license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-tur - source_languages: ukr - target_languages: tur - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-tur/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'tr'] - src_constituents: {'ukr'} - tgt_const... | fdf7d8be0ce28117b32ead6027c96da5 |
mit | ['bert', 'pytorch', 'tsdae'] | false | Introduction Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large. "BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recogniti... | 6e5c489d4e88e84079ee27b0c2344afa |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper largeV2 Italian MLS This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/multilingual_librispeech italian dataset. It achieves the following results on the evaluation set: - Loss: 0.2051 - Wer: 8.3353 | 90cbd0001bb20efab92c3606090ac1d0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Model description The model is fine-tuned for 4000 updates/steps on multilingual librispeech Italian train data. - Zero-shot - 13.8 (MLS Italian test) - Fine-tune MLS Italian train - 8.33 (MLS Italian test) (-40%) | 3ca7d55ee355d0e07720a8e6a0574dec |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 6ce397f642e6103316be992e210bd4cb |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1115 | 1.02 | 1000 | 0.2116 | 9.4217 | | 0.0867 | 2.03 | 2000 | 0.1964 | 9.7823 | | 0.0447 | 3.05 | 3000 | 0.2001 | 9.6409 | |... | 2495623a660406858ee76f2b149c7423 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper-large-v2-Irish This model is a fine-tuned version of [kpriyanshu256/whisper-large-v2-cy-500-32-1e-05](https://huggingface.co/kpriyanshu256/whisper-large-v2-cy-500-32-1e-05) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.1613 - Wer: 40.8827 | a83dfeb80dcfd95d3dc813b1458fabfa |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4018 | 3.01 | 100 | 0.9510 | 47.8513 | | 0.0465 | 6.02 | 200 | 0.9984 | 42.4797 | | 0.0127 | 9.02 | 300 | 1.0906 | 42.915... | c273efcd9f6d11202e5919d346ae77cc |
apache-2.0 | ['translation'] | false | opus-mt-fi-mg * source languages: fi * target languages: mg * OPUS readme: [fi-mg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-mg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 8f7a3ee10e2b0f3584fc1090dddfcd32 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | tedx100-xlsr: Wav2vec 2.0 with TEDx Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [TEDx multilingual in Portuguese](http://www.openslr.org/100) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. | Dataset ... | ad61142de56905b3f841531f28985b5f |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | tedx\_1... | e4c4e3bbd021241302bf7bdbe1d7a2b5 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.13846663354859937 | 2cbee1190db93e1029d2e98d03a6d862 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.36960721735520236 | d46958ce6e5dce69ade3bbb727c759a2 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.16941287878787875 | a4954f1d900275360e11a30c56c7c78e |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.16586103382107384 | b63d5944cd15de5fcd444ced8a6732fe |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.7943364822145216 | e95110489312fa32c9b17f1b2be3c947 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.22221476803982182 | 47adf767d8cb4d513eba1bf9b5d16936 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.39486066017315996 | 754273d1ba4bcd6de719994c389bb943 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.12338749517028079 | 16fd93da1173d3bd57c8ad6594fa8266 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.4146185693398481 | e48f89fc50cb903d14fab8f95e8c2f5c |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.17142676767676762 | 919bc49b6bff9fce0bab648d5b2a8236 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.15212081808962674 | 475dfea29694c23158370e0adda64f34 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.982518441309493 | f054e78e1dd3b30b4f0ddcf59d7abcf8 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.21567860841157235 | deaea19443d09f3f448c8625b2b2accc |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.3952218614718614 | 46b5f6c318c11c8593301023f2a8dd95 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Tacotron 2 with Guided Attention trained on Baker (Chinese) This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Baker dataset (Ch). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https:/... | 3ce4fb4a79e3091446ead010aef16a85 |
