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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apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2 This model is a fine-tuned version of [anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol](https://huggingface.co/anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0069 - Bleu: 28.1311 - Gen Len: 18.7412 | b206f24b20025b376ff1c2fe0ce2a0d0 |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP | 0398187751e49a1eced2d297b57714d1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 22 | 0.0692 | 27.4908 | 18.7353 | | No log | 2.0 | 44 | 0.0631 | 27.554 | 18.7294 | | No log | 3.0 | 66 | 0.0533 | 27.6007 | 18.7294 | | No log | 4.0 | 88 | 0.0484 | 27.6446 | 18.7294 | | No log | 5.0 | 110 | 0.0439 | 27.6401 | 18.7294 | | No log | 6.0 | 132 | 0.0404 | 27.5117 | 18.7294 | | No log | 7.0 | 154 | 0.0389 | 27.6358 | 18.7294 | | No log | 8.0 | 176 | 0.0362 | 27.6358 | 18.7294 | | No log | 9.0 | 198 | 0.0339 | 27.5731 | 18.7294 | | No log | 10.0 | 220 | 0.0319 | 27.2326 | 18.6882 | | No log | 11.0 | 242 | 0.0298 | 27.2326 | 18.6882 | | No log | 12.0 | 264 | 0.0293 | 27.5498 | 18.7294 | | No log | 13.0 | 286 | 0.0276 | 27.6566 | 18.7294 | | No log | 14.0 | 308 | 0.0268 | 27.6566 | 18.7294 | | No log | 15.0 | 330 | 0.0251 | 27.6107 | 18.7294 | | No log | 16.0 | 352 | 0.0239 | 27.7096 | 18.7294 | | No log | 17.0 | 374 | 0.0228 | 27.6716 | 18.7294 | | No log | 18.0 | 396 | 0.0231 | 27.8083 | 18.7294 | | No log | 19.0 | 418 | 0.0218 | 27.4838 | 18.6882 | | No log | 20.0 | 440 | 0.0212 | 27.4712 | 18.6882 | | No log | 21.0 | 462 | 0.0197 | 27.8787 | 18.7353 | | No log | 22.0 | 484 | 0.0207 | 27.6899 | 18.6941 | | 0.1026 | 23.0 | 506 | 0.0186 | 27.6376 | 18.6941 | | 0.1026 | 24.0 | 528 | 0.0202 | 27.6672 | 18.6941 | | 0.1026 | 25.0 | 550 | 0.0174 | 28.0172 | 18.7412 | | 0.1026 | 26.0 | 572 | 0.0170 | 27.8714 | 18.7412 | | 0.1026 | 27.0 | 594 | 0.0164 | 27.7423 | 18.7412 | | 0.1026 | 28.0 | 616 | 0.0164 | 27.8278 | 18.7412 | | 0.1026 | 29.0 | 638 | 0.0163 | 27.8278 | 18.7412 | | 0.1026 | 30.0 | 660 | 0.0158 | 27.907 | 18.7412 | | 0.1026 | 31.0 | 682 | 0.0165 | 27.7752 | 18.7412 | | 0.1026 | 32.0 | 704 | 0.0147 | 27.8284 | 18.7412 | | 0.1026 | 33.0 | 726 | 0.0150 | 27.8862 | 18.7412 | | 0.1026 | 34.0 | 748 | 0.0148 | 27.8402 | 18.7412 | | 0.1026 | 35.0 | 770 | 0.0141 | 27.8353 | 18.7412 | | 0.1026 | 36.0 | 792 | 0.0142 | 27.858 | 18.7412 | | 0.1026 | 37.0 | 814 | 0.0143 | 27.858 | 18.7412 | | 0.1026 | 38.0 | 836 | 0.0158 | 27.8353 | 18.7412 | | 0.1026 | 39.0 | 858 | 0.0125 | 27.8913 | 18.7412 | | 0.1026 | 40.0 | 880 | 0.0121 | 27.9167 | 18.7412 | | 0.1026 | 41.0 | 902 | 0.0122 | 27.9569 | 18.7412 | | 0.1026 | 42.0 | 924 | 0.0126 | 27.9569 | 18.7412 | | 0.1026 | 43.0 | 946 | 0.0120 | 28.001 | 18.7412 | | 0.1026 | 44.0 | 968 | 0.0125 | 28.0079 | 18.7412 | | 0.1026 | 45.0 | 990 | 0.0115 | 28.0079 | 18.7412 | | 0.072 | 46.0 | 1012 | 0.0113 | 27.9851 | 18.7412 | | 0.072 | 47.0 | 1034 | 0.0113 | 28.0184 | 18.7412 | | 0.072 | 48.0 | 1056 | 0.0110 | 28.0184 | 18.7412 | | 0.072 | 49.0 | 1078 | 0.0108 | 28.0184 | 18.7412 | | 0.072 | 50.0 | 1100 | 0.0107 | 28.0184 | 18.7412 | | 0.072 | 51.0 | 1122 | 0.0101 | 28.0184 | 18.7412 | | 0.072 | 52.0 | 1144 | 0.0102 | 28.0184 | 18.7412 | | 0.072 | 53.0 | 1166 | 0.0099 | 28.0184 | 18.7412 | | 0.072 | 54.0 | 1188 | 0.0100 | 28.0184 | 18.7412 | | 0.072 | 55.0 | 1210 | 0.0102 | 28.0184 | 18.7412 | | 0.072 | 56.0 | 1232 | 0.0095 | 28.0184 | 18.7412 | | 0.072 | 57.0 | 1254 | 0.0098 | 28.0184 | 18.7412 | | 0.072 | 58.0 | 1276 | 0.0092 | 28.0184 | 18.7412 | | 0.072 | 59.0 | 1298 | 0.0090 | 28.0184 | 18.7412 | | 0.072 | 60.0 | 1320 | 0.0095 | 28.0184 | 18.7412 | | 0.072 | 61.0 | 1342 | 0.0092 | 27.9674 | 18.7412 | | 0.072 | 62.0 | 1364 | 0.0091 | 27.9419 | 18.7412 | | 0.072 | 63.0 | 1386 | 0.0100 | 27.9419 | 18.7412 | | 0.072 | 64.0 | 1408 | 0.0084 | 28.0752 | 18.7412 | | 0.072 | 65.0 | 1430 | 0.0086 | 28.0192 | 18.7412 | | 0.072 | 66.0 | 1452 | 0.0084 | 28.0192 | 18.7412 | | 0.072 | 67.0 | 1474 | 0.0085 | 28.0192 | 18.7412 | | 0.072 | 68.0 | 1496 | 0.0087 | 28.0192 | 18.7412 | | 0.0575 | 69.0 | 1518 | 0.0084 | 28.0192 | 18.7412 | | 0.0575 | 70.0 | 1540 | 0.0080 | 28.0192 | 18.7412 | | 0.0575 | 71.0 | 1562 | 0.0082 | 28.0192 | 18.7412 | | 0.0575 | 72.0 | 1584 | 0.0080 | 28.0192 | 18.7412 | | 0.0575 | 73.0 | 1606 | 0.0075 | 28.0192 | 18.7412 | | 0.0575 | 74.0 | 1628 | 0.0079 | 28.0192 | 18.7412 | | 0.0575 | 75.0 | 1650 | 0.0078 | 28.0752 | 18.7412 | | 0.0575 | 76.0 | 1672 | 0.0076 | 28.1311 | 18.7412 | | 0.0575 | 77.0 | 1694 | 0.0073 | 28.1311 | 18.7412 | | 0.0575 | 78.0 | 1716 | 0.0074 | 28.1311 | 18.7412 | | 0.0575 | 79.0 | 1738 | 0.0072 | 28.1311 | 18.7412 | | 0.0575 | 80.0 | 1760 | 0.0078 | 28.1311 | 18.7412 | | 0.0575 | 81.0 | 1782 | 0.0077 | 28.1311 | 18.7412 | | 0.0575 | 82.0 | 1804 | 0.0071 | 28.1311 | 18.7412 | | 0.0575 | 83.0 | 1826 | 0.0072 | 28.1311 | 18.7412 | | 0.0575 | 84.0 | 1848 | 0.0075 | 28.1311 | 18.7412 | | 0.0575 | 85.0 | 1870 | 0.0071 | 28.1311 | 18.7412 | | 0.0575 | 86.0 | 1892 | 0.0070 | 28.1311 | 18.7412 | | 0.0575 | 87.0 | 1914 | 0.0069 | 28.1311 | 18.7412 | | 0.0575 | 88.0 | 1936 | 0.0069 | 28.1311 | 18.7412 | | 0.0575 | 89.0 | 1958 | 0.0069 | 28.1311 | 18.7412 | | 0.0575 | 90.0 | 1980 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 91.0 | 2002 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 92.0 | 2024 | 0.0070 | 28.1311 | 18.7412 | | 0.0509 | 93.0 | 2046 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 94.0 | 2068 | 0.0070 | 28.1311 | 18.7412 | | 0.0509 | 95.0 | 2090 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 96.0 | 2112 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 97.0 | 2134 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 98.0 | 2156 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 99.0 | 2178 | 0.0069 | 28.1311 | 18.7412 | | 0.0509 | 100.0 | 2200 | 0.0069 | 28.1311 | 18.7412 | | 641b65acb84ed903726d051da3698ac1 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_wav2vec2_s847 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | d5373b554e329a5893e8ff9575a7785a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 9d8b7db70c2aab673634ddde6978d2d6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0862 - Accuracy: 0.9828 | 03aa3d161d8cdf85694525b9453fd33f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.668 | 1.0 | 399 | 0.5462 | 0.9588 | | 0.2728 | 2.0 | 798 | 0.1750 | 0.9766 | | 0.1846 | 3.0 | 1197 | 0.1166 | 0.9785 | | 0.1642 | 4.0 | 1596 | 0.0930 | 0.9813 | | 0.1522 | 5.0 | 1995 | 0.0862 | 0.9828 | | 8556616a02d93b03bd95025960448734 |
