% ... | e7ae8ac408a3edb88b1b66fca87cf758 |
mit | ['One-Class Image Classification'] | false | How to Get Started with Patacotrón Import the required modules, and with this four lines you're good to go: img = cv2.imread(image_dir) resize = tf.image.resize(cv2.cvtColor(img, cv2.COLOR_BGR2RGB),(IMAGE_WIDTH, IMAGE_HEIGHT)) ptctrn = load_model(model_dir) y_gorrito = ptctrn.predict(np.expand_dims(resize/255, 0)... | 7c2d890f7cc223d4f4ca7df2c22d2696 |
mit | ['One-Class Image Classification'] | false | Evaluation A novel formula of efficiency was made, which uses average prediction, score on an image dataset, and weights to each variable. It is as follows: -For positive classes: $$E = \frac{(S * {S}')+(P * {P}')}{{S}'+{P}'}$$ -For negative classes: $$E = \frac{(S * {S}')+((1-P) * {P}')}{{S}'+{P}'}$$ Where: S re... | f294e51d98cfc43bdf0bf679cbeb7bb2 |
mit | ['One-Class Image Classification'] | false | Results For efficiency: 'ptctrn_v1.1.h5': 0.4805495065119731 'ptctrn_v1.2.h5': 0.4238817456329115 'ptctrn_v1.3.h5': 0.5343622414829778 'ptctrn_v1.4.h5': 0.6059606705949329 'ptctrn_v1.5.h5': 0.5040440155920757 'ptctrn_v1.6.h5': 0.6889029338405537 'ptctrn_v1.7.h5': 0.7112169071407513 ... | 27b889792fb4f01089efb685e96459b5 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-cnndm3-wikihow3 This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm3-wikihow2](https://huggingface.co/Chikashi/t5-small-finetuned-cnndm3-wikihow2) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.3138 - Rouge1: 27.2654 - Rouge2: 10.5461... | db855f773c1e95c250a3754b55883013 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.5019 | 1.0 | 39313 | 2.3138 | 27.2654 | 10.5461 | 23.2451 | 26.6151 |... | d5cca9f4dca5d2f901fae800bb04c5fa |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Hindi This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2680 - eval_wer: 15.6053 - eval_runtime: 3549.0061 - eval_samples_per_second: 0.815... | 083a63f217081a6a78706d163a6ee40d |
gpl-3.0 | [] | false | [UNOFFICIAL] This is the pretrained CTransPath model that accompanies the manuscript Transformer-based Unsupervised Contrastive Learning for Histopathological Image Classification, published by Xiyue Wang *et al* in Medical Image Analysis (October 2022, DOI: https://doi.org/10.1016/j.media.2022.102559) This model ha... | 8fcfc665f58f3739e8c99d30e5648fb7 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-emotion-17-labels 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: 1.4087 - Accuracy: 0.6495 - F1: 0.6481 | 3227c047a35fa93df034473143e9a23e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | 5759046c61714802d28091b4cb6f66c6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.5228 | 1.0 | 177 | 2.0913 | 0.3364 | 0.3043 | | 1.9362 | 2.0 | 354 | 1.7457 | 0.4353 | 0.4037 | | 1.5719 |... | a37b5b9feda37ccd822a23372e4db263 |
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 None dataset. It achieves the following results on the evaluation set: - Loss: 1.7093 | 12c7b0a1d3805c70239a5f94e08c2819 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | ab2d43b50f60f6cde10c428cdf9a2838 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2755 | 1.0 | 553 | 2.1210 | | 1.8766 | 2.0 | 1106 | 1.7363 | | 1.5381 | 3.0 | 1659 | 1.7093 | | ecd980c96ff21a6748cda755216e52ae |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | SepFormer trained on WHAM! for speech enhancement (8k sampling frequency) This repository provides all the necessary tools to perform speech enhancement (denoising) with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAM!](http://wham.whisper.ai/) dataset wit... | 41c70d8aa02784f2a0f03eb889a373f8 |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | Perform speech enhancement on your own audio file ```python from speechbrain.pretrained import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-wham-enhancement", savedir='pretrained_models/sepformer-wham-enhancement') | 288a601b2ea04c8895317d6a87595816 |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-wham-enhancement/example_wham.wav') torchaudio.save("enhanced_wham.wav", est_sources[:, :, 0].detach().cpu(), 8000) ``` | cd630384dd364109076efe657bfb7b2d |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - 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: 2000 - mixed_precis... | 5ecd49ae01507f8ca07f1afb76d72bbc |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.1285 | 1.03 | 200 | 1.0640 | 53.4934 | | 0.5163 | 2.05 | 400 | 0.6450 | 41.2428 | | 0.2005 | 4.01 | 600 | 0.5600 | 36.679... | b02d2365f813d1c2dcf694903dc9899c |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat-kornia 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: 0.0540 - Accuracy: 0.9830 | ce8bbe7e186808001c4f516e58d52567 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0859 | 1.0 | 190 | 0.0969 | 0.9685 | | 0.0664 | 2.0 | 380 | 0.0627 | 0.9815 | | 0.0359 | 3.0 | 570 | 0.0540 | 0.... | e067b0de74d2324d71b461015fc94489 |
