license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.6348 | 2.2921 | 0 | | 2.3547 | 2.1969 | 1 | | 2.2381 | 2.0656 | 2 | | 2.1568 | 2.0696 | 3 | | 2.1510 | 1.9786 | 4 | | 2.1493 | 2.0436 | 5 | | 2.1469 | 2.0735 | 6 | | 2.1520 | 2.0695 | 7 | | 2.1617 | 2.0451 | 8 | | 2.1600 | 2.0358 | 9 | | feef6dcd1aad54ee2cb992bb8514316b |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-cuad-full This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.0274 | 423b716297037c2401089a4265918249 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.0323 | 1.0 | 47569 | 0.0280 | | 0.0314 | 2.0 | 95138 | 0.0265 | | 0.0276 | 3.0 | 142707 | 0.0274 | | c41c3ed8a763b0c270f242366bb94841 |
mit | ['torch'] | false | BERT BASE (cased) finetuned on Bulgarian part-of-speech data Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is cased: it does make a difference between bulgarian and Bulgarian. The training data is Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](https://bg.wikipedia.org/). It was finetuned on public part-of-speech Bulgarian data. Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925). | 724a6c293e8a091bcc544208a99d5e7f |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import pipeline >>> >>> model = pipeline( >>> 'token-classification', >>> model='rmihaylov/bert-base-pos-theseus-bg', >>> tokenizer='rmihaylov/bert-base-pos-theseus-bg', >>> device=0, >>> revision=None) >>> output = model('Здравей, аз се казвам Иван.') >>> print(output) [{'end': 7, 'entity': 'INTJ', 'index': 1, 'score': 0.9640711, 'start': 0, 'word': '▁Здравей'}, {'end': 8, 'entity': 'PUNCT', 'index': 2, 'score': 0.9998927, 'start': 7, 'word': ','}, {'end': 11, 'entity': 'PRON', 'index': 3, 'score': 0.9998872, 'start': 8, 'word': '▁аз'}, {'end': 14, 'entity': 'PRON', 'index': 4, 'score': 0.99990034, 'start': 11, 'word': '▁се'}, {'end': 21, 'entity': 'VERB', 'index': 5, 'score': 0.99989736, 'start': 14, 'word': '▁казвам'}, {'end': 26, 'entity': 'PROPN', 'index': 6, 'score': 0.99990785, 'start': 21, 'word': '▁Иван'}, {'end': 27, 'entity': 'PUNCT', 'index': 7, 'score': 0.9999685, 'start': 26, 'word': '.'}] ``` | 6a6cec8a82e378f656f0fcc6f4749160 |
mit | [] | false | lofa on Stable Diffusion This is the `<lofa>` 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 an `object`:      | 3dd2b83ca684d884ef17d9801d75ebf1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-fine-tuned-emotions 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: 0.1377 - Accuracy: 0.9335 - F1 Score: 0.9338 | 4777709b99652d5091827e3c6308328e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | db8e1511edf06f1b112c7aa060ff72e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 0.478 | 1.0 | 125 | 0.1852 | 0.931 | 0.9309 | | 0.1285 | 2.0 | 250 | 0.1377 | 0.9335 | 0.9338 | | cab551f8829da7664b1cd3235735c64a |
mit | ['generated_from_trainer'] | false | Multilingual Verdict Classifier This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 2,500 deduplicated multilingual verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into 65 languages with the [Google Cloud Translation API](https://cloud.google.com/translate/docs/reference/rest/). It achieves the following results on the evaluation set, being 1,000 such verdicts, but here including duplicates to represent the true distribution: - Loss: 0.2238 - F1 Macro: 0.8540 - F1 Misinformation: 0.9798 - F1 Factual: 0.9889 - F1 Other: 0.5934 - Prec Macro: 0.8348 - Prec Misinformation: 0.9860 - Prec Factual: 0.9889 - Prec Other: 0.5294 | 0f921edb5b508b1c10f3eb2a2e698667 |
mit | ['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 - gradient_accumulation_steps: 8 - 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: 162525 - num_epochs: 1000 | 4ec6393ba2f3d8f97b0b84d974a9e618 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:| | 1.1109 | 0.1 | 2000 | 1.2166 | 0.0713 | 0.1497 | 0.0 | 0.0640 | 0.2451 | 0.7019 | 0.0 | 0.0334 | | 0.9551 | 0.2 | 4000 | 0.7801 | 0.3611 | 0.8889 | 0.0 | 0.1943 | 0.3391 | 0.8915 | 0.0 | 0.1259 | | 0.9275 | 0.3 | 6000 | 0.7712 | 0.3468 | 0.9123 | 0.0 | 0.1282 | 0.3304 | 0.9051 | 0.0 | 0.0862 | | 0.8881 | 0.39 | 8000 | 0.5386 | 0.3940 | 0.9524 | 0.0 | 0.2297 | 0.3723 | 0.9748 | 0.0 | 0.1420 | | 0.7851 | 0.49 | 10000 | 0.3298 | 0.6886 | 0.9626 | 0.7640 | 0.3393 | 0.6721 | 0.9798 | 0.7727 | 0.2639 | | 0.639 | 0.59 | 12000 | 0.2156 | 0.7847 | 0.9633 | 0.9355 | 0.4554 | 0.7540 | 0.9787 | 0.9062 | 0.3770 | | 0.5677 | 0.69 | 14000 | 0.1682 | 0.7877 | 0.9694 | 0.9667 | 0.4270 | 0.7763 | 0.9745 | 0.9667 | 0.3878 | | 0.5218 | 0.79 | 16000 | 0.1475 | 0.8037 | 0.9692 | 0.9667 | 0.4752 | 0.7804 | 0.9812 | 0.9667 | 0.3934 | | 0.4682 | 0.89 | 18000 | 0.1458 | 0.8097 | 0.9734 | 0.9667 | 0.4889 | 0.7953 | 0.9791 | 0.9667 | 0.44 | | 0.4188 | 0.98 | 20000 | 0.1416 | 0.8370 | 0.9769 | 0.9724 | 0.5618 | 0.8199 | 0.9826 | 0.9670 | 0.5102 | | 0.3735 | 1.08 | 22000 | 0.1624 | 0.8094 | 0.9698 | 0.9368 | 0.5217 | 0.7780 | 0.9823 | 0.89 | 0.4615 | | 0.3242 | 1.18 | 24000 | 0.1648 | 0.8338 | 0.9769 | 0.9727 | 0.5517 | 0.8167 | 0.9826 | 0.9570 | 0.5106 | | 0.2785 | 1.28 | 26000 | 0.1843 | 0.8261 | 0.9739 | 0.9780 | 0.5263 | 0.8018 | 0.9836 | 0.9674 | 0.4545 | | 0.25 | 1.38 | 28000 | 0.1975 | 0.8344 | 0.9744 | 0.9834 | 0.5455 | 0.8072 | 0.9859 | 0.9780 | 0.4576 | | 0.2176 | 1.48 | 30000 | 0.1849 | 0.8209 | 0.9691 | 0.9889 | 0.5047 | 0.7922 | 0.9846 | 0.9889 | 0.4030 | | 0.1966 | 1.58 | 32000 | 0.2119 | 0.8194 | 0.9685 | 0.9944 | 0.4954 | 0.7920 | 0.9846 | 1.0 | 0.3913 | | 0.1738 | 1.67 | 34000 | 0.2110 | 0.8352 | 0.9708 | 0.9944 | 0.5405 | 0.8035 | 0.9881 | 1.0 | 0.4225 | | 0.1625 | 1.77 | 36000 | 0.2152 | 0.8165 | 0.9709 | 0.9834 | 0.4950 | 0.7905 | 0.9835 | 0.9780 | 0.4098 | | 0.1522 | 1.87 | 38000 | 0.2300 | 0.8097 | 0.9697 | 0.9832 | 0.4762 | 0.7856 | 0.9835 | 0.9888 | 0.3846 | | 0.145 | 1.97 | 40000 | 0.1955 | 0.8519 | 0.9774 | 0.9889 | 0.5895 | 0.8280 | 0.9860 | 0.9889 | 0.5091 | | 0.1248 | 2.07 | 42000 | 0.2308 | 0.8149 | 0.9703 | 0.9889 | 0.4854 | 0.7897 | 0.9835 | 0.9889 | 0.3968 | | 0.1186 | 2.17 | 44000 | 0.2368 | 0.8172 | 0.9733 | 0.9834 | 0.4948 | 0.7942 | 0.9836 | 0.9780 | 0.4211 | | 0.1122 | 2.26 | 46000 | 0.2401 | 0.7968 | 0.9804 | 0.8957 | 0.5143 | 0.8001 | 0.9849 | 1.0 | 0.4154 | | 0.1099 | 2.36 | 48000 | 0.2290 | 0.8119 | 0.9647 | 0.9834 | 0.4874 | 0.7777 | 0.9880 | 0.9780 | 0.3671 | | 0.1093 | 2.46 | 50000 | 0.2256 | 0.8247 | 0.9745 | 0.9889 | 0.5106 | 0.8053 | 0.9825 | 0.9889 | 0.4444 | | 0.1053 | 2.56 | 52000 | 0.2416 | 0.8456 | 0.9799 | 0.9889 | 0.5679 | 0.8434 | 0.9805 | 0.9889 | 0.5610 | | 0.1049 | 2.66 | 54000 | 0.2850 | 0.7585 | 0.9740 | 0.8902 | 0.4112 | 0.7650 | 0.9802 | 0.9865 | 0.3284 | | 0.098 | 2.76 | 56000 | 0.2828 | 0.8049 | 0.9642 | 0.9889 | 0.4615 | 0.7750 | 0.9856 | 0.9889 | 0.3506 | | 0.0962 | 2.86 | 58000 | 0.2238 | 0.8540 | 0.9798 | 0.9889 | 0.5934 | 0.8348 | 0.9860 | 0.9889 | 0.5294 | | 0.0975 | 2.95 | 60000 | 0.2494 | 0.8249 | 0.9715 | 0.9889 | 0.5143 | 0.7967 | 0.9858 | 0.9889 | 0.4154 | | 0.0877 | 3.05 | 62000 | 0.2464 | 0.8274 | 0.9733 | 0.9889 | 0.5200 | 0.8023 | 0.9847 | 0.9889 | 0.4333 | | 0.0848 | 3.15 | 64000 | 0.2338 | 0.8263 | 0.9740 | 0.9889 | 0.5161 | 0.8077 | 0.9814 | 0.9889 | 0.4528 | | 0.0859 | 3.25 | 66000 | 0.2335 | 0.8365 | 0.9750 | 0.9889 | 0.5455 | 0.8108 | 0.9859 | 0.9889 | 0.4576 | | 0.084 | 3.35 | 68000 | 0.2067 | 0.8343 | 0.9763 | 0.9889 | 0.5376 | 0.8148 | 0.9837 | 0.9889 | 0.4717 | | 0.0837 | 3.45 | 70000 | 0.2516 | 0.8249 | 0.9746 | 0.9889 | 0.5111 | 0.8097 | 0.9803 | 0.9889 | 0.46 | | 0.0809 | 3.54 | 72000 | 0.2948 | 0.8258 | 0.9728 | 0.9944 | 0.5102 | 0.8045 | 0.9824 | 1.0 | 0.4310 | | 0.0833 | 3.64 | 74000 | 0.2457 | 0.8494 | 0.9744 | 0.9944 | 0.5794 | 0.8173 | 0.9893 | 1.0 | 0.4627 | | 0.0796 | 3.74 | 76000 | 0.3188 | 0.8277 | 0.9733 | 0.9889 | 0.5208 | 0.8059 | 0.9825 | 0.9889 | 0.4464 | | 0.0821 | 3.84 | 78000 | 0.2642 | 0.8343 | 0.9714 | 0.9944 | 0.5370 | 0.8045 | 0.9870 | 1.0 | 0.4265 | | 4425cb8f6094a9af178668e9a22dcc4a |
mit | [] | false | DarkPlane on Stable Diffusion This is the `<DarkPlane>` 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`:                      | f09f36a247957fcd0780aca555db48da |
gpl-2.0 | ['corenlp'] | false | Core NLP model for french CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. Find more about it in [our website](https://stanfordnlp.github.io/CoreNLP) and our [GitHub repository](https://github.com/stanfordnlp/CoreNLP). This card and repo were automatically prepared with `hugging_corenlp.py` in the `stanfordnlp/huggingface-models` repo Last updated 2023-01-21 01:37:03.293 | fdbbafc2e4ccd66052c3b62dd241f7dd |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-3-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2614 - Accuracy: 0.4289 | 80487f90205185690d95f6f6cdc73ee3 |
mit | ['generated_from_trainer'] | false | afro-xlmr-large AfroXLMR-large was created by MLM adaptation of XLM-R-large model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Nigerian-Pidgin, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high resource languages (Arabic, French, and English). | f8b6f294794b0877ad30bffc86fa8c56 |
mit | ['generated_from_trainer'] | false | Eval results on MasakhaNER (F-score) language| XLM-R-miniLM| XLM-R-base |XLM-R-large | afro-xlmr-large | afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|-|- amh |69.5|70.6|76.2|79.7|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.4|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.7|87.4|86.6|83.5 kin |68.6|73.3|73.8|79.1|78.0|77.5|74.1 lug |64.7|79.7|81.6|86.7|82.9|83.2|77.4 luo |11.7|74.9|73.6|78.1|75.1|75.4|17.5 pcm |83.2|87.3|89.0|91.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|90.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|69.6|67.4|65.9|59.0 yor |72.0|78.3|78.9|85.2|82.1|81.3|75.1 avg |66.4|79.0|80.5|83.9|81.8|80.9|71.6 | 7f1db1b1fd84ca602557a834ad3b804a |
