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 | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for China-Chic-illustration This is the first model for `China-Chic illustration` (国潮插画) style painting. The model is based on Stable Diffusion model, fine-tuned the `China-Chic illustration` style taught to Stable Diffusion with DreamBooth. It is trained by tilake AIGC group on the own dataset. It c... | 66b6765ad84e47123e71cfb1ccbc4ebe |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run China-Chic-illustration: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756... | a0c55c1aac901ada67883a668c1cbe38 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on `China-Chic illustration` style images. It is trained by Tilake AIGC Group. If you like this model, click the \[❤ like\] button! 国潮插画是将传统文化和现代潮流审美进行结合的一种插画形式，开放该模型旨在给相关艺术家和工作者提供灵感和创作思路。如果喜欢该模型，欢迎点亮网页最上方的【like】按钮~ | b71257aec53b9791895bc69b14940477 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Examples - Prompt: ```a cute rabbit in red clothes, in the style of <guo-chao> illustration, trending on artstation, masterpiece, best quality```（国潮兔） <img width="200px" height="200px" src="https://huggingface.co/tilake/China-Chic-illustration/resolve/main/example/1.jpg"> - Prompt: ```dragon dance, in the style of <... | a0901958f83f3aac33dcc949bd419479 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | guidance_scale=8.8 may be the best pipeline = StableDiffusionPipeline.from_pretrained('tilake/China-Chic-illustration') image = pipeline("style of <guo-chao> illustration, a rabbit wearing sunglasses").images[0] image ``` | 626a35a4d84925fb86305b9990a03fa4 |
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: 0.2187 - Accuracy: 0.9255 - F1: 0.9255 | 1ed9d1c5b2cb3aa5775ad9a8dc120388 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.855 | 1.0 | 250 | 0.3211 | 0.905 | 0.9017 | | 0.2561 | 2.0 | 500 | 0.2187 | 0.9255 | 0.9255 | | 70f39645e3d338bf090852f28449bacd |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0959 - Accuracy: 0.9769 | de3b29c46d26f2b94685f25dfbb7a312 |
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... | 7d332a60e469c18405e311a79f66ca63 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.206 | 1.0 | 168 | 0.1753 | 0.9613 | | 0.0904 | 2.0 | 336 | 0.0959 | 0.9769 | | be2b2b3af2c474b4d208fbc1fb402c67 |
apache-2.0 | [] | false | Introduction This is the **medium** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-... | db2a00680d5acbdf1ac3107ada59ed74 |
apache-2.0 | [] | false | How to use To use it in transformers, please refer to https://github.com/OFA-Sys/OFA/tree/feature/add_transformers. Install the transformers and download the models as shown below. ```bash git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git pip install OFA/transformers/ git c... | 06304d2733d9ae9d00d95adc819cc86f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Ta - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3096 - Wer: 30.1027 | c75f9f231acda8d22021495dfa436f70 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 7107467a34e98d87e17b71fa92b77282 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.5622 | 0.2 | 1000 | 0.4460 | 41.4141 | | 0.4151 | 0.4 | 2000 | 0.3657 | 35.1390 | | 0.3727 | 0.6 | 3000 | 0.3417 | 3... | 04bacfedd2876502394c8809907661eb |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | beit-base-ches-demo-v0 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0420 - Accuracy: 0.9871 | 4b8881c679a541743409f36edb0e9775 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 4 - mixed_precision_training: Native AMP | a710b14e3ea94a5fc5672b21f1270028 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0183 | 3.45 | 300 | 0.0420 | 0.9871 | | fb04bfe744c84754e94b296aba834084 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9611 - Mae: 0.4749 | 0a565a05cf50c34aa238383beba575b4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0431 | 1.0 | 860 | 0.9819 | 0.4985 | | 0.9079 | 2.0 | 1720 | 0.9611 | 0.4749 | | e4aa8b68fc573b5f3d1baed1b6f236f6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/facebook-dpr-ctx_encoder-multiset-base This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [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 ... | 7b4725651329d949f4629659b8c1e0a8 |