apache-2.0 | ['generated_from_keras_callback'] | false | aphostrophy/jessonyo-finetuned-ja-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2713 - Validation Loss: 1.4892 - Epoch: 2 | ea75e9849baec4713e5ed17fc985c838 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1107, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | e9a9d6d446ba84a3d63f8242413c333b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9784 | 1.5943 | 0 | | 1.4695 | 1.5160 | 1 | | 1.2713 | 1.4892 | 2 | | 7de84a874f5d7eedf2c332f24f38dd4a |
mit | ['vision', 'image-classification', 'tensorflow'] | false | Bokeh (ボケ Japanese word for blur) Bokeh model is based on a densenet like architecture trained on Unsplash images at 300x200 resolution. It classifies whether an photo is capture with bokeh producing a shallow depth of field | d06286dc8858da8a7a59c3521a990822 |
mit | ['vision', 'image-classification', 'tensorflow'] | false | Model description Bokeh model is based on a DenseNet architecture. The model is trained with a mini-batch size of 32 samples with Adam optimizer and a learning rate $0.0001$. It has 3.632 trainable parameters, 8 convolution filters are used for the network's input, with $7\times7$ kernel size. | 07b339267fc29a7796550620d8c32bc7 |
mit | ['vision', 'image-classification', 'tensorflow'] | false | BibTeX entry and citation info ``` @article{sniafas2021, title={DoF: An image dataset for depth of field classification}, author={Niafas, Stavros}, doi= {10.13140/RG.2.2.17217.89443}, url= {https://www.researchgate.net/publication/355917312_Photography_Style_Analysis_using_Machine_Learning} year={2021} } ``... | e0da0672b2489121e42e7ae0adb0acac |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_audio-roberta-large-v1-5-0 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... | da219802fcc1a5c5288789013d490b91 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Model provided by: rjadr Pretrained musika_irish_jigs model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | 317ebc2bc6468bc2cd51e7eb24a5e858 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7760 - Accuracy: 0.9148 | 71a805dcdfb8a6dd90e2c3e1960f35e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2994 | 1.0 | 318 | 3.3016 | 0.7442 | | 2.6387 | 2.0 | 636 | 1.8892 | 0.8339 | | 1.5535 | 3.0 | 954 | 1.1602 | 0.... | 210e52464f353271d7fcf3a1a358357f |
apache-2.0 | ['speech'] | false | Hubert-Base [Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This mode... | e598441b3b675eb7cf7ef4b47c39ebb2 |
apache-2.0 | ['speech'] | false | Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `HubertForCTC`. | 7d01ce7ebe6e3895a4d15f017136bbec |
mit | ['keytotext', 'k2t', 'Keywords to Sentences'] | false | keytotext  Idea is to build a model which will take keywords as inputs and generate sentences as outputs. | f4045fc27c37221588a1f00fd6c6c695 |
mit | ['keytotext', 'k2t', 'Keywords to Sentences'] | false | Keytotext is powered by Huggingface 🤗 [](https://pypi.org/project/keytotext/) [ - `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny) - `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base) Training Notebooks can be found in the [`Training Notebooks`](https://github.com/g... | ee0c148f9cea88a04dd015c3b74f3708 |
mit | ['keytotext', 'k2t', 'Keywords to Sentences'] | false | Usage: Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb) Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder ``` pip... | f7f74f031de244e8129b6c119375fb92 |
mit | ['keytotext', 'k2t', 'Keywords to Sentences'] | false | UI: UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py) ``` pip install streamlit-tags ``` This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)  task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a ... | 70dabe94a5a7630cf3a322b3b444b593 |
apache-2.0 | [] | false | Usage with SentenceTransformers The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) scores = model.predict([('Que... | d9513203173afe28c5ad766f6c2b59fd |
apache-2.0 | ['translation'] | false | opus-mt-vsl-es * source languages: vsl * target languages: es * OPUS readme: [vsl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/vsl-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | b58ad551dc153aaf3e5e1c23403d6f95 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2349 - Accuracy: 0.9165 - F1: 0.9164 | 1f56a1d94cbce3503995101748e570f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.837 | 1.0 | 250 | 0.3317 | 0.9015 | 0.8999 | | 0.2563 | 2.0 | 500 | 0.2349 | 0.9165 | 0.9164 | | 0c4caa5bcb95b1c08d2afa0f83021924 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1333 - F1: 0.8551 | 2ccc1a9d618b3e29be56e5da8b04dcfc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 263 | 0.1573 | 0.8137 | | 0.2142 | 2.0 | 526 | 0.1386 | 0.8466 | | 0.2142 | 3.0 | 789 | 0.1333 | 0.8551 | ... | e4042f4ca57577e95d2115bc4043c180 |
