license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | Inference import pandas as pd from tabulate import tabulate text = """When Member States adopt those measures, they shall contain a reference to this Directive or be accompanied by such reference on the occasion of their official publication. They shall also include a statement that references in existing laws, regulations and administrative provisions to Article 9 of Directive 97/23/EC shall be construed as references to Article 13 of this Directive. Member States shall determine how such reference is to be made and how that statement is to be formulated.""" entities = pypeline(text) df = pd.DataFrame(entities) print(tabulate(df, showindex=True, headers=df.columns)) ``` ``` | 512b95a44968a4c0c09da4976737635d |
mit | [] | false | Output entity_group score word start end -- ------------------------------ -------- ------------------ ------- ----- 0 current_act 0.999999 Directive 80 89 1 article_relevant_following_act 0.999995 9 296 297 2 another_act 0.999999 Directive 97/23/EC 301 319 3 article_relevant_following_act 0.999996 13 364 366 4 current_act 0.999999 Directive 375 384 ``` | b1d9271cca204a9feb18106d50020eba |
mit | [] | false | phoenix-01 on Stable Diffusion This is the `<phoenix-style>` 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`:     | b197436201dd7f73eff8d53ddb0ab309 |
mit | [] | false | anime boy on Stable Diffusion This is the `<myAItestShota>` 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`:      | 56269953512a487d7a31f7ea141e095a |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_cola_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6753 - Matthews Correlation: 0.0 | 70e0499247602fa0ed8f5a13673b95e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.8155 | 1.0 | 67 | 0.6867 | 0.0 | | 0.797 | 2.0 | 134 | 0.6862 | 0.0 | | 0.7961 | 3.0 | 201 | 0.6836 | 0.0 | | 0.7944 | 4.0 | 268 | 0.6821 | 0.0 | | 0.7863 | 5.0 | 335 | 0.6753 | 0.0 | | 0.7138 | 6.0 | 402 | 0.6790 | 0.1085 | | 0.6262 | 7.0 | 469 | 0.7238 | 0.1231 | | 0.5782 | 8.0 | 536 | 0.7285 | 0.1281 | | 0.5482 | 9.0 | 603 | 0.7484 | 0.1281 | | 0.5318 | 10.0 | 670 | 0.7918 | 0.1182 | | 3a9b7fd4bf57feed75511ccc8297173a |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_model4 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: 25.4886 - Accuracy: 0.0 | 6cd70b9718ffdf0a7328815596c98f4c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.02 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 25e6d268c783e5b4ab6509deadf3945c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6252 | 1.0 | 1 | 3.9768 | 0.0 | | 1.0027 | 2.0 | 2 | 25.4886 | 0.0 | | f8ed50e4354cb30c215784c832b1c80a |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_mrpc_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 - F1: 1.0 - Combined Score: 1.0 | 60507cdf93609ebf777c18c8964c4a1e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.1854 | 1.0 | 1959 | 0.0199 | 0.9975 | 0.9982 | 0.9979 | | 0.04 | 2.0 | 3918 | 0.0050 | 0.9975 | 0.9982 | 0.9979 | | 0.0253 | 3.0 | 5877 | 0.0015 | 1.0 | 1.0 | 1.0 | | 0.0175 | 4.0 | 7836 | 0.0003 | 1.0 | 1.0 | 1.0 | | 0.0134 | 5.0 | 9795 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0107 | 6.0 | 11754 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0081 | 7.0 | 13713 | 0.0012 | 1.0 | 1.0 | 1.0 | | 0.0062 | 8.0 | 15672 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0061 | 9.0 | 17631 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0044 | 10.0 | 19590 | 0.0002 | 1.0 | 1.0 | 1.0 | | 0.0041 | 11.0 | 21549 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0034 | 12.0 | 23508 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0029 | 13.0 | 25467 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0016 | 14.0 | 27426 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0019 | 15.0 | 29385 | 0.0140 | 0.9975 | 0.9982 | 0.9979 | | 0.0018 | 16.0 | 31344 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0012 | 17.0 | 33303 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0013 | 18.0 | 35262 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0008 | 19.0 | 37221 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0011 | 20.0 | 39180 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0005 | 21.0 | 41139 | 0.0007 | 1.0 | 1.0 | 1.0 | | 0.0009 | 22.0 | 43098 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0004 | 23.0 | 45057 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0004 | 24.0 | 47016 | 0.0000 | 1.0 | 1.0 | 1.0 | | 7e447f9c6a8a5cd39211d293c4bb2940 |
mit | ['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 - lr_scheduler_warmup_steps: 200 - num_epochs: 1 - mixed_precision_training: Native AMP | 1ca9e6f7f1d1b8e9a32ca2c78c65b09c |