apache-2.0 | ['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... | d95e6af23f1e8b396e30d976a784eb5f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/facebook-dpr-ctx_encoder-multiset-base') model = AutoModel.from_pretrained('sentence-transformers/facebook-dpr-ctx_encoder-multiset-base') | 4232edae718411a7325c2549ac862f34 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/facebook-dpr-ctx_encoder-multiset-base) | 9a368d6ac8b014c27ff9525e951fca3c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mea... | 299b3eca09a6c0b544b3fb8509f5fba1 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-qg-context-dataset-2-hard-medium This model is a fine-tuned version of [Deigant/t5-base-finetuned-qg-context-dataset-2](https://huggingface.co/Deigant/t5-base-finetuned-qg-context-dataset-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1877 - Rouge1: 27.906... | f1dc2d588976698c07b3c8e3936532b1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP | 236c03e647d4714a1317bb126199f678 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 73 | 2.1134 | 27.571 | 8.3183 | 25.3973 | 25.2743 | | No log | 2.0 ... | 860ea86c7db599c6217f056c433e909b |
apache-2.0 | ['bert', 'stsb', 'glue', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as tho... | 8712c17ad9b039f1718c5af1ca20b925 |
other | ['text-to-image', 'stable-diffusion', 'finetune', 'icons', 'art'] | false | about - this was made with SD 1.4 - generates icons inspired by fantasy games with mostly plain backgrounds, as seen [here](https://huggingface.co/proxima/fantassified_icons/blob/main/comparisons/euler_a_steps_vs_scale.jpg) - struggles with things that are not fantasy-ish/not in the dataset, like sunglasses. best wi... | 8a089c1f225cb9aa72fb593e42270d94 |
other | ['text-to-image', 'stable-diffusion', 'finetune', 'icons', 'art'] | false | license This model is licensed under a modified CreativeML OpenRAIL-M license. * Utilizing and hosting the Fantassified Icons 1.0 model and its derivatives on platforms that earn, will earn, or plan to earn revenue or donations requires prior authorization. **To request permission, please email proximasan@protonmail.... | 43f42a8ee473de6ea8f312d0b54a2fc4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_5_ternary_v1 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: 2.1368 - F1: 0.7682 | 10011f0254848b4e7dc8390ae3e01f90 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.6423 | 0.7465 | | 0.5563 | 2.0 | 582 | 0.6001 | 0.7631 | | 0.5563 | 3.0 | 873 | 0.6884 | 0.7785 | |... | 7d5974ec7f2b560d8e6b0524e6aade13 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-combinedmodel1-ner 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: 2.3126 - Precision: 0.0289 - Recall: 0.1443 - F1: 0.0481 - Accuracy... | 58a7d03af5e2ce2a03d9438f2f77dcd5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 312 | 1.5290 | 0.0431 | 0.2278 | 0.0725 | 0.6990 | | 0.1106 | 2.0 |... | 6354c2f25250f78de0cd0bfe94b55b84 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-qnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3208 - Accuracy: 0.9125 | 948c71286879ea05a02131767848278c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.289 | 1.0 | 3274 | 0.2289 | 0.9094 | | 0.1801 | 2.0 | 6548 | 0.2493 | 0.9118 | | 0.1074 | 3.0 | 9822 | 0.3208 | 0.... | 3bd19e351c07882f7d8ce8753238bcf3 |
mit | ['generated_from_keras_callback'] | false | juro95/xlm-roberta-finetuned-ner-cased_0.8_ratio This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0820 - Validation Loss: 0.1369 - Epoch: 3 | dea2ec8f983cc579cb3202c89fcf3016 |
mit | ['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': 2e-05, 'decay_steps': 17152, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 30c74a1180bec2d193da938783bf6b10 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3781 | 0.2062 | 0 | | 0.1790 | 0.1571 | 1 | | 0.1170 | 0.1408 | 2 | | 0.0820 | 0.1369 | 3 | | 03c08b2c2d5b1f5db9d61a6ae346e00d |
