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 | swin-small-finetuned-cifar100 This model is a fine-tuned version of [microsoft/swin-small-patch4-window7-224](https://huggingface.co/microsoft/swin-small-patch4-window7-224) on the cifar100 dataset. It achieves the following results on the evaluation set: - Loss: 0.6281 - Accuracy: 0.8938 | 3d5f7c462ddf2306bcfcdf8f1a90b007 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 0cc9d69459781e8d0c542d5a13e6bf9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.72 | 1.0 | 781 | 0.6691 | 0.8077 | | 0.6944 | 2.0 | 1562 | 0.4797 | 0.8495 | | 0.2794 | 3.0 | 2343 | 0.4338 ... | 2010f343a1029bec5c69dc6f10560fb4 |
apache-2.0 | ['translation'] | false | opus-mt-tzo-es * source languages: tzo * target languages: es * OPUS readme: [tzo-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tzo-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 6f7aac7fee7780c737f7e2d064fb79f6 |
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: 0.1928 - F1: 0.8388 | 172b4b45f1143e554d3ee4709bae1be6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3375 | 1.0 | 525 | 0.2216 | 0.7952 | | 0.1749 | 2.0 | 1050 | 0.1996 | 0.8206 | | 0.1094 | 3.0 | 1575 | 0.1928 | 0.8388 | ... | 98b8c4a29bab529b136e00b6c9017f44 |
mit | [] | false | Phan on Stable Diffusion This is the `<phan>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your ... | 687a69104b5680028a92c9c70f7913a7 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0296 - Rouge1: 18.0335 - Rouge2: 8.816 - Rougel: 17.5279 - Rougelsum: 17.6189 | 8de1373ad3ef5f7772520bcc07322672 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.9312 | 1.0 | 1209 | 3.2984 | 14.4268 | 6.4451 | 14.0547 | 14.1363 | | 3.8882 | 2.0 |... | 5acb10510345cbeb58cca045f8461bbb |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-mbti-0901 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0780 | 4bf7b330166bd5839ce7f600f5de8550 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.3179 | 1.0 | 9920 | 4.1970 | | 4.186 | 2.0 | 19840 | 4.1264 | | 4.1057 | 3.0 | 29760 | 4.0955 | | 4.0629 | 4.0 | 39680 | 4.0826 ... | c87a5e7c39f1a3f06b5b2844ca7de313 |
apache-2.0 | ['translation'] | false | opus-mt-es-tw * source languages: es * target languages: tw * OPUS readme: [es-tw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-tw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 82e058c2757199d71cf331a0a2a75245 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_gst+xvector_tacotron2` ♻️ Imported from https://zenodo.org/record/4394598/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 4e7a0b97786117fee731cdf0a9cfa730 |
apache-2.0 | ['exbert', 'multiberts'] | false | MultiBERTs Seed 17 (uncased) Seed 17 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language... | b2ec650ad8d4fb24fedaeb9be6d525ed |
apache-2.0 | ['exbert', 'multiberts'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-17') model = BertModel.from_pretrained("multiberts-seed-17") text = "Replace me by any text you'd like." enco... | 2c0b9e21985e084e070446c33d549b74 |
mit | [] | false | million-live-spade-q-style-3k on Stable Diffusion This is the `<spade_q>` 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) notebo... | 0d5992cf86696c1f622f594273fef944 |
apache-2.0 | ['translation'] | false | opus-mt-mh-es * source languages: mh * target languages: es * OPUS readme: [mh-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mh-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 990e1007b14f7edc67ad54638eaf36ea |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'science'] | false | DreamBooth model for the StarTrek concept trained by vumichien on the vumichien/spaceship_star_trek dataset. <img src="https://huggingface.co/vumichien/StarTrek-starship/resolve/main/1_dlgd3k5ZecT17cJOrg2NdA.jpeg" alt="StarTrek starship"> This is a Stable Diffusion model fine-tuned on the StarTrek concept with Dream... | 5a804633cfad6b268869ad0249745d8b |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'science'] | false | Examples <figure> <img src="https://huggingface.co/vumichien/StarTrek-starship/resolve/main/Leonardo%20Da%20Vinci%20style.png" alt="StarTrek starship - Leonardo Da Vinci style"> <figcaption>Text prompts for generated: A painting of StarTrek starship, Leonardo Da Vinci style </figcaption> </figure> <figure> <... | 5836e978c77d4fb81d941ff860676702 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-2-2-1 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: 2.6817 - Accuracy: 0.1022 | dd9f89236bbaba5ea08e5a2eb415e7dc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.653 | 1.0 | 5 | 2.6817 | 0.1022 | | 2e7739971bb85a4246fb6e0670648e8a |