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-baker-ch") tacotro... | ced2ceaef9a3fa1df39b9a831a69a393 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1909 | 997c8b5cef313bc8983525e4a04d6fef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 18 | 4.2070 | | No log | 2.0 | 36 | 4.1958 | | No log | 3.0 | 54 | 4.1909 | | bbd06d20ae20e2d033f983c3810eb5ef |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Russian Chit-chat, Deductive and Common Sense reasoning model Модель является ядром прототипа [диалоговой системы](https://github.com/Koziev/chatbot) с двумя основными функциями. Первая функция - **генерация реплик чит-чата**. В качестве затравки подается история диалога (предшествующие несколько реплик, от 1 до 10)... | 1fbc0e4416bfb354787728e2b391d12f |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Варианты модели и метрики Выложенная на данный момент модель имеет 760 млн. параметров, т.е. уровня sberbank-ai/rugpt3large_based_on_gpt2. Далее приводится результат замера точности решения арифметических задач на отложенном тестовом наборе сэмплов: | base model | arith. accuracy | | ---... | 23d53bff815821940d8c538614a93357 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Пример использования ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "inkoziev/rugpt_chitchat" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>... | ea0a1334c4a2e2e5a904365c976ea424 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | На вход модели подаем последние 2-3 реплики диалога. Каждая реплика на отдельной строке, начинается с символа "-" input_text = """<s>- Привет! Что делаешь? - Привет :) В такси еду -""" encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.gen... | 3168a234880bb55134ca2cf916d6f1f1 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Citation: ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Russian Chit-chat with Common sence Reasoning}, url = {https://huggingface.co/inkoziev/rugpt_chitchat}, year = 2022 } ``` | 5c98416baa76f46e359c6db7873b5174 |
apache-2.0 | ['luke', 'named entity recognition', 'relation classification', 'question answering'] | false | mLUKE **mLUKE** (multilingual LUKE) is a multilingual extension of LUKE. Please check the [official repository](https://github.com/studio-ousia/luke) for more details and updates. This is the mLUKE large model with 24 hidden layers, 768 hidden size. The total number of parameters in this model is 868M (561M for the... | b7cecc00667c63d0b3cc7cfc7136195e |
apache-2.0 | ['luke', 'named entity recognition', 'relation classification', 'question answering'] | false | Citation If you find mLUKE useful for your work, please cite the following paper: ```latex @inproceedings{ri-etal-2022-mluke, title = "m{LUKE}: {T}he Power of Entity Representations in Multilingual Pretrained Language Models", author = "Ri, Ryokan and Yamada, Ikuya and Tsuruoka, Yoshimasa", ... | bce73ccb486f75fa46cbec0504cd3a96 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | 简介 Brief Introduction 采用统一的框架处理多种抽取任务，AIWIN2022的冠军方案，1.1亿参数量的中文UBERT-Base。 Adopting a unified framework to handle multiple information extraction tasks, AIWIN2022's champion solution, Chinese UBERT-Base (110M). | 841acf4e89db96064d1c5ff546e7d786 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | UBERT | 110M | 中文 Chinese | | fa4d926ecb32c4022fe78a67ed6e1885 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | 模型信息 Model Information 参考论文：[Unified BERT for Few-shot Natural Language Understanding](https://arxiv.org/abs/2206.12094) UBERT是[2022年AIWIN世界人工智能创新大赛：中文保险小样本多任务竞赛](http://ailab.aiwin.org.cn/competitions/68 | 180c7713140197ecfe5ff2c80d28e4e9 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | results)的冠军解决方案。