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: 0.7781 - Accuracy: 0.9161 | a3c9c5fbc79360af8965f590eae05e5f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2788 | 0.7458 | | 3.78 | 2.0 | 636 | 1.8706 | 0.8339 | | 3.78 | 3.0 | 954 | 1.1620 | 0.... | 272bbf0e80edbe33a326c1daf36674e2 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-10Epochs64Batch 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: 0.6931 - Accuracy: 0.4993 | 5a003163e8d2f2f46d60f2865eee1e1d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 10 | 317f1f03c9ef86f4f9b136de8b139fcd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6962 | 1.0 | 569 | 0.6931 | 0.5094 | | 0.6969 | 2.0 | 1138 | 0.6931 | 0.5062 | | 0.6959 | 3.0 | 1707 | 0.6931 | 0.... | 852df0e67c02d2705a0a0af4f65bc57e |
mit | ['generated_from_trainer'] | false | roberta-base_edos_b 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: 1.3797 - Accuracy: 0.6337 - F1: 0.6259 - Precision: 0.6395 - Recall: 0.6155 | 262c1119f87035076b35a15421dd26a9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.9275 | 1.0 | 638 | 0.8373 | 0.6502 | 0.6579 | 0.6348 | 0.6989 | | 0.5744 | 2.0 |... | 92f94629187627b207b39cb51b55c5f0 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-mbert-finetuned-non-code-mixed-DS This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6572 - Accuracy: 0.6429 - Precision: 0.6334 - Recall: 0.6231 - F1: 0.6262 | 8cd2406467f946c8911003ac073e129b |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.932923543227153e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 | bcd9c1243ea39ba1e603d2f5bcd2d511 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.005 | 0.5 | 926 | 0.9346 | 0.5707 | 0.5844 | 0.5274 | 0.5108 | | 0.969 | 1.0 |... | 2924bb315b9eab71e13cae1e44ff3ffb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_8_binary_v1 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: 1.6283 - F1: 0.8178 | 2383f3250752f939a7db9b5d7a55b9ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4038 | 0.7981 | | 0.409 | 2.0 | 580 | 0.4023 | 0.8176 | | 0.409 | 3.0 | 870 | 0.5245 | 0.8169 | |... | 6879e9f456cb40b9a7a3d3220dc307a6 |
apache-2.0 | ['translation'] | false | epo-bul * source group: Esperanto * target group: Bulgarian * OPUS readme: [epo-bul](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-bul/README.md) * model: transformer-align * source language(s): epo * target language(s): bul * model: transformer-align * pre-processing: normalization + ... | f5371ab190c7ea89f792f0e23e894447 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: epo-bul - source_languages: epo - target_languages: bul - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-bul/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'bg'] - src_constituents: {'epo'} - tgt_const... | f3dcf9682c1dbfcb2033e1203cc24aed |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-est This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8077 | c6ae7f2e1858f487f07f415124024a20 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 52 | 4.2865 | | No log | 2.0 | 104 | 4.0711 | | No log | 3.0 | 156 | 3.9351 | | No log | 4.0 | 208 | 3.8885 ... | 477c418afb0328e24f76b8ab15a105ca |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola 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: 0.8124 - Matthews Correlation: 0.5341 | 37dcbc2e0f7bb58fb0991185c42e8775 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5227 | 1.0 | 535 | 0.5222 | 0.4210 | | 0.3467 | 2.0 | 1070 | 0.5046 | 0.4855 | | 0.2... | ea4f825b7162a31ad50194386e3c704e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-indonesia-squadv2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9144 | 88c245a0625b075cc6012353464347ed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.1676 | 1.0 | 14833 | 2.0658 | | 1.865 | 2.0 | 29666 | 1.9552 | | 1.6669 | 3.0 | 44499 | 1.9144 | | 0ebcdb234964c5ce443f6015ddd87eb8 |