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: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 19ece0fc08de4981f9aec02bbef0a412 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the landscape concept trained by nahidalam on the nahidalam/landscape dataset. This is a Stable Diffusion model fine-tuned on the landscape concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of landscape ocean** This model was created as part of the DreamBooth ... | ca28c784dbf9584c5412cf4a7c65cd2b |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Km - Kak Soky This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SLR42 dataset. It achieves the following results on the evaluation set: - Loss: 0.1471 - Wer: 35.6654 | 3fd0f41b2cca18b2f2bbeb6916839e95 |
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: 2 - eval_batch_size: 4 - 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: 4000 - mixed_precisio... | a39bd79383a3b10551a158be7063daec |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3639 | 0.76 | 1000 | 0.3452 | 71.9392 | | 0.1553 | 1.53 | 2000 | 0.2025 | 49.0494 | | 0.0565 | 2.29 | 3000 | 0.1664 | 39.924... | 00edae06c9b2fcac00d0667ce507df70 |
apache-2.0 | ['translation'] | false | opus-mt-et-es * source languages: et * target languages: es * OPUS readme: [et-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/et-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | b676df5883ab05a6348985f4ec852911 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_nosp_0020 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: 0.1825 - Train Accuracy: 0.0228 - Validation Loss: 0.8115 - Validation Accuracy: 0.0203 - Epoch: 19 | 239f5c69ee59c447407b20a678b21de6 |
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 ... | 70302b576c4f688dfe92707fb0cf5700 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-patch16-224 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1510 - Accuracy: 0.9443 | c60e3f84e8fe6a4187ced4535697200a |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 7e8f581e156bfda9790ee30425233f6b |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1438 | 1.0 | 150 | 0.1645 | 0.9353 | | 36eb576ec58e82d1c1094d2da4061fd1 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-base-224_finetuned_on_unlabelled_IA_with_snorkel_labels This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3443 - Precision: 0.9864 - Recall: 0.9822 - F1:... | 603c05c81e322465d8a76ae124368c32 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 10 - mixed_precision_training: Native AMP - label_smooth... | 4685f66c4435f70d58bb5adc36f36dae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3611 | 1.0 | 2021 | 0.3467 | 0.9843 | 0.9729 | 0.9784 | 0.9842 | | 0.3524 | 2.0 ... | 90c88af43ec0341602b0d7d9327990b7 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_large_tf_224.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. | 0b141edeeb5dc9beec64b76b0dffb71c |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 211.8 - GMACs: 43.7 - Activations (M): 127.3 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 6a8b7cd83950ba0fa40f9cbc307e3d64 |
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_large_tf_224.in1k', pretrained=True) model = mo... | a26b4cfc1db07b6c53ac9831abe048e0 |
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_large_tf_224.in1k', pretrained=True,... | 2bee8e0fe77e6ed9534971f63b09b5e8 |
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_large_tf_224.in1k', pretrained=True, n... | b26f98e0d7bcc19131d0505ccc37debf |
cc-by-4.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3011 - Accuracy: 0.9185 | 87de9caf33a321ef13efac7cb0bbb726 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2427 | 1.0 | 125 | 0.2109 | 0.919 | | 0.0986 | 2.0 | 250 | 0.3011 | 0.9185 | | 94c564679b84686f7564f56c960facf6 |
apache-2.0 | ['t5', 'seq2seq'] | false | t5-v1.1-base-dutch-cased A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned). This **t5-v1.1** model has **247M** parameters. It was pre-train... | 39609a9a18ad4c5a5437569b8167ee11 |
