license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | 🚋 Usage **Below is a quick way to get up and running with the model.** 1. Download files from hub ```python import os import shutil import sys from huggingface_hub import snapshot_download cache_dir = "./capu" def download_files(repo_id, cache_dir=None, ignore_regex=None): download_dir = snapshot_download(repo... | 17c3489975d447abd0659fa676f1768a |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | ['Những gói cước 5G MobiFone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời. So với mạng 4G thì tốc độ truy cập mạng 5G MobiFone được nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần.'] ``` **This model can work on arbitrarily large text in Vietnamese language.** ------------------------------... | d4ec55aa56bb3d4dcfe090d2c7755430 |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | 🎯 Accuracy Below is a breakdown of the performance of the model by each label on 10,000 held-out text samples: | label | precision | recall | f1-score | support | | --- | --- | --- | --- | --- | | **Upper** | 0.88 | 0.89 | 0.89 | 56497 | | **Complex-Upper** | 0.92 | ... | ada5aa8ec8cb3d546ab48027fe7ddc11 |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | 6b17d60bd94d96164e2475ee6286cde5 |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-Mono 2B) was firstly initialized with *CodeGen-Multi 2B*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. | 68566ccfc2b74c3d9d6cf72b628f733d |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-2B-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-2B-mono") text = ... | 225e0b00603d5b860efb02cce70b00f2 |
apache-2.0 | ['translation'] | false | ukr-pol * source group: Ukrainian * target group: Polish * OPUS readme: [ukr-pol](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-pol/README.md) * model: transformer-align * source language(s): ukr * target language(s): pol * model: transformer-align * pre-processing: normalization + Sen... | f942bf25e181967332ccbd080831842e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-pol - source_languages: ukr - target_languages: pol - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-pol/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'pl'] - src_constituents: {'ukr'} - tgt_const... | e165dc68b584e9e890feb21a3152400d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Hindi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 hi dataset. It achieves the following results on the evaluation set: - Loss: 0.2325 - Wer: 10.3608 | 654088c5f0ef3e0b942eceeef398628c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0067 | 4.18 | 1000 | 0.2325 | 10.3608 | | 75255ab2e5eb1e2c0cec27b7db6e4a7d |
cc-by-sa-4.0 | [] | false | Training Data * Subset of [CC-100/el](https://data.statmt.org/cc-100/) : Monolingual Datasets from Web Crawl Data * Subset of [oscar](https://huggingface.co/datasets/oscar) * [wiki40b/el](https://www.tensorflow.org/datasets/catalog/wiki40b | 56d55e6de4e0e1de08b208624f68821c |
apache-2.0 | [] | false | IcelandicNER DistilBERT This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language. The [MIM-GOLD-NER](http://hdl.handle.net/20.500.12537/42) corpus was developed at [Reykjavik University](https://en.ru.is/) in 2018–2020 that covered eight types of entities: - Date - Location - Miscellaneous ... | 9534f45e358a38abcafeae0cd7d36dc6 |
apache-2.0 | [] | false | Dataset Information | | Records | B-Date | B-Location | B-Miscellaneous | B-Money | B-Organization | B-Percent | B-Person | B-Time | I-Date | I-Location | I-Miscellaneous | I-Money | I-Organization | I-Percent | I-Person | I-Time | |:------|----------:|---------:|-------------:... | 0f2deb3fc9e6e31ce23bf683ed7228d8 |
apache-2.0 | [] | false | Evaluation The following tables summarize the scores obtained by model overall and per each class. | entity | precision | recall | f1-score | support | |:-------------:|:---------:|:--------:|:--------:|:-------:| | Date | 0.969309 | 0.973042 | 0.971172 | 779.0 | | Location | 0.941221 | 0.... | b507cf27273d43552703343da3c08593 |
apache-2.0 | [] | false | for tensorflow from transformers import pipeline model_name_or_path = "m3hrdadfi/icelandic-ner-distilbert" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) | f1649b166852396dc53a0c89be548f27 |