我们开发了一个基于类似BERT的骨干的多任务、多目标、统一的抽取任务框架。我们的UBERT在比赛A榜和B榜上均取得了第一名。因为比赛中的数据集在比赛结束后不再可用，我们开源的UBERT从多个任务中收集了70多个数据集（共1,065,069个样本）来进行预训练，并且我们选择了[MacBERT-Base](https://huggingface.co/hfl/chinese-macbert-base)作为骨干网络。除了支持开箱即用之外，我们的UBERT还可以用于各种场景，如NLI、实体识别和阅读理解。示例代码可以在[Github](https://github.com/IDEA-CCNL/Fengshen... | 16d46e8542dbb0d9e7da0755d2a8a272 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | results). We developed a unified framework based on BERT-like backbone for multiple tasks and objectives. Our UBERT owns first place, as described in leaderboards A and B. In addition to the unavailable datasets in the challenge, we carefully collect over 70 datasets (1,065,069 samples in total) from a variety of tasks... | 7a312d69b3faaa1c47999bf7af846ea1 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | 使用 Usage Pip install fengshen: ```python git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git cd Fengshenbang-LM pip install --editable ./ ``` Run the code: ```python import argparse from fengshen import UbertPiplines total_parser = argparse.ArgumentParser("TASK NAME") total_parser = UbertPiplines.piplines_... | 29f8503040e76201debdb161ac353116 |
apache-2.0 | ['bert', 'NLU', 'Sentiment', 'Chinese'] | false | 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文： If you are using the resource for your work, please cite the our paper for this model: ```text @article{fengshenbang/ubert, author = {JunYu Lu and Ping Yang and Jiaxing Zhang and Ruyi Gan and Jing Yang}, ... | 56d51e46d34fbab25a3e585ad0fe6b85 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | bert-base-japanese-jsnli This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) on the [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) dataset. It achieve... | 5db1fe78a6ae81ea2d92c1b4e3fdd92b |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="Formzu/bert-base-japanese-jsnli") sequence_to_classify = "いつか世界を見る。" candidate_labels = ['旅行', '料理', '踊り'] out = classifier(sequence_to_classify, candidate_labels, hypothesis_... | 35bbba338e0401528212fa0eb9bd8568 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "Formzu/bert-base-japanese-jsnli" model = AutoModelForSequenceClassification.from_pretrained(model_name).to(d... | ecc92d4290c26ab3ddbaebc284d889ea |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | 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: 3.0 | f530bcceef35822b8b935166108d23a7 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | | :-----------: | :---: | :---: | :-------------: | :------: | | 0.4054 | 1.0 | 16657 | 0.2141 | 0.9216 | | 0.3297 | 2.0 | 33314 | 0.2145 | 0.9236 | | 0.2645 | 3.0 | 49971 | 0.2085 | 0.9... | ad5c8462215af746fd89f07c474fd699 |
mit | ['spacy', 'token-classification'] | false | en_core_web_lg English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `en_core_web_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `... | 1d01e0cf92734c33b1dfba54a79b2117 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.86 | | `TOKEN_P` | 99.57 | | `TOKEN_R` | 99.58 | | `TOKEN_F` | 99.57 | | `TAG_ACC` | 97.35 | | `SENTS_P` | 92.19 | | `SENTS_R` | 89.27 | | `SENTS_F` | 90.71 | | `DEP_UAS` | 92.08 | | `DEP_LAS` | 90.27 | | `ENTS_P` | 85.16 | | `ENTS_R` | 85.70 | | `ENTS_F` | 8... | 37b8793a57256f5cee9c3c47f4da0364 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-3feb-2022-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1470 | 3aff5a72421795e68f750615197083fa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2276 | 1.0 | 5533 | 1.1641 | | 0.9614 | 2.0 | 11066 | 1.1225 | | 0.7769 | 3.0 | 16599 | 1.1470 | | 4e178c33f7330f57d9d910835dbb870a |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8956 - Rouge1: 58.1914 - Rouge2: 45.822 - Rougel: 49.4407 - Rougelsum: 56.6379 - Ge... | 761a0c3206bff72a877293d07e237f95 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.2575 | 1.0 | 795 | 0.9154 | 53.8792 | 34.3203 | 35.8768 | 51.1789 ... | 9d89b8bddf21a9edddc5c20f25522ed5 |