mit | ['generated_from_trainer'] | false | BibTeX entry and citation info ``` @inproceedings{alabi-etal-2022-adapting, title = "Adapting Pre-trained Language Models to {A}frican Languages via Multilingual Adaptive Fine-Tuning", author = "Alabi, Jesujoba O. and Adelani, David Ifeoluwa and Mosbach, Marius and Klakow, Dietrich", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.382", pages = "4336--4349", abstract = "Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is language adaptive fine-tuning (LAFT) {---} fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to target language individually takes large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50{\%}. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.", } ``` | aa5021d2197a72c77e316f3c6e838cce |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-10 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0354 - Wer: 1.0 | c1913c630a3966fe3b40126ca271a72d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 | a3ace68023effaac9a83dc5fb2bece99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.2231 | 0.78 | 200 | 3.0442 | 1.0 | | 2.8665 | 1.57 | 400 | 3.0081 | 1.0 | | 2.8596 | 2.35 | 600 | 3.0905 | 1.0 | | 2.865 | 3.14 | 800 | 3.0443 | 1.0 | | 2.8613 | 3.92 | 1000 | 3.0316 | 1.0 | | 2.8601 | 4.71 | 1200 | 3.0574 | 1.0 | | 2.8554 | 5.49 | 1400 | 3.0261 | 1.0 | | 2.8592 | 6.27 | 1600 | 3.0785 | 1.0 | | 2.8606 | 7.06 | 1800 | 3.1129 | 1.0 | | 2.8547 | 7.84 | 2000 | 3.0647 | 1.0 | | 2.8565 | 8.63 | 2200 | 3.0624 | 1.0 | | 2.8633 | 9.41 | 2400 | 2.9900 | 1.0 | | 2.855 | 10.2 | 2600 | 3.0084 | 1.0 | | 2.8581 | 10.98 | 2800 | 3.0092 | 1.0 | | 2.8545 | 11.76 | 3000 | 3.0299 | 1.0 | | 2.8583 | 12.55 | 3200 | 3.0293 | 1.0 | | 2.8536 | 13.33 | 3400 | 3.0566 | 1.0 | | 2.8556 | 14.12 | 3600 | 3.0385 | 1.0 | | 2.8573 | 14.9 | 3800 | 3.0098 | 1.0 | | 2.8551 | 15.69 | 4000 | 3.0623 | 1.0 | | 2.8546 | 16.47 | 4200 | 3.0964 | 1.0 | | 2.8569 | 17.25 | 4400 | 3.0648 | 1.0 | | 2.8543 | 18.04 | 4600 | 3.0377 | 1.0 | | 2.8532 | 18.82 | 4800 | 3.0454 | 1.0 | | 2.8579 | 19.61 | 5000 | 3.0301 | 1.0 | | 2.8532 | 20.39 | 5200 | 3.0364 | 1.0 | | 2.852 | 21.18 | 5400 | 3.0187 | 1.0 | | 2.8561 | 21.96 | 5600 | 3.0172 | 1.0 | | 2.8509 | 22.75 | 5800 | 3.0420 | 1.0 | | 2.8551 | 23.53 | 6000 | 3.0309 | 1.0 | | 2.8552 | 24.31 | 6200 | 3.0416 | 1.0 | | 2.8521 | 25.1 | 6400 | 3.0469 | 1.0 | | 2.852 | 25.88 | 6600 | 3.0489 | 1.0 | | 2.854 | 26.67 | 6800 | 3.0394 | 1.0 | | 2.8572 | 27.45 | 7000 | 3.0336 | 1.0 | | 2.8502 | 28.24 | 7200 | 3.0363 | 1.0 | | 2.8557 | 29.02 | 7400 | 3.0304 | 1.0 | | 2.8522 | 29.8 | 7600 | 3.0354 | 1.0 | | 143c35efe63711012c30b88b0c120034 |
apache-2.0 | ['generated_from_keras_callback'] | false | afrodp95/distilbert-base-uncased-finetuned-job-skills-ner 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: 0.0923 - Validation Loss: 0.1313 - Train Precision: 0.3601 - Train Recall: 0.4922 - Train F1: 0.4159 - Train Accuracy: 0.9522 - Epoch: 5 | 373858b0682aa32ce70538e9f43a8b33 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1386, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 794eafa00699c8cf6a8f327dc00167ad |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3257 | 0.1935 | 0.3122 | 0.2144 | 0.2542 | 0.9521 | 0 | | 0.1564 | 0.1464 | 0.3503 | 0.3423 | 0.3463 | 0.9546 | 1 | | 0.1257 | 0.1365 | 0.3593 | 0.4893 | 0.4143 | 0.9522 | 2 | | 0.1102 | 0.1318 | 0.3607 | 0.4692 | 0.4079 | 0.9521 | 3 | | 0.1002 | 0.1305 | 0.3504 | 0.4941 | 0.4100 | 0.9515 | 4 | | 0.0923 | 0.1313 | 0.3601 | 0.4922 | 0.4159 | 0.9522 | 5 | | 280a1e7639ac5028bd29cbec43447b82 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-nl_s354 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 (et)](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. | 9f55ee25d5ebce706c6c14896cbf3224 |
apache-2.0 | ['generated_from_trainer'] | false | bart-large-commentaries_hdwriter This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1619 - Rouge1: 26.1101 - Rouge2: 9.928 - Rougel: 22.9007 - Rougelsum: 23.117 - Gen Len: 15.9536 | 944ece00fcac0df2d1e7e6800de5ec96 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.6237 | 1.0 | 5072 | 2.5309 | 26.4063 | 9.1795 | 22.6699 | 22.9125 | 17.3103 | | 1.8808 | 2.0 | 10144 | 2.5049 | 25.3706 | 8.7568 | 21.8594 | 22.1233 | 15.8579 | | 1.3084 | 3.0 | 15216 | 2.6680 | 26.6284 | 9.9914 | 23.1477 | 23.3625 | 16.8832 | | 0.9247 | 4.0 | 20288 | 2.8923 | 26.3827 | 9.8217 | 22.9524 | 23.1651 | 15.4529 | | 0.692 | 5.0 | 25360 | 3.1619 | 26.1101 | 9.928 | 22.9007 | 23.117 | 15.9536 | | 9378c9a720c41de885b0528e72b95abb |
apache-2.0 | ['translation'] | false | run-fra * source group: Rundi * target group: French * OPUS readme: [run-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-fra/README.md) * model: transformer-align * source language(s): run * target language(s): fra * 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/run-fra/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.eval.txt) | 555587af4dab8212d38e8856f5dc9b2c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: run-fra - source_languages: run - target_languages: fra - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-fra/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['rn', 'fr'] - src_constituents: {'run'} - tgt_constituents: {'fra'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.test.txt - src_alpha3: run - tgt_alpha3: fra - short_pair: rn-fr - chrF2_score: 0.397 - bleu: 18.2 - brevity_penalty: 1.0 - ref_len: 7496.0 - src_name: Rundi - tgt_name: French - train_date: 2020-06-16 - src_alpha2: rn - tgt_alpha2: fr - prefer_old: False - long_pair: run-fra - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 57ab89df96f7e9d88ed7036e9a7251d5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_pre-training-dim-96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikitext wikitext-103-raw-v1 dataset. It achieves the following results on the evaluation set: - Loss: 6.6092 - Accuracy: 0.1494 | 4fb8d94fb84fb970eed970e38fa58515 |