lgpl | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sdadas/st-polish-paraphrase-from-mpnet 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. <!--- Describe your model here --> | 264e525e95b045143ecd65a1d2526f4f |
lgpl | ['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 sen... | 0ab3a3d89f8b954518e7db335cee3d80 |
lgpl | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sdadas/st-polish-paraphrase-from-mpnet) | 963d34afcdd2398c47b4e45278d6b752 |
lgpl | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_... | 30d598c6e8c10e2420b96db181715f2e |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Examples We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ```bash pip install --upgrade diffusers transformers scipy ``` Run this command to log in with your HF Hub token if you haven't before: ```bash huggingface-cli login ``` Running the pipeline wit... | a4e8b6971689ff5a787c26b4f1591020 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Use the K-LMS scheduler here instead scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000) pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True) pipe = pipe.to("cuda") prompt = "a photo of an astronaut... | 57912377a16888496508e2bc23da4a05 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conferenc... | a86a6f09256f9bc346f065e5b49d9013 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Art Model This model combines three different artstyles 1. [Flower Style](https://huggingface.co/datasets/Nerfgun3/flower_style) 2. [Wlop Style](https://huggingface.co/datasets/Nerfgun3/wlop_style) 3. [Sciamano Style](https://huggingface.co/datasets/Nerfgun3/sciamano) | 1a99c6a63904766873c7f67c9dda3690 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Usage To use this model you have to download the file aswell as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder Class names: 1. ```m_flower``` - For more Flower Style Art 2. ```m_wlop``` - For more Wlop Style Art 3. ```m_sas``` - For more Sciamano Style Art If it is to strong just add [] a... | a074a57cc79d343b2096ef6df668f594 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Example Pictures <table> <tr> <td><img src=https://i.imgur.com/2PFk7WC.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/qSFfDGC.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/PLzVdu5.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/kmu1Huy.png ... | 1cafad2a8bfa6b6b76ba04db14cde416 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outp... | 5ebb5e8a3cbc0c2b88ef0c64c755e518 |
apache-2.0 | ['vision', 'image-segmentation'] | false | DPT (large-sized model) fine-tuned on ADE20k Dense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl... | 183fef6fe4f2a0cf26816eeee740ae9d |
apache-2.0 | ['vision', 'image-segmentation'] | false | Model description DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for semantic segmentation.  | 566c889bed40a065a04c7f61f86f7e04 |
apache-2.0 | ['vision', 'image-segmentation'] | false | Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=dpt) to look for fine-tuned versions on a task that interests you. | 9b4644aee82f4622cfbb19e8d1a28ec2 |
apache-2.0 | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation 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 = DPTFeatureExtr... | d2932f747ac8a4898f49709fdf9fa4d7 |
apache-2.0 | ['vision', 'image-segmentation'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-13413, author = {Ren{\'{e}} Ranftl and Alexey Bochkovskiy and Vladlen Koltun}, title = {Vision Transformers for Dense Prediction}, journal = {CoRR}, volume = {abs/2103.13413}, year = ... | 5261fee8550ad82368bec3241e95a9fd |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'anime', 'aiart'] | false | Example Generations Prompt: `BoMaple uniform BoSally unfirom, yuri, in classroom, 4K wallpaper, beautiful eyes`  Prompt: `2girls, BoMay BoYui, yuri, half body, floating in the sky, cloud... | da856aae08019cab43693743554f8592 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'anime', 'aiart'] | false | Concepts The 33 concepts are listed in `concept_list` and demonstrated below. BoMaple +  BoSally + ![00159-20230129224620.jpg](https://huggingface.co/alea31415/bofuri-full/resolve/... | 17dafe22612c3f4c883b804d1bcbd50b |
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