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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cc-by-4.0 | [] | false | | Language | Setting | |----------------------------------------------------------------------|-------|:--------------:|---------------| | [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune | | [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune | | [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot | | [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune | | [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune | | [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune | | [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1) | 1 | Portuguese | fine-tune | | **[prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2)** | **2** | **Portuguese** | **fine-tune** | | [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3) | 3 | Portuguese | fine-tune | For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above. | 41a2c8d423847af2af102f41dd77e6d6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-bert-large-max-pooling This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 656573f68476840368c42b0a66cfe113 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/nli-bert-large-max-pooling') embeddings = model.encode(sentences) print(embeddings) ``` | b09642d8cc4c33d2ffbe0ac0119beea9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-bert-large-max-pooling') model = AutoModel.from_pretrained('sentence-transformers/nli-bert-large-max-pooling') | 07930aa3a26571a2d3da2b9ed9c41f79 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/nli-bert-large-max-pooling) | f56e29c33e2a323d5b31605ba1f8cde6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': True, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` | 15bdc0401e22b4f7c0418792b37c1eea |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-de-to-en-lr1e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.8228 - Bleu: 11.427 - Gen Len: 17.2674 | 16859e3ffed9fbabb9afdfec2843bbed |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 10 | 71b4db6049bff71dfeb84cdc055762f2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 272 | 1.9605 | 9.0786 | 17.3148 | | 2.3992 | 2.0 | 544 | 1.8884 | 10.1443 | 17.3301 | | 2.3992 | 3.0 | 816 | 1.8647 | 10.4816 | 17.3258 | | 2.0832 | 4.0 | 1088 | 1.8473 | 10.7396 | 17.3231 | | 2.0832 | 5.0 | 1360 | 1.8343 | 11.0937 | 17.2621 | | 1.9193 | 6.0 | 1632 | 1.8282 | 11.1303 | 17.3098 | | 1.9193 | 7.0 | 1904 | 1.8234 | 11.2971 | 17.2991 | | 1.8351 | 8.0 | 2176 | 1.8241 | 11.3433 | 17.2621 | | 1.8351 | 9.0 | 2448 | 1.8224 | 11.394 | 17.2691 | | 1.7747 | 10.0 | 2720 | 1.8228 | 11.427 | 17.2674 | | d82c3c2850e49bc3f03bd508a8855581 |
apache-2.0 | ['translation'] | false | opus-mt-tpi-en * source languages: tpi * target languages: en * OPUS readme: [tpi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tpi-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tpi-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tpi-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tpi-en/opus-2020-01-16.eval.txt) | 425c04162c2ab56034fa5f3a0377f20a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_norm500_aug2-3 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: 2.5766 - Mse: 5.1532 - Mae: 1.3526 - R2: -0.0072 - Accuracy: 0.4734 | d89cefb2170b4d51585fb707891c2a2e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:--------:| | 1.0562 | 1.0 | 15533 | 2.5766 | 5.1532 | 1.3526 | -0.0072 | 0.4734 | | bb0fe67eea5739928b63b7194b8eeede |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_qqp_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6884 - Accuracy: 0.7872 - F1: 0.7062 - Combined Score: 0.7467 | 7a4996fd7b9e48ccfdbce88140f0c957 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.9518 | 1.0 | 2843 | 0.8352 | 0.7536 | 0.6530 | 0.7033 | | 0.8249 | 2.0 | 5686 | 0.7766 | 0.7607 | 0.6219 | 0.6913 | | 0.7847 | 3.0 | 8529 | 0.7625 | 0.7648 | 0.6402 | 0.7025 | | 0.7498 | 4.0 | 11372 | 0.7551 | 0.7638 | 0.6197 | 0.6917 | | 0.7137 | 5.0 | 14215 | 0.7387 | 0.7691 | 0.6545 | 0.7118 | | 0.6762 | 6.0 | 17058 | 0.7165 | 0.7753 | 0.6720 | 0.7237 | | 0.6373 | 7.0 | 19901 | 0.7042 | 0.7783 | 0.6765 | 0.7274 | | 0.6045 | 8.0 | 22744 | 0.7075 | 0.7799 | 0.6902 | 0.7350 | | 0.5729 | 9.0 | 25587 | 0.7233 | 0.7792 | 0.6639 | 0.7215 | | 0.545 | 10.0 | 28430 | 0.7088 | 0.7805 | 0.7180 | 0.7493 | | 0.5183 | 11.0 | 31273 | 0.6884 | 0.7872 | 0.7062 | 0.7467 | | 0.4948 | 12.0 | 34116 | 0.7064 | 0.7869 | 0.7076 | 0.7472 | | 0.4724 | 13.0 | 36959 | 0.7053 | 0.7884 | 0.7120 | 0.7502 | | 0.4514 | 14.0 | 39802 | 0.7314 | 0.7903 | 0.7024 | 0.7464 | | 0.4321 | 15.0 | 42645 | 0.7112 | 0.7891 | 0.7228 | 0.7560 | | 0.4152 | 16.0 | 45488 | 0.7410 | 0.7909 | 0.7211 | 0.7560 | | 73079ec1c6fb38d2e62e5a19fee68cd9 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. | 54da255a04b99e82ebf624cc61ff0966 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Model XLM-Roberta : [https://huggingface.co/xlm-roberta-base](https://huggingface.co/xlm-roberta-base) Paper : [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/pdf/1911.02116.pdf) | 7d77b726b7cc320b98f46a26486cbcfd |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Demo: How to use in HuggingFace Transformers Pipeline Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline model_name = 'qanastek/51-languages-classifier' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer) res = classifier("פרק הבא בפודקאסט בבקשה") print(res) ``` Outputs: ```python [{'label': 'he-IL', 'score': 0.9998375177383423}] ``` | b33631d84fbb93936117358a74784dc8 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Training data [MASSIVE](https://huggingface.co/datasets/qanastek/MASSIVE) is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | 28e805ad3bf27f2c12d0aeddb0a5826c |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Languages Thee model is capable of distinguish 51 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `German - Germany (de-DE)` - `Greek - Greece (el-GR)` - `English - United States (en-US)` - `Spanish - Spain (es-ES)` - `Farsi - Iran (fa-IR)` - `Finnish - Finland (fi-FI)` - `French - France (fr-FR)` - `Hebrew - Israel (he-IL)` - `Hungarian - Hungary (hu-HU)` - `Armenian - Armenia (hy-AM)` - `Indonesian - Indonesia (id-ID)` - `Icelandic - Iceland (is-IS)` - `Italian - Italy (it-IT)` - `Japanese - Japan (ja-JP)` - `Javanese - Indonesia (jv-ID)` - `Georgian - Georgia (ka-GE)` - `Khmer - Cambodia (km-KH)` - `Korean - Korea (ko-KR)` - `Latvian - Latvia (lv-LV)` - `Mongolian - Mongolia (mn-MN)` - `Malay - Malaysia (ms-MY)` - `Burmese - Myanmar (my-MM)` - `Norwegian - Norway (nb-NO)` - `Dutch - Netherlands (nl-NL)` - `Polish - Poland (pl-PL)` - `Portuguese - Portugal (pt-PT)` - `Romanian - Romania (ro-RO)` - `Russian - Russia (ru-RU)` - `Slovanian - Slovania (sl-SL)` - `Albanian - Albania (sq-AL)` - `Swedish - Sweden (sv-SE)` - `Swahili - Kenya (sw-KE)` - `Hindi - India (hi-IN)` - `Kannada - India (kn-IN)` - `Malayalam - India (ml-IN)` - `Tamil - India (ta-IN)` - `Telugu - India (te-IN)` - `Thai - Thailand (th-TH)` - `Tagalog - Philippines (tl-PH)` - `Turkish - Turkey (tr-TR)` - `Urdu - Pakistan (ur-PK)` - `Vietnamese - Vietnam (vi-VN)` - `Welsh - United Kingdom (cy-GB)` | 2ebfbbaf689f6ca33324c5705fefcc95 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Evaluation results ```plain precision recall f1-score support af-ZA 0.9821 0.9805 0.9813 2974 am-ET 1.0000 1.0000 1.0000 2974 ar-SA 0.9809 0.9822 0.9815 2974 az-AZ 0.9946 0.9845 0.9895 2974 bn-BD 0.9997 0.9990 0.9993 2974 cy-GB 0.9970 0.9929 0.9949 2974 da-DK 0.9575 0.9617 0.9596 2974 de-DE 0.9906 0.9909 0.9908 2974 el-GR 0.9997 0.9973 0.9985 2974 en-US 0.9712 0.9866 0.9788 2974 es-ES 0.9825 0.9842 0.9834 2974 fa-IR 0.9940 0.9973 0.9956 2974 fi-FI 0.9943 0.9946 0.9945 2974 fr-FR 0.9963 0.9923 0.9943 2974 he-IL 1.0000 0.9997 0.9998 2974 hi-IN 1.0000 0.9980 0.9990 2974 hu-HU 0.9983 0.9950 0.9966 2974 hy-AM 1.0000 0.9993 0.9997 2974 id-ID 0.9319 0.9291 0.9305 2974 is-IS 0.9966 0.9943 0.9955 2974 it-IT 0.9698 0.9926 0.9811 2974 ja-JP 0.9987 0.9963 0.9975 2974 jv-ID 0.9628 0.9744 0.9686 2974 ka-GE 0.9993 0.9997 0.9995 2974 km-KH 0.9867 0.9963 0.9915 2974 kn-IN 1.0000 0.9993 0.9997 2974 ko-KR 0.9917 0.9997 0.9956 2974 lv-LV 0.9990 0.9950 0.9970 2974 ml-IN 0.9997 0.9997 0.9997 2974 mn-MN 0.9987 0.9966 0.9976 2974 ms-MY 0.9359 0.9418 0.9388 2974 my-MM 1.0000 0.9993 0.9997 2974 nb-NO 0.9600 0.9533 0.9566 2974 nl-NL 0.9850 0.9748 0.9799 2974 pl-PL 0.9946 0.9923 0.9934 2974 pt-PT 0.9885 0.9798 0.9841 2974 ro-RO 0.9919 0.9916 0.9918 2974 ru-RU 0.9976 0.9983 0.9980 2974 sl-SL 0.9956 0.9939 0.9948 2974 sq-AL 0.9936 0.9896 0.9916 2974 sv-SE 0.9902 0.9842 0.9872 2974 sw-KE 0.9867 0.9953 0.9910 2974 ta-IN 1.0000 1.0000 1.0000 2974 te-IN 1.0000 0.9997 0.9998 2974 th-TH 1.0000 0.9983 0.9992 2974 tl-PH 0.9929 0.9899 0.9914 2974 tr-TR 0.9869 0.9872 0.9871 2974 ur-PK 0.9983 0.9929 0.9956 2974 vi-VN 0.9993 0.9973 0.9983 2974 zh-CN 0.9812 0.9832 0.9822 2974 zh-TW 0.9832 0.9815 0.9823 2974 accuracy 0.9889 151674 macro avg 0.9889 0.9889 0.9889 151674 weighted avg 0.9889 0.9889 0.9889 151674 ``` Keywords : language identification ; language identification ; multilingual ; classification | 4ad6216a7ef4f9f7cea8d1c7c760f582 |
mit | [] | false | uma-clean-object on Stable Diffusion This is the `<uma-clean-object>` 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`:          | 0e20b9e5e3ee8e804c304da18b8c8052 |
apache-2.0 | [] | false | Model Details **Model Description:** This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). - **Developed by:** Hugging Face - **Model Type:** Text Classification - **Language(s):** English - **License:** Apache-2.0 - **Parent Model:** For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased). - **Resources for more information:** - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert | bed3b3a039c1df6da9ca3a6705ba5e5f |
apache-2.0 | [] | false | How to Get Started With the Model Example of single-label classification: ```python import torch from transformers import DistilBertTokenizer, DistilBertForSequenceClassification tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits predicted_class_id = logits.argmax().item() model.config.id2label[predicted_class_id] ``` | e12984aff6281cdee4210098c97bc462 |
apache-2.0 | [] | false | Direct Use This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. | c35b178db12c0d8a2f18e87ae7a428cb |
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. | cdd3cac18de383bf4684e7283ea39aca |
