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 | ['LABEL-0 = NONE', 'LABEL-1 = B-DATE', 'LABEL-2 = I-DATE', 'LABEL-3 = B-TIME', 'LABEL-4 = I-TIME', 'LABEL-5 = B-DURATION', 'LABEL-6 = B-DURATION', 'LABEL-7 = B-SET', 'LABEL-8 = B-SET'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 82 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 24 | e88c5fb0d59e27c28256c973db79cc0c |
apache-2.0 | ['LABEL-0 = NONE', 'LABEL-1 = B-DATE', 'LABEL-2 = I-DATE', 'LABEL-3 = B-TIME', 'LABEL-4 = I-TIME', 'LABEL-5 = B-DURATION', 'LABEL-6 = B-DURATION', 'LABEL-7 = B-SET', 'LABEL-8 = B-SET'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0658 | 1.0 | 8 | 0.0613 | 0.3704 | 0.4514 | 0.4069 | 0.9758 | | 0.038 | 2.0 |... | f27e9f8db56931a6e39124c8079d0597 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Samples The top 4 samples are "pure" while others are mixed with other artists and modifiers. I hope it still gives you an idea of what kind of styles can be created with this model. <img src="https://huggingface.co/Froddan/saidoudicko/resolve/main/index.png" width="256px"/> <img src="https://huggingface.co/Froddan/... | 59c51774b02e7bc44e78ea1152ae8e1c |
apache-2.0 | ['generated_from_keras_callback'] | false | distilgpt_oscarth_0040 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0004 - Validation Loss: 2.8864 - Epoch: 39 | 81bebd2268002af2003af8b8583b4da2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.6021 | 4.5759 | 0 | | 4.4536 | 4.1235 | 1 | | 4.1386 | 3.9013 | 2 | | 3.9546 | 3.7563 | 3 | | 3.8255 | 3.6477 | 4 | | 3.7271 |... | a8e52dc849240f0aea5d29ca0dc84538 |
mit | ['generated_from_trainer'] | false | roberta_RCADE_fine_tuned_sentiment_covid_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1662 - Accuracy: 0.9700 - F1 Score: 0.9700 | 365e57937438a26a340aca9dfa3f2ac0 |
mit | ['generated_from_trainer'] | false | predict-perception-bert-focus-concept This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8129 - Rmse: 1.0197 - Rmse Focus::a Su un concetto astratt... | 5a804b46568f1aa56ea3008eb6ab7d06 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un concetto astratto o un'emozione | Mae | Mae Focus::a Su un concetto astratto o un'emozione | R2 | R2 Focus::a Su un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:----... | 3c28c827379b33cd9613016ca975f906 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | bert-base-cased-finetuned-qqp This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3752 - Accuracy: 0.9084 - F1: 0.8768 - Combined Score: 0.8926 The model was fine-tuned to compare [... | afc7a425c51ff35af43c71e43a7b0ccc |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qqp \\n --push_to_hub \\n ... | 17283e9417c263c96449599952de775c |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.308 | 1.0 | 22741 | 0.2548 | 0.8925 | 0.8556 | 0.8740 | | 0.201 | 2.0 | 45482 | ... | eb15ba03b219a33926a937c40e4bc9ba |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-spam-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: 0.0238 - Accuracy: 0.9933 - F1: 0.9933 | 18d72a6daf6e26ee3b1e16e2173a6d42 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1739 | 1.0 | 260 | 0.0391 | 0.9894 | 0.9894 | | 0.0181 | 2.0 | 520 | 0.0238 | 0.9933 | 0.9933 | | d7d0886403ffef3bfdce36bfdf3252ec |
apache-2.0 | ['generated_from_keras_callback'] | false | vanichandna/bert-base-multilingual-cased-finetuned-squad This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5313 - Epoch: 3 | 1acb4cda3a704234c211f30a5d848973 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 43880, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 4c943473754cc9b89705254477fa9ad6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mrpc_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5873 - Accuracy: 0.6887 - F1: 0.7829 - Combined Score: 0.7358 | fba01b13cd5a3c4866f0409c0c4bc77c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6677 | 1.0 | 15 | 0.6479 | 0.6838 | 0.8122 | 0.7480 | | 0.6455 | 2.0 | 30 | 0.63... | 275eb0082a2b55a2c5917e6e0e2075a5 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_recommendation-roberta-large-v1-5-18 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) wi... | 70ab55f4328289aa53b022305619fcfc |