mit | ['ukrainian', 'english'] | false | This is a variant of the [google/mt5-base](https://huggingface.co/google/mt5-base) model, in which Ukrainian and 9% English words remain. This model has 252M parameters - 43% of the original size. Special thanks for the practical example and inspiration: [cointegrated ](https://huggingface.co/cointegrated) | d4692647f9a44d262716ff2a273dc6a1 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-sqac This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the sqac dataset. It achieves the following results on the evaluation set: - Loss: 1.2111 | 10423484e708f172a2191a1e5203aeb2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9971 | 1.0 | 1196 | 0.8646 | | 0.482 | 2.0 | 2392 | 0.9334 | | 0.1652 | 3.0 | 3588 | 1.2111 | | f54cc793fd99110f4a85ddcf8451e564 |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on COCO panoptic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. | 4c0cc0259a21a65a9c3ba255086d1456 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on COCO panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-coco-panoptic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) | d2347b4dd0343b9cb610611c220529b1 |
other | ['vision', 'image-segmentation'] | false | we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | 3357e3bf34df0ac93cfd85a67868a0a7 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/distilbert-base-uncased-trained-on-open-and-closed-source This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0039 - Validation Loss: 0.2082 - Train Precision: 0.9374 - Train Recall: 0.9714 - Train F1: 0.9541 - Epoch: 9 | a5b156c6bf33747040c50859cd8b77cf |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:-----:| | 0.2472 | 0.1604 | 0.8967 | 0.9771 | 0.9352 | 0 | | 0.0924 | 0.1266 | 0.9330 | 0.9561 | 0.9444 | 1 | | 0.0439 | 0.1281 | 0.9543 | 0.9561 | 0.9552 | 2 | | 0.0258 | 0.2058 | 0.8995 | 0.9905 | 0.9428 | 3 | | 0.0136 | 0.1767 | 0.9418 | 0.9580 | 0.9499 | 4 | | 0.0134 | 0.2637 | 0.8927 | 0.9847 | 0.9365 | 5 | | 0.0074 | 0.2197 | 0.9144 | 0.9790 | 0.9456 | 6 | | 0.0049 | 0.2140 | 0.9355 | 0.9695 | 0.9522 | 7 | | 0.0058 | 0.2117 | 0.9360 | 0.9771 | 0.9561 | 8 | | 0.0039 | 0.2082 | 0.9374 | 0.9714 | 0.9541 | 9 | | 0389965313454c25700738da6a7da0ef |
mit | ['generated_from_trainer'] | false | ukrainian-qa This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on the [UA-SQuAD](https://github.com/fido-ai/ua-datasets/tree/main/ua_datasets/src/question_answering) dataset. Link to training scripts - [https://github.com/robinhad/ukrainian-qa](https://github.com/robinhad/ukrainian-qa) It achieves the following results on the evaluation set: - Loss: 1.4778 | 4fc8061befc5a51c25ceec72918d1037 |
mit | ['generated_from_trainer'] | false | How to use ```python from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering model_name = "robinhad/ukrainian-qa" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForQuestionAnswering.from_pretrained(model_name) qa_model = pipeline("question-answering", model=model.to("cpu"), tokenizer=tokenizer) question = "Де ти живеш?" context = "Мене звати Сара і я живу у Лондоні" qa_model(question = question, context = context) ``` | a02cc23f068c02f135db41a27f304cd9 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 6 | b35a6f4fde6d7771c945a672b37575d5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4526 | 1.0 | 650 | 1.3631 | | 1.3317 | 2.0 | 1300 | 1.2229 | | 1.0693 | 3.0 | 1950 | 1.2184 | | 0.6851 | 4.0 | 2600 | 1.3171 | | 0.5594 | 5.0 | 3250 | 1.3893 | | 0.4954 | 6.0 | 3900 | 1.4778 | | 02df506c62bbff325ab6cd77c6aca680 |
cc0-1.0 | [] | false | I created this embedding for SD 2.x 768x768 models, it turns everything into your favorite Christmas classic AniMagic stop motion style as popularized by Rudolf the Red Nosed Reindeer and Santa Claus is Coming to Town among several others produced by the same studio! The Unreleased Christmas Stop Motion Mario Kart Movie!  Prompt: mario kart toy, (rnknbss16 :1.3), highly textured, figurine Negative prompt: cgi, 3d render, videogame Steps: 34, Sampler: Euler a, CFG scale: 7, Seed: 2737353293, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned, Denoising strength: 0.79, Mask blur: 3, aesthetic_score: 4.9 The Upcoming Stop Action Pikachu Movie!  Prompt: pikachu in the style of rnknbss16 Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 459369051, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned, aesthetic_score: 5.2  Prompt: pikachu in the style of rnknbss16-100 Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 4076512951, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned, aesthetic_score: 5.2 Some 2022 Holiday Ads for the Latest Celebs! Donald Trump  Prompt: a close up of (donald trump:1.) in the style of (rnknbss16 :1.0) Negative prompt: blurry, text, words Steps: 29, Sampler: Euler a, CFG scale: 7, Seed: 1397465632, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned, aesthetic_score: 5.4 Morgan Freeman  Prompt: morgan freeman in the style of (rnknbss16 :1.0) Steps: 29, Sampler: DPM++ 2S a Karras, CFG scale: 7, Seed: 1868403973, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned, aesthetic_score: 5.7 Barack Obama  Prompt: barack obama in the style of rnknbss16v2-775 Steps: 47, Sampler: Euler a, CFG scale: 7, Seed: 3661737292, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned And Lastly, The Remake of A Lifetime, Hogwarts Castle From the New Harry Potter Series  Prompt: Hogwarts school of witchcraft and wizardry in the style of (rnknbss16 :1.0), highly detailed, intricate Negative prompt: blurry Steps: 60, Sampler: Euler a, CFG scale: 7, Seed: 2909664084, Size: 768x768, Model: SD 2.0_Standard_512-depth-ema, Denoising strength: 0.66, Mask blur: 3, aesthetic_score: 6.2 Notes on the use of these: So I didn't really get a chance to fine-tune them as well as I would have liked, but I wanted to get them out there for people to enjoy so I've included the best of what I have. All of these were trained with 90-ish upscaled screen grabs from high quality DVDs of just the 2 movies mentioned above. I did use some of the letters, and postcards, and packages from the opening credits scenes in hopes to be able to reproduce those or something similar (haven't tried) so you will probably want to include the usual "words, text, letters, logos, watermarks..." in your negative prompts to try to weed those out. I also included some of the limited 2d artwork found in those movies, again in hopes to be able to generate that style as well. but that hasn't seemed to affect much except possibly when generating things that have a lot of 2d variations (i.e. comic book characters) so specifying 3d, or that you want a doll of the thing, or a model, or toy of the thing might help a lot with prompting. Otherwise, just saying " thing in the style of rnknbss16" should do the trick! The Models: They're all 16 vectors. rnknbss16: pretty good but was trained too far and/or fast and tends to make hybrid elf/Santa creatures out of everything and is hard to get it to do anything else, although if your concept is strong or present enough in the model it can do pretty well (i.e. Cinderella's castle which is on EVERYTHING Disney). Models rnknbss16-100 through rnknbss16-150 do much better, however these do less well with people and faces, they're better suited for things, creatures, animals, scenery, places, etc. rnknbss16v2: pretty sure this one is overtrained by a good deal, but you might have success with it. rnknbss16v2-750 and rnknbss16v2-775 are the sweet spot for people and characters with this v2 model, it also tends to get clearer outputs without looking as "fuzzy" or "blurry" and almost as a similar quality as VintageHelper embedding. Which brings me to mixing this with things: Using VingateHelper tends to enhance the "old school" vibes and film grain look as well as thematic props and other elements that may appear in the scene, and PhotoHelper embedding tends to create more "clay" models out of things, like with the Hogwarts castle it made it a wide angle clay diorama model of sorts which was cool and unexpected (see below).  