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: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 15 - mixed_precision_training: Native AMP | f5fa74d4882d99f64bfd92be10270e54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 14.685 | 1.0 | 3573 | 9.3922 | 0.1240 | | 8.0255 | 2.0 | 7146 | 7.1510 | 0.1315 | | 7.0152 | 3.0 | 10719 | 6.7861 | 0.1482 | | 6.8127 | 4.0 | 14292 | 6.7053 | 0.1493 | | 6.74 | 5.0 | 17865 | 6.6695 | 0.1474 | | 6.7067 | 6.0 | 21438 | 6.6431 | 0.1491 | | 6.6871 | 7.0 | 25011 | 6.6204 | 0.1483 | | 6.6748 | 8.0 | 28584 | 6.6250 | 0.1473 | | 6.6649 | 9.0 | 32157 | 6.6108 | 0.1486 | | 6.6596 | 10.0 | 35730 | 6.6140 | 0.1497 | | 6.6536 | 11.0 | 39303 | 6.6067 | 0.1493 | | 6.6483 | 12.0 | 42876 | 6.6140 | 0.1489 | | 6.6463 | 13.0 | 46449 | 6.6096 | 0.1484 | | 6.6434 | 14.0 | 50022 | 6.5570 | 0.1526 | | 6.6414 | 15.0 | 53595 | 6.5836 | 0.1526 | | 3fc9a4a213ccb761852c69ecf6d91681 |
apache-2.0 | ['generated_from_trainer'] | false | mt5_fill_puntuation This model is a fine-tuned version of [jamie613/mt5_fill_puntuation](https://huggingface.co/jamie613/mt5_fill_puntuation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0717 | 782c9a18488d89012f1c6c6dd6ee2b57 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | b2b2d06a931fbc0f9a6b4858e4090b76 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0918 | 0.04 | 500 | 0.0803 | | 0.0894 | 0.07 | 1000 | 0.0773 | | 0.0905 | 0.11 | 1500 | 0.0822 | | 0.0908 | 0.15 | 2000 | 0.0833 | | 0.0868 | 0.18 | 2500 | 0.0840 | | 0.09 | 0.22 | 3000 | 0.0811 | | 0.0868 | 0.26 | 3500 | 0.0735 | | 0.0869 | 0.29 | 4000 | 0.0805 | | 0.0874 | 0.33 | 4500 | 0.0742 | | 0.088 | 0.37 | 5000 | 0.0749 | | 0.0884 | 0.4 | 5500 | 0.0730 | | 0.0861 | 0.44 | 6000 | 0.0749 | | 0.0804 | 0.48 | 6500 | 0.0739 | | 0.0845 | 0.51 | 7000 | 0.0717 | | 0.0861 | 0.55 | 7500 | 0.0743 | | 0.0812 | 0.59 | 8000 | 0.0726 | | 0.0824 | 0.62 | 8500 | 0.0729 | | 0.0836 | 0.66 | 9000 | 0.0751 | | 0.079 | 0.7 | 9500 | 0.0731 | | 0.0806 | 0.73 | 10000 | 0.0725 | | 0.0798 | 0.77 | 10500 | 0.0749 | | 0.0794 | 0.81 | 11000 | 0.0725 | | 0.0795 | 0.84 | 11500 | 0.0726 | | 0.0755 | 0.88 | 12000 | 0.0732 | | 0.0815 | 0.92 | 12500 | 0.0722 | | 0.0776 | 0.95 | 13000 | 0.0719 | | 0.0838 | 0.99 | 13500 | 0.0717 | | 45bbb17fd789b8c58838dd841395410e |
mit | ['generated_from_keras_callback'] | false | deepiit98/Catalan_language-clustered This model is a fine-tuned version of [nandysoham16/13-clustered_aug](https://huggingface.co/nandysoham16/13-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5877 - Train End Logits Accuracy: 0.8681 - Train Start Logits Accuracy: 0.8507 - Validation Loss: 0.4207 - Validation End Logits Accuracy: 0.8182 - Validation Start Logits Accuracy: 0.8182 - Epoch: 0 | 1cce319ac6488bcfd585772744569f56 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.5877 | 0.8681 | 0.8507 | 0.4207 | 0.8182 | 0.8182 | 0 | | 1981b366c370e11afb8ce0421fc3b4a8 |
creativeml-openrail-m | ['text-to-image'] | false | Roy Dreambooth model trained by duja1 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: r123oy (use that on your prompt)  | cb56e90b0a45535ab557c06837349d10 |
apache-2.0 | [] | false | bert-base-en-es-pt-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | e8e2fb8a959734be41611629cd3fd427 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-es-pt-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-es-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | 19ac9c95eefb0be97d512b95275d05d9 |
mit | [] | false | Collage14 on Stable Diffusion This is the `<C14>` 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`:       | 0b5cf2627c48eb42c6fce5b464f4fc7b |
apache-2.0 | ['translation'] | false | opus-mt-en-he * source languages: en * target languages: he * OPUS readme: [en-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-he/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-he/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-he/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-he/opus-2019-12-18.eval.txt) | 585eaf263ce59c645b89edec57617694 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-6 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2459 - Wer: 1.0 | 6f431d5d65e6a3ccc6379d7fda7050e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - 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: 400 - num_epochs: 30 | 95e13a5c734d37b650ac47e14ef887a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.5873 | 1.56 | 200 | 5.4586 | 1.0 | | 4.1846 | 3.12 | 400 | 5.2278 | 1.0 | | 4.1711 | 4.69 | 600 | 5.3131 | 1.0 | | 4.1581 | 6.25 | 800 | 5.2558 | 1.0 | | 4.1275 | 7.81 | 1000 | 5.2556 | 1.0 | | 4.1452 | 9.38 | 1200 | 5.2637 | 1.0 | | 4.1614 | 10.94 | 1400 | 5.2847 | 1.0 | | 4.1667 | 12.5 | 1600 | 5.2349 | 1.0 | | 4.1471 | 14.06 | 1800 | 5.2850 | 1.0 | | 4.1268 | 15.62 | 2000 | 5.2510 | 1.0 | | 4.1701 | 17.19 | 2200 | 5.2605 | 1.0 | | 4.1459 | 18.75 | 2400 | 5.2493 | 1.0 | | 4.1411 | 20.31 | 2600 | 5.2649 | 1.0 | | 4.1351 | 21.88 | 2800 | 5.2541 | 1.0 | | 4.1442 | 23.44 | 3000 | 5.2459 | 1.0 | | 4.1805 | 25.0 | 3200 | 5.2232 | 1.0 | | 4.1262 | 26.56 | 3400 | 5.2384 | 1.0 | | 4.145 | 28.12 | 3600 | 5.2522 | 1.0 | | 4.142 | 29.69 | 3800 | 5.2459 | 1.0 | | 7e71483d37ee1edd4a5d4d2f665f0c2e |