mit | ['token-classification', 'fill-mask'] | false | This model is the combined camembert-base model, with the pretrained lilt checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding", with the visual backbone built from the pretrained checkpoint "microsoft/dit-base". *Note:* This model shou... | 67a5c76361cfd52d13c2d030448bf6f9 |
mit | ['token-classification', 'fill-mask'] | false | patch_transformers() must have been executed beforehand tokenizer = AutoTokenizer.from_pretrained("camembert-base") model = AutoModel.from_pretrained("manu/lilt-camembert-dit-base-hf") model = AutoModelForTokenClassification.from_pretrained("manu/lilt-camembert-dit-base-hf") | db55f16873a7076c91ef630cc69380d1 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the torta concept trained by morgan on the morgan/tortas dataset. This is a Stable Diffusion model fine-tuned on the torta concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of a torta sandwich** See the ongoing experiments here in this [Weights & Biases traini... | 0e3ce5210ca73c60c0348e6ff0ec0e3e |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | jan-1,-11:50---try-add-%22sandwich%22-class-to-the-training-prompt---success!) 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! Note that commit `8f89857b2b2a6f75c443eac298f022483ef23a3f` use... | d08c6cf02e41fa223317d34468246a6e |
apache-2.0 | ['translation'] | false | spa-cat * source group: Spanish * target group: Catalan * OPUS readme: [spa-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-cat/README.md) * model: transformer-align * source language(s): spa * target language(s): cat * model: transformer-align * pre-processing: normalization + Sent... | 0d2cbdad64138abd251ee1bb871a064d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-cat - source_languages: spa - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'ca'] - src_constituents: {'spa'} - tgt_const... | 1ed86d29db71ce1d0a95a6fe769b1ff0 |
apache-2.0 | ['generated_from_trainer'] | false | BERTModified-fullsize-finetuned-wikitext-test 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: 6.7813 - Precision: 0.1094 - Recall: 0.1094 - F1: 0.1094 - Accuracy: 0.1... | 52fbd2b6295b9ea79fa8b018f7b26ab8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 9.2391 | 1.0 | 4382 | 8.1610 | 0.0373 | 0.0373 | 0.0373 | 0.0373 | | 7.9147 | 2.0 ... | 40dfb2b774610b7204550305d06c82c8 |
mit | [] | false | roy-lichtenstein on Stable Diffusion This is the `<roy-lichtenstein>` 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. ... | ceac2407bd19df925ceed0fe7321623b |
apache-2.0 | [] | false | bert-base-en-ar-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the ... | 02ffb64b1cae61f8672ee5c96be33109 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-ar-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-ar-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](h... | 4a3ebf92beac9fd55e79079edeb5d30c |
bsd-3-clause | ['codet5'] | false | CodeT5-base for Code Summarization [CodeT5-base](https://huggingface.co/Salesforce/codet5-base) model fine-tuned on CodeSearchNet data in a multi-lingual training setting ( Ruby/JavaScript/Go/Python/Java/PHP) for code summarization. It was introduced in this EMNLP 2021 paper [CodeT5: Identifier-aware Unified Pre-trai... | a85b434a94945c3bb7c26db684ec8ea2 |
bsd-3-clause | ['codet5'] | false | How to use Here is how to use this model: ```python from transformers import RobertaTokenizer, T5ForConditionalGeneration if __name__ == '__main__': tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base-multi-sum') model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base-mul... | 6e615f02b9d3d2940d55f7c891b4b4fd |
bsd-3-clause | ['codet5'] | false | Fine-tuning data We employ the filtered version of CodeSearchNet data [[Husain et al., 2019](https://arxiv.org/abs/1909.09436)] from [CodeXGLUE](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text) benchmark for fine-tuning on code summarization. The data is tokenized with our pre-trained code-spe... | d76854ca3a3b5656340b9298e36c7d78 |