Prompt: Hogwarts castle in the style of (rnknbss16 :1.2), highly detailed, very textured, intricate, shallow depth of field, photohelper Negative prompt: blurry, text, words Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3448665914, Size: 768x768, Model: SD 2.0_Standard_v2-1_768-ema-pruned, aesthetic_score: 5.6 | 7eb5450e26b6874f4f9a595ab43555b1 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_xlsr-53_s948 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | ce711b1bf5c474af8f32d86aed550bbd |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-8-16-5-oos 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: 2.3789 - Accuracy: 0.3356 | 6bf856204f351e1e6e51b6f51fa630e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7614 | 1.0 | 1 | 2.6146 | 0.1889 | | 2.2082 | 2.0 | 2 | 2.5232 | 0.2667 | | 1.8344 | 3.0 | 3 | 2.4516 | 0.2933 | | 1.4601 | 4.0 | 4 | 2.4033 | 0.3267 | | 1.2748 | 5.0 | 5 | 2.3789 | 0.3356 | | 08b3b7185fe33546be8eea579e1f91f0 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Using dbwhitemane.ckpt   %2C%20best%20quality%2C%20(masterpiece_1.3)%2C%20(red%20eyes_1.2)%2C%20blush%2C%20embarrassed.png) %2C%20lush%2C%20%20blond.png)      Clip skip comparsion  I uploaded for now 3 models (more incoming for whitemane): -[whitemanedb_step_2500.ckpt](https://huggingface.co/sd-dreambooth-library/sally-whitemanev/blob/main/whitemanedb_step_2500.ckpt) -[whitemanedb_step_3500.ckpt](https://huggingface.co/sd-dreambooth-library/sally-whitemanev/blob/main/whitemanedb_step_3500.ckpt) Are trained with 21 images and the trigger is "whitemanedb", this is my first attempts and I didn't get the final file because I ran out of space on drive :\ but model seems to work just fine. The second model is [dbwhitemane.ckpt](https://huggingface.co/sd-dreambooth-library/sally-whitemanev/blob/main/dbwhitemane.ckpt) This one has a total of 39 images used for training that you can find [here](https://huggingface.co/sd-dreambooth-library/sally-whitemanev/tree/main/dataset) **Model is based on AnythingV3 FP16 [38c1ebe3] And so I would recommend to use a VAE from NAI, Anything or WaifuDiffusion** **Also set clip skip to 2 will help because its based on NAI model** | 7b052c6f09a237af9a67dd30ba8100fd |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Promt examples This one is for the comparsion on top > whitemanedb , 8k, 4k, (highres:1.1), best quality, (masterpiece:1.3), (red eyes:1.2), blush, embarrassed > Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, deformed face, (poorly drawn face)),((buckteeth)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck))), 1boy, > Steps: 45, Sampler: Euler a, CFG scale: 7, Seed: 772493513, Size: 512x512, Model hash: 313ad056, Eta: 0.07, Clip skip: 2 > whitemanedb taking a bath, 8k, 4k, (highres:1.1), best quality, (masterpiece:1.3), (red eyes:1.2), nsfw, nude, blush, nipples, > Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, deformed face, (poorly drawn face)),((buckteeth)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck))), 1boy, > Steps: 45, Sampler: Euler a, CFG scale: 7, Seed: 3450621385, Size: 512x512, Model hash: 313ad056, Eta: 0.07, Clip skip: 2 > whitemanedb in a forest > Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, deformed face > Steps: 35, Sampler: Euler a, CFG scale: 10.0, Seed: 2547952708, Size: 512x512, Model hash: 313ad056, Eta: 0.07, Clip skip: 2 > lying in the ground , princess, 1girl, solo, sbwhitemane in forest , leather armor, red eyes, lush > Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, deformed face, (poorly drawn face)),((buckteeth)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck))), 1boy, > Steps: 58, Sampler: Euler a, CFG scale: 7, Seed: 1390776440, Size: 512x512, Model hash: 8b1a4378, Clip skip: 2 > sbwhitemane leaning forward, princess, 1girl, solo,elf in forest , leather armor, large eyes, (ice green eyes:1.1), lush, blonde hair, realistic photo > Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, deformed face, (poorly drawn face)),((buckteeth)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck))), 1boy, > Steps: 45, Sampler: Euler a, CFG scale: 7, Seed: 1501953711, Size: 512x512, Model hash: 8b1a4378, Clip skip: 2 Enjoy, any recommendation or help is welcome, this is my first model and probably a lot of things can be improved! | a742c9646621d071737de42459508bbe |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-uk-hu Neural machine translation model for translating from Ukrainian (uk) to Hungarian (hu). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` | 359b8a451666adefd39586950b7ed114 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-08 * source language(s): ukr * target language(s): hun * model: transformer-align * data: opusTCv20210807+pft ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pft_transformer-align_2022-03-08.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-hun/opusTCv20210807+pft_transformer-align_2022-03-08.zip) * more information released models: [OPUS-MT ukr-hun README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-hun/README.md) | 12696b3b2f88ecb93fcdb7a36916c71a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Я тобі винний 1000 доларів.", "Я п'ю воду." ] model_name = "pytorch-models/opus-mt-tc-base-uk-hu" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | 180b4280d23db9e9fd6ca387f2bf7496 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Vizet iszom. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-hu") print(pipe("Я тобі винний 1000 доларів.")) | 8ef632d9c5842765f7d7a50fe0c90074 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+pft_transformer-align_2022-03-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-hun/opusTCv20210807+pft_transformer-align_2022-03-08.test.txt) * test set scores: [opusTCv20210807+pft_transformer-align_2022-03-08.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-hun/opusTCv20210807+pft_transformer-align_2022-03-08.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 7564af4406d30bfd96b07a96f929964f |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-EL12 (Deep-Narrow version) T5-Efficient-LARGE-EL12 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | d6961c1fddf90536966e427bb2fd1f7a |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-el12** - is of model type **Large** with the following variations: - **el** is **12** It has **586.69** million parameters and thus requires *ca.