apache-2.0 | ['generated_from_trainer'] | false | amk-whisper This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1902 - Wer: 40.3587 | b3a066c15d6966d46bbb12f13f7d875a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - 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 - lr_scheduler_warmup_steps: 50 - training_steps: 100 - mixed_precisio... | b4379e620ca3738ff1dccf67f36d4838 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 20.0 | 20 | 0.7838 | 30.9417 | | 0.8511 | 40.0 | 40 | 1.0878 | 44.8430 | | 0.0794 | 60.0 | 60 | 1.1466 | 39.461... | dd166dd51f6501ae2a90cc11fe86b47b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-timit-tokenizer This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4285 - Wer: 0.3662 | ffb3fb88b584745a84d5a6c5e98aef62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1571 | 4.03 | 500 | 0.5235 | 0.5098 | | 0.2001 | 8.06 | 1000 | 0.4172 | 0.4375 | | 0.0968 | 12.1 | 1500 | 0.4562 | 0.4016 | |... | 0d43c0139fe17c60e4af3801841bb66e |
apache-2.0 | ['sentence-transformers', 'sentence-similarity', 'feature-extraction', 'transformers', 'onnx'] | false | This is the ONNX model of sentence-transformers/gtr-t5-xl [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). Currently, Hugging Face does not support downloading ONNX files with external format files. I have created a workaround using sbert and optimum together to generate embeddin... | 19e077bce2e53e1e288a05609c9ae296 |
apache-2.0 | ['sentence-transformers', 'sentence-similarity', 'feature-extraction', 'transformers', 'onnx'] | false | First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) def generate_embedding(text): token = tokeni... | f05a7f02dacbdbf829e9f99a2e83b5a0 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.5452 - Accuracy: 0.4657 | aa1a238d4f1eb72c9efbbc3a1a951e1c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.28 | 1.0 | 1136 | 0.5452 | 0.4657 | | 0.2191 | 2.0 | 2272 | 0.5774 | 0.4765 | | 0.2124 | 3.0 | 3408 | 0.5632 | 0.... | 31ae58881c8dcf83b67062528d9fccbf |
mit | [] | false | Models At the moment, the following models are available on the model hub: | Model identifier | Model Hub link | --------------------------------------------- | -------------------------------------------------------------------------- | `dbmdz/bert-base-historic-multilingual-cased` | [h... | e84d9b8c16a3430d242333d3ce2cab04 |
mit | [] | false | Smaller multilingual models Inspired by the ["Well-Read Students Learn Better: On the Importance of Pre-training Compact Models"](https://arxiv.org/abs/1908.08962) paper, we train smaller models (different layers and hidden sizes), and report number of parameters and pre-training costs: | Model (Layer / Hidden size)... | fb1fbaed71664de4f5b68852df270d05 |
mit | [] | false | Multilingual model - hmBERT Base We train a multilingual BERT model using the 32k vocab with the official BERT implementation on a v3-32 TPU using the following parameters: ```bash python3 run_pretraining.py --input_file gs://histolectra/historic-multilingual-tfrecords/*.tfrecord \ --output_dir gs://histolectra/bert... | fa1c3b56d59778816fabfcf4a9f211b3 |
mit | ['generated_from_trainer'] | false | airberta_airbnb_dat_lang8_xml_roberta_base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the Airbnb 1T dataset. It achieves the following results on the evaluation set: - Loss: 0.8560 | 9222f5681aa1314d1c10ae2cae66617c |
mit | ['generated_from_trainer'] | false | Model description AirBert is trained on a combination airbnb in-house data including reviews, listing descriptions, customer messages, message between guest and host, agent message and notes, house manual descriptions and customer emails, which contains a total size of 1T. | a1442a0c8cc5a4a3155255219e4d33eb |