mit | ['generated_from_trainer'] | false | distilcamembert-cae-territory This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7346 - Precision: 0.7139 - Recall: 0.6835 - F1: 0.6887 | 87983868f748fe3efb96990486e00fa0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.1749 | 1.0 | 40 | 1.0498 | 0.1963 | 0.4430 | 0.2720 | | 0.9833 | 2.0 | 80 | 0.8853 | 0.7288 | 0.6709 | 0.6625 | | 0.6263 | 3.0 | 120 | 0.7503 | 0.7237 | 0.6709 | 0.6689 | | 0.3563 | 4.0 | 160 | 0.7346 | 0.7139 | 0.6835 | 0.6887 | | 0.2253 | 5.0 | 200 | 0.7303 | 0.7139 | 0.6835 | 0.6887 | | 57f2b5997a589f5b21bad76c23d79830 |
mit | [] | false | hmBERT: Historical Multilingual Language Models for Named Entity Recognition More information about our hmBERT model can be found in our new paper: ["hmBERT: Historical Multilingual Language Models for Named Entity Recognition"](https://arxiv.org/abs/2205.15575). | 3a1a7089f180894c476b673badb4630d |
mit | [] | false | Smaller Models We have also released smaller models for the multilingual model: | Model identifier | Model Hub link | ----------------------------------------------- | --------------------------------------------------------------------------- | `dbmdz/bert-tiny-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-tiny-historic-multilingual-cased) | `dbmdz/bert-mini-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-mini-historic-multilingual-cased) | `dbmdz/bert-small-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-small-historic-multilingual-cased) | `dbmdz/bert-medium-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) | f764a08c2e112e8e0b02f0b7863f0f10 |
cc-by-4.0 | ['roberta', 'roberta-base', 'masked-language-modeling'] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Fill-Mask **Training data:** wikimovies **Eval data:** wikimovies **Infrastructure**: 2x Tesla v100 **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/shell_scripts/train_movie_roberta.sh) | cb877c761e41d2729dd36c172e1bc3d0 |
cc-by-4.0 | ['roberta', 'roberta-base', 'masked-language-modeling'] | false | Hyperparameters ``` num_examples = 4346 batch_size = 16 n_epochs = 3 base_LM_model = "roberta-base" learning_rate = 5e-05 max_query_length=64 Gradient Accumulation steps = 1 Total optimization steps = 816 evaluation_strategy=IntervalStrategy.NO prediction_loss_only=False per_device_train_batch_size=8 per_device_eval_batch_size=8 adam_beta1=0.9 adam_beta2=0.999 adam_epsilon=1e-08, max_grad_norm=1.0 lr_scheduler_type=SchedulerType.LINEAR warmup_ratio=0.0 seed=42 eval_steps=500 metric_for_best_model=None greater_is_better=None label_smoothing_factor=0.0 ``` | b07ad75066c84330d51cf1b019f4292a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_1_ternary 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: 2.0582 - F1: 0.7326 | 5c2c0d139186852163cdc0b9a289c809 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.5524 | 0.6755 | | 0.5648 | 2.0 | 580 | 0.5654 | 0.7124 | | 0.5648 | 3.0 | 870 | 0.6547 | 0.6896 | | 0.2712 | 4.0 | 1160 | 0.8916 | 0.7263 | | 0.2712 | 5.0 | 1450 | 1.1187 | 0.7120 | | 0.1147 | 6.0 | 1740 | 1.2778 | 0.7114 | | 0.0476 | 7.0 | 2030 | 1.4441 | 0.7151 | | 0.0476 | 8.0 | 2320 | 1.5535 | 0.7133 | | 0.0187 | 9.0 | 2610 | 1.6439 | 0.7212 | | 0.0187 | 10.0 | 2900 | 1.7261 | 0.7313 | | 0.0138 | 11.0 | 3190 | 1.6806 | 0.7292 | | 0.0138 | 12.0 | 3480 | 1.8425 | 0.7111 | | 0.009 | 13.0 | 3770 | 1.9207 | 0.7213 | | 0.0045 | 14.0 | 4060 | 1.8900 | 0.7202 | | 0.0045 | 15.0 | 4350 | 1.9730 | 0.7216 | | 0.0042 | 16.0 | 4640 | 2.0775 | 0.7041 | | 0.0042 | 17.0 | 4930 | 2.0514 | 0.7106 | | 0.0019 | 18.0 | 5220 | 2.0582 | 0.7326 | | 0.0039 | 19.0 | 5510 | 2.1010 | 0.7081 | | 0.0039 | 20.0 | 5800 | 2.0487 | 0.7273 | | 0.0025 | 21.0 | 6090 | 2.0415 | 0.7254 | | 0.0025 | 22.0 | 6380 | 2.0753 | 0.7157 | | 0.0017 | 23.0 | 6670 | 2.0554 | 0.7246 | | 0.0017 | 24.0 | 6960 | 2.0644 | 0.7290 | | 0.0001 | 25.0 | 7250 | 2.0711 | 0.7310 | | 1aac2426f03df0f6b1614a3c557d82ec |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-vi This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 1.2134 - Bleu: 51.2085 | 9bdf8ac7bf03d7d11137b1b923de777a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | stargate-diffusion-sg1-1 Dreambooth model trained by Aphophis420 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook USE: *prompt*, still from stargate sg1 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)        | b28386794e6cf78faaee78c24af8a528 |
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: 0.8442 - Matthews Correlation: 0.5443 | bcfa0e2dc5d96cd5c05287c7b9b98051 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5267 | 1.0 | 535 | 0.5646 | 0.3655 | | 0.3477 | 2.0 | 1070 | 0.5291 | 0.4898 | | 0.2324 | 3.0 | 1605 | 0.5629 | 0.5410 | | 0.1774 | 4.0 | 2140 | 0.7630 | 0.5370 | | 0.1248 | 5.0 | 2675 | 0.8442 | 0.5443 | | 7f78403f9aa806d20495dd7c83e7d779 |