apache-2.0 | ['t5', 'seq2seq'] | false | Tokenizer The model uses a cased SentencePiece tokenizer configured with the `Nmt, NFKC, Replace multi-space to single-space` normalizers and has 32003 tokens. It was trained on Dutch mc4 with scripts from the Huggingface Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/fl... | 214b45ec156c5e5e1ee194495eb73bb3 |
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: 0.0599 - Precision: 0.9360 - Recall: 0.9520 - F1: 0.9439 - Accuracy: 0.9869 | 8be1d14958b4a0c262cb8468bb435b35 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0879 | 1.0 | 1756 | 0.0652 | 0.9236 | 0.9379 | 0.9307 | 0.9832 | | 0.0343 | 2.0 |... | 4e348f1e78da4ef9568d598c11e4b969 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium VI - Multi - Augmented This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the following datasets: - [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) - [google/fleurs](https://huggingf... | 2c4fdbfb4d989b7c3db0451f2adbf075 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training and evaluation data Training: - [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation) - [google/fleurs](https://huggingface.co/datasets/google/fleurs) (train+validation) - [vivos](https://huggingface.co/datasets/vivos) (train) Evaluat... | dadc82970d4b618ec5d1c952912a0d4b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.1992 | 1.8 | 1000 | 0.2726 | 17.4929 | 8.2562 | | 0.0402 | 3.6 | 2000 | 0.3317 | 17.4929 | 8.2588 | | 0.0073 | 5.4... | a45d4f25454c68e9e5df4647953e9c0e |
afl-3.0 | ['t5'] | false | chunked T5 - small (cT5-small) Github: https://github.com/mtreviso/chunked-t5 A T5 model that uses a new loss where a special end-of-chunk token `</c>` is appended after sentinel tokens. The decoder has to predict the full input with masked tokens followed by `</c>`. This allows a much faster auto-regressive gener... | 0da5ee187274a104ca052b9f036c1166 |
afl-3.0 | ['t5'] | false | Training details cT5 models used T5's weights as a starting point, and then it was finetuned on the English [wikipedia](https://huggingface.co/datasets/wikipedia) for 3 epochs, achieving ~74% validation accuracy (ct5-small). The training script is in JAX + Flax and can be found in `pretrain_ct5.py`. Flax checkpoin... | 7e05ba378782ae858991167f9567a5b1 |
afl-3.0 | ['t5'] | false | Usage ```python from transformers import AutoTokenizer from modeling_ct5 import CT5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("mtreviso/ct5-small-en-wiki") model = CT5ForConditionalGeneration.from_pretrained("mtreviso/ct5-small-en-wiki") ``` For training: ```python input_ids = tokenizer("Th... | aabbbf02e4aa7bd6e74057e6b139cf49 |
mit | ['object-detection', 'computer-vision', 'sort', 'tracker', 'bytetracker'] | false | Model Description [ByteTrack](https://arxiv.org/abs/2110.06864): Multi-Object Tracking by Associating Every Detection Box <img src="https://raw.githubusercontent.com/ifzhang/ByteTrack/main/assets/sota.png" width="500"/> | ea2126ec993d95da165321f57395d1f3 |
mit | ['object-detection', 'computer-vision', 'sort', 'tracker', 'bytetracker'] | false | BibTeX Entry and Citation Info ``` @article{zhang2022bytetrack, title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box}, author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Weng, Fucheng and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang}, booktitle={Proceedin... | d5dadff04819748b26f873e6accbf674 |
mit | ['binary_segmentation', 'image_differences'] | false | Image Difference Segmentation For the main repository and code, please refer to the [GitHub Repo](https://github.com/Brikwerk/image-difference-segmentation). This project enables creation of large binary segmentation datasets through use of image differences. Certain domains, such as comic books or manga, take parti... | 8227bce14a750f60bc2744e8dfc8acd0 |
mit | ['binary_segmentation', 'image_differences'] | false | Prerequisites The following must be on your system: - Python 3.6+ - An accompanying Pip installation - Python and Pip must be accessible from the command line - An NVIDIA GPU that is CUDA-capable (6GB+ of VRAM likely needed) | 526574568d76ee3b77c66e41e32a44f7 |
mit | ['binary_segmentation', 'image_differences'] | false | Downloading the Weights File Weights for this project are hosted at [HuggingFace](https://huggingface.co/brikwerk/image-difference-segmentation) under `weights` directory. Currently, a DiffNet instance trained on text differences is provided. To use this model, download it and move it to the weights directory in your... | e50534d2f89aba264371cb195960053f |