apache-2.0 | [] | false | Risks, Limitations and Biases Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations. For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https://twitter.com/aureliengeron) made an interesting map plotting these probabilities for each country. <img src="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/map.jpeg" alt="Map of positive probabilities per country." width="500"/> We strongly advise users to thoroughly probe these aspects on their use-cases in order to evaluate the risks of this model. We recommend looking at the following bias evaluation datasets as a place to start: [WinoBias](https://huggingface.co/datasets/wino_bias), [WinoGender](https://huggingface.co/datasets/super_glue), [Stereoset](https://huggingface.co/datasets/stereoset). | 57f5f272ff273bffa7604cd0fe8bcd85 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-SarcojiComplEmojisDistilRoberta-baseCLM This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8277 | 6aae130a5c5bca5fcc8ca338dfab9e2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2083 | 1.0 | 768 | 2.9175 | | 2.9739 | 2.0 | 1536 | 2.7931 | | 2.9174 | 3.0 | 2304 | 2.8351 | | e9eed5d90756f5d8ba60d378ccefaa8a |
apache-2.0 | ['generated_from_keras_callback'] | false | Okyx/finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0154 - Validation Loss: 3.3292 - Epoch: 7 | a4f10e66b880a24849ce1269190fb7d1 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.2009 | 4.0465 | 0 | | 5.7436 | 3.6640 | 1 | | 5.0419 | 3.5296 | 2 | | 4.6412 | 3.4582 | 3 | | 4.3722 | 3.3943 | 4 | | 4.1947 | 3.3610 | 5 | | 4.0747 | 3.3295 | 6 | | 4.0154 | 3.3292 | 7 | | 897f8d3fe2a92bc9c7cd08529336d33f |
mit | ['generated_from_trainer'] | false | celt-covid-twitter-bert-v2 This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4237 - F1: 0.8495 | 42f503f35e76363ea7fca2eeb71c2739 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5772 | 1.0 | 988 | 0.3683 | 0.8449 | | 0.3161 | 2.0 | 1976 | 0.4237 | 0.8495 | | 6989596fe9bd772e23a6b9f4435eb919 |
apache-2.0 | ['translation'] | false | opus-mt-fj-fr * source languages: fj * target languages: fr * OPUS readme: [fj-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fj-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fj-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fj-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fj-fr/opus-2020-01-09.eval.txt) | b821c15d0675e50d116b9ff1012763ad |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1100k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1100k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 2d7ab29f6bc86732909b7c4ec0a17329 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1100k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1100k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_1100k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1100k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_1100k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 79e6921873e5395548ea4ea6890c0ad6 |
mit | ['generated_from_trainer'] | false | agitated_jones This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | b173b1a67df2d198a3a8eaf247a1a638 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'agitated_jones', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 752b7c9b818081a476e3ad5f459ab0a4 |
apache-2.0 | ['translation'] | false | lit-rus * source group: Lithuanian * target group: Russian * OPUS readme: [lit-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-rus/README.md) * model: transformer-align * source language(s): lit * target language(s): rus * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-rus/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-rus/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-rus/opus-2020-06-17.eval.txt) | 58b858274fd4c395aba49dad669d6b33 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: lit-rus - source_languages: lit - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['lt', 'ru'] - src_constituents: {'lit'} - tgt_constituents: {'rus'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/lit-rus/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/lit-rus/opus-2020-06-17.test.txt - src_alpha3: lit - tgt_alpha3: rus - short_pair: lt-ru - chrF2_score: 0.695 - bleu: 51.7 - brevity_penalty: 0.982 - ref_len: 15395.0 - src_name: Lithuanian - tgt_name: Russian - train_date: 2020-06-17 - src_alpha2: lt - tgt_alpha2: ru - prefer_old: False - long_pair: lit-rus - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | fe7111eae693dc28b77d04ed9238cf7a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2474 - F1: 0.8270 | ac2877989dc162339c1f797f22613ba7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 70 | 0.3527 | 0.7372 | | 0.5173 | 2.0 | 140 | 0.2580 | 0.7916 | | 0.5173 | 3.0 | 210 | 0.2474 | 0.8270 | | 894046a6d63000b22ba3ffb1b4421e6a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_rte_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6918 - Accuracy: 0.5271 | ba85135e5f5f1d9453d619a030722af7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6948 | 1.0 | 10 | 0.6991 | 0.4729 | | 0.6969 | 2.0 | 20 | 0.6918 | 0.5271 | | 0.6939 | 3.0 | 30 | 0.6945 | 0.4729 | | 0.6948 | 4.0 | 40 | 0.6926 | 0.5271 | | 0.6935 | 5.0 | 50 | 0.6950 | 0.4729 | | 0.6936 | 6.0 | 60 | 0.6924 | 0.5271 | | 0.6941 | 7.0 | 70 | 0.6926 | 0.5271 | | 5c5289209ff6ad0ec4c55409499e22f3 |
mit | [] | false | T5-base model fine-tuned on BioASQ for Biological Question Answering 👩⚕️👨⚕️ [Google's T5-base](https://huggingface.co/t5-base) fine-tuned on [BioASQ](https://github.com/dmis-lab/biobert) (secondary task) for **Q&A** downstream task. | 300c245afecf5fad6ac96b6104c49c96 |
mit | [] | false | Details of T5 [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* | f7761d2d9194d2998c93688daf5d61a2 |