apache-2.0 | ['translation'] | false | cpp-cpp * source group: Creoles and pidgins, Portuguese-based * target group: Creoles and pidgins, Portuguese-based * OPUS readme: [cpp-cpp](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cpp-cpp/README.md) * model: transformer * source language(s): ind pap * target language(s): ind pap * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cpp-cpp/opus-2020-07-26.zip) * test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cpp-cpp/opus-2020-07-26.test.txt) * test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cpp-cpp/opus-2020-07-26.eval.txt) | 55d2d5f6c957589a2ee2118eb8db3171 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.msa-msa.msa.msa | 0.7 | 0.149 | | Tatoeba-test.msa-pap.msa.pap | 31.7 | 0.577 | | Tatoeba-test.multi.multi | 21.1 | 0.369 | | Tatoeba-test.pap-msa.pap.msa | 17.7 | 0.197 | | 8d63d6872ba9033db5fdf0a4ae57a657 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cpp-cpp - source_languages: cpp - target_languages: cpp - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cpp-cpp/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['id', 'cpp'] - src_constituents: {'zsm_Latn', 'ind', 'pap', 'min', 'tmw_Latn', 'max_Latn', 'zlm_Latn'} - tgt_constituents: {'zsm_Latn', 'ind', 'pap', 'min', 'tmw_Latn', 'max_Latn', 'zlm_Latn'} - src_multilingual: True - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/cpp-cpp/opus-2020-07-26.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/cpp-cpp/opus-2020-07-26.test.txt - src_alpha3: cpp - tgt_alpha3: cpp - short_pair: cpp-cpp - chrF2_score: 0.369 - bleu: 21.1 - brevity_penalty: 0.882 - ref_len: 18.0 - src_name: Creoles and pidgins, Portuguese-based - tgt_name: Creoles and pidgins, Portuguese-based - train_date: 2020-07-26 - src_alpha2: cpp - tgt_alpha2: cpp - prefer_old: False - long_pair: cpp-cpp - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | c53ac8ff5fd04bdf6e8c57da6bd65746 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_mnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4989 - Accuracy: 0.6525 | 48eeb10761266fa80b76ea93c02dcd55 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.575 | 1.0 | 1534 | 0.5428 | 0.5554 | | 0.5345 | 2.0 | 3068 | 0.5205 | 0.5987 | | 0.511 | 3.0 | 4602 | 0.5105 | 0.6222 | | 0.4917 | 4.0 | 6136 | 0.5021 | 0.6360 | | 0.4735 | 5.0 | 7670 | 0.5004 | 0.6470 | | 0.4557 | 6.0 | 9204 | 0.4976 | 0.6534 | | 0.4391 | 7.0 | 10738 | 0.4982 | 0.6606 | | 0.4231 | 8.0 | 12272 | 0.4982 | 0.6586 | | 0.4082 | 9.0 | 13806 | 0.5020 | 0.6587 | | 0.394 | 10.0 | 15340 | 0.5082 | 0.6561 | | 0.3816 | 11.0 | 16874 | 0.5140 | 0.6617 | | c1a3e8c5ad7e4bb2e4f2dbf3b5f47719 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_50v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6180 - Precision: 0.1063 - Recall: 0.0090 - F1: 0.0166 - Accuracy: 0.7870 | adc0b4b80155d5a46be89784bd13eae7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 14 | 0.7325 | 0.0 | 0.0 | 0.0 | 0.7803 | | No log | 2.0 | 28 | 0.6458 | 0.0860 | 0.0039 | 0.0075 | 0.7838 | | No log | 3.0 | 42 | 0.6180 | 0.1063 | 0.0090 | 0.0166 | 0.7870 | | 0f804d2181a87cb14b627baa82bc87ca |
mit | [] | false | roberta-base-wechsel-ukrainian [`roberta-base`](https://huggingface.co/roberta-base) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://aclanthology.org/2022.naacl-main.293/). | 4a1c49b0b2527a7d0cc44f4c21a74095 |
mit | [] | false | Evaluation Evaluation was done on [lang-uk's ner-uk project](https://github.com/lang-uk/ner-uk), the Ukrainian portion of [WikiANN](https://huggingface.co/datasets/wikiann) and the [Ukrainian IU corpus from the Universal Dependencies project](https://github.com/UniversalDependencies/UD_Ukrainian-IU). Evaluation results are the mean of 5 runs with different seeds. __Validation Results__ | | lang-uk NER (Micro F1) | WikiANN (Micro F1) | UD Ukrainian IU POS (Accuracy) | |:-------------------------------------------------|:-------------------------|:-------------|:-------------------------| | roberta-base-wechsel-ukrainian | 88.06 (0.50) | 92.96 (0.08) | 98.70 (0.05) | | roberta-large-wechsel-ukrainian | __89.27 (0.53)__ | __93.22 (0.15)__ | __98.86 (0.03)__ | | | roberta-base-scratch-ukrainian* | 85.49 (0.88) | 91.91 (0.08) | 98.49 (0.04) | | roberta-large-scratch-ukrainian* | 86.54 (0.70) | 92.39 (0.16) | 98.65 (0.09) | | | dbmdz/electra-base-ukrainian-cased-discriminator | 87.49 (0.52) | 93.20 (0.16) | 98.60 (0.03) | | xlm-roberta-base | 86.68 (0.44) | 92.41 (0.13) | 98.53 (0.02) | | xlm-roberta-large | 86.64 (1.61) | 93.01 (0.13) | 98.71 (0.04) | __Test Results__ | | lang-uk NER (Micro F1) | WikiANN (Micro F1) | UD Ukrainian IU POS (Accuracy) | |:-------------------------------------------------|:-------------------------|:-------------|:-------------------------| | roberta-base-wechsel-ukrainian | 90.81 (1.51) | 92.98 (0.12) | 98.57 (0.03) | | roberta-large-wechsel-ukrainian | __91.24 (1.16)__ | __93.22 (0.17)__ | __98.74 (0.06)__ | | | roberta-base-scratch-ukrainian* | 89.57 (1.01) | 92.05 (0.09) | 98.31 (0.08) | | roberta-large-scratch-ukrainian* | 89.96 (0.89) | 92.49 (0.15) | 98.52 (0.04) | | | dbmdz/electra-base-ukrainian-cased-discriminator | 90.43 (1.29) | 92.99 (0.11) | 98.59 (0.06) | | xlm-roberta-base | 90.86 (0.81) | 92.27 (0.09) | 98.45 (0.07) | | xlm-roberta-large | 90.16 (2.98) | 92.92 (0.19) | 98.71 (0.04) | \*trained using the same exact training setup as the wechsel-\* models, but without parameter transfer from WECHSEL. | 187c7e038863b49098eeaa80581f73bf |