apache-2.0 | [] | false | Tensorflow nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ." ner_results = nlp(example) print(ner_results) ``` | 8ff761aea8f11409964aaa3dcda60ec5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2317 - Accuracy: 0.923 - F1: 0.9233 | d470bce500fa4674181ab78d2bc2431b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8669 | 1.0 | 250 | 0.3344 | 0.9025 | 0.9004 | | 0.2607 | 2.0 | 500 | 0.2317 | 0.923 | 0.9233 | | b04a58333c7b9127f07f64223665df2a |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-v3-e2 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9021 - Rouge1: 53.515 - Rouge2: 33.4314 - Rou... | 7916a6b870e841cb95947a0544090707 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9656 | 52.7601 | 33.0555 | 34.4738 | 50.449 | ... | 664e96eff10d43e1160e43072f4c9f76 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mnli_256 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: 0.8831 - Accuracy: 0.5886 | 865bf25864547ea3e192fc569d6435c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0194 | 1.0 | 1534 | 0.9641 | 0.5250 | | 0.9428 | 2.0 | 3068 | 0.9256 | 0.5586 | | 0.9042 | 3.0 | 4602 | 0.9137 ... | c6ca1916f878588bb5e6b853bc8207e0 |
mit | ['timelms', 'twitter'] | false | Twitter December 2021 (RoBERTa-base, 124M) This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021. More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829). Below, we provide some usage examples using the standard Transformers interface... | 0e2c339ff71c9a18c9d22a5ead0dec2c |
mit | ['timelms', 'twitter'] | false | Example Masked Language Model ```python from transformers import pipeline, AutoTokenizer MODEL = "cardiffnlp/twitter-roberta-base-dec2021" fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) def pprint(candidates, n): for i in range(n): token... | 587cd7a5a39d2e1a4843d3eca7c70ef6 |
mit | ['timelms', 'twitter'] | false | naive approach for demonstration text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') features = model(**encoded_input) features = features[0].detach().cpu().numpy() return np.mean(features[0], axis=0) MODEL = "cardiffnlp/twitter-roberta-base-dec2021" tokenizer = AutoTokenizer.fro... | 9dd6d71d5b119166f50036e6f9ce4b38 |
mit | ['timelms', 'twitter'] | false | Example Feature Extraction ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np MODEL = "cardiffnlp/twitter-roberta-base-dec2021" tokenizer = AutoTokenizer.from_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) | ad29dbb4e51def97bb70bbe50ec4aa11 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from North Germanic languages (gmq) to Arabic (ar). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world.... | e558b769386f9a7b6c651658f9090e75 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ara<< Jeg elsker semitiske sprog.", ">>ara<< Vad handlar boken om?" ] model_name = "pytorch-models/opus-mt-tc-big-gmq-ar" tokenizer = MarianTokenizer.from_pretrained(mod... | 0c23ff631a2b5e87896f5412220df326 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | عن ماذا يتحدث الكتاب؟ ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-ar") print(pipe(">>ara<< Jeg elsker semitiske sprog.")) | 1b1aab52b32bff4fd1bf595884eee60d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-a... | d02ee1d721e005d791549c46b40ea968 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gm... | 2026ec3b26c425fc61e0ab1569d0413f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | dan-ara | flores101-devtest | 0.52841 | 19.9 | 1012 | 21357 | | nob-ara | flores101-devtest | 0.49670 | 16.8 | 1012 | 21357 | | swe-ara | flores101-devtest | 0.51882 | 19.3 | 1012 | 21357 | | 6acc0376c4d05c5b19693a5bd585e485 |
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: 0.2380 - F1: 0.8289 | 26c860ff38eb5a2636ba74c641e80d32 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7058 | 1.0 | 70 | 0.3183 | 0.7480 | | 0.2808 | 2.0 | 140 | 0.2647 | 0.8070 | | 0.1865 | 3.0 | 210 | 0.2380 | 0.8289 | ... | 89ef4dfa91bd2b13bf900b3242a19a7d |