* **2346.78 MB** of memory in full precision (*fp32*) or **1173.39 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | f0c36b70b9b527ece8d38e194f265cf7 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-nl_s158 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 45fc69f94dabfd99d34b0c486739dd97 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6247 | fb6ced7a09609d6a38460349d8841847 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9872 | 1.0 | 554 | 1.7933 | | 1.6189 | 2.0 | 1108 | 1.6159 | | 1.3125 | 3.0 | 1662 | 1.6247 | | 9764db42db998ff9cfd79b2fcc218d67 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | test1 Dreambooth model trained by ukeeba 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 0ce2e73ad5ae6e6ff7feeba5eba8d7a9 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_inv_gamma: 1.0 - ema_inv_gamma: 0.75 - ema_inv_gamma: 0.9999 - mixed_precision: fp16 | 72712858905ab1d5e505197d0f721647 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_xls-r_s287 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 772077fc21012a5c86beb99834b3a753 |
apache-2.0 | ['thai', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-thai-char-upos](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char-upos). | 538b5260cb6255be677b0890ef0949f0 |
apache-2.0 | ['thai', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/roberta-base-thai-char-ud-goeswith") print(nlp("หลายหัวดีกว่าหัวเดียว")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-thai-char-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("หลายหัวดีกว่าหัวเดียว")) ``` | 23fc6e7505fda07de63c6de5f40d2f7f |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Wav2Vec2 XLS-R 300M Cantonese (zh-HK) Wav2Vec2 XLS-R 300M Cantonese (zh-HK) is an automatic speech recognition model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `zh-HK` subset of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. This model was trained using HuggingFace's PyTorch framework and is part of the [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by HuggingFace. All training was done on a Tesla V100, sponsored by OVH. All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-zh-HK-v2/tree/main) tab, as well as the [Training metrics](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-zh-HK-v2/tensorboard) logged via Tensorboard. | 7a4407881ba995806f90ca6cc6fbed9a |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | params | Arch. | Training/Validation data (text) | | ------------------------------ | ------- | ----- | ------------------------------- | | `wav2vec2-xls-r-300m-zh-HK-v2` | 300M | XLS-R | `Common Voice zh-HK` Dataset | | 1fade30c966eaf2e03c87c151c8acf91 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Evaluation Results The model achieves the following results on evaluation: | Dataset | Loss | CER | | -------------------------------- | ------ | ------ | | `Common Voice` | 0.8089 | 31.73% | | `Common Voice 7` | N/A | 23.11% | | `Common Voice 8` | N/A | 23.02% | | `Robust Speech Event - Dev Data` | N/A | 56.60% | | 53efcc67a471aaa180d1f5a312e567b7 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - `learning_rate`: 0.0001 - `train_batch_size`: 8 - `eval_batch_size`: 8 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 32 - `optimizer`: Adam with `betas=(0.9, 0.999)` and `epsilon=1e-08` - `lr_scheduler_type`: linear - `lr_scheduler_warmup_steps`: 2000 - `num_epochs`: 100.0 - `mixed_precision_training`: Native AMP | d12b84bc51eb69ad9ded22eb52ff93ae |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | | :-----------: | :---: | :---: | :-------------: | :----: | :----: | | 69.8341 | 1.34 | 500 | 80.0722 | 1.0 | 1.0 | | 6.6418 | 2.68 | 1000 | 6.6346 | 1.0 | 1.0 | | 6.2419 | 4.02 | 1500 | 6.2909 | 1.0 | 1.0 | | 6.0813 | 5.36 | 2000 | 6.1150 | 1.0 | 1.0 | | 5.9677 | 6.7 | 2500 | 6.0301 | 1.1386 | 1.0028 | | 5.9296 | 8.04 | 3000 | 5.8975 | 1.2113 | 1.0058 | | 5.6434 | 9.38 | 3500 | 5.5404 | 2.1624 | 1.0171 | | 5.1974 | 10.72 | 4000 | 4.5440 | 2.1702 | 0.9366 | | 4.3601 | 12.06 | 4500 | 3.3839 | 2.2464 | 0.8998 | | 3.9321 | 13.4 | 5000 | 2.8785 | 2.3097 | 0.8400 | | 3.6462 | 14.74 | 5500 | 2.5108 | 1.9623 | 0.6663 | | 3.5156 | 16.09 | 6000 | 2.2790 | 1.6479 | 0.5706 | | 3.32 | 17.43 | 6500 | 2.1450 | 1.8337 | 0.6244 | | 3.1918 | 18.77 | 7000 | 1.8536 | 1.9394 | 0.6017 | | 3.1139 | 20.11 | 7500 | 1.7205 | 1.9112 | 0.5638 | | 2.8995 | 21.45 | 8000 | 1.5478 | 1.0624 | 0.3250 | | 2.7572 | 22.79 | 8500 | 1.4068 | 1.1412 | 0.3367 | | 2.6881 | 24.13 | 9000 | 1.3312 | 2.0100 | 0.5683 | | 2.5993 | 25.47 | 9500 | 1.2553 | 2.0039 | 0.6450 | | 2.5304 | 26.81 | 10000 | 1.2422 | 2.0394 | 0.5789 | | 2.4352 | 28.15 | 10500 | 1.1582 | 1.9970 | 0.5507 | | 2.3795 | 29.49 | 11000 | 1.1160 | 1.8255 | 0.4844 | | 2.3287 | 30.83 | 11500 | 1.0775 | 1.4123 | 0.3780 | | 2.2622 | 32.17 | 12000 | 1.0704 | 1.7445 | 0.4894 | | 2.2225 | 33.51 | 12500 | 1.0272 | 1.7237 | 0.5058 | | 2.1843 | 34.85 | 13000 | 0.9756 | 1.8042 | 0.5028 | | 2.1 | 36.19 | 13500 | 0.9527 | 1.8909 | 0.6055 | | 2.0741 | 37.53 | 14000 | 0.9418 | 1.9026 | 0.5880 | | 2.0179 | 38.87 | 14500 | 0.9363 | 1.7977 | 0.5246 | | 2.0615 | 40.21 | 15000 | 0.9635 | 1.8112 | 0.5599 | | 1.9448 | 41.55 | 15500 | 0.9249 | 1.7250 | 0.4914 | | 1.8966 | 42.89 | 16000 | 0.9023 | 1.5829 | 0.4319 | | 1.8662 | 44.24 | 16500 | 0.9002 | 1.4833 | 0.4230 | | 1.8136 | 45.58 | 17000 | 0.9076 | 1.1828 | 0.2987 | | 1.7908 | 46.92 | 17500 | 0.8774 | 1.5773 | 0.4258 | | 1.7354 | 48.26 | 18000 | 0.8727 | 1.5037 | 0.4024 | | 1.6739 | 49.6 | 18500 | 0.8636 | 1.1239 | 0.2789 | | 1.6457 | 50.94 | 19000 | 0.8516 | 1.2269 | 0.3104 | | 1.5847 | 52.28 | 19500 | 0.8399 | 1.3309 | 0.3360 | | 1.5971 | 53.62 | 20000 | 0.8441 | 1.3153 | 0.3335 | | 1.602 | 54.96 | 20500 | 0.8590 | 1.2932 | 0.3433 | | 1.5063 | 56.3 | 21000 | 0.8334 | 1.1312 | 0.2875 | | 1.4631 | 57.64 | 21500 | 0.8474 | 1.1698 | 0.2999 | | 1.4997 | 58.98 | 22000 | 0.8638 | 1.4279 | 0.3854 | | 1.4301 | 60.32 | 22500 | 0.8550 | 1.2737 | 0.3300 | | 1.3798 | 61.66 | 23000 | 0.8266 | 1.1802 | 0.2934 | | 1.3454 | 63.0 | 23500 | 0.8235 | 1.3816 | 0.3711 | | 1.3678 | 64.34 | 24000 | 0.8550 | 1.6427 | 0.5035 | | 1.3761 | 65.68 | 24500 | 0.8510 | 1.6709 | 0.4907 | | 1.2668 | 67.02 | 25000 | 0.8515 | 1.5842 | 0.4505 | | 1.2835 | 68.36 | 25500 | 0.8283 | 1.5353 | 0.4221 | | 1.2961 | 69.7 | 26000 | 0.8339 | 1.5743 | 0.4369 | | 1.2656 | 71.05 | 26500 | 0.8331 | 1.5331 | 0.4217 | | 1.2556 | 72.39 | 27000 | 0.8242 | 1.4708 | 0.4109 | | 1.2043 | 73.73 | 27500 | 0.8245 | 1.4469 | 0.4031 | | 1.2722 | 75.07 | 28000 | 0.8202 | 1.4924 | 0.4096 | | 1.202 | 76.41 | 28500 | 0.8290 | 1.3807 | 0.3719 | | 1.1679 | 77.75 | 29000 | 0.8195 | 1.4097 | 0.3749 | | 1.1967 | 79.09 | 29500 | 0.8059 | 1.2074 | 0.3077 | | 1.1241 | 80.43 | 30000 | 0.8137 | 1.2451 | 0.3270 | | 1.1414 | 81.77 | 30500 | 0.8117 | 1.2031 | 0.3121 | | 1.132 | 83.11 | 31000 | 0.8234 | 1.4266 | 0.3901 | | 1.0982 | 84.45 | 31500 | 0.8064 | 1.3712 | 0.3607 | | 1.0797 | 85.79 | 32000 | 0.8167 | 1.3356 | 0.3562 | | 1.0119 | 87.13 | 32500 | 0.8215 | 1.2754 | 0.3268 | | 1.0216 | 88.47 | 33000 | 0.8163 | 1.2512 | 0.3184 | | 1.0375 | 89.81 | 33500 | 0.8137 | 1.2685 | 0.3290 | | 0.9794 | 91.15 | 34000 | 0.8220 | 1.2724 | 0.3255 | | 1.0207 | 92.49 | 34500 | 0.8165 | 1.2906 | 0.3361 | | 1.0169 | 93.83 | 35000 | 0.8153 | 1.2819 | 0.3305 | | 1.0127 | 95.17 | 35500 | 0.8187 | 1.2832 | 0.3252 | | 0.9978 | 96.51 | 36000 | 0.8111 | 1.2612 | 0.3210 | | 0.9923 | 97.85 | 36500 | 0.8076 | 1.2278 | 0.3122 | | 1.0451 | 99.2 | 37000 | 0.8086 | 1.2451 | 0.3156 | | 39374d4d8bee50c0bdb75cf2c6b8cb46 |