bsd-3-clause | ['codet5'] | false | Data statistic | Programming Language | Training | Dev | Test | | :------------------- | :------: | :----: | :----: | | Python | 251,820 | 13,914 | 14,918 | | PHP | 241,241 | 12,982 | 14,014 | | Go | 167,288 | 7,325 | 8,122 | | Java | 164,923 ... | 8ee4d9dac2d347f28c8b650673b341ac |
bsd-3-clause | ['codet5'] | false | Training procedure We fine-tune codet5-base on these six programming languages (Ruby/JavaScript/Go/Python/Java/PHP) in the multi-task learning setting. We employ the balanced sampling to avoid biasing towards high-resource tasks. Please refer to the [paper](https://arxiv.org/abs/2109.00859) for more details. | bb50731c92daa916c5c750b50cd8b5c2 |
bsd-3-clause | ['codet5'] | false | Evaluation results Unlike the paper allowing to select different best checkpoints for different programming languages (PLs), here we employ one checkpoint for all PLs. Besides, we remove the task control prefix to specify the PL in training and inference. The results on the test set are shown as below: | Model ... | 79302ea63a195ee310a4e5783eca0827 |
bsd-3-clause | ['codet5'] | false | Citation ```bibtex @inproceedings{ wang2021codet5, title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation}, author={Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi}, booktitle={Proceedings of the 2021 Conference on Empirical Methods in Nat... | 27cab9203989c108bd58ac511dd44765 |
cc-by-4.0 | ['Transformers', 'text-classification', 'intent-classification', 'multi-class-classification', 'natural-language-understanding'] | false | Demo: How to use in HuggingFace Transformers Pipeline Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline model_name = 'qanastek/XLMRoberta-Alexa-Intents-Class... | 8d8450cb07d8bba7f551fb7e71f4e830 |
cc-by-4.0 | ['Transformers', 'text-classification', 'intent-classification', 'multi-class-classification', 'natural-language-understanding'] | false | Intents * audio_volume_other * play_music * iot_hue_lighton * general_greet * calendar_set * audio_volume_down * social_query * audio_volume_mute * iot_wemo_on * iot_hue_lightup * audio_volume_up * iot_coffee * takeaway_query * qa_maths * play_game * cooking_query * iot_hue_lightdim * iot_wemo_off * music_settings * ... | c9e8ef0e32ddd1bb5ba5025f26d46062 |
cc-by-4.0 | ['Transformers', 'text-classification', 'intent-classification', 'multi-class-classification', 'natural-language-understanding'] | false | Evaluation results ```plain precision recall f1-score support alarm_query 0.9661 0.9037 0.9338 1734 alarm_remove 0.9484 0.9608 0.9545 1071 alarm_set 0.8611 0.9254 0.8921 2091 audio_volume_down ... | b8495cb7c932059c029961bacfd7ee03 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | d0c123b2fbd0bc3996b64fe5f36af969 |
cc-by-2.0 | ['text2image', 'prompting'] | false | Created based on my cat, Garry. used 600+ images to train the model. You can view the images I used to train the model over on my [Facebook](https://www.facebook.com/media/set/?vanity=patrick.caulton&set=a.1003539599707037/) It's a public album. Some examples from the model.  on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5530 - Wer: 17.0761 | 68868f09b32226223552614573fb23d5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 79d469dd2786c2b147bdbaf504680a21 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0012 | 21.01 | 150 | 0.5211 | 17.2845 | | 0.0006 | 42.02 | 300 | 0.5530 | 17.0761 | | 3574e07c78be808a40e1bfc685bcd371 |
apache-2.0 | ['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | Post-training static quantization This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [jimypbr/bert-base-uncased-squad](https://huggingface.co/jimypbr/bert-base-uncased-squad). The calibration ... | aacb0a578f8a715eea5cc7fa5062fd23 |