apache-2.0 | ['generated_from_trainer'] | false | bert-uncased-massive-intent-classification_banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6770 - Accu... | 613840e7c7d67037330ecfb4e9c49ac8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 2b5a3a82de55d79b2ebb0c6e236e8fe7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8977 | 1.0 | 3 | 2.7353 | 0.0622 | | 2.5889 | 2.0 | 6 | 2.7109 | 0.0933 | | 2.4362 | 3.0 | 9 | 2.6940 | 0.... | 5ed98effd98126a7aaf816bbdfff0c7b |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8560 - Bleu: 52.8324 | ac0b9a2cea01d24d9e7c8d8c181ca6c1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-fintuend-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2081 - Accuracy: 0.9225 - F1: 0.9225 | b5688bed6d180ad45ea1acee4650d0a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7993 | 1.0 | 250 | 0.3078 | 0.908 | 0.9055 | | 0.2437 | 2.0 | 500 | 0.2081 | 0.9225 | 0.9225 | | dc56e434ddc8e1fab7113dd90436ec73 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-small-itquad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gene... | 8f014125c7e0f131ebebb385297bfe86 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qg"... | d366f7e0e2a9c09b9d17c0bd1702c7cf |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:-----... | 646433a8a98afa423226483cd7007918 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_itquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 15 - ... | 237b31dc04510b1d03230f8ea1494798 |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/albert-base-v2-mlm-electronics-attribute-correction This model is a fine-tuned version of [ksabeh/albert-base-v2-mlm-electronics](https://huggingface.co/ksabeh/albert-base-v2-mlm-electronics) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0541 - Validation Loss:... | 63fb5415db1663af93c1a5ef9dd9cf33 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36852, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | db72b8864aff2ffa6aad8b9c318d91ca |
apache-2.0 | ['generated_from_trainer'] | false | this_is_my_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1143 - Precision: 0.8674 - Recall: 0.9021 - F1: 0.8844 - Accuracy: 0.9762 | 974e64ae0ec03a9857e4eae24449414e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2292 | 1.0 | 878 | 0.1048 | 0.8683 | 0.8973 | 0.8825 | 0.9763 | | 0.0493 | 2.0 |... | 59ee6572196f13c09ca29ef7fd1eb019 |
apache-2.0 | ['translation'] | false | opus-mt-es-war * source languages: es * target languages: war * OPUS readme: [es-war](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-war/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | c674d0a3fb78fbf633857fb5d9193e8e |
mit | ['Nasopharyngeal carcinoma', 'Cancer'] | false | Background This model was built on Microsoft's BERT trained on PubMed uncased database (`microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext`). A number of (~500) radiology reports for staging nasopharyngeal carcinoma (NPC) written in our center by board-certified radiologist were retrospectively retrieved w... | 2ab36053039f623040dc2cdc8e162b3f |
mit | ['Nasopharyngeal carcinoma', 'Cancer'] | false | Training Losses | Epoch | Training Loss | Validation Loss | |-------|---------------|-----------------| | 1 | No log | 3.474347 | | 2 | No log | 3.174083 | | 3 | No log | 2.944307 | | 4 | No log | 2.674384 | | 5 | No log | 2.574261 ... | 1578467cb6dd84c098d4373403e198d0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Mohammed245/bert-base-uncased-finetuned-ED_BERT_test 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: 4.9663 - Validation Loss: 5.2474 - Epoch: 1 | 414fa9c4e61e6c86704fa134a6c6ea38 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9302 - Mae: 0.5 | 65537a7277a1279a2d77514e2c27d006 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1253 | 1.0 | 235 | 0.9756 | 0.5488 | | 0.9465 | 2.0 | 470 | 0.9302 | 0.5 | | ce290907bdca11daced48363abd8631d |
apache-2.0 | ['generated_from_trainer'] | false | bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8760 - Matthews Correlation: 0.5676 | 23bb88c0ce2ca6ded6e97559df79c26e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4768 | 1.0 | 1069 | 0.5682 | 0.5183 | | 0.3134 | 2.0 | 2138 | 0.6110 | 0.5789 | | 0.1... | baf4412d674161e9eb755020ea1989d1 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-custom-tokenizer-target-glue-mnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-glue-mrpc-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8198 - Accuracy: 0... | ed13690a90bf29f5e83e5389f60dbd3b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.052 | 0.04 | 500 | 1.0262 | 0.4857 | | 0.9703 | 0.08 | 1000 | 0.9454 | 0.5575 | | 0.9365 | 0.12 | 1500 | 0.9063 | 0.... | 904e3042f162bc334b5e8118487b2e4c |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_hubert_s601 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 76eddb12e1aecf4d98de256c315cd552 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'art', 'style'] | false | Shinkai-Art ✨ Stable diffusion model Pretrained from `andite/anything-v4.0`. This model can generate output like **Makoto Shinkai** (Japanese