mit | [] | false | Usage 🚀 ```python import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("ozcangundes/T5-base-for-BioQA") model = T5ForConditionalGeneration.from_pretrained("ozcangundes/T5-base-for-BioQA") def get_answer(question,context): source_encoding=tokenizer( question, context, max_length=512, padding="max_length", truncation="only_second", return_attention_mask=True, add_special_tokens=True, return_tensors="pt") generated_ids=model.generate( input_ids=source_encoding["input_ids"], attention_mask=source_encoding["attention_mask"]) preds=[tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) for gen_id in generated_ids] return "".join(preds) ``` | cd58c8111d9a0c88ee4268473230e6d2 |
mit | [] | false | Example 1 ```python question={ "context":"Effect of food on the pharmacokinetics of empagliflozin, a sodium glucose cotransporter 2 (SGLT2) inhibitor, and assessment of dose proportionality in healthy volunteers. OBJECTIVES: Empagliflozin is an orally available, potent and highly selective inhibitor of the sodium glucose cotransporter 2 (SGLT2). This study was undertaken to investigate the effect of food on the pharmacokinetics of 25 mg empagliflozin and to assess dose proportionality between 10 mg and 25 mg empagliflozin under fasted conditions. MATERIALS AND METHODS: In this open-label, 3-way, cross-over study, 18 healthy volunteers received 3 single doses of empagliflozin in a randomized sequence (25 mg empagliflozin under fasted conditions, 25 mg empagliflozin after a high-fat, high-calorie breakfast and 10 mg empagliflozin under fasted conditions), each separated by a washout period of at least 7 days. Serial plasma samples were collected at selected time points over a period of 72 hours. RESULTS: Administration with food had no clinically relevant effect on the area under the plasma concentration-time curve (AUC0-∞) of empagliflozin (geometric mean ratio (GMR): 84.04, 90% confidence interval (CI): 80.86 - 87.34). The decrease observed in the maximum plasma concentrations (Cmax) of empagliflozin (GMR: 63.22, 90% CI: 56.74 - 70.44) when administered with food was not considered clinically meaningful. The increases in AUC0-∞ and Cmax for 10 mg vs. 25 mg empagliflozin administered under fasting conditions were roughly dose-proportional, as demonstrated by the slope β of the regression lines being slightly less than 1 (slope β for AUC0-∞: 0.94, 95% CI: 0.90 - 0.97; slope β for Cmax: 0.91, 95% CI: 0.80 - 1.01). Empagliflozin was well tolerated under fed and fasting conditions. CONCLUSIONS: The results support administration of empagliflozin tablets independently of food. Increases in empagliflozin exposure under fasting conditions were roughly dose-proportional between 10 mg and 25 mg empagliflozin.", "question":"Which protein does empagliflozin inhibit?" } get_answer(question["question"],question["context"]) ``` > SGLT2 | 681dfa507e5f96a7180e045997a4eb8f |
mit | [] | false | Example 2 ```python question2={ "context":"Dermatitis herpetiformis: jejunal findings and skin response to gluten free diet. Fifty seven children with dermatitis herpetiformis, 18 from Finland and 39 from Hungary, were studied. Diagnostic criteria included the finding of granular IgA deposits in the skin of all patients. The mean age at onset of the rash was 7 X 2 years and favoured sites were the elbows, knees, and buttocks. Symptoms suggesting small intestinal disease were rare but in 35 (61%) of the children subtotal villous atrophy and in 16 (28%) partial villous atrophy were found on jejunal biopsy. Eighteen children underwent a second biopsy after a mean of 21 months on a gluten free diet; villous height was found to be increased and the intraepithelial lymphocyte count decreased in all these patients. Gluten challenge caused a reversal in the two children who underwent a third biopsy. The effect of the gluten free diet on the rash was examined in Finnish children by observing the daily requirements of dapsone, a drug used to control the rash at the beginning of the diet. Eight (67%) of the 12 children were able to stop taking dapsone after a mean of 11 months on the diet and all three patients treated with diet alone became asymptomatic after three to 6 months on the diet. These results confirm that most children with dermatitis herpetiformis have jejunal villous atrophy, though they rarely have gastrointestinal symptoms. The central role of gluten in childhood dermatitis herpetiformis is evidenced by the fact that a gluten free diet helps the damaged jejunal mucosa to recover and controls the rash even in those children who do not have an abnormal jejunal biopsy.", "question":"What is the typical rash associated with gluten?" } get_answer(question2["question"],question2["context"]) ``` > dermatitis herpetiformis Created by Özcan Gündeş ✌️ --- Twitter: <a href="https://twitter.com/ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/twitter.svg" alt="ozcangundes" height="30" width="30" /></a> Linkedin: <a href="https://www.linkedin.com/in/%C3%B6zcan-g%C3%BCnde%C5%9F-7693055b/" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/linkedin.svg" alt="13198517" height="30" width="30" /></a> Medium: <a href="https://medium.com/@ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/medium.svg" alt="@ozcangundes" height="30" width="30" /></a> Github: <a href="https://github.com/ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/github.svg" alt="@ozcangundes" height="30" width="30" /></a> | 3a0b9fe9a5d85221e22fa909ca97c51d |