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 the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3404 - Accuracy: 0.8667 - F1: 0.8734 | d7302df606f579a8b256876a9bde1a55 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mn', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-mongolian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MN dataset. It achieves the following results on the evaluation set: - Loss: 0.6003 - Wer: 0.4473 | 1616b789641e32b3991ce6c9c32b86fd |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mn', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 1 - seed: 42 - 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 | d2be108dff6c18ce8cb7c65b6996c914 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mn', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.3677 | 15.87 | 2000 | 0.6432 | 0.6198 | | 1.1379 | 31.75 | 4000 | 0.6196 | 0.5592 | | 1.0093 | 47.62 | 6000 | 0.5828 | 0.5117 | | 0.8888 | 63.49 | 8000 | 0.5754 | 0.4822 | | 0.7985 | 79.37 | 10000 | 0.5987 | 0.4690 | | 0.697 | 95.24 | 12000 | 0.6014 | 0.4471 | | 39c3562a727243424b5fd2e043c2be5e |
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: 0.2251 - Accuracy: 0.9215 - F1: 0.9215 | 9c6dee935f92d9b9055f697701e668be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.851 | 1.0 | 250 | 0.3314 | 0.8985 | 0.8952 | | 0.2565 | 2.0 | 500 | 0.2251 | 0.9215 | 0.9215 | | e51debd62d42b290a24add79737ff425 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.observational.sa.5-class.seed_43 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.9838 - Accuracy: 0.5918 - Macro-f1: 0.4948 - Weighted-macro-f1: 0.5380 | 2279e04fe5b7d5dfc17d2015b1b3813f |
mit | ['generated_from_trainer'] | false | xlnet-base-cased-IUChatbot-ontologyDts-localParams This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0238 | bd2a565e9a10cac7ff562f61b2cb65aa |
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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | efe01ca76074b3abdc454e07f1e59638 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1172 | 1.0 | 1119 | 0.0657 | | 0.0564 | 2.0 | 2238 | 0.0237 | | 0.033 | 3.0 | 3357 | 0.0238 | | 1f7dbb9aa9b99d7915da2d5bfa0f19c3 |
mit | ['generated_from_trainer'] | false | baitblocker This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5G](https://huggingface.co/cahya/bert-base-indonesian-1.5G) on the [id_clickbait](https://huggingface.co/datasets/id_clickbait) dataset. It achieves the following results on the evaluation set: - Loss: 0.4660 - Accuracy: 0.8347 | d9b27cc9914b921de4fbc6a81a6fca0b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - 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: 3 | 814be693faeb665bf71f3c1bfa8fd538 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4025 | 1.0 | 1500 | 0.4074 | 0.827 | | 0.3581 | 2.0 | 3000 | 0.4090 | 0.8283 | | 0.333 | 3.0 | 4500 | 0.4660 | 0.8347 | | ad28f2783dbcfb74ff0223c8a95dd8a1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | <center><b>HighRiseMixV2.5</b></center> <p align="center"><img src="https://huggingface.co/0RisingStar0/HighRiseMixV2/resolve/main/00733-2938506110-(masterpiece%2C%20best%20quality%2C%20excellent%20quality)%2C%20((1girl%2C%20solo))%2C%20(gradient%20pink%20eye%2C%20black%20hair%2C%20short%20hair%2C%20school%20uniform%2C%20mic.png"> <img src="https://huggingface.co/0RisingStar0/HighRiseMixV2/resolve/main/00729-221520444-(masterpiece%2C%20best%20quality%2C%20excellent%20quality)%2C%20((1girl%2C%20solo))%2C%20(gradient%20pink%20eye%2C%20black%20hair%2C%20short%20hair%2C%20school%20uniform%2C%20mic.png"></p> <center><b>HighRiseMixV2</b></center> <p align="center"><img src="https://huggingface.co/0RisingStar0/HighRiseMixV2/resolve/main/00016-3185527639-(masterpiece%2C%20excellent%20quality%2C%20high%20quality)%2C%20(1girl%2C%20solo%2C%20cowboy%20shot)%2C%20looking%20at%20viewer%2C%20sky%2C%20city%2C%20skyscrapers%2C%20pavement%2C.png"> <img src="https://huggingface.co/0RisingStar0/HighRiseMixV2/resolve/main/00021-3185527644-(masterpiece%2C%20excellent%20quality%2C%20high%20quality)%2C%20(1girl%2C%20solo%2C%20cowboy%20shot)%2C%20looking%20at%20viewer%2C%20sky%2C%20city%2C%20skyscrapers%2C%20pavement%2C.png"></p> U-Net mixed model <b>specialized for city and skyscrapers background.</b> <b>FP16 Pruned version</b>(No EMA). (Quality change may occur in very small details on buildings' textures) <b>V2 Update Log : </b> Added models : AikimixPv1.0, Counterfeit V2.0, pastelmix-better-vae Adjusted character style(more cute, anime style) <b>V2.5 Update Log : </b> Added models : AikimixCv1.5 Just some very little changes to textures adjusted to my taste. It doesn't matter which one to use. There are pros and cons between V2 and V2.5 so you can just use what you want. <b>Recommended prompts : </b> (masterpiece, best quality, excellent quality), ((1girl, solo)), sky, city, (skyscrapers), trees, pavement, lens flare EasyNegative, moss, phone, man, pedestrians, extras, border, outside border, white border (EasyNegative is a negative embedding : https://huggingface.co/datasets/gsdf/EasyNegative) <b>Recommended settings : </b> Sampler : DPM++ 2M Karras OR DPM++ SDE Karras Sampling steps : 25 ~ 30 Resolution : 512x768 OR 768x512 CFG Scale : 9 <b> Upscale is a must-do!! </b> Otherwise, you won't get intended results. Upscaler : Latent (nearest) Hires steps : 0 Denoise : 0.6 Upscale 2x <b>Recommended VAEs : </b> kl-f8-anime2 orangemix.vae.pt <b> Mixed models : </b> AbyssOrangeMix2_NSFW, AnythingV4.5, AikimiXPv1.0, BasilMixFixed, Counterfeit V2.0, CounterfeitV2.5, EerieOrangeMix2, pastelmix-better-vae, PowercolorV2 (Thanks to everyone who made above models!) This is my first mixed model being uploaded to public site, so feel free to give feedbacks as you wish, I'll try and work around with it. | 58ce30b1246c6b7540f1a928fee47fa9 |