Anime Director) movies style image, his anime movies style deeply inspired me to create seperate model in his style. Some Anime Movies from him is: 1. [Your Name](https://www... | b115d2a1c53f84caa92c29f54dac76c3 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'art', 'style'] | false | Trigger Word: `shinkai-art`, `shinkaiart`, `portrait` 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) | 23b3582cd0ab93cf407b92cebd6a1361 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'art', 'style'] | false | Sample pictures Generated using this Model:-   * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | da651d3bcd253081fcd4412c0f128d9a |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-d-4 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0456 - Rouge1: 93.3486 - Rouge2: 82.1873 - Rougel: 92.8611 - Rougelsum: 92.7768 - Gen Len: 14.9953 | b85ec2ef69762c0830adc324567f4ee9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.0873 | 1.0 | 8043 | 0.0456 | 93.3486 | 82.1873 | 92.8611 | 92.7768 | 14... | b21ca233f58d5f7e22d244128d3244fa |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_vp-nl_s496 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 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 4577fdd02c1d5dd754aeed6039ead4a2 |
apache-2.0 | ['automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Fine-tuned XLS-R 1B model for speech recognition in Spanish Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Spanish using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [MediaSpeech](https://www.... | ada17b2015f9a04454f5daec2eb4a5b4 |
apache-2.0 | ['automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Usage Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-spanish") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transc... | 9e92fa084f37eebc510377c3ed4555fe |
apache-2.0 | ['automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-spanish --dataset mozilla-foundation/common_voice_8_0 --config es --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash py... | 8cfd9eaaeb1ae96bb0cdf2bb4d95a4e3 |
apache-2.0 | ['automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr-1b-spanish, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {S}panish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish}}, year={2022} } ``` | 4ddd9de1679c8c2b9dc27aace26f4a0f |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4393279/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 4b090795bf6da08ecd711a81957ec459 |
mit | [] | false | Naoki Saito on Stable Diffusion This is the `<naoki_saito>` 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 al... | b2b06488ebed096dc4a9f1ab3896de8d |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'xls_r_repro_common_voice_tr'] | false | wav2vec2-large-xlsr-53-common_voice-tr-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.4231 - Wer: 0.3104 - Cer: 0.0737 | dd80d1da9660cff0eb11cac969f6942e |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'xls_r_repro_common_voice_tr'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | d31d8370b46369211e083e5248cf3267 |
apache-2.0 | ['translation'] | false | opus-mt-en-id * source languages: en * target languages: id * OPUS readme: [en-id](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-id/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 434ae1a6db6a7307eb636b91c7bca707 |
apache-2.0 | ['bert', 'punctuation restoration'] | false | How to use The model requires some additional inference code, hence we created an awesome little pip package for inference. The inference code is based on the `TokenClassificationPipeline` pipeline from huggingface. First, install the little package by running ``` pip install punctfix ``` Then restoration is as s... | 3890c3cdf2902b077f2b11380e8d805b |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Ts-En_update This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ts-en](https://huggingface.co/Helsinki-NLP/opus-mt-ts-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2030 - Bleu: 44.6835 | 10baa57c2cca94c10a65ac6845053d0d |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results |Epoch| Training Loss | Validation Loss| Bleu | |:---:|:---------------:|:----------------:|:-------:| | 1 | 1.650900 | 1.676816 | 31.785027| | 2 | 1.365800 | 1.478832 | 35.611479| | 3 | 1.205400 | 1.376320 | 38.719939| | 4 | 1.095300 | 1.31... | 1857cd2667f207edbc57c5e23e9409db |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_700k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 