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: 0.2149 - Accuracy: 0.9265 - F1: 0.9266 | 09b92bfe5e5c1b2aacf3aa07360e6f2d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8307 | 1.0 | 250 | 0.3103 | 0.9065 | 0.9038 | | 0.2461 | 2.0 | 500 | 0.2149 | 0.9265 | 0.9266 | | 0c887f65f6cc946754047932c0ac3cf0 |
cc | ['text classification'] | false | Model information: This model is the [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report texts into two classes. Labels of [0,1] were assigned to radiology reports in MIMIC-III that were linked to an ICD9 diagnosis code for lung cancer = 1 and a random sample of reports which were not linked to any type of cancer diagnosis code at all = 0. | 8fde37ade488235a8316cbcd86333174 |
cc | ['text classification'] | false | Limitations: Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before use - - [MIMIC-III](https://www.nature.com/articles/sdata201635.pdf) - [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) | dff997f5c33d90613f3cef0f0684ec1f |
cc | ['text classification'] | false | How to use: Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-m3-lc") model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-m3-lc") ``` | 8329ccceb47d3823327dfa760980bfbf |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_gender_male-2_female-8_s364 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | fd19560ecd20ca133ecb130212867ee2 |
apache-2.0 | [] | false | AIShell-1 and Wenetspeech testset results with modified-beam-search streaming decode using epoch-14.pt | decode_chunk_len | AIShell-1 | TEST_NET | TEST_MEETING | |------------------|-----------|----------|--------------| | 32 | 3.19 | 9.58 | 9.51 || | 64 | 3.04 | 8.97 | 8.83 || | 233934d1f7c65446b75906b1b85f8fab |
apache-2.0 | [] | false | Training and decoding commands ``` nohup ./pruned_transducer_stateless7_streaming/train.py --world-size 8 --num-epochs 30 --start-epoch 1 --feedforward-dims "1024,1024,1536,1536,1024" --exp-dir pruned_transducer_stateless7_streaming/exp --max-duration 360 > pruned_transducer_stateless7_streaming/exp/nohup.zipformer & nohup ./pruned_transducer_stateless7_streaming/decode.py --epoch 6 --avg 1 --exp-dir ./pruned_transducer_stateless7_streaming/exp --max-duration 600 --decode-chunk-len 32 --decoding-method modified_beam_search --beam-size 4 > nohup.zipformer.deocode & ``` | 6a8dbf1574edba65f17e9559f35deb52 |
apache-2.0 | [] | false | Tips some k2-fsa version and parameter is ``` {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'lo g_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.23.2', 'k2-build -type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'a74f59dba1863cd9386ba4d8815850421260eee7', 'k2-git-date': 'Fri Dec 2 08:32:22 2022', 'lhotse-version': '1.5.0.dev+gi t.8ce38fc.dirty', 'torch-version': '1.11.0+cu113', 'torch-cuda-available': True, 'torch-cuda-version': '11.3', 'python-version': '3.7', 'icefall-git-branch': 'master', 'icef all-git-sha1': '11b08db-dirty', 'icefall-git-date': 'Thu Jan 12 10:19:21 2023', 'icefall-path': '/opt/conda/lib/python3.7/site-packages', 'k2-path': '/opt/conda/lib/python3. 7/site-packages/k2/__init__.py', 'lhotse-path': '/opt/conda/lib/python3.7/site-packages/lhotse/__init__.py', 'hostname': 'xxx', 'IP add ress': 'x.x.x.x'}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 30, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('pruned_trans ducer_stateless7_streaming/exp'), 'lang_dir': 'data/lang_char_bpe', 'base_lr': 0.01, 'lr_batches': 5000, 'lr_epochs': 3.5, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0 .25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'inf_check': False, 'save_every_n': 2000, 'keep_last_k': 30, 'average_period': 200, ' use_fp16': False, 'num_encoder_layers': '2,4,3,2,4', 'feedforward_dims': '1024,1024,1536,1536,1024', 'nhead': '8,8,8,8,8', 'encoder_dims': '384,384,384,384,384', 'attention_ dims': '192,192,192,192,192', 'encoder_unmasked_dims': '256,256,256,256,256', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder _dim': 512, 'joiner_dim': 512, 'short_chunk_size': 50, 'num_left_chunks': 4, 'decode_chunk_len': 32, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 360, 'bucketing _sampler': True, 'num_buckets': 300, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_wor kers': 8, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'training_subset': '12k_hour', 'blank_id': 0, 'vocab_size': 6254} ``` | 839fbf2f442d294c8bf434bcd2ae1cde |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3185 - Accuracy: 0.8667 - F1: 0.8675 | a7df9895fb129e65011045127514de5b |
mit | [] | false | guttestreker on Stable Diffusion This is the `<guttestreker>` 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 own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:              | 7fa34036dc69067d108271043827d9ca |
apache-2.0 | ['generated_from_keras_callback'] | false | opus-mt-ar-en-finetunedTanzil-v5-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8101 - Validation Loss: 0.9477 - Train Bleu: 9.3241 - Train Gen Len: 88.73 - Train Rouge1: 56.4906 - Train Rouge2: 34.2668 - Train Rougel: 53.2279 - Train Rougelsum: 53.7836 - Epoch: 2 | 92380d030caa58449cb05658e8a1a243 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Bleu | Train Gen Len | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Epoch | |:----------:|:---------------:|:----------:|:-------------:|:------------:|:------------:|:------------:|:---------------:|:-----:| | 0.8735 | 0.9809 | 11.0863 | 78.68 | 56.4557 | 33.3673 | 53.4828 | 54.1197 | 0 | | 0.8408 | 0.9647 | 9.8543 | 88.955 | 57.3797 | 34.3539 | 53.8783 | 54.3714 | 1 | | 0.8101 | 0.9477 | 9.3241 | 88.73 | 56.4906 | 34.2668 | 53.2279 | 53.7836 | 2 | | bdf80f9cda83fffe58eac80070e76f70 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xls-r-1b-Indonesian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9550 - Wer: 0.4551 - Cer: 0.1643 | 31cbb507f002f1d61dc98a7a069b80c6 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.663 | 7.69 | 200 | 0.7898 | 0.6039 | 0.1848 | | 0.7424 | 15.38 | 400 | 1.0215 | 0.5615 | 0.1924 | | 0.4494 | 23.08 | 600 | 1.0901 | 0.5249 | 0.1932 | | 0.5075 | 30.77 | 800 | 1.1013 | 0.5079 | 0.1935 | | 0.4671 | 38.46 | 1000 | 1.1034 | 0.4916 | 0.1827 | | 0.1928 | 46.15 | 1200 | 0.9550 | 0.4551 | 0.1643 | | aada2564ff4d0ddbe01bdc47a26fe8dd |