mit | ['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 - distributed_type: sagemaker_data_parallel - num_devices: 64 - total_train_batch_size: 512 - total_eval_batch_size: 512 - optimizer: Adam with betas=(0.9,0.99... | 49a191b5e06e705a89e3482ad129499b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 1.5943 | 0.02 | 5000 | 1.3627 | | 1.4333 | 0.04 | 10000 | 1.2376 | | 1.3562 | 0.06 | 15000 | 1.1754 | | 1.3105 | 0.08 | 20000... | b4e00095e85692eecafeecaa2a97ef9a |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-qg-hard-medium This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4711 - Rouge1: 44.656 - Rouge2: 24.9885 - Rougel: 40.9697 - Rougelsum: 41.1529 | 4fad296e050db4f2a2380b0282c15369 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 135 | 1.5611 | 37.7779 | 19.4817 | 34.3244 | 34.3904 | | No log | 2.0 ... | 2f3a245fbce4efef2a9ee9dac4ca3f6b |
cc-by-4.0 | ['translation'] | false | Model Details **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Transformer-align - **Language(s):** - Source Language: Russian - Target Language: English - **License:** CC-BY-4.0 - **Resources for more information:** - [GitHub Repo](h... | 8e398d92fc992698d3f2857bad0fb94a |
cc-by-4.0 | ['translation'] | false | Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclan... | 73e4d50581f245b2f1d7aa0230cce870 |
cc-by-4.0 | ['translation'] | false | Preprocessing * Pre-processing: Normalization + SentencePiece * Dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) * Download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.zip) * Test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc... | 047d91285733e1795977604db1ed0273 |
cc-by-4.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.ru.en | 34.8 | 0.603 | | newstest2013.ru.en | 27.9 | 0.545 | | newstest2014-ruen.ru.en | 31.9 | 0.591 | | newstest2015-enru.ru.en | 30.4 | 0.568 | | newstest2016-enru.ru.en | 30.1 | 0.565 | | newste... | 53215c7cdd47dccd7d39a2ceb2e64cc6 |
cc-by-4.0 | ['translation'] | false | How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en") ``` | 16eb64e066a320ddeaee0ccaec7f1d71 |
apache-2.0 | ['conditional text generation', 'data augmentation'] | false | 如何使用 ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM pretrained = "Maciel/T5_Mask_Completion" tokenizer = AutoTokenizer.from_pretrained(pretrained) model = AutoModelForSeq2SeqLM.from_pretrained(pretrained) sentence = "[mask]疫情[mask]公园[mask]散步[mask]" max_input_length = 128 input_encodings = tokenizer... | 5a027010bd49415bccb09726decd6015 |
apache-2.0 | ['conditional text generation', 'data augmentation'] | false | 案例展示 ``` 1) 原始文本：今天[mask]篮球[mask]学校[mask] 补全文本：今天,我们来谈谈篮球与学校的关系。 2) 原始文本：[mask]疫情[mask]公园[mask]散步[mask] 补全文本：在疫情发生之前,人们可以在公园里散步。 3) 原始文本：[mask]感染新冠[mask]身体不舒服[mask]多休息[mask] 补全文本：如果你感染新冠了,身体不舒服,建议你多休息,不要吃辛辣刺激性的食物,以免加重病情。 ``` | 87f7a7e125d8dc9d40c84b5088a02899 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Examples  ``` (masterpiece, best quality:1.2), 1girl, NP: (worst quality, low quality, medium quality:1.4), (depth of field, blurry:1.2), Steps: 35, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2405394752, Size:... | a6279280db82b79333cb024ba779579a |
mit | ['generated_from_keras_callback'] | false | sachinsahu/Web_browser-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0993 - Train End Logits Accuracy: 0.9722 - Train Start Logits Acc... | a1357e17ad4ec52157f2cef98b05e0eb |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | ca1d7b2afe930878a82b313eaceafe39 |
apache-2.0 | ['text-classification', 'sentiment-analysis', 'finance-sentiment-detection', 'finance-sentiment'] | false | nickwong64/bert-base-uncased-finance-sentiment Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective. [bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the [cyrilzhang/financial_phrasebank_split](https://huggingface.co/datasets/cyrilzha... | 7bc1930c788783a434a74850472b7ca4 |
apache-2.0 | ['text-classification', 'sentiment-analysis', 'finance-sentiment-detection', 'finance-sentiment'] | false | How to Use the Model ```python from transformers import pipeline nlp = pipeline(task='text-classification', model='nickwong64/bert-base-uncased-finance-sentiment') p1 = "HK stocks open lower after Fed rate comments" p2 = "US stocks end lower on earnings worries" p3 = "Muted Fed, AI hopes send Wall Str... | 39673ee8fccb081b6818a20b3440c3db |