mit | ['generated_from_trainer'] | false | xlnet-base-cased-IUChatbot-ontologyDts This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4965 | 128641411304aca5c45c26ba6fd05439 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 318 | 0.5005 | | 0.8222 | 2.0 | 636 | 0.4488 | | 0.8222 | 3.0 | 954 | 0.4965 | | d6334836a0400c4f1208d9496ead6f6b |
apache-2.0 | ['Super-Resolution', 'computer-vision', 'RealSR', 'gan'] | false | Model Description [RealSR](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Ji_Real-World_Super-Resolution_via_Kernel_Estimation_and_Noise_Injection_CVPRW_2020_paper.pdf): Real-World Super-Resolution via Kernel Estimation and Noise Injection. [NTIRE 2020 Challenge on Real-World Image Super-Resolution](https://arxiv.org/abs/2005.01996): Methods and Results [Paper Repo](https://github.com/Tencent/Real-SR): Implementation of paper. | 1c121f8059f089926ef044d75a58e066 |
apache-2.0 | ['Super-Resolution', 'computer-vision', 'RealSR', 'gan'] | false | BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @article{Lugmayr2020ntire, title={NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results}, author={Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy Chiu, Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete, Michelini Christian, Micheloni Kalpesh, Prajapati Haoyu, Ren Yong, Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Haoning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou}, journal={CVPR Workshops}, year={2020}, } ``` | 338824f84102e702ff77e00b70c70452 |
apache-2.0 | ['generated_from_trainer'] | false | favs_token_classification_v2_updated_data This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the token_classification_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5346 - Precision: 0.6923 - Recall: 0.8357 - F1: 0.7573 - Accuracy: 0.8493 | 57afb1e646c64ae9ea53eaa10297519f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 2.3096 | 1.0 | 13 | 1.9927 | 0.3011 | 0.2 | 0.2403 | 0.3726 | | 2.038 | 2.0 | 26 | 1.7093 | 0.2569 | 0.2643 | 0.2606 | 0.4274 | | 1.8391 | 3.0 | 39 | 1.4452 | 0.3057 | 0.4214 | 0.3544 | 0.5562 | | 1.4912 | 4.0 | 52 | 1.2176 | 0.4130 | 0.5429 | 0.4691 | 0.6493 | | 1.3296 | 5.0 | 65 | 1.0368 | 0.4973 | 0.6643 | 0.5688 | 0.7123 | | 1.2036 | 6.0 | 78 | 0.9084 | 0.5053 | 0.6786 | 0.5793 | 0.7260 | | 0.9244 | 7.0 | 91 | 0.8148 | 0.5543 | 0.7286 | 0.6296 | 0.7616 | | 0.8293 | 8.0 | 104 | 0.7482 | 0.5698 | 0.7286 | 0.6395 | 0.7726 | | 0.7422 | 9.0 | 117 | 0.6961 | 0.5833 | 0.75 | 0.6562 | 0.7836 | | 0.6379 | 10.0 | 130 | 0.6613 | 0.6124 | 0.7786 | 0.6855 | 0.8027 | | 0.6071 | 11.0 | 143 | 0.6357 | 0.6193 | 0.7786 | 0.6899 | 0.8082 | | 0.5526 | 12.0 | 156 | 0.6033 | 0.6433 | 0.7857 | 0.7074 | 0.8164 | | 0.537 | 13.0 | 169 | 0.5813 | 0.6512 | 0.8 | 0.7179 | 0.8301 | | 0.4806 | 14.0 | 182 | 0.5706 | 0.6608 | 0.8071 | 0.7267 | 0.8329 | | 0.4503 | 15.0 | 195 | 0.5594 | 0.6647 | 0.8071 | 0.7290 | 0.8356 | | 0.4149 | 16.0 | 208 | 0.5503 | 0.6805 | 0.8214 | 0.7443 | 0.8438 | | 0.4175 | 17.0 | 221 | 0.5430 | 0.6824 | 0.8286 | 0.7484 | 0.8438 | | 0.4337 | 18.0 | 234 | 0.5396 | 0.6923 | 0.8357 | 0.7573 | 0.8493 | | 0.3965 | 19.0 | 247 | 0.5361 | 0.6882 | 0.8357 | 0.7548 | 0.8493 | | 0.3822 | 20.0 | 260 | 0.5346 | 0.6923 | 0.8357 | 0.7573 | 0.8493 | | 0b1b14661abae153c7f42e48bbaa4f95 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_gender_male-2_female-8_s728 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 (fr)](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. | 4bfe301fbe69cad04a08b1f28d8120a7 |
apache-2.0 | ['generated_from_trainer'] | false | ner_peoples_daily This model is a fine-tuned version of [hfl/rbt6](https://huggingface.co/hfl/rbt6) on the peoples_daily_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0249 - Precision: 0.9205 - Recall: 0.9365 - F1: 0.9285 - Accuracy: 0.9930 | 1c5e3c727dcc4ab11e58931d8b51ea20 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 0a930ac60959605e3c7dc72ff2ce8187 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3154 | 1.0 | 164 | 0.0410 | 0.8258 | 0.8684 | 0.8466 | 0.9868 | | 0.0394 | 2.0 | 328 | 0.0287 | 0.8842 | 0.9070 | 0.8954 | 0.9905 | | 0.0293 | 3.0 | 492 | 0.0264 | 0.8978 | 0.9168 | 0.9072 | 0.9916 | | 0.02 | 4.0 | 656 | 0.0254 | 0.9149 | 0.9226 | 0.9188 | 0.9923 | | 0.016 | 5.0 | 820 | 0.0250 | 0.9167 | 0.9281 | 0.9224 | 0.9927 | | 0.0124 | 6.0 | 984 | 0.0252 | 0.9114 | 0.9328 | 0.9220 | 0.9928 | | 0.0108 | 7.0 | 1148 | 0.0249 | 0.9169 | 0.9339 | 0.9254 | 0.9928 | | 0.0097 | 8.0 | 1312 | 0.0249 | 0.9205 | 0.9365 | 0.9285 | 0.9930 | | 635e533419fa9d47e889e8d456af9235 |
creativeml-openrail-m | [] | false | Usage ```python from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/miniSD-diffusers") pipe = pipe.to("cuda") prompt = "a photograph of an wrinkly old man laughing" image = pipe(prompt, width=256, height=256).images[0] image.save('test.jpg') ``` | 56b97428c45f42d83f8c6f9e660f837c |