gpl | ['corenlp'] | false | Core NLP model for en 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). | 3f261c4ea793c077b5ac26084a969cea |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Jak's Voxel-ish Image Pack for Stable Diffusion Another fantastic image pack brought to you by 143 training images through 8000 training steps, 20% Training text crafted by Jak_TheAI_Artist Include Prompt trigger: "voxel-ish" to activate. Tip: add "intricate detail" in prompt to make a semi-realistic image. | 8c4b77887ddab811eee23233d3d9a205 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | UPDATE: Version 1.2 available [here](https://huggingface.co/plasmo/vox2) Sample pictures of this concept: voxel-ish         | fd4527fe67f0b1d23bb4c88c3629bd77 |
cc-by-sa-4.0 | ['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 - training_steps: 1000 - mixed_precision_training: Native AMP | 024cfcea1ab109f58c82ff0ba9dbc98a |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 & NST dataset. It achieves the following results on the evaluation set: - Loss: 0.3551 - Wer: 19.2143 | 77e7114c7f175073d0cddde698057952 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 500 - training_steps: 8000 - mixed_precision_training: Native AMP | 3aa91b46ce62dfa3412308deb447109e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2128 | 0.85 | 1000 | 0.2955 | 22.1613 | | 0.0871 | 1.71 | 2000 | 0.2790 | 20.8034 | | 0.0373 | 2.56 | 3000 | 0.2884 | 19.9269 | | 0.0163 | 3.41 | 4000 | 0.3082 | 19.5477 | | 0.0046 | 4.27 | 5000 | 0.3183 | 19.5881 | | 0.0023 | 5.12 | 6000 | 0.3397 | 19.3757 | | 0.0023 | 5.97 | 7000 | 0.3468 | 19.3219 | | 0.0013 | 6.83 | 8000 | 0.3551 | 19.2143 | | f0d35442b7af1a706ce4bd0e74bb408a |
apache-2.0 | ['translation'] | false | opus-mt-es-ht * source languages: es * target languages: ht * OPUS readme: [es-ht](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ht/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-ht/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-ht/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-ht/opus-2020-01-16.eval.txt) | 51473354655c325240e5e182db99a45b |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_unispeech_s149 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (id)](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. | 08872c24dab61d50eabb2c6681fa5ab1 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-wnli-target-glue-rte This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6882 - Accuracy: 0.5596 | 76bfc5ebfa0b454d2f9835cb5c5c0e99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6475 | 6.41 | 500 | 0.7071 | 0.5596 | | 0.4526 | 12.82 | 1000 | 0.8708 | 0.5704 | | 0.2668 | 19.23 | 1500 | 1.1317 | 0.5704 | | 0.162 | 25.64 | 2000 | 1.4052 | 0.5704 | | 0.0978 | 32.05 | 2500 | 1.8224 | 0.5812 | | 0.0658 | 38.46 | 3000 | 2.0893 | 0.5668 | | 0.0488 | 44.87 | 3500 | 2.4656 | 0.5560 | | 0.0409 | 51.28 | 4000 | 2.6882 | 0.5596 | | d25ff2f72afa3808a986f278bcdd8ff7 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'nl', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-nl This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the test set: - Loss: 0.3923 - Wer: 0.1748 | 30183bd72dc6d99b6d7b6f5db8f59261 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'nl', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.5787 | 0.89 | 400 | 0.6354 | 0.5643 | | 0.3036 | 1.78 | 800 | 0.3690 | 0.3552 | | 0.188 | 2.67 | 1200 | 0.3239 | 0.2958 | | 0.1434 | 3.56 | 1600 | 0.3093 | 0.2515 | | 0.1245 | 4.44 | 2000 | 0.3024 | 0.2433 | | 0.1095 | 5.33 | 2400 | 0.3249 | 0.2643 | | 0.0979 | 6.22 | 2800 | 0.3191 | 0.2281 | | 0.0915 | 7.11 | 3200 | 0.3152 | 0.2216 | | 0.0829 | 8.0 | 3600 | 0.3419 | 0.2218 | | 0.0777 | 8.89 | 4000 | 0.3432 | 0.2132 | | 0.073 | 9.78 | 4400 | 0.3223 | 0.2131 | | 0.0688 | 10.67 | 4800 | 0.3094 | 0.2152 | | 0.0647 | 11.56 | 5200 | 0.3411 | 0.2152 | | 0.0639 | 12.44 | 5600 | 0.3762 | 0.2135 | | 0.0599 | 13.33 | 6000 | 0.3790 | 0.2137 | | 0.0572 | 14.22 | 6400 | 0.3693 | 0.2118 | | 0.0563 | 15.11 | 6800 | 0.3495 | 0.2139 | | 0.0521 | 16.0 | 7200 | 0.3800 | 0.2023 | | 0.0508 | 16.89 | 7600 | 0.3678 | 0.2033 | | 0.0513 | 17.78 | 8000 | 0.3845 | 0.1987 | | 0.0476 | 18.67 | 8400 | 0.3511 | 0.2037 | | 0.045 | 19.56 | 8800 | 0.3794 | 0.1994 | | 0.044 | 20.44 | 9200 | 0.3525 | 0.2050 | | 0.043 | 21.33 | 9600 | 0.4082 | 0.2007 | | 0.0409 | 22.22 | 10000 | 0.3866 | 0.2004 | | 0.0393 | 23.11 | 10400 | 0.3899 | 0.2008 | | 0.0382 | 24.0 | 10800 | 0.3626 | 0.1951 | | 0.039 | 24.89 | 11200 | 0.3936 | 0.1953 | | 0.0361 | 25.78 | 11600 | 0.4262 | 0.1928 | | 0.0362 | 26.67 | 12000 | 0.3796 | 0.1934 | | 0.033 | 27.56 | 12400 | 0.3616 | 0.1934 | | 0.0321 | 28.44 | 12800 | 0.3742 | 0.1933 | | 0.0325 | 29.33 | 13200 | 0.3582 | 0.1869 | | 0.0309 | 30.22 | 13600 | 0.3717 | 0.1874 | | 0.029 | 31.11 | 14000 | 0.3814 | 0.1894 | | 0.0296 | 32.0 | 14400 | 0.3698 | 0.1877 | | 0.0281 | 32.89 | 14800 | 0.3976 | 0.1899 | | 0.0275 | 33.78 | 15200 | 0.3854 | 0.1858 | | 0.0264 | 34.67 | 15600 | 0.4021 | 0.1889 | | 0.0261 | 35.56 | 16000 | 0.3850 | 0.1830 | | 0.0242 | 36.44 | 16400 | 0.4091 | 0.1878 | | 0.0245 | 37.33 | 16800 | 0.4012 | 0.1846 | | 0.0243 | 38.22 | 17200 | 0.3996 | 0.1833 | | 0.0223 | 39.11 | 17600 | 0.3962 | 0.1815 | | 0.0223 | 40.0 | 18000 | 0.3898 | 0.1832 | | 0.0219 | 40.89 | 18400 | 0.4019 | 0.1822 | | 0.0211 | 41.78 | 18800 | 0.4035 | 0.1809 | | 0.021 | 42.67 | 19200 | 0.3915 | 0.1826 | | 0.0208 | 43.56 | 19600 | 0.3934 | 0.1784 | | 0.0188 | 44.44 | 20000 | 0.3912 | 0.1787 | | 0.0195 | 45.33 | 20400 | 0.3989 | 0.1766 | | 0.0186 | 46.22 | 20800 | 0.3887 | 0.1773 | | 0.0188 | 47.11 | 21200 | 0.3982 | 0.1758 | | 0.0175 | 48.0 | 21600 | 0.3933 | 0.1755 | | 0.0172 | 48.89 | 22000 | 0.3921 | 0.1749 | | 0.0187 | 49.78 | 22400 | 0.3923 | 0.1748 | | 83c7b77e5b387a37b172abfedd10ca7b |