apache-2.0 | ['3rd', 'generated_from_trainer'] | false | bert-finetuned-sem_eval-english This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5536 - F1: 0.5455 - Roc Auc: 0.6968 - Accuracy: 0.1839 | 58b0fe3018fd8ca98ccde9164ae87334 |
apache-2.0 | ['3rd', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 8906097ed933f1af6563baa28f2c86dd |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_hubert_s875 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](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. | cc8319ec1c604c2782e93d9545187389 |
apache-2.0 | ['translation'] | false | opus-mt-ca-en * source languages: ca * target languages: en * OPUS readme: [ca-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ca-en/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/ca-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.eval.txt) | c6af00f258bf8808beebc53a42639768 |
apache-2.0 | [] | false | ParsBERT (v2.0) A Transformer-based Model for Persian Language Understanding We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models. | 4c584f26ac2f5d0e8864129a270807f9 |
apache-2.0 | [] | false | Persian Sentiment [Digikala, SnappFood, DeepSentiPers] It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types. | a31ecc16c1e5a74bccfce706a56065a5 |
apache-2.0 | [] | false | DeepSentiPers which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset. **Binary:** 1. Negative (Furious + Angry) 2. Positive (Happy + Delighted) **Multi** 1. Furious 2. Angry 3. Neutral 4. Happy 5. Delighted | Label | | 983a4fe7537c2070cd2fcacbd93255e8 |
apache-2.0 | [] | false | | |:---------:|:----:| | Furious | 236 | | Angry | 1357 | | Neutral | 2874 | | Happy | 2848 | | Delighted | 2516 | **Download** You can download the dataset from: - [SentiPers](https://github.com/phosseini/sentipers) - [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers) | fe12735c6d39b2924b4e72d11a913ace |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------:|:-----------:|:-----:|:-------------:| | SentiPers (Multi Class) | 71.31* | 71.11 | - | 69.33 | | SentiPers (Binary Class) | 92.42* | 92.13 | - | 91.98 | | 178a41a3fac47147268052a44db1f42b |
apache-2.0 | [] | false | How to use :hugs: | Task | Notebook | |---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) | | 306d8cc215192c91e5b41f29e0ff092f |
apache-2.0 | [] | false | BibTeX entry and citation info Please cite in publications as the following: ```bibtex @article{ParsBERT, title={ParsBERT: Transformer-based Model for Persian Language Understanding}, author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, journal={ArXiv}, year={2020}, volume={abs/2005.12515} } ``` | 29717726973e3959993eaa1ea0718cf5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-8-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1019 - Accuracy: 0.139 | 24c8cbc22156b2421be5ce92ab28f3c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1082 | 1.0 | 5 | 1.1432 | 0.0 | | 1.0524 | 2.0 | 10 | 1.1613 | 0.0 | | 1.0641 | 3.0 | 15 | 1.1547 | 0.0 | | 0.9592 | 4.0 | 20 | 1.1680 | 0.0 | | 0.9085 | 5.0 | 25 | 1.1762 | 0.0 | | 0.8508 | 6.0 | 30 | 1.1809 | 0.2 | | 0.7263 | 7.0 | 35 | 1.1912 | 0.2 | | 0.6448 | 8.0 | 40 | 1.2100 | 0.2 | | 0.5378 | 9.0 | 45 | 1.2037 | 0.2 | | 0.5031 | 10.0 | 50 | 1.2096 | 0.2 | | 0.4041 | 11.0 | 55 | 1.2203 | 0.2 | | 504a41dbf42bfd1e4f3d933ec85c20de |
apache-2.0 | ['generated_from_trainer'] | false | presentation_hate_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8632 - F1: 0.7730 | 8f0dd32aa8bb1ff9c054cc1be54e2f2d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.436235805743952e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 0003f0aae84a98a99419ee85a46c5aa4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.363 | 1.0 | 282 | 0.4997 | 0.7401 | | 0.2145 | 2.0 | 564 | 0.5071 | 0.7773 | | 0.1327 | 3.0 | 846 | 0.7109 | 0.7645 | | 0.0157 | 4.0 | 1128 | 0.8632 | 0.7730 | | c89c6d9e0687ef1cc062921174c98c93 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-mutation-recognition-3 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 - Dnamutation F1: 0.6484 - Proteinmutation F1: 0.8571 - Snp F1: 1.0 - Precision: 0.7966 - Recall: 0.7625 - F1: 0.7792 - Accuracy: 0.9872 | 42d83f01c5de291de197578bfd1de8e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Dnamutation F1 | Proteinmutation F1 | Snp F1 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------------:|:------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 324 | 0.0323 | 0.5996 | 0.7886 | 1.0 | 0.6583 | 0.7982 | 0.7215 | 0.9901 | | 0.0788 | 2.0 | 648 | 0.0314 | 0.6765 | 0.8783 | 1.0 | 0.7453 | 0.8571 | 0.7973 | 0.9907 | | 0.0788 | 3.0 | 972 | 0.0306 | 0.6391 | 0.8679 | 1.0 | 0.7341 | 0.8232 | 0.7761 | 0.9903 | | 0.0273 | 4.0 | 1296 | 0.0424 | 0.6360 | 0.8714 | 1.0 | 0.7792 | 0.775 | 0.7771 | 0.9885 | | 0.0178 | 5.0 | 1620 | 0.0462 | 0.5885 | 0.8683 | 1.0 | 0.7576 | 0.7589 | 0.7583 | 0.9869 | | 0.0178 | 6.0 | 1944 | 0.0531 | 0.6176 | 0.8701 | 1.0 | 0.7734 | 0.7679 | 0.7706 | 0.9873 | | 0.0165 | 7.0 | 2268 | 0.0573 | 0.6597 | 0.8658 | 1.0 | 0.8022 | 0.775 | 0.7884 | 0.9881 | | 0.0144 | 8.0 | 2592 | 0.0636 | 0.6596 | 0.8454 | 1.0 | 0.7919 | 0.7679 | 0.7797 | 0.9871 | | 0.0144 | 9.0 | 2916 | 0.0710 | 0.6568 | 0.8748 | 1.0 | 0.8159 | 0.7679 | 0.7912 | 0.9872 | | 0.0108 | 10.0 | 3240 | 0.0727 | 0.6484 | 0.8571 | 1.0 | 0.7966 | 0.7625 | 0.7792 | 0.9872 | | 0b4a86a493a7a35e26e72f5fc83cde38 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_unispeech_s683 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 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | feef46ef8d13a99c4179143ea6ac4faa |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 160k (uncased) Seed 2 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-2](https://hf.co/multberts-seed-2). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 33879f4a91a42aa587cbdba56a1f5927 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-160k') model = BertModel.from_pretrained("multiberts-seed-2-160k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | da75d682397665b02eeb59856d1b8138 |
apache-2.0 | ['hf-ast-leaderboard', 'generated_from_trainer'] | false | Whisper Small arb - GP This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Dialect Arabic dataset. It achieves the following results on the evaluation set: - Loss: 2.1489 - Wer: 110.7984 | d0d7417c51a8f2e8a42f50f2c4569b6c |
apache-2.0 | ['hf-ast-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9933 | 1.89 | 1000 | 2.0970 | 125.2555 | | 1.3119 | 3.79 | 2000 | 1.9818 | 113.1290 | | 0.7643 | 5.68 | 3000 | 2.0559 | 115.4176 | | 0.5144 | 7.58 | 4000 | 2.1489 | 110.7984 | | 92ee8e57a48a121335ee0e0321623c93 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_8_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8474 - F1: 0.8022 | 767eec04fe64bf09ce0b5307d145b10f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5398 | 0.7838 | | 0.5509 | 2.0 | 578 | 0.6062 | 0.7703 | | 0.5509 | 3.0 | 867 | 0.6563 | 0.7666 | | 0.2366 | 4.0 | 1156 | 0.7688 | 0.7961 | | 0.2366 | 5.0 | 1445 | 1.0968 | 0.7690 | | 0.1247 | 6.0 | 1734 | 1.1414 | 0.7924 | | 0.0482 | 7.0 | 2023 | 1.2159 | 0.7875 | | 0.0482 | 8.0 | 2312 | 1.2703 | 0.7887 | | 0.0245 | 9.0 | 2601 | 1.3401 | 0.7985 | | 0.0245 | 10.0 | 2890 | 1.4645 | 0.7961 | | 0.0149 | 11.0 | 3179 | 1.5632 | 0.7801 | | 0.0149 | 12.0 | 3468 | 1.5249 | 0.7875 | | 0.0124 | 13.0 | 3757 | 1.6263 | 0.7948 | | 0.0038 | 14.0 | 4046 | 1.8059 | 0.7764 | | 0.0038 | 15.0 | 4335 | 1.7649 | 0.7776 | | 0.0061 | 16.0 | 4624 | 1.8293 | 0.7850 | | 0.0061 | 17.0 | 4913 | 1.8316 | 0.7887 | | 0.0022 | 18.0 | 5202 | 1.7628 | 0.7973 | | 0.0022 | 19.0 | 5491 | 1.8763 | 0.7862 | | 0.002 | 20.0 | 5780 | 1.8409 | 0.7899 | | 0.0026 | 21.0 | 6069 | 1.8146 | 0.8022 | | 0.0026 | 22.0 | 6358 | 1.8420 | 0.7973 | | 0.0008 | 23.0 | 6647 | 1.8683 | 0.8010 | | 0.0008 | 24.0 | 6936 | 1.8571 | 0.8010 | | 0.0015 | 25.0 | 7225 | 1.8474 | 0.8022 | | 52883d35a17dc0e25cf247981002687d |
apache-2.0 | ['align', 'clip'] | false | Model Details This is an unofficial implementation of [ALIGN](https://arxiv.org/abs/2102.05918) trained on [COYO-700M](https://github.com/kakaobrain/coyo-dataset). The official ALIGN is trained on its dataset of 1.8B samples. That dataset is not released to the public. Instead, we trained our implementation of ALIGN model on [COYO-700M](https://github.com/kakaobrain/coyo-dataset). It's developed by Kakao Brain to validate the performance of COYO-700M dataset on a large-scale model. The training took about 8 days on TPU V3-512. | b79e0c2777a50217524dd95a5907992e |
apache-2.0 | ['align', 'clip'] | false | Evaluation results | | Dataset | ImageNet | Flickr30k | | MsCOCO | | |----------------------------------|:----------:|:--------:|:---------:|:-------:|:-------:|:-------:| | | | KNN | I2T R@1 | T2I R@1 | I2T R@1 | T2I R@1 | | ALIGN-L2-Large(Google) | ALIGN 1.8B | 76.4 | 88.6 | 75.7 | 58.6 | 45.6 | | ALIGN-B7-Base(Google) | ALIGN 1.8B | 69.3 | - | - | 55.4 | 41.7 | | COYO-ALIGN-B7-Base(Kakao Brain) | COYO-700M | 68.6 | 88.1 | 73.2 | 61.2 | 43.1 | | e6a24af04ff794784cb9eb326ccc8526 |
mit | [] | false | Model miniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency. Since miniALBERT is a compact model, it is trained using a layer-to-layer distillation technique, using the BioClinicalBERT model as the teacher. This model is trained for 3 epochs on the MIMIC-III notes dataset. In terms of architecture, this model uses an embedding dimension of 312, a hidden size of 768, an MLP expansion rate of 4, and a reduction factor of 16 for bottleneck adapters. In general, this model uses 6 recursions and has a unique parameter count of 18 million parameters. | 90c1832336202708796178f4e11b4617 |
mit | [] | false | For Sequence Classification use the below code model = MiniAlbertForTokenClassification.from_pretrained("nlpie/clinical-miniALBERT-312") ``` In addition, For efficient fine-tuning using the pre-trained bottleneck adapters use the below code: ```Python model.trainAdaptersOnly() ``` | 28463ee8f09edfd736845a4e2fea8f79 |