mit | ['binary_segmentation', 'image_differences'] | false | Using Pretrained Weights Pretrained weights can be used in the `batch_process.py` file and the `evaluate.py` file. For both files, specify the path to your weights file using the `--weights_path` CLI argument. | 34f1a230977b6e4ba731ed3bf4ca4378 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the fruins concept trained on the CCMat/db-forest-ruins dataset. This is a Stable Diffusion model fine-tuned on the fruins concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of fruins ruins** This model was created as part of the DreamBooth Hackathon 🔥. Visit ... | 645ac2ec17271b30418eb7a8745f98b5 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Description This is a Stable Diffusion model fine-tuned on `ruins` images for the landscape theme.<br> Concept: **fruins** : forest ruins, greenery ruins<br> Pretrained Model: [prompthero/openjourney](https://huggingface.co/prompthero/openjourney)<br> Learning rate: 1e-6<br> | 27666fb68ef173db8089c0550e3e6e3b |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Samples Prompt: "a photo fruins ruins in Paris in front of the Arc de triomphe, in the 1970s, vivid colors"  <br> Prompt: "high quality photo of Rome in fruins ruins with the Colosseum in the background"  on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7523 | cefdf34aea4d086229ab4b05e07cfd83 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.681 | 1.0 | 17307 | 0.7207 | | 0.4682 | 2.0 | 34614 | 0.7523 | | 41efd632def359cd4b758e007ade727c |
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/resnet34").eval() img = Imag... | 761aebd7360c3cc7e651b4cd32405f95 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 0d8cd47dd3572248b502bc831cd305e648170233 pip install -e . cd egs2/csj/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/kan-bayashi_csj_asr_train_asr_conformer ``` | a3599e1a4a024b0a327418ea5fd0af48 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_raw_char_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist... | 91cb9e78160291173d55ac3f0bc60125 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_mrpc This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 - F1: 1.0 - Combined Score: 1.0 | d5148771d7e25ded73bb3442b900e6e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.1838 | 1.0 | 1959 | 0.0138 | 0.9951 | 0.9964 | 0.9958 | | 0.0406 | 2.0 | 3918 | ... | 7673d76d2cca2b91acbf83f9cb1d6a5c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-16-8 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: 1.0704 - Accuracy: 0.394 | aef47aa32a59fe0ca20a7f5505e73322 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1031 | 1.0 | 10 | 1.1286 | 0.1 | | 1.0648 | 2.0 | 20 | 1.1157 | 0.3 | | 0.9982 | 3.0 | 30 | 1.1412 | 0.... | 4f997e8e251e065dc277386410abb0eb |
apache-2.0 | ['generated_from_trainer'] | false | local_test_model_with_local_dataset 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: 0.5566 - Wer: 0.0 | 953c14e13d55ef281f30f5799fe4012b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 3.4660 | 85.7143 | | No log | 20.0 | 20 | 0.7373 | 10.7143 | | 3.3998 | 30.0 | 30 | 0.5920 | 0.0 ... | 6b04cde866816d7e83c60f46c814e91d |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-target This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2793 - Precision: 0.6688 - Recall: 0.7 - F1: 0.6840 - Accuracy: 0.9170 | eda04ab8aaa43cd73ed37bb9fa46159e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 218 | 0.2489 | 0.6034 | 0.7 | 0.6481 | 0.9106 | | No log | 2.0 |... | 52e225bfdbf0f36d258d5a43ab1a09ae |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | PyTorch ```bash pip install --upgrade diffusers transformers scipy ``` Running the pipeline with the default PNDM scheduler: ```python import torch from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, tor... | fecb30b35e4366a1a9b0ea8b97db5998 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pip... | 0fb673d2d7e38909a382931434d7b532 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, num_samples) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` ... | 38eabc412d3d84641530aab6e683dbbe |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, num_samples) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` ... | 01ac48adcf8a60e977d13a9cd596c837 |