apache-2.0 | ['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | Load with Intel® Neural Compressor: ```python from optimum.intel.neural_compressor import IncQuantizedModelForQuestionAnswering int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained( "Intel/bert-base-uncased-squad-int8-static", ) ``` | e430c3e118d35f0d301f65245d3a1619 |
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: 0.1870 - Wer: 12.4577 | f06e0ef5792b6f61939759b5909420c9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 70e9795db24a624c24e14c3506161f45 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2097 | 0.37 | 100 | 0.2616 | 17.6701 | | 0.1578 | 0.73 | 200 | 0.2108 | 14.0990 | | 0.0806 | 1.1 | 300 | 0.1870 | 12.457... | 66dec033d20b221b8e5ccf4c624ce0ed |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3953 - Wer: 19.6472 | 319706c99c4fef1d6fc775b65e184f50 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - 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: 500 - training_steps: 1 | 710c091f9f5b445a6f098cffa37247c8 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1331 | 1.29 | 1000 | 0.3014 | 22.3602 | | 0.0537 | 2.59 | 2000 | 0.2988 | 20.8572 | | 0.0217 | 3.88 | 3000 | 0.3093 | 20.564... | 12d1f198fb69c2397201c8a9279935f3 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_xlsr-53_s182 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | b015a4f79e1998da8e31a10c5b3979c5 |
mit | [] | false | Cute Game Style on Stable Diffusion This is the `<cute-game-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. Yo... | 778455fa3cf7174cb20d4722f65e74fd |
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: 0.1611 - Accuracy: 0.938 - F1: 0.9382 | eee675a1bd52837620e2c17f9201678d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2043 | 1.0 | 250 | 0.1804 | 0.9275 | 0.9270 | | 0.1334 | 2.0 | 500 | 0.1611 | 0.938 | 0.9382 | | c8d4f60ee786254d7d861358d57fa6cd |
mit | ['generated_from_trainer'] | false | language-detection-RoBerta-base-additional This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1367 - Accuracy: 0.9874 | 3fa4c09580e26d5b770e588390a0c5cc |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | MobileNet V3 - Small model Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in [this paper](https://arxiv.org/pdf/1905.02244.pdf). | b30943ee71bed37ff16902a9620776ce |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from pyrovision.models import model_from_hf_hub model = model_from_hf_hub("pyronear/mobilenet_v3_small").eva... | b8bfe1e7c16c317c4997194cd590ca6b |
apache-2.0 | ['translation'] | false | opus-mt-sv-sl * source languages: sv * target languages: sl * OPUS readme: [sv-sl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-sl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | f0d65d93820add37960c82162b8bc989 |
cc-by-sa-4.0 | ['finance'] | false | Additional pretrained BERT base Japanese finance This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). | 06088e43eac90eaa2ff70eccc1ec00e2 |
cc-by-sa-4.0 | ['finance'] | false | Model architecture The model architecture is the same as BERT small in the [original BERT paper](https://arxiv.org/abs/1810.04805); 12 layers, 768 dimensions of hidden states, and 12 attention heads. | d10d36f0d1c4856ca80bbe74c09377f0 |
cc-by-sa-4.0 | ['finance'] | false | Training Data The models are additionally trained on financial corpus from [Tohoku University's BERT base Japanese model (cl-tohoku/bert-base-japanese)](https://huggingface.co/cl-tohoku/bert-base-japanese). The financial corpus consists of 2 corpora: - Summaries of financial results from October 9, 2012, to Decembe... | 17f1c302a8f82157dd600f3ced6f43c3 |
cc-by-sa-4.0 | ['finance'] | false | Tokenization You can use tokenizer [Tohoku University's BERT base Japanese model (cl-tohoku/bert-base-japanese)](https://huggingface.co/cl-tohoku/bert-base-japanese). You can use the tokenizer: ``` tokenizer = transformers.BertJapaneseTokenizer.from_pretrained('cl-tohoku/bert-base-japanese') ``` | b52e6cc68361fea8393e1b7f2589c79b |
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