mit | [] | false | Citation If you use the model, please cite our paper: ```bibtex @misc{https://doi.org/10.48550/arxiv.2302.04725, doi = {10.48550/ARXIV.2302.04725}, url = {https://arxiv.org/abs/2302.04725}, author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Group, ISARIC Clinical Characterisation and Clifton, Lei and Merson, Laura and Clifton, David A.}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7, 68T50}, title = {Lightweight Transformers for Clinical Natural Language Processing}, publisher = {arXiv}, year = {2023}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` | a1b4fd0de578f847a4678aef732ca145 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-nl_s756 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 (ar)](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. | 493e869b93e52bc4e043d3f9c81ec7af |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-multilingual-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2352 - Precision: 0.8109 - Recall: 0.8332 - F1: 0.8219 - Accuracy: 0.9264 | aec9054779273a53a29165a0c0513829 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7301 | 0.16 | 100 | 0.3827 | 0.6189 | 0.7009 | 0.6573 | 0.8734 | | 0.3841 | 0.32 | 200 | 0.3195 | 0.7057 | 0.7511 | 0.7277 | 0.8922 | | 0.3451 | 0.48 | 300 | 0.2862 | 0.7094 | 0.7750 | 0.7407 | 0.8952 | | 0.3187 | 0.65 | 400 | 0.2735 | 0.7372 | 0.7802 | 0.7581 | 0.9019 | | 0.3058 | 0.81 | 500 | 0.2533 | 0.7536 | 0.8015 | 0.7768 | 0.9052 | | 0.2918 | 0.97 | 600 | 0.2458 | 0.7587 | 0.8085 | 0.7828 | 0.9126 | | 0.2425 | 1.13 | 700 | 0.2379 | 0.7742 | 0.7976 | 0.7857 | 0.9150 | | 0.2387 | 1.29 | 800 | 0.2300 | 0.7772 | 0.8108 | 0.7936 | 0.9165 | | 0.2125 | 1.45 | 900 | 0.2387 | 0.7900 | 0.8130 | 0.8014 | 0.9180 | | 0.2026 | 1.62 | 1000 | 0.2317 | 0.7877 | 0.8152 | 0.8012 | 0.9186 | | 0.1963 | 1.78 | 1100 | 0.2326 | 0.7842 | 0.8269 | 0.8049 | 0.9220 | | 0.2052 | 1.94 | 1200 | 0.2247 | 0.7924 | 0.8234 | 0.8076 | 0.9212 | | 0.1868 | 2.1 | 1300 | 0.2410 | 0.7903 | 0.8282 | 0.8088 | 0.9204 | | 0.1556 | 2.26 | 1400 | 0.2428 | 0.8064 | 0.8317 | 0.8189 | 0.9256 | | 0.153 | 2.42 | 1500 | 0.2316 | 0.8017 | 0.8282 | 0.8147 | 0.9238 | | 0.1484 | 2.58 | 1600 | 0.2379 | 0.8054 | 0.8338 | 0.8194 | 0.9258 | | 0.137 | 2.75 | 1700 | 0.2331 | 0.8101 | 0.8324 | 0.8211 | 0.9270 | | 0.1638 | 2.91 | 1800 | 0.2352 | 0.8109 | 0.8332 | 0.8219 | 0.9264 | | 96d020cc7bb4cfcb8d4afb305cd663e4 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | 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 - TA dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 1.0 | f6e698a38d834371646624ae35b067b9 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 - mixed_precision_training: Native AMP | 209176b05d852b22eef7c5ffe36330de |
mit | ['automatic-speech-recognition', 'generated_from_trainer'] | false | Model description We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 4h of labelled Luxembourgish speech from the same domain. Additionally, we rescore the output transcription with a 5-gram language model trained on text corpora from RTL.lu and the Luxembourgish parliament. | 2b484dae6494345141cd4b27dee85497 |
mit | ['bert', 'language-model', 'flaubert', 'flue', 'french', 'flaubert-small', 'cased'] | false | FlauBERT: Unsupervised Language Model Pre-training for French **FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer. Along with FlauBERT comes [**FLUE**](https://github.com/getalp/Flaubert/tree/master/flue): an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language.For more details please refer to the [official website](https://github.com/getalp/Flaubert). | fad28aea2bea4270b9f9cdf33043e52e |
mit | ['bert', 'language-model', 'flaubert', 'flue', 'french', 'flaubert-small', 'cased'] | false | FlauBERT models | Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters | | :------: | :---: | :---: | :---: | :---: | | `flaubert-small-cased` | 6 | 8 | 512 | 54 M | | `flaubert-base-uncased` | 12 | 12 | 768 | 137 M | | `flaubert-base-cased` | 12 | 12 | 768 | 138 M | | `flaubert-large-cased` | 24 | 16 | 1024 | 373 M | **Note:** `flaubert-small-cased` is partially trained so performance is not guaranteed. Consider using it for debugging purpose only. | 6b75247cf9c6cb10375be15243a938f0 |
mit | ['bert', 'language-model', 'flaubert', 'flue', 'french', 'flaubert-small', 'cased'] | false | Load pretrained model and tokenizer flaubert, log = FlaubertModel.from_pretrained(modelname, output_loading_info=True) flaubert_tokenizer = FlaubertTokenizer.from_pretrained(modelname, do_lowercase=False) | 3e9469e7a02de3ffcff5161010448d6a |
mit | ['bert', 'language-model', 'flaubert', 'flue', 'french', 'flaubert-small', 'cased'] | false | do_lowercase=False if using cased models, True if using uncased ones sentence = "Le chat mange une pomme." token_ids = torch.tensor([flaubert_tokenizer.encode(sentence)]) last_layer = flaubert(token_ids)[0] print(last_layer.shape) | e1b5cc7b2e9478f2a12bfaef2243d695 |