mit | ['generated_from_trainer'] | false | xlnet-base-cased_fold_9_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7204 - F1: 0.8203 | f4a67de62b409c7a02e44eabb494c38c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.4045 | 0.8001 | | 0.4262 | 2.0 | 582 | 0.3914 | 0.8297 | | 0.4262 | 3.0 | 873 | 0.5050 | 0.8029 | | 0.2488 | 4.0 | 1164 | 0.7681 | 0.8007 | | 0.2488 | 5.0 | 1455 | 0.8349 | 0.8262 | | 0.1483 | 6.0 | 1746 | 0.9045 | 0.8220 | | 0.0894 | 7.0 | 2037 | 1.1584 | 0.8165 | | 0.0894 | 8.0 | 2328 | 1.1818 | 0.8300 | | 0.0389 | 9.0 | 2619 | 1.3332 | 0.8147 | | 0.0389 | 10.0 | 2910 | 1.2373 | 0.8285 | | 0.038 | 11.0 | 3201 | 1.3156 | 0.8234 | | 0.038 | 12.0 | 3492 | 1.3251 | 0.8341 | | 0.0211 | 13.0 | 3783 | 1.3144 | 0.8255 | | 0.0158 | 14.0 | 4074 | 1.5686 | 0.8168 | | 0.0158 | 15.0 | 4365 | 1.5382 | 0.8185 | | 0.0165 | 16.0 | 4656 | 1.5203 | 0.8282 | | 0.0165 | 17.0 | 4947 | 1.5352 | 0.8136 | | 0.0142 | 18.0 | 5238 | 1.4799 | 0.8243 | | 0.0062 | 19.0 | 5529 | 1.5030 | 0.8294 | | 0.0062 | 20.0 | 5820 | 1.6264 | 0.8094 | | 0.0078 | 21.0 | 6111 | 1.6949 | 0.8122 | | 0.0078 | 22.0 | 6402 | 1.7106 | 0.8139 | | 0.0043 | 23.0 | 6693 | 1.7234 | 0.8218 | | 0.0043 | 24.0 | 6984 | 1.7344 | 0.8208 | | 0.0028 | 25.0 | 7275 | 1.7204 | 0.8203 | | 08b34d7a55a599a8cbf070fcd2ecba16 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - walter-white These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "break bad" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: break bad     | 3e1d3b86afeadcaaf94ce330fd718f37 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-ner-false-finetuned-ner-2002 This model is a fine-tuned version of [StivenLancheros/xlm-roberta-base-finetuned-ner-false](https://huggingface.co/StivenLancheros/xlm-roberta-base-finetuned-ner-false) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0725 - Precision: 0.9412 - Recall: 0.9507 - F1: 0.9459 - Accuracy: 0.9904 | 977512200d392f28de4daba9b3ef856e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.086 | 1.0 | 7021 | 0.0709 | 0.9221 | 0.9261 | 0.9241 | 0.9872 | | 0.0352 | 2.0 | 14042 | 0.0871 | 0.9243 | 0.9354 | 0.9298 | 0.9879 | | 0.0203 | 3.0 | 21063 | 0.0747 | 0.9398 | 0.9490 | 0.9444 | 0.9901 | | 0.0184 | 4.0 | 28084 | 0.0725 | 0.9412 | 0.9507 | 0.9459 | 0.9904 | | 30ba9e5080b666e26733ae679a122020 |
mit | ['generated_from_trainer'] | false | final_model_output_subreddit-wallstreetbets This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5351 | 65060d901b0b4367f1563513aae1f184 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP | 05b6259467bb48ab3f7b4744c5dd5d87 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.7979 | 1.25 | 5000 | 3.6293 | | 3.4998 | 2.49 | 10000 | 3.5351 | | 34fcf3908a85d2fa8f21ad4d5613d536 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  | cbc3b2e70e3eb6cae6ddccf60e8f37b0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-ar-7 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 61.6652 - Wer: 0.2222 | 98b3f5bf2a232b1d93427865091d2997 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6306.7719 | 4.71 | 400 | 617.7255 | 1.0 | | 1222.8073 | 9.41 | 800 | 81.7446 | 0.3820 | | 326.9842 | 14.12 | 1200 | 67.3986 | 0.2859 | | 223.859 | 18.82 | 1600 | 60.8896 | 0.2492 | | 175.5662 | 23.53 | 2000 | 59.2339 | 0.2256 | | 146.3602 | 28.24 | 2400 | 61.6652 | 0.2222 | | ef9d005dca66fb6863103364e6698b6a |
['apache-2.0'] | ['causal-lm', 'text-generation'] | false | How to use ```python from transformers import GPT2LMHeadModel, GPT2Tokenizer import torch DEVICE = torch.device("cuda:0") model_name_or_path = "radm/rugpt3medium-tathagata" tokenizer = GPT2Tokenizer.from_pretrained("sberbank-ai/rugpt3medium_based_on_gpt2") model = GPT2LMHeadModel.from_pretrained(model_name_or_path).to(DEVICE) text = "В чем смысл жизни?\n" input_ids = tokenizer.encode(text, return_tensors="pt").to(DEVICE) model.eval() with torch.no_grad(): out = model.generate(input_ids, do_sample=True, num_beams=4, temperature=1.1, top_p=0.9, top_k=50, max_length=250, min_length=50, early_stopping=True, no_repeat_ngram_size=2 ) generated_text = list(map(tokenizer.decode, out))[0] print() print(generated_text) ``` | 566a9df570337d45c3bfbb202b7799e6 |
['apache-2.0'] | ['causal-lm', 'text-generation'] | false | Dataset Dataset based on summaries of major Buddhist, Hindu and Advaita texts such as: - Diamond Sutra - Lankavatara Sutra - Sri Nisargadatta Maharaj quotes - Quotes from the Bhagavad Gita Dataset link: [tathagata](https://huggingface.co/datasets/radm/tathagata) | c68b6e2ae0e61d84ba7cb4457acd3b3d |