apache-2.0 | ['text-classification', 'sentiment-analysis', 'finance-sentiment-detection', 'finance-sentiment'] | false | Evaluation ``` {'test_loss': 0.9547446370124817, 'test_accuracy': 0.8536082474226804, 'test_f1': 0.8543579048224414, 'test_runtime': 4.9865, 'test_samples_per_second': 97.263, 'test_steps_per_second': 12.233} ``` | 585fd8ee6ed78a764b23224627ebbb9a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-german-with-lm This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the German set of the Common Voice dataset. It achieves a Word Error Rate of 8,8 percent on the evaluation set | 4bd1b3e5cc13e962a3cfb18619792904 |
apache-2.0 | ['generated_from_trainer'] | false | Model description German wav2vec2-xls-r-300m trained on the full train set of Common Voice dataset with a n-gram language model. Full code available in [my Github repository](https://github.com/MichaelFleck92/asr-wav2vec) | 68e766099703a682922d019313e1496e |
apache-2.0 | ['generated_from_trainer'] | false | Citation Feel free to cite this work by ``` @misc{mfleck/wav2vec2-large-xls-r-300m-german-with-lm, title={XLS-R-300 Wav2Vec2 German with language model}, author={Fleck, Michael}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mfleck/wav2vec2-large-xls-r-300m-... | 9a8e583d155b2cdaad456f990110ccce |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations Inference Usage ```python from transformers import pipeline pipe = pipeline(model="mfleck/wav2vec2-large-xls-r-300m-german-with-lm") output = pipe("/path/to/file.wav",chunk_length_s=5, stride_length_s=1) print(output["text"]) ``` | 003ad87f1083f559dd7dc840427c543b |
apache-2.0 | ['generated_from_trainer'] | false | Training and evaluation data Script used for training (takes about 80 hours on a single A100 40GB) ```python import random import re import json from typing import Any, Dict, List, Optional, Union import pandas as pd import numpy as np import torch | 445ece6aecf54c2e645f98e88ea92fae |
apache-2.0 | ['generated_from_trainer'] | false | import soundfile from datasets import load_dataset, load_metric, Audio from dataclasses import dataclass, field from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, TrainingArguments, Trainer, Wav2Vec2ForCTC ''' Most parts of this script are following the tutorial: https:... | b9df5ebd45117316fa5833676fd7d9e4 |
apache-2.0 | ['generated_from_trainer'] | false | Remove unused columns common_voice_train = common_voice_train.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"]) common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"]) | 31deaa015be85f5477b822066728bc6e |
apache-2.0 | ['generated_from_trainer'] | false | Remove batches with chars which do not exist in German print(len(common_voice_train)) regex = "[^A-Za-zäöüÄÖÜß,?.! ]+" common_voice_train = common_voice_train.filter(lambda example: bool(re.search(regex, example['sentence']))==False) common_voice_test = common_voice_test.filter(lambda example: bool(re.search(regex, ex... | 405a0ea2854bd244897e27a260b05e9b |
apache-2.0 | ['generated_from_trainer'] | false | Remove special chars from transcripts chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']' def remove_special_characters(batch): batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower() return batch common_voice_train = common_voice_train.map(remove_special_characters, num_proc=10) ... | 0b3f1a311e1c5fdde8f0ccbd8d7a6071 |
apache-2.0 | ['generated_from_trainer'] | false | Show some random transcripts to proof that preprocessing worked as expected def show_random_elements(dataset, num_examples=10): assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset." picks = [] for _ in range(num_examples): pick = random.randint(0, len(datase... | c4f91271b05086a14ce8fdb72618e51d |
apache-2.0 | ['generated_from_trainer'] | false | Extract all chars which exist in datasets and add wav2vek tokens def extract_all_chars(batch): all_text = " ".join(batch["sentence"]) vocab = list(set(all_text)) return {"vocab": [vocab], "all_text": [all_text]} vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_me... | 31f9d5976e5cf433b9f7f5389a385f79 |