mit | ['bert', 'language-model', 'flaubert', 'flue', 'french', 'flaubert-small', 'cased'] | false | The BERT [CLS] token correspond to the first hidden state of the last layer cls_embedding = last_layer[:, 0, :] ``` **Notes:** if your `transformers` version is <=2.10.0, `modelname` should take one of the following values: ``` ['flaubert-small-cased', 'flaubert-base-uncased', 'flaubert-base-cased', 'flaubert-large-cased'] ``` | 4d1083793b601e0e59214c37233e3cbb |
mit | ['bert', 'language-model', 'flaubert', 'flue', 'french', 'flaubert-small', 'cased'] | false | References If you use FlauBERT or the FLUE Benchmark for your scientific publication, or if you find the resources in this repository useful, please cite one of the following papers: [LREC paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.302.pdf) ``` @InProceedings{le2020flaubert, author = {Le, Hang and Vial, Lo\"{i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb\'{e}, Beno\^{i}t and Besacier, Laurent and Schwab, Didier}, title = {FlauBERT: Unsupervised Language Model Pre-training for French}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {2479--2490}, url = {https://www.aclweb.org/anthology/2020.lrec-1.302} } ``` [TALN paper](https://hal.archives-ouvertes.fr/hal-02784776/) ``` @inproceedings{le2020flaubert, title = {FlauBERT: des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais}, author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb{\'e}, Beno{\^\i}t and Besacier, Laurent and Schwab, Didier}, booktitle = {Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 31e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2: Traitement Automatique des Langues Naturelles}, pages = {268--278}, year = {2020}, organization = {ATALA} } ``` | 77800cb422c437608926da203ad08425 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1654 - F1: 0.8590 | a2fad795cde60e7908933a2153921e4e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2845 | 1.0 | 715 | 0.1831 | 0.8249 | | 0.1449 | 2.0 | 1430 | 0.1643 | 0.8479 | | 0.0929 | 3.0 | 2145 | 0.1654 | 0.8590 | | bc46d3376e0478e3bae4abf55d7242a5 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 26.3 - GMACs: 2.6 - Activations (M): 18.5 - Image size: 224 x 224 - **Original:** https://github.com/snap-research/EfficientFormer - **Papers:** - Rethinking Vision Transformers for MobileNet Size and Speed: https://arxiv.org/abs/2212.08059 - **Dataset:** ImageNet-1k | 54bcf031fe9ad150ae5f2ffa289dee20 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('efficientformerv2_l.snap_dist_in1k', pretrained=True) model = model.eval() | b08d96ab32f613e0b0abc51daa1c96b5 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'efficientformerv2_l.snap_dist_in1k', pretrained=True, num_classes=0, | 32fd026892157270d257eef25144956c |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'efficientformerv2_l.snap_dist_in1k', pretrained=True, features_only=True, ) model = model.eval() | 805fc779e60cf5a88649203738ff3dd4 |
apache-2.0 | ['image-classification', 'timm'] | false | Citation ```bibtex @article{li2022rethinking, title={Rethinking Vision Transformers for MobileNet Size and Speed}, author={Li, Yanyu and Hu, Ju and Wen, Yang and Evangelidis, Georgios and Salahi, Kamyar and Wang, Yanzhi and Tulyakov, Sergey and Ren, Jian}, journal={arXiv preprint arXiv:2212.08059}, year={2022} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` | aeee581cf1bb420361dc2739614174d4 |
cc | ['text classification'] | false | Model information: This model is the [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report texts into two classes. Labels of [0,1] were assigned to radiology reports in MIMIC-III that were linked to an ICD9 diagnosis code for lung cancer = 1 and a random sample of reports which were not linked to any type of cancer diagnosis code at all = 0. | c1929c84659e8d5d894f4a8a65b11a0c |
cc | ['text classification'] | false | Limitations: Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before use - - [MIMIC-III](https://www.nature.com/articles/sdata201635.pdf) - [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) | b4a744a5063370d3b7fce0fe9851238c |
cc | ['text classification'] | false | How to use: Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/bioclinical-bert-ft-m3-lc") model = AutoModel.from_pretrained("sarahmiller137/bioclinical-bert-ft-m3-lc") ``` | 276e256c2b525f933aae9498b09583c5 |
mit | ['conversational'] | false | Aeona | Chatbot  An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small). Recommended to use along with an [AIML Chatbot](https://github.com/deepsarda/Aeona-Aiml) to reduce load, get better replies, add name and personality to your bot. Using an AIML Chatbot will allow you to hardcode some replies also. | 96e2b8f124cf70179f80f70297fa0048 |
mit | ['conversational'] | false | AEONA Aeona is an chatbot which hope's to be able to talk with humans as if its an friend! It's main target platform is discord. You can invite the bot [here](https://aeona.xyz). To learn more about this project and chat with the ai, you can use this [website](https://aeona.xyz/). Aeona works why using context of the previous messages and guessing the personality of the human who is talking with it and adapting its own personality to better talk with the user. | 302b95c785cbc1c80216aaf90fd3fb3b |