mit | ['token-classification', 'sequence-tagger-model', 'pytorch', 'transformers', 'pubmedbert', 'uncased', 'radiology', 'biomedical'] | false | Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. These model weights are the recommended ones among all available deidentifier weights... | a5b3a71581bada067d3d948fd76c2d94 |
mit | ['generated_from_trainer'] | false | kobart_32_4e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6131 - Rouge1: 35.7499 - Rouge2: 13.0188 - Rougel: 23.5089 - Bleu1: 29.... | c23fa7b7023d745f7689e7edcbb008a0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 44388b242920d102c283666eb2c370f8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 1.7368 | 3.78 | 5000 | 2.6131 ... | 5d021e0564ef96931195d4c5df200af3 |
apache-2.0 | ['t5', 'seq2seq'] | false | t5-small-24L-dutch-english A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned). This **t5 eff** model has **249M**... | ab09eaaa7fbb7835b51b8095c08f73a1 |
apache-2.0 | ['generated_from_trainer'] | false | Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss: 0.175... | 4bf7fa067e50ca3994db3946a6ec3595 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the [CRAFT](https://github.com/UCDenver-ccp/CRAFT/releases)(Colorado Richly Annotated Full Text) Corpus in Spanish and English. Entity tags have been normalized and replaced from ... | f78dc74c7d2b5b65f83aa4a92668c944 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0564 | 1.0 | 1360 | 0.1459 | 0.8296 | 0.8489 | 0.8392 | 0.9696 | | 0.0222 | 2.0 |... | 9ac10deb0510040a4b647827f1326f30 |
apache-2.0 | ['generated_from_trainer'] | false | GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text. ... | 1d6d45633d23fb36e09df3670a3b902c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 5 | d08cd1713649581dc87571f1c23eb332 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4438 | 1.0 | 18 | 1.8004 | | 2.1981 | 2.0 | 36 | 1.6985 | | 1.9766 | 3.0 | 54 | 1.6487 | | 1.8233 | 4.0 | 72 | 1.6384 ... | e2ad1f8b5f05ff484977c2e0f461de94 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout e62de171f1d11015cb856f83780c61bd5ca7fa8f pip install -e . cd egs2/tedlium2/asr1 ./run.sh --skip_data_prep false --skip_train tr... | 34d906c7e6c4eaacac5a9b485c6e973d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Dec 30 14:56:03 CST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `e62de171f1d11015cb856f83780c61bd5ca7fa8f` - Commit date: `Thu Dec 29 14:18:44 2022 -0500` | 819a1805450b6a3f8d8c629492cd903e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_ctc_asr_model_valid.cer_ctc.ave/dev|466|14671|92.4|5.4|2.2|1.2|8.9|75.1| |decode_asr_ctc_asr_model_valid.cer_ctc.ave/test|1155|27500|92.6|5.0|2.5|1.1|8.5|70.3| | 246986e8a5f33abd34cbe6f1b392af6f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_ctc_asr_model_valid.cer_ctc.ave/dev|466|78259|97.0|0.9|2.1|1.2|4.2|75.1| |decode_asr_ctc_asr_model_valid.cer_ctc.ave/test|1155|145066|97.0|0.9|2.1|1.2|4.2|70.3| | ec787815dacbcf6d774d7071050f4f97 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_ctc_asr_model_valid.cer_ctc.ave/dev|466|28296|94.6|3.1|2.4|1.2|6.6|75.1| |decode_asr_ctc_asr_model_valid.cer_ctc.ave/test|1155|52113|94.9|2.7|2.4|1.2|6.3|70.3| | 549f81c8df03b64860e98950633c4483 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_ctc_conformer_e12_linear2048.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_ctc_conformer_e12_linear2048_raw_en_bpe500_sp ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 3 di... | 2d16caa700ee0145da2aaa31741c479c |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [roberta-base-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora-char) and [UD_Japanese-GSDLUW](https://github.com/UniversalD... | ca22078ab3de085af9cf55bb39829c0e |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-japanese-aozora-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/roberta-base-japanese-aozora-ud-head") qap=Qu... | 41176a723895a917a74e9b90ece45502 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD... | 71c08ab08cb9a3484c548c388237bc30 |
mit | [] | false | She Mask on Stable Diffusion This is the `<she-mask>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also tra... | e58fcd25ffae33535118cef5fcd333ca |
apache-2.0 | ['translation'] | false | opus-mt-ln-en * source languages: ln * target languages: en * OPUS readme: [ln-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ln-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 4e73ba6af8d69d2dd9ccffeba258038a |
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