apache-2.0 | ['generated_from_trainer'] | false | Evaluations result ``` {'exact': 42.29721064443732, 'f1': 54.120071422699546, 'total': 24952, 'HasAns_exact': 42.29721064443732, 'HasAns_f1': 54.120071422699546, 'HasAns_total': 24952, 'best_exact': 42.29721064443732, 'best_exact_thresh': 0.0, 'best_f1': 54.120071422699546, 'best_f1_thresh': 0.0} ``` | c207ebd3f1762a3f0f7182d193bbdcb5 |
apache-2.0 | ['generated_from_trainer'] | false | Simple Usage ``` from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="asaduas/distilbert-base-uncased-indonesia-squadv2", tokenizer="asaduas/distilbert-base-uncased-indonesia-squadv2" ) qa_pipeline( { 'context': "Pada tahun 1512 juga Afonso de Albuquerque meng... | 24a72372421dda4df66d7f8bfc06f370 |
mit | ['generated_from_trainer'] | false | rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4049 - Accuracy: 0.8779 - F1: 0.8775 | 5ab6b5f6f5928de213863f401fb86306 |
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: 40 | 7e55b782c73bdb07ad105e2918edfeab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.3097 | 1.0 | 69 | 1.1369 | 0.6628 | 0.6210 | | 0.949 | 2.0 | 138 | 0.7114 | 0.8225 | 0.8202 | | 0.6288 |... | 708ce93ed791e6c969453f005bd0ac21 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Yohan Diffusion **Welcome to Yohan Diffusion** - a latent diffusion model that has been trained on a Japanese artist's artworks, [yohan1754/Free Style](https://www.pixiv.net/en/users/4446354). This model has been fine-tuned with a learning rate of `2.0e-6` for `20000 training steps` on `226 images` collected from Dan... | 924d81c80c0ab5cb32bc0bedd80e3fbc |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 4567478be904fdc9071b31c0a48aa6ea |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Examples Below are some examples of images generated using this model:   with their [Kohya Trainer](https://note.com/kohya_ss/n/ne17e34dd51bf) - [Linaqruf](https://huggingface.co/Linaqruf), he's a really huge help and responds to my questions thoughtfully even though I've been asking alot xD. | 2b1a2eb766798f94c9c8afd43142de7c |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_hubert_s807 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 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 39f7878905fd6410f5d037d614bebdfa |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-lowR100-3-cased-DA-20 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5250 | 39d058b79307190db0a955c9dc6fc9e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 6.8611 | | 6.5268 | 2.0 | 2 | 8.5069 | | 6.5268 | 3.0 | 3 | 6.4383 | | 6.3552 | 4.0 | 4 | 5.2540 ... | 4af9608e1871cc23106360c49d8716b2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr4_2e-05_all_26_02_2022-04_20_09 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4676 - Accuracy:... | 6ace2bd45316dae4d1c3a8a15f57fd68 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 | | No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 | | 0.4084 |... | 68f8d116cfb1f0506538b90581374892 |
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: 0.0612 - Precision: 0.9259 - Recall: 0.9369 - F1: 0.9314 - Accuracy: 0.9839 | 50b671feacab226962f609414203efee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.243 | 1.0 | 878 | 0.0703 | 0.9134 | 0.9181 | 0.9158 | 0.9806 | | 0.0515 | 2.0 |... | 7c79cc1788eb8ec666c68655858ab25b |
apache-2.0 | ['translation'] | false | opus-mt-kwy-en * source languages: kwy * target languages: en * OPUS readme: [kwy-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kwy-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 4a56fa737a58f7bb0a5ff8f0a660953a |
unknown | [] | false | Just a place to keep my LoRAs. Currently contains: Single Subject\ kanna128 - Kanna Kamui (Kobayashi-san chi no Dragon Maid)\ lum128 - Lum (Urusei Yatsura!)\ mahiro128 - Oyama Mahiro (Onii-chan wa Oshimai!)\ ononoki128 - O... | cba54186803b7aaa95e1f6d7447d6b24 |
apache-2.0 | ['automatic-speech-recognition', 'librispeech_asr', 'generated_from_trainer'] | false | sew-d-mid-400k-librispeech-clean-100h-ft This model is a fine-tuned version of [asapp/sew-d-mid-400k](https://huggingface.co/asapp/sew-d-mid-400k) on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 2.3540 - Wer: 1.0536 | 3fea42dabb5435939cff80d284c5f045 |