apache-2.0 | ['translation'] | false | opus-mt-yap-en * source languages: yap * target languages: en * OPUS readme: [yap-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yap-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yap-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yap-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yap-en/opus-2020-01-16.eval.txt) | dfba6f38d73dcce6739ef7cc35f9b086 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Turkish This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 Turkish dataset. It achieves the following results on the evaluation set: - Loss: 0.1879 - Wer: 10.5033 | ad387fc78722ff5056ebb3f9f8676947 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Model description The model is fine-tuned for 1000 steps/updates. - Zero-shot - 20.89 (CV11) - Fine-tune on CV11 - 10.50 (CV11) (-49%) ------------------------------------------------------------------- - Zeroshot - 10.4 (Google Fluers) - Fine-tune on CV11 - 9.26 (Google Fluers) | 466f4e82ee0fda1fafc92c1315807395 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0348 | 3.05 | 1000 | 0.1879 | 10.5033 | | 9ebda24a85ff362af19f76a77cefaf90 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_vp-it_s609 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](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. | def9a2ed927836cbd7d408bc271f8bc2 |
apache-2.0 | ['summarization', 'translation'] | false | Model Card for T5 11B  | dc28e73795d2c90c7436bfa1e2c04a2d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - 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: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP | 77d7264fa37afd1e709ae993ba7b3436 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_no-pretraining_s842 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (it)](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. | 009f22a75f50d3470c03d70c29c62a47 |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep100 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: - Train Loss: 0.7195 - Epoch: 99 | bb856fa28da33450680cf47ea217b0c9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.8730 | 0 | | 3.0562 | 1 | | 3.0168 | 2 | | 3.0032 | 3 | | 2.9954 | 4 | | 2.9951 | 5 | | 2.9904 | 6 | | 2.9765 | 7 | | 2.9788 | 8 | | 2.9692 | 9 | | 2.9656 | 10 | | 2.9761 | 11 | | 2.9643 | 12 | | 2.9393 | 13 | | 2.9026 | 14 | | 2.8685 | 15 | | 2.8438 | 16 | | 2.8279 | 17 | | 2.8107 | 18 | | 2.7896 | 19 | | 2.7716 | 20 | | 2.7458 | 21 | | 2.7118 | 22 | | 2.6519 | 23 | | 2.5933 | 24 | | 2.4702 | 25 | | 2.2842 | 26 | | 2.0712 | 27 | | 1.8406 | 28 | | 1.6374 | 29 | | 1.4836 | 30 | | 1.3824 | 31 | | 1.3079 | 32 | | 1.2538 | 33 | | 1.2054 | 34 | | 1.1700 | 35 | | 1.1432 | 36 | | 1.1122 | 37 | | 1.0939 | 38 | | 1.0645 | 39 | | 1.0465 | 40 | | 1.0248 | 41 | | 1.0069 | 42 | | 0.9902 | 43 | | 0.9769 | 44 | | 0.9510 | 45 | | 0.9394 | 46 | | 0.9316 | 47 | | 0.9181 | 48 | | 0.9090 | 49 | | 0.9010 | 50 | | 0.8934 | 51 | | 0.8791 | 52 | | 0.8759 | 53 | | 0.8652 | 54 | | 0.8566 | 55 | | 0.8511 | 56 | | 0.8414 | 57 | | 0.8373 | 58 | | 0.8302 | 59 | | 0.8241 | 60 | | 0.8246 | 61 | | 0.8207 | 62 | | 0.8110 | 63 | | 0.8081 | 64 | | 0.8010 | 65 | | 0.7995 | 66 | | 0.7965 | 67 | | 0.7941 | 68 | | 0.7849 | 69 | | 0.7866 | 70 | | 0.7874 | 71 | | 0.7796 | 72 | | 0.7742 | 73 | | 0.7706 | 74 | | 0.7687 | 75 | | 0.7686 | 76 | | 0.7663 | 77 | | 0.7586 | 78 | | 0.7554 | 79 | | 0.7563 | 80 | | 0.7541 | 81 | | 0.7527 | 82 | | 0.7482 | 83 | | 0.7460 | 84 | | 0.7436 | 85 | | 0.7423 | 86 | | 0.7422 | 87 | | 0.7385 | 88 | | 0.7367 | 89 | | 0.7321 | 90 | | 0.7320 | 91 | | 0.7354 | 92 | | 0.7271 | 93 | | 0.7270 | 94 | | 0.7210 | 95 | | 0.7236 | 96 | | 0.7263 | 97 | | 0.7237 | 98 | | 0.7195 | 99 | | 451a9248104abf33e39a8bbc3a3915ca |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | cifar10_outputs This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0806 - Accuracy: 0.9914 | eaa9b1133d9728ce073a671f8a1dab82 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 17 - eval_batch_size: 17 - seed: 1337 - distributed_type: IPU - gradient_accumulation_steps: 128 - total_train_batch_size: 8704 - total_eval_batch_size: 272 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.25 - num_epochs: 100.0 - training precision: Mixed Precision | b4f952929abdcd5cfd4615aeee82c3c4 |
cc-by-sa-4.0 | [] | false | BERT Base Japanese for Irony This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection. The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a dataset containing ironic and sarcastic tweets. | 8feb720665f8b00c5d4482be21fdb3be |
cc-by-sa-4.0 | [] | false | Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022bert-base-irony02, title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (daigo ver.)}, author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-sentiment-irony" } ``` | 6ba6141885ff540a8a2b6d17675f6228 |
apache-2.0 | Text Classification | false | BatteryBERT-uncased for Battery Abstract Classification
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
| ac94a588db094686b33592435446cdb4 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-mbert-finetuned-TRAC-DS This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9044 - Accuracy: 0.7010 - Precision: 0.6772 - Recall: 0.6723 - F1: 0.6740 | 5cf6ee6a52f8a69024cd214b8682b3fe |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.824279936868144e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 97ed213d2eb6f542ea8a47d067f1fa75 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.837 | 1.0 | 1224 | 0.7640 | 0.6422 | 0.6377 | 0.6475 | 0.6277 | | 0.6164 | 2.0 | 2448 | 0.8456 | 0.6724 | 0.6581 | 0.6623 | 0.6547 | | 0.434 | 3.0 | 3672 | 1.0284 | 0.6969 | 0.6715 | 0.6771 | 0.6729 | | 0.267 | 4.0 | 4896 | 1.5533 | 0.6912 | 0.6644 | 0.6675 | 0.6655 | | 0.1542 | 5.0 | 6120 | 1.9044 | 0.7010 | 0.6772 | 0.6723 | 0.6740 | | 