apache-2.0 | ['generated_from_trainer'] | false | Create tokenizer and repo at Huggingface tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("./", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|") repo_name = "wav2vec2-large-xls-r-300m-german-with-lm" tokenizer.push_to_hub(repo_name) print("pushed to hub") | 8155c20e559c04eb6e2cfe0f119235a0 |
apache-2.0 | ['generated_from_trainer'] | false | Create feature extractor and processor feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) | 821067a6f3d57ef8f52ef71395759021 |
apache-2.0 | ['generated_from_trainer'] | false | soundfile.write("/home/debian/trainnew/test.wav",batch["input_values"],audio["sampling_rate"]) batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["sentence"]).input_ids return batch common_voice_train = common_voice_train... | 0201331b702337341a22d27cf1002c49 |
apache-2.0 | ['generated_from_trainer'] | false | different padding methods input_features = [{"input_values": feature["input_values"]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.pad( input_features, padding=self.padding, retur... | 048238dfa4f753ee18da1fc1703facfd |
apache-2.0 | ['generated_from_trainer'] | false | replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels return batch data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) | 71dc447c3ea23041187d5f140d33fba5 |
apache-2.0 | ['generated_from_trainer'] | false | Use word error rate as metric wer_metric = load_metric("wer") def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids) | 88bad259b8adf44605b7f5a1f99123bf |
apache-2.0 | ['generated_from_trainer'] | false | we do not want to group tokens when computing the metrics label_str = processor.batch_decode(pred.label_ids, group_tokens=False) wer = wer_metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} | fbed036ec6de47359a0830f4adbaebcc |
apache-2.0 | ['generated_from_trainer'] | false | Model and training parameters model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-xls-r-300m", attention_dropout=0.094, hidden_dropout=0.01, feat_proj_dropout=0.04, mask_time_prob=0.08, layerdrop=0.04, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, ... | 38e49e139a57baf9ad0e097f7e6d9f26 |
apache-2.0 | ['generated_from_trainer'] | false | When done push final model to Huggingface hub trainer.push_to_hub() ``` The model achieves a Word Error Rate of 8,8% using the following script: ```python import argparse import re from typing import Dict import torch from datasets import Audio, Dataset, load_dataset, load_metric from transformers import AutoFeatu... | 73b12d1110a4674e6f41813a1c339f2b |
apache-2.0 | ['generated_from_trainer'] | false | load processor feature_extractor = AutoFeatureExtractor.from_pretrained("mfleck/wav2vec2-large-xls-r-300m-german-with-lm") sampling_rate = feature_extractor.sampling_rate dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate)) | 53ad46e36736d827e2d17c7a5493326c |
apache-2.0 | ['generated_from_trainer'] | false | Remove batches with chars which do not exist in German regex = "[^A-Za-zäöüÄÖÜß,?.! ]+" dataset = dataset.filter(lambda example: bool(re.search(regex, example['sentence']))==False) chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']' | e3658b5f461af9aab81f032c34684174 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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_sch... | 043384acfc014d39e5ddbff879176d05 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1396 | 1.42 | 5000 | 0.1449 | 0.1479 | | 0.1169 | 2.83 | 10000 | 0.1285 | 0.1286 | | 0.0938 | 4.25 | 15000 | 0.1277 | 0.123... | 89b013d4976563310b19eb83169ee7c7 |
mit | ['generated_from_trainer'] | false | BERiT_2000_custom_architecture_100_epochs 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: 4.3615 | e71bb45e96e0938f1578fd71a1000243 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 | 3016676ff72fbdae7879ce4f758765f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 16.6444 | 0.19 | 500 | 8.8209 | | 8.1904 | 0.39 | 1000 | 7.5197 | | 7.3572 | 0.58 | 1500 | 7.1037 | | 7.0042 | 0.77 | 2000 | 6... | 5bea7ec271195100c0b625f08ad70cc8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | isometric-floating-icons Dreambooth model trained by viba98 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/f... | fb101911cb93d56845fd543de43c9e3f |