apache-2.0 | ['automatic-speech-recognition', 'librispeech_asr', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | 97c3453193e652cb3c4b66b5b2c33cd7 |
apache-2.0 | ['automatic-speech-recognition', 'librispeech_asr', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.319 | 0.11 | 100 | 11.0572 | 1.0 | | 3.6726 | 0.22 | 200 | 4.2003 | 1.0 | | 2.981 | 0.34 | 300 | 3.5742 | 0.9919 | |... | 3106decd3f7e800a454b96fdbe5f1650 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0665 - Accuracy: 0.9785 | ca85651d052ec9b312d1f975b868cdf8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.286 | 1.0 | 190 | 0.1254 | 0.9581 | | 0.1916 | 2.0 | 380 | 0.0802 | 0.9744 | | 0.1155 | 3.0 | 570 | 0.0665 | 0.... | 18ae5d4c78b366600acad1d7e7c7b7a2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0755 - Wer: 1.0 | 7567bc75884e52b328fb41c1d8fd4153 |
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: 10 - num_epochs: 2 - mixed_precision_trai... | d8b7f28108003d82e8ce5ca9651a6342 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 8.0894 | 0.34 | 50 | 3.8065 | 1.0 | | 3.2971 | 0.69 | 100 | 3.0704 | 1.0 | | 3.1262 | 1.03 | 150 | 3.0153 | 1.0 | | 2.9925 ... | 6b62f4ee6f5c3b211d20c1e79d274625 |
mit | ['text-classification'] | false | Multi2ConvAI-Quality: finetuned MBert for French
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: French (fr)
- model type:... | 73b6fc96779cbf15d22abd4ca3ccd142 |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-fr-mbert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-fr-mbert")
````
| be587313e95ef7df0227258f1ff531f1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-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: 0.2926 - Accuracy: 0.9490 | f6de197b19fdb301c4af3516a30c20ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.7039 | 1.0 | 318 | 2.7703 | 0.7519 | | 2.1213 | 2.0 | 636 | 1.3972 | 0.8590 | | 1.0629 | 3.0 | 954 | 0.7295 | 0.... | 0201bac1f08b489f096774b5340e003d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3255 - Precision: ... | ae3b1a37db8eb2c09bb549a61faf8c43 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4549 | 0.0228 | 0.0351 | 0.0276 | 0.7734 | | No log | 2.0 |... | e0b9c898385c33a76ffbaf813dcc2c14 |
mit | [] | false | Bengali GPT-2 Bengali GPT-2 demo. Part of the [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/). Also features a [finetuned](https://huggingface.co/khalidsaifullaah/bengali-lyricist-gpt2?) model on bengali song lyrics. | ef7e2d0aebbc5b6cbc15dcb329dc3972 |
mit | [] | false | Model Description OpenAI GPT-2 model was proposed in [Language Models are Unsupervised Multitask Learners](https://paperswithcode.com/paper/language-models-are-unsupervised-multitask) paper .Original GPT2 model was a causal (unidirectional) transformer pretrained using language modeling on a very large corpus of ~40 ... | 62c4a51dd000a2b2eab32feee1a0709d |
mit | [] | false | Training Details Overall Result: ```Eval loss : 1.45, Eval Perplexity : 3.141``` Data: [mC4-bn](https://huggingface.co/datasets/mc4) Train Steps: 250k steps link 🤗 flax-community/gpt2-bengali Demo : https://huggingface.co/spaces/flax-community/Gpt2-bengali | 84c3df9912f9cb40219914eb7b89310a |
mit | [] | false | Usage For using the model there are multiple options available. For example using the pipeline directly we can try to generate sentences. ``` from transformers import pipeline gpt2_bengali = pipeline('text-generation',model="flax-community/gpt2-bengali", tokenizer='flax-community/gpt2-bengali') ``` Similarly for ... | 142e02787c4c3a6839f7543a01101b64 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1042 | 2685832ebd21604627c17bd8d02d46f2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 0.5793 | | No log | 2.0 | 2 | 0.1730 | | No log | 3.0 | 3 | 0.1042 | | f318e8edac2507e67f3fda1dba4c7b98 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | trbse Dreambooth model trained by cdefghijkl 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-diff... | ac9d2996ccae82269b243a12b4ffbb8c |
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