mit | ['conversational'] | false | Why not an AI on its own? For AI it is not possible (realistically) to learn about the user and store data on them, when compared to an AIML which can even execute code! The goal of the AI is to generate responses where the AIML fails. Hence the goals becomes to make an AI which has a wide variety of knowledge, yet be as small as possible! So we use 3 dataset:- 1. [Movielines](https://www.kaggle.com/Cornell-University/movie-dialog-corpus) The movie lines promote longer and more thought out responses but it can be very random. About 200k lines! 2. [Discord Messages](https://www.kaggle.com/jef1056/discord-data) The messages are on a wide variety of topics filtered and removed spam which makes the AI highly random but gives it a very random response to every days questions! about 120 million messages! 3. Custom dataset scrapped from my messages, These messages are very narrow teaching this dataset and sending a random reply will make the AI say sorry loads of time! | f8241d72d83ce5814b0681c2d668a006 |
mit | ['conversational'] | false | Training The Discord Messages Dataset simply dwarfs the other datasets, Hence the data sets are repeated. This leads to them covering each others issues! The AI has a context of 6 messages which means it will reply until the 4th message from user. [Example](https://huggingface.co/deepparag/Aeona-Beta/discussions/1) | bab8d6f3dc35ae637e5e5132ff45526a |
mit | ['conversational'] | false | Tips for Hugging Face interference I recommend send the user input, previous 3 AI and human responses. Using more context than this will lead to useless responses but using less is alright but the responses may be random. | b18d63d3d6fbd639d6a98f6aecd612c3 |
mit | ['conversational'] | false | Evaluation Below is a comparison of Aeona vs. other baselines on the mixed dataset given above using automatic evaluation metrics. | Model | Perplexity | |---|---| | Seq2seq Baseline [3] | 29.8 | | Wolf et al. [5] | 16.3 | | GPT-2 baseline | 99.5 | | DialoGPT baseline | 56.6 | | DialoGPT finetuned | 11.4 | | PersonaGPT | 10.2 | | **Aeona** | **7.9** | | 3e814e5ceb8b7ba255bdede95b097f11 |
mit | ['conversational'] | false | Usage Example: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("deepparag/Aeona") model = AutoModelWithLMHead.from_pretrained("deepparag/Aeona") | 81d0b439a1b282d46936f74df511a791 |
mit | ['conversational'] | false | generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=4, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) | c71b00813de3afaecd7a73c9a2fbdee4 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepclean_8k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `sep_clean` task of the Libri3Mix dataset. Training config: ```yml data: n_src: 3 sample_rate: 8000 segment: 3 task: sep_clean train_dir: data/wav8k/min/train-360 valid_dir: data/wav8k/min/dev filterbank: kernel_size: 16 n_filters: 512 stride: 8 masknet: bn_chan: 128 hid_chan: 512 mask_act: relu n_blocks: 8 n_repeats: 3 n_src: 3 skip_chan: 128 optim: lr: 0.001 optimizer: adam weight_decay: 0.0 training: batch_size: 24 early_stop: true epochs: 200 half_lr: true num_workers: 4 ``` Results : On Libri3Mix min test set : ```yaml si_sdr: 8.581797049575108 si_sdr_imp: 11.977037288467368 sdr' 9.305885208641385 sdr_imp: 12.3943409734845 sir: 16.42030534048559 sir_imp: 19.508759460400984 sar: 10.641943911079238 sar_imp: -56.4345187842095 stoi: 0.8365148408724333 stoi_imp: 0.24401766199806396 ``` License notice: This work "ConvTasNet_Libri3Mix_sepclean_8k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov, used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). "ConvTasNet_Libri3Mix_sepclean_8k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Cosentino Joris. | 078e79440aa7e7293f04c0b7ac39241b |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the spaeti concept trained by malysheva42 on the malysheva42/spaeti_store dataset. This is a Stable Diffusion model fine-tuned on the spaeti (späti) concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of spaeti store** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 08e5cb2caa85ff60824df584d29e8f19 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Examples 1. a picture of spaeti store in the forest  2. a picture of spaeti store on the beach near the sea, best quality  3. a picture of spaeti store in the snow  | aa9f9cc6e1a5cea449cef493f79bef52 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Croatian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | f2667054cf6d257edb8128fdabfb348d |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr") ``` | b82dcbd649e219a85756fd4af3bc0fa7 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | MVP-question-generation The MVP-question-generation model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). | ae2de5f0c7048183d78cd65f68bcb446 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Model