apache-2.0 | ['image-classification'] | false | MindSpore Image Classification models with MNIST on the 🤗Hub! This repository contains the model from [this notebook on image classification with MNIST dataset using LeNet architecture](https://gitee.com/mindspore/mindspore/blob/r1.2/model_zoo/official/cv/lenet/README.md | 7037282e890cec7e4c12f09da54d531d |
apache-2.0 | ['image-classification'] | false | LeNet Description Lenet-5 is one of the earliest pre-trained models proposed by Yann LeCun and others in the year 1998, in the research paper Gradient-Based Learning Applied to Document Recognition. They used this architecture for recognizing the handwritten and machine-printed characters. The main reason behind the popularity of this model was its simple and straightforward architecture. It is a multi-layer convolution neural network for image classification.  [source](https://www.analyticsvidhya.com/blog/2021/03/the-architecture-of-lenet-5/) | 32bb0e9802001168568acbfa8a5852ca |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT_fold_6_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7302 - F1: 0.8128 | a276289f627ecfe15a4002d1ca4e69c1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 292 | 0.5359 | 0.7833 | | 0.5585 | 2.0 | 584 | 0.5376 | 0.8026 | | 0.5585 | 3.0 | 876 | 0.6117 | 0.8038 | | 0.2314 | 4.0 | 1168 | 0.8036 | 0.7974 | | 0.2314 | 5.0 | 1460 | 0.9467 | 0.8179 | | 0.1093 | 6.0 | 1752 | 1.2957 | 0.7923 | | 0.0384 | 7.0 | 2044 | 1.3423 | 0.8026 | | 0.0384 | 8.0 | 2336 | 1.2644 | 0.8218 | | 0.021 | 9.0 | 2628 | 1.3093 | 0.8231 | | 0.021 | 10.0 | 2920 | 1.3282 | 0.8179 | | 0.0129 | 11.0 | 3212 | 1.3853 | 0.8295 | | 0.0078 | 12.0 | 3504 | 1.4705 | 0.8154 | | 0.0078 | 13.0 | 3796 | 1.5063 | 0.8167 | | 0.0064 | 14.0 | 4088 | 1.5293 | 0.8179 | | 0.0064 | 15.0 | 4380 | 1.6303 | 0.8128 | | 0.0085 | 16.0 | 4672 | 1.5945 | 0.8115 | | 0.0085 | 17.0 | 4964 | 1.6899 | 0.8103 | | 0.0056 | 18.0 | 5256 | 1.6952 | 0.8064 | | 0.0055 | 19.0 | 5548 | 1.7550 | 0.7936 | | 0.0055 | 20.0 | 5840 | 1.6779 | 0.8141 | | 0.003 | 21.0 | 6132 | 1.7064 | 0.8128 | | 0.003 | 22.0 | 6424 | 1.7192 | 0.8154 | | 0.0013 | 23.0 | 6716 | 1.8188 | 0.7974 | | 0.0014 | 24.0 | 7008 | 1.7273 | 0.8128 | | 0.0014 | 25.0 | 7300 | 1.7302 | 0.8128 | | d239ddd6d0cdbc98d3f8a96bb6b81329 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-summarization This model is a fine-tuned version of t5-small (https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6477 | 18ba5cd6bd5cd8e65feeae1520761a69 |
apache-2.0 | ['generated_from_trainer'] | false | Model description The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 1 - mixed_precision_training: Native AMP | 7869c41c946b519187cc59fcf9fb6968 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_general-roberta-large-v1-5-95 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. | d733dd56f77faa597e0e51ebf3378e77 |
openrail++ | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-guided-to-image-inpainting', 'endpoints-template'] | false | Fork of [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) > Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. > For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). For more information about the model, license and limitations check the original model card at [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting). --- This repository implements a custom `handler` task for `text-guided-to-image-inpainting` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [handler.py](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/handler.py). There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py`  | 89fee600ceca7acf5958deaf2824ed67 |
openrail++ | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-guided-to-image-inpainting', 'endpoints-template'] | false | expected Request payload ```json { "inputs": "A prompt used for image generation", "image" : "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC", "mask_image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC", } ``` below is an example on how to run a request using Python and `requests`. | e012e4502025defbe30e495926c5af84 |
openrail++ | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-guided-to-image-inpainting', 'endpoints-template'] | false | helper image utils def encode_image(image_path): with open(image_path, "rb") as i: b64 = base64.b64encode(i.read()) return b64.decode("utf-8") def predict(prompt, image, mask_image): image = encode_image(image) mask_image = encode_image(mask_image) | 2bce5c8671ee63656ba125f8db3318ab |
openrail++ | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-guided-to-image-inpainting', 'endpoints-template'] | false | important to get an image back } response = r.post(ENDPOINT_URL, headers=headers, json=payload) img = Image.open(BytesIO(response.content)) return img prediction = predict( prompt="Face of a bengal cat, high resolution, sitting on a park bench", image="dog.png", mask_image="mask_dog.png" ) ``` expected output  | ea357773f2d5297e86c1f0a3823cec73 |
bsd-3-clause | ['microsoft/MiniLM-L12-H384-uncased'] | false | test-minilm-finetuned-emotion fine-tuned model (uncased) This model is a fine-tuned extension of the [Microsoft MiniLM distilled model](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased). This is the result of the learning exercise for [Simple Training with the 🤗 Transformers Trainer](https://www.youtube.com/watch?v=u--UVvH-LIQ&t=198s) and also going through Chapter 2, Text Classification in [Natural Language Processing with Transformers](https://transformersbook.com/), Revised Color Edition, May 2022. This model is uncased: it does not make a difference between english and English. | f133a3e4195bdda30d357a99bcf903e2 |
mit | [] | false | Anime Background style (v2) on Stable Diffusion This is the `<anime-background-style-v2>` 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 own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:               Here are images generated with this style:     | 82e20ae67fa60befeb57712f39ca0fb9 |
apache-2.0 | ['translation'] | false | epo-rus * source group: Esperanto * target group: Russian * OPUS readme: [epo-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-rus/README.md) * model: transformer-align * source language(s): epo * target language(s): rus * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.eval.txt) | 9c1e7c187a55e912e43a57189fcdf9c0 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: epo-rus - source_languages: epo - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'ru'] - src_constituents: {'epo'} - tgt_constituents: {'rus'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.test.txt - src_alpha3: epo - tgt_alpha3: rus - short_pair: eo-ru - chrF2_score: 0.379 - bleu: 17.7 - brevity_penalty: 0.9179999999999999 - ref_len: 71288.0 - src_name: Esperanto - tgt_name: Russian - train_date: 2020-06-16 - src_alpha2: eo - tgt_alpha2: ru - prefer_old: False - long_pair: epo-rus - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 2327bf088f4cc66c7ab000689700318d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-convincingness-IBM 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: 0.6537 - Accuracy: 0.7511 | 43d40968ba4554c80208fa9b61c8dba9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 270 | 0.5707 | 0.7337 | | 0.4673 | 2.0 | 540 | 0.6059 | 0.7221 | | 0.4673 | 3.0 | 810 | 0.6537 | 0.7511 | | 0.2218 | 4.0 | 1080 | 0.8485 | 0.7467 | | 0.2218 | 5.0 | 1350 | 0.9221 | 0.7438 | | 357230fe6933f7142be1413fd7891421 |