700k 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 ... | 3900c792014af374d8959cb7f10b5d0c |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_700k'] | 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_3-step_700k') model = TFBertModel.from_pretrained("google/multibe... | addef3374ad466112e20c723c57533ed |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_unispeech_s364 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 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 7f7c5961883a0ba0833361a804b430c1 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-mic-nlp This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0049 - Accuracy: 0.9993 - F1: 0.9993 | 8d8c3c181c48b1c716dd6e6a6fe5c90f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.740146306575944e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | f8dac45f8866819e4d50e4b404130df0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 188 | 0.0027 | 0.9997 | 0.9997 | | No log | 2.0 | 376 | 0.0049 | 0.9993 | 0.9993 | | 13771373a44d962bf9acd7b440535fcf |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_mrpc_128 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: 0.6220 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | ea73eb13b83a4da183cda341ade8096f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6454 | 1.0 | 29 | 0.6241 | 0.6838 | 0.8122 | 0.7480 | | 0.63 | 2.0 | 58 | 0.62... | 002e9e1eaeeb288b224a11abe15aad15 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion x4 upscaler model card This model card focuses on the model associated with the Stable Diffusion Upscaler, available [here](https://github.com/Stability-AI/stablediffusion). This model is trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of... | 3872139647ce8d58155a0f73369aa07a |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` ```python import requests from PIL import Image from io import BytesIO from diffusers import S... | 8566bc871c146d42fecd1ce7afb91e0b |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | load model and scheduler model_id = "stabilityai/stable-diffusion-x4-upscaler" pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipeline = pipeline.to("cuda") | 96f923b4eaa2845d3526b7fc85159a8c |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | let's download an image url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png" response = requests.get(url) low_res_img = Image.open(BytesIO(response.content)).convert("RGB") low_res_img = low_res_img.resize((128, 128)) prompt = "a white cat" upscaled_i... | 073bd40997f103002229dcc5fa23d54c |
mit | ['image_restoration', 'superresolution'] | false | @inproceedings{wan2020bringing, title={Bringing Old Photos Back to Life}, author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={2747--2757}, year={2020} } @art... | 2fc343e674ca56fae6ab993a26b16de7 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1100k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, 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... | 28911f51bc64634a588517d9c4e3f3b9 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-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_1-step_1100k') model = TFBertModel.from_pretrained("google/multib... | a285941e3555d098de391bb0f5417e22 |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | ad00dc5ee1d7aaf9e3b7a44183553e14 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'm-vote-strict-epoch-3' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceCl... | 6db5494ec9263cb058333340315e10f1 |
apache-2.0 | ['bert', 'qqp', 'glue', 'torchdistill'] | false | `bert-large-uncased` fine-tuned on QQP dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as thos... | 6b13db1beb6eb5eb3a2a617df92367c8 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-xl-finetuned-unnatural-instructions This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1224 | 135f911e218a69d126c12e64cc5c754a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 | 5a65e9cecec3beced846e80530b92d6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1345 | 0.02 | 1000 | 0.1386 | | 0.14 | 0.03 | 2000 | 0.1336 | | 0.1402 | 0.05 | 3000 | 0.1282 | | 0.129 | 0.07 | 4000 | 0.1235 ... | 0000f5f558c8fcf04cc0d7278bdf7b05 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Tamil Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 35429f508a26b3d0b50b613b896c6996 |
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