0d81c7fe996140894d0a6d2e6b154cef |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | thilinamethsahan Dreambooth model trained by Thilinameths 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:     | e533eff933ecca7487b162cc4540a68e |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | Model Description This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`. | 7f8e1423b661416152df11503c222242 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-wikipedia-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-base-japanese-wikipedia-ud-head") qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False) print(qap(question="国語",context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/)) ```py class TransformersUD(object): def __init__(self,bert): import os from transformers import (AutoTokenizer,AutoModelForQuestionAnswering, AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline) self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForQuestionAnswering.from_pretrained(bert) x=AutoModelForTokenClassification.from_pretrained if os.path.isdir(bert): d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger")) else: from transformers.utils import cached_file c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json")) d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c) s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json")) t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s) self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer, aggregation_strategy="simple") self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)] z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w) r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan) v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[] for i,t in enumerate(v): q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id] c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]]) b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c] with torch.no_grad(): d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]), token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b])) s,e=d.start_logits.tolist(),d.end_logits.tolist() for i in range(n): for j in range(n): m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: i=([p for s,e,p in w]+["root"]).index("root") j=i+1 if i<n else numpy.nanargmax(m[:,0]) m[0:j,0]=m[j+1:,0]=numpy.nan h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u=" | fbf331fbfc52059c98b0c6e2d159ba03 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD("KoichiYasuoka/bert-base-japanese-wikipedia-ud-head") print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` | 2096509f89e9175fd27bee9e2120bcc7 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small sv-SE - KTH This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3310 - Wer: 19.1193 | 708980b7894f025c805a0e694e9c96b0 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1015 | 1.29 | 1000 | 0.2880 | 20.4134 | | 0.0387 | 2.59 | 2000 | 0.2959 | 19.6810 | | 0.0126 | 3.88 | 3000 | 0.3103 | 19.2990 | | 0.0035 | 5.17 | 4000 | 0.3310 | 19.1193 | | ba72b48019d055131d50645db98bf5d7 |
creativeml-openrail-m | [] | false | Preview Images https://imgur.com/a/vwO6f5A IMPORTANT INSTRUCTIONS!! This model was trained on SD base 1.5 version BUT It does also work for 1.4 as they both share the same Clip encoder. Install instructions. Simply place the chimp.pt file inside the \stable-diffusion-webui\models\hypernetworks folder. Load the model inside the Automatic1111 interface under settings hypernetwork. Use instructions. Use between 0.55-1.0 hypernetwork strength, more strength will give a more real chimpl look while .55 gives a more human form chimp look. I find .7 works well enough. Use DPM++ SDE Karras sampler with 15 steps and CFG of 6.0. Make sure and always include the word chimp somewhere in the prompt. For people always preface the subject with chimp, example "chimp man walking", "chimp girl playing in the backyard", etc... VERY IMPORTANT! Always describe the background in some detail or you WILL get a very generic boring background.. So for example DON'T just say "an old chimp man". DO say "an old chimp man inside a rustic hut". Some fun info. People have been sleeping on hypernetworks and I plan to change that. Hopefully the flexibility of this hypernetwok will show everyone their true potential. Because this model is a hypernetwork it can be used in conjunction with ANY model based on the 1.4 CLIP architecture. That means this model will work on any custom 1.4 or 1.5 model, like the modern disney model, or classic disney, etc… for example, let's say you want to load classic disney as base. Well simply load the classic disney model, make sure and preface every prompt with classic disney. As per instructions of the model. Then follow up with my “chimp” tag as instructed once you have loaded the hypernetwork. So the prompt should look something like this “classic disney. chimp girl playing in the backyard.” Make sure and adjust the hypernetwork strength to .5 for a more cartoon look or .7 for a realistic chimp look. Have fun folks! | 1d7074023a33e754ae4fb4599ecc5f79 |
wtfpl | [] | false | Embedding in a Dishonored-ish style. Works really well with other embeddings for a dystopian, sad, painterly vibe. No training settings this time, as I completely forgot to write those down. My apologies.        | 597deae9b63f060667f233dc58ffaad7 |
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