mit | ['generated_from_trainer'] | false | predict-perception-bert-cause-human This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7139 - Rmse: 1.2259 - Rmse Cause::a Causata da un essere umano: 1.2259 - Mae: 1.0480 - Mae Cause::a Causata da un essere umano: 1.0480 - R2: 0.4563 - R2 Cause::a Causata da un essere umano: 0.4563 - Cos: 0.4783 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.3953 - Rsa: nan | 6cdf131d9fb67eb0bf16802a167a4f68 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un essere umano | Mae | Mae Cause::a Causata da un essere umano | R2 | R2 Cause::a Causata da un essere umano | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:------:|:--------------------------------------:|:------:|:----:|:----:|:---------:|:---:| | 1.0874 | 1.0 | 15 | 1.2615 | 1.6296 | 1.6296 | 1.3836 | 1.3836 | 0.0393 | 0.0393 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan | | 0.9577 | 2.0 | 30 | 1.1988 | 1.5886 | 1.5886 | 1.3017 | 1.3017 | 0.0870 | 0.0870 | 0.4783 | 0.0 | 0.5 | 0.3944 | nan | | 0.8414 | 3.0 | 45 | 0.9870 | 1.4414 | 1.4414 | 1.1963 | 1.1963 | 0.2483 | 0.2483 | 0.3913 | 0.0 | 0.5 | 0.3048 | nan | | 0.7291 | 4.0 | 60 | 0.9098 | 1.3839 | 1.3839 | 1.1297 | 1.1297 | 0.3071 | 0.3071 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.5949 | 5.0 | 75 | 0.9207 | 1.3921 | 1.3921 | 1.2079 | 1.2079 | 0.2988 | 0.2988 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.4938 | 6.0 | 90 | 0.8591 | 1.3448 | 1.3448 | 1.1842 | 1.1842 | 0.3458 | 0.3458 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.3611 | 7.0 | 105 | 0.8176 | 1.3119 | 1.3119 | 1.1454 | 1.1454 | 0.3774 | 0.3774 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan | | 0.2663 | 8.0 | 120 | 0.6879 | 1.2034 | 1.2034 | 1.0300 | 1.0300 | 0.4761 | 0.4761 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan | | 0.1833 | 9.0 | 135 | 0.7704 | 1.2735 | 1.2735 | 1.1031 | 1.1031 | 0.4133 | 0.4133 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan | | 0.1704 | 10.0 | 150 | 0.7097 | 1.2222 | 1.2222 | 1.0382 | 1.0382 | 0.4596 | 0.4596 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.1219 | 11.0 | 165 | 0.6872 | 1.2027 | 1.2027 | 1.0198 | 1.0198 | 0.4767 | 0.4767 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.1011 | 12.0 | 180 | 0.7201 | 1.2312 | 1.2312 | 1.0466 | 1.0466 | 0.4516 | 0.4516 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan | | 0.0849 | 13.0 | 195 | 0.7267 | 1.2368 | 1.2368 | 1.0454 | 1.0454 | 0.4466 | 0.4466 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0818 | 14.0 | 210 | 0.7361 | 1.2448 | 1.2448 | 1.0565 | 1.0565 | 0.4394 | 0.4394 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0634 | 15.0 | 225 | 0.7158 | 1.2275 | 1.2275 | 1.0384 | 1.0384 | 0.4549 | 0.4549 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.065 | 16.0 | 240 | 0.7394 | 1.2475 | 1.2475 | 1.0659 | 1.0659 | 0.4369 | 0.4369 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.0541 | 17.0 | 255 | 0.7642 | 1.2683 | 1.2683 | 1.0496 | 1.0496 | 0.4181 | 0.4181 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0577 | 18.0 | 270 | 0.7137 | 1.2257 | 1.2257 | 1.0303 | 1.0303 | 0.4565 | 0.4565 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0474 | 19.0 | 285 | 0.7393 | 1.2475 | 1.2475 | 1.0447 | 1.0447 | 0.4370 | 0.4370 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.0494 | 20.0 | 300 | 0.7157 | 1.2274 | 1.2274 | 1.0453 | 1.0453 | 0.4550 | 0.4550 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.0434 | 21.0 | 315 | 0.7248 | 1.2352 | 1.2352 | 1.0462 | 1.0462 | 0.4480 | 0.4480 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.049 | 22.0 | 330 | 0.7384 | 1.2467 | 1.2467 | 1.0613 | 1.0613 | 0.4377 | 0.4377 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0405 | 23.0 | 345 | 0.7420 | 1.2498 | 1.2498 | 1.0653 | 1.0653 | 0.4349 | 0.4349 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.0398 | 24.0 | 360 | 0.7355 | 1.2442 | 1.2442 | 1.0620 | 1.0620 | 0.4399 | 0.4399 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0398 | 25.0 | 375 | 0.7570 | 1.2623 | 1.2623 | 1.0698 | 1.0698 | 0.4235 | 0.4235 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.0345 | 26.0 | 390 | 0.7359 | 1.2446 | 1.2446 | 1.0610 | 1.0610 | 0.4396 | 0.4396 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan | | 0.0345 | 27.0 | 405 | 0.7417 | 1.2495 | 1.2495 | 1.0660 | 1.0660 | 0.4352 | 0.4352 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0386 | 28.0 | 420 | 0.7215 | 1.2323 | 1.2323 | 1.0514 | 1.0514 | 0.4506 | 0.4506 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.0372 | 29.0 | 435 | 0.7140 | 1.2260 | 1.2260 | 1.0477 | 1.0477 | 0.4562 | 0.4562 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan | | 0.0407 | 30.0 | 450 | 0.7139 | 1.2259 | 1.2259 | 1.0480 | 1.0480 | 0.4563 | 0.4563 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | fb98ac5f5bc6cd2226b26178f6bad167 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_mrpc_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5199 - Accuracy: 0.3284 - F1: 0.0616 - Combined Score: 0.1950 | 2e95d5a0ff049b7f39bd540a4e3774f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.5375 | 1.0 | 15 | 0.5292 | 0.3162 | 0.0 | 0.1581 | | 0.5305 | 2.0 | 30 | 0.5292 | 0.3162 | 0.0 | 0.1581 | | 0.5294 | 3.0 | 45 | 0.5293 | 0.3162 | 0.0 | 0.1581 | | 0.5283 | 4.0 | 60 | 0.5284 | 0.3162 | 0.0 | 0.1581 | | 0.5258 | 5.0 | 75 | 0.5260 | 0.3162 | 0.0 | 0.1581 | | 0.519 | 6.0 | 90 | 0.5199 | 0.3284 | 0.0616 | 0.1950 | | 0.5036 | 7.0 | 105 | 0.5200 | 0.3848 | 0.2462 | 0.3155 | | 0.4916 | 8.0 | 120 | 0.5226 | 0.4167 | 0.3239 | 0.3703 | | 0.4725 | 9.0 | 135 | 0.5298 | 0.4289 | 0.3581 | 0.3935 | | 0.4537 | 10.0 | 150 | 0.5333 | 0.6152 | 0.6736 | 0.6444 | | 0.4382 | 11.0 | 165 | 0.5450 | 0.6201 | 0.6906 | 0.6554 | | 10c03d03936dcb94a881282838f2a7d2 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_vp-es_s533 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 01b11d57af3b8bd539963ae87c826109 |
apache-2.0 | [] | false | Model Details **Model Description:** The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than *bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark. This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned using (a second step of) knowledge distillation on [SQuAD v1.1](https://huggingface.co/datasets/squad). - **Developed by:** Hugging Face - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** Apache 2.0 - **Related Models:** [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased) - **Resources for more information:** - See [this repository](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for more about Distil\* (a class of compressed models including this model) - See [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) for more information about knowledge distillation and the training procedure | bb01a35d6af767309b732ba88e739477 |