Description MVP-question-generation is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question generation datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts. MVP-question-generation is specially designed for question generation tasks, such as SQuAD and CoQA. | cee7f4021eb457cf6a8a01ded9585981 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-question-generation") >>> inputs = tokenizer( ... "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing .", ... return_tensors="pt", ... ) >>> generated_ids = model.generate(**inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['A bolo punch and a hook are both punches used in what sport?'] ``` | 24ecffcf6239d3442eb4733b181fb586 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Related Models **MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp). **Prompt-based models**: - MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task). - MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization). - MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog). - MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text). - MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story). - MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering). - MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation). - MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog). **Multi-task models**: - MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization). - MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog). - MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text). - MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story). - MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering). - MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation). - MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog). | 1a27159d326f18d2109d659ab1fc29e7 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Citation ```bibtex @article{tang2022mvp, title={MVP: Multi-task Supervised Pre-training for Natural Language Generation}, author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2206.12131}, year={2022}, url={https://arxiv.org/abs/2206.12131}, } ``` | ac366490a3203b58a2b8281b639949e6 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Tacotron 2 with Guided Attention trained on Synpaflex (Fr) This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). | f51f1b443089c3fde75e92674adffc45 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-synpaflex-fr") tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-synpaflex-fr") text = "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous étions amis" input_ids = processor.text_to_sequence(text) decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32), speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), ) ``` | 3743294c81defc0262d91cf37d87efe9 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Referencing Tacotron 2 ``` @article{DBLP:journals/corr/abs-1712-05884, author = {Jonathan Shen and Ruoming Pang and Ron J. Weiss and Mike Schuster and Navdeep Jaitly and Zongheng Yang and Zhifeng Chen and Yu Zhang and Yuxuan Wang and R. J. Skerry{-}Ryan and Rif A. Saurous and Yannis Agiomyrgiannakis and Yonghui Wu}, title = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions}, journal = {CoRR}, volume = {abs/1712.05884}, year = {2017}, url = {http://arxiv.org/abs/1712.05884}, archivePrefix = {arXiv}, eprint = {1712.05884}, timestamp = {Thu, 28 Nov 2019 08:59:52 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | 8629cc2a12a457b3c10c0997bab1ae80 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | laywaxys Dreambooth model trained by NOISK8 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) Or you can 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) Sample pictures of this concept: | 7863143b09d7c7a9eb1db837ce9da0ee |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | MTL-question-generation The MTL-question-generation model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). | 3384d80a60b16c19c5cd6b5d5d589404 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Model Description MTL-question-generation is supervised pre-trained using a mixture of labeled question generation datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture. MTL-question-generation is specially designed for question generation tasks, such as SQuAD and CoQA. | b70d2f03802de089ebe7a30648aad0f5 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-question-generation") >>> inputs = tokenizer( ... "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing .", ... return_tensors="pt", ... ) >>> generated_ids = model.generate(**inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['A bolo punch and a hook are both punches used in what sport?] ``` | 7df267f3bbaf7ea4f9c8f68879cbb364 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.4571 | 6fe3882a7bbd914a5bb5a48b70a60f93 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | d8c9f8e17773623b974aa67a1bd77966 |
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