apache-2.0 | ['generated_from_trainer'] | false | tamil-sentiment-distilbert This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tamilmixsentiment dataset. It achieves the following results on the evaluation set: - Loss: 1.0230 - Accuracy: 0.665 | 2bf714e3d5d411ce8690411b32baf4b8 |
apache-2.0 | ['generated_from_trainer'] | false | Dataset Information - text: Tamil-English code-mixed comment. - label: list of the possible sentiments - LABEL_0: "Positive", - LABEL_1: "Negative", - LABEL_2: "Mixed_feelings", - LABEL_3: "unknown_state", - LABEL_4: "not-Tamil" | 45341925e5acdd26624ae21313476d71 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | ebc1ade8f12bc91be027445a227bcb20 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0442 | 1.0 | 250 | 0.9883 | 0.674 | | 0.9227 | 2.0 | 500 | 0.9782 | 0.673 | | 0.7591 | 3.0 | 750 | 1.0230 | 0.... | 358fe0110e9f9f3ec885e1e6fcaca8fb |
unknown | [] | false | Example prompts `woman near a fountain by laze opera panda`: <img src="https://huggingface.co/cyburn/laze_opera_panda/resolve/main/1.png" alt="Picture." width="500"/> `woman in taxi by laze opera panda`: <img src="https://huggingface.co/cyburn/laze_opera_panda/resolve/main/2.png" alt="Picture." width="500"/> `man... | a390a7702cad51e3a531928c1f2e4372 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small German This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 de dataset. It achieves the following results on the evaluation set: - Loss: 0.2490 - Wer: 12.5194 | 2c2d28ce82f5f39256d7fd9748c4908c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2189 | 1.0 | 1000 | 0.2490 | 12.5194 | | 4ba0ba2ce11574104fb93491a50d9ae3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1405 - F1: 0.8655 | c3d0a5ecade5011724f56012e3363a85 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2495 | 1.0 | 787 | 0.1764 | 0.8184 | | 0.1299 | 2.0 | 1574 | 0.1427 | 0.8562 | | 0.0771 | 3.0 | 2361 | 0.1405 | 0.8655 | ... | ab066238d0a5d6a0bc8cf0975b1a7f54 |
apache-2.0 | ['summarization', 'pegasus'] | false | pszemraj/pegasus-large-summary-explain This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs. It achieves the following results on the evaluation set: - eval_loss: 1.1193 - eval_r... | 0870cc26267cb3fe104e094847325df2 |
apache-2.0 | ['summarization', 'pegasus'] | false | Model description - After some initial tests, it was found that models trained on the [booksum](https://github.com/salesforce/booksum) dataset seem to inherit the summaries' SparkNotes-style explanations; so the user gets a shorter and easier-to-understand version of the text instead of **just** more compact. - Thi... | 78180a0883110c5e7352fba75f79b7c5 |
apache-2.0 | ['summarization', 'pegasus'] | false | Intended uses & limitations - standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer ... | 42cd6abaa6fc008c6dfbef697f26f1af |
apache-2.0 | ['summarization', 'pegasus'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | 0215681a0f5c7826cb56d413c10d3ff5 |
apache-2.0 | ['generated_from_trainer'] | false | MIX3_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4832 | cb5482ee2f44dc21c24b11ae078ac221 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP | 9081b6693b57f0dbb2e93c882a2ebcea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 2.8699 | 0.01 | 5000 | 2.3465 | | 2.6168 | 0.02 | 10000 | 2.2205 | | 2.5083 | 0.03 | 15000 | 2.2382 | | 2.4359 | 0.04 | 20000... | 822c4ae9a3ea716e6d4d0fdbf1f6be5e |
mit | ['bert', 'language-model', 'flaubert', 'french', 'flaubert-base', 'uncased', 'asr', 'speech', 'oral', 'natural language understanding', 'NLU', 'spoken language understanding', 'SLU', 'understanding'] | false | FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaubert... | 3e0ea79581d320bc84804430d2b3e830 |
mit | ['bert', 'language-model', 'flaubert', 'french', 'flaubert