apache-2.0 | [] | false | How to Get Started with the Model Use the code below to get started with the model. ```python >>> from transformers import pipeline >>> question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad') >>> context = r""" ... Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a ... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune ... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script. ... """ >>> result = question_answerer(question="What is a good example of a question answering dataset?", context=context) >>> print( ... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}" ...) Answer: 'SQuAD dataset', score: 0.4704, start: 147, end: 160 ``` Here is how to use this model in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering import torch tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad') model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad') question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" inputs = tokenizer(question, text, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) answer_start_index = torch.argmax(outputs.start_logits) answer_end_index = torch.argmax(outputs.end_logits) predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens) ``` And in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering import tensorflow as tf tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-distilled-squad") model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased-distilled-squad") question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" inputs = tokenizer(question, text, return_tensors="tf") outputs = model(**inputs) answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0]) answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0]) predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens) ``` | 387d04014953a79b98b6fc95ac65cce5 |
apache-2.0 | [] | false | Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. | 8a35e4484197f85d2534c0740b0e65db |
apache-2.0 | [] | false | Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline >>> question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad') >>> context = r""" ... Alice is sitting on the bench. Bob is sitting next to her. ... """ >>> result = question_answerer(question="Who is the CEO?", context=context) >>> print( ... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}" ...) Answer: 'Bob', score: 0.4183, start: 32, end: 35 ``` Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. | bae36f3199d62b66433f216241f6d8f0 |
apache-2.0 | [] | false | Training Data The [distilbert-base-uncased model](https://huggingface.co/distilbert-base-uncased) model describes it's training data as: > DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). To learn more about the SQuAD v1.1 dataset, see the [SQuAD v1.1 data card](https://huggingface.co/datasets/squad). | d9460fb2a68d4edfd0c8bf823abc7221 |
apache-2.0 | [] | false | Evaluation As discussed in the [model repository](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md) > This model reaches a F1 score of 86.9 on the [SQuAD v1.1] dev set (for comparison, Bert bert-base-uncased version reaches a F1 score of 88.5). | f7b9030b85aea156c2c05238008fd354 |
apache-2.0 | [] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1910.01108.pdf). Note that these details are just for training DistilBERT, not including the fine-tuning with SQuAD. - **Hardware Type:** 8 16GB V100 GPUs - **Hours used:** 90 hours - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown | 4f40b8eb3851f7f8f86352af08b834fe |
apache-2.0 | [] | false | Citation Information ```bibtex @inproceedings{sanh2019distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas}, booktitle={NeurIPS EMC^2 Workshop}, year={2019} } ``` APA: - Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. | 275cf2f1404b7118738312202b844152 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 200 - num_epochs: 60 - mixed_precision_training: Native AMP | 8a2959fdf5afa173259003ee01ac53f2 |
apache-2.0 | ['generated_from_trainer'] | false | squad-bn-qgen-mt5-all-metric This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the squad_bn dataset. It achieves the following results on the evaluation set: - Loss: 0.7273 - Rouge1 Precision: 35.8589 - Rouge1 Recall: 29.7041 - Rouge1 Fmeasure: 31.6373 - Rouge2 Precision: 15.4203 - Rouge2 Recall: 12.5155 - Rouge2 Fmeasure: 13.3978 - Rougel Precision: 34.4684 - Rougel Recall: 28.5887 - Rougel Fmeasure: 30.4627 - Rougelsum Precision: 34.4252 - Rougelsum Recall: 28.5362 - Rougelsum Fmeasure: 30.4053 - Sacrebleu: 6.4143 - Meteor: 0.1416 - Gen Len: 16.7199 | 5931befe97507fe142564a8b3a86d163 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 5 | a575b7163f434ba156b6e6a6fa1d26b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Sacrebleu | Meteor | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:---------:|:------:|:-------:| | 0.8449 | 1.0 | 16396 | 0.7340 | 31.6476 | 26.8901 | 28.2871 | 13.621 | 11.3545 | 11.958 | 30.3276 | 25.7754 | 27.1048 | 30.3426 | 25.7489 | 27.0991 | 5.9655 | 0.1336 | 16.8685 | | 0.7607 | 2.0 | 32792 | 0.7182 | 33.7173 | 28.6115 | 30.1049 | 14.8227 | 12.2059 | 12.9453 | 32.149 | 27.2036 | 28.6617 | 32.2479 | 27.2261 | 28.7272 | 6.6093 | 0.138 | 16.8522 | | 0.7422 | 3.0 | 49188 | 0.7083 | 34.6128 | 29.0223 | 30.7248 | 14.9888 | 12.3092 | 13.1021 | 33.2507 | 27.8154 | 29.4599 | 33.2848 | 27.812 | 29.5064 | 6.2407 | 0.1416 | 16.5806 | | 0.705 | 4.0 | 65584 | 0.7035 | 34.156 | 29.0012 | 30.546 | 14.72 | 12.0251 | 12.8161 | 32.7527 | 27.6511 | 29.1955 | 32.7692 | 27.6627 | 29.231 | 6.1784 | 0.1393 | 16.7793 | | 0.6859 | 5.0 | 81980 | 0.7038 | 35.1405 | 29.6033 | 31.2614 | 15.5108 | 12.6414 | 13.5059 | 33.8335 | 28.4264 | 30.0745 | 33.8782 | 28.4349 | 30.0901 | 6.5896 | 0.144 | 16.6651 | | 64cb5264985cac5059e791a2ca09ed62 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-nl_s253 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | f0613f0eb009a8b7c0f51adf7c32093f |
apache-2.0 | ['vision', 'image-classification'] | false | Vision Transformer (base-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. | 26d732bc252983cdd38c023586947af7 |
apache-2.0 | ['vision', 'image-classification'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. | a3675a9f52ed82c87cd8db7138a0c67b |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ViTFeatureExtractor, ViTForImageClassification 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 = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits | 27df18fe0fe9de88d4e04dc1ce06a21b |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html | 31eb653385aa365edbfe94a896bf2d5a |
apache-2.0 | ['vision', 'image-classification'] | false | Training data The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. | 327fe480dd18f772b821e0b5a61f1f61 |
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