creativeml-openrail-m | [] | false | Training details Fine tuned from the stable-diffusion 1.4 checkpoint as follows: - 22,000 steps fine-tuning only the attention layers of the unet, learn rate=1e-5, batch size=256 - 66,000 steps training the full unet, learn rate=5e-5, batch size=552 - GPUs provided by [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud) - Trained on [LAION Improved Aesthetics 6plus](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus). - Trained using https://github.com/justinpinkney/stable-diffusion, original [checkpoint available here](https://huggingface.co/justinpinkney/miniSD) | 91f3e010611d9086dadcd28d7b8d1e0e |
creativeml-openrail-m | [] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here | 9eb06f426888bdd965d280cdc414191c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | TARDISfusion <p> <img src="https://huggingface.co/Guizmus/Tardisfusion/raw/main/showcase.jpg"/><br/> This is a Dreamboothed Stable Diffusion model trained on 3 Style concepts.<br/> The total dataset is made of 209 pictures, and the training has been done on runawayml 1.5 with 2500 steps and the new VAE. The following tokens will add their corresponding concept :<br/> <ul> <li><b>Classic Tardis style</b> : Architectural and furniture style seen inside the TARDIS in the series before the reboot.</li> <li><b>Modern Tardis style</b>: Architectural and furniture style seen inside the TARDIS in the series after the reboot</li> <li><b>Tardis Box style</b>: A style made from the TARDIS seen from the outside. Summons a TARDIS anywhere.</li> </ul> </p> [CKPT download link](https://huggingface.co/Guizmus/Tardisfusion/resolve/main/Tardisfusion-v2.ckpt) | f9615be88345b496e4a7d50a588481b5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-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), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "Guizmus/Tardisfusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a bedroom, Classic Tardis style" image = pipe(prompt).images[0] image.save("./TARDIS Style.png") ``` | ae8fe24f0241150366b8a881275652c6 |
apache-2.0 | ['automatic-speech-recognition', 'NyanjaSpeech', 'generated_from_trainer'] | false | xls-r-300m-nyanja-model_v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NYANJASPEECH - NYA dataset. It achieves the following results on the evaluation set: - Loss: 0.2772 - Wer: 0.9074 | a298d298db29d54ea49c35a93023b262 |
apache-2.0 | ['automatic-speech-recognition', 'NyanjaSpeech', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 5.0 - mixed_precision_training: Native AMP | d29b666b3a9dcfe44da525c1dc2ae54b |
apache-2.0 | ['automatic-speech-recognition', 'NyanjaSpeech', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7585 | 1.58 | 500 | 0.3574 | 0.9679 | | 0.4736 | 3.16 | 1000 | 0.2772 | 0.9074 | | 0.4776 | 4.75 | 1500 | 0.2853 | 0.9578 | | d4c2285b4d7016ef8a740ab2bab95607 |
mit | ['fill-mask'] | false | ClinicalBERT - Bio + Clinical BERT Model The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Clinical BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on all MIMIC notes. | ed91e23c8ff34f7bdd92c6430080b105 |
mit | ['fill-mask'] | false | Pretraining Data The `Bio_ClinicalBERT` model was trained on all notes from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words). | fc766da5acb064cb9a83a3fa7bc2a8c5 |
mit | ['fill-mask'] | false | Note Preprocessing Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer). | 420691debb0cf1460c4966bfd4225163 |
mit | ['fill-mask'] | false | Pretraining Procedures The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`). | 8a00d40c0845c9a16ce2365e529bf632 |
mit | ['fill-mask'] | false | Pretraining Hyperparameters We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20). | 56077944a7bfe3e29be31d9785bb5fd3 |
mit | ['fill-mask'] | false | How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") ``` | b423bee422de38d268d2c7d371f7dda9 |
mit | ['fill-mask'] | false | More Information Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks. | 9b666b5a2758b0965756c015257a3ce7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-checkpoint-9 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-8](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-8) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9203 - Wer: 0.3258 | fd0504cfaa87ad18d7b1f2a200432e83 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2783 | 1.58 | 1000 | 0.5610 | 0.3359 | | 0.2251 | 3.16 | 2000 | 0.5941 | 0.3374 | | 0.173 | 4.74 | 3000 | 0.6026 | 0.3472 | | 0.1475 | 6.32 | 4000 | 0.6750 | 0.3482 | | 0.1246 | 7.9 | 5000 | 0.6673 | 0.3414 | | 0.1081 | 9.48 | 6000 | 0.7072 | 0.3409 | | 0.1006 | 11.06 | 7000 | 0.7413 | 0.3392 | | 0.0879 | 12.64 | 8000 | 0.7831 | 0.3394 | | 0.0821 | 14.22 | 9000 | 0.7371 | 0.3333 | | 0.0751 | 15.8 | 10000 | 0.8321 | 0.3445 | | 0.0671 | 17.38 | 11000 | 0.8362 | 0.3357 | | 0.0646 | 18.96 | 12000 | 0.8709 | 0.3367 | | 0.0595 | 20.54 | 13000 | 0.8352 | 0.3321 | | 0.0564 | 22.12 | 14000 | 0.8854 | 0.3323 | | 0.052 | 23.7 | 15000 | 0.9031 | 0.3315 | | 0.0485 | 25.28 | 16000 | 0.9171 | 0.3278 | | 0.046 | 26.86 | 17000 | 0.9390 | 0.3254 | | 0.0438 | 28.44 | 18000 | 0.9203 | 0.3258 | | 5e84e38fdf682adec0a585b3d6b6391e |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_wavlm_s753 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 466c9434c59698a9cc6d8212a9856d68 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 2701a0164fe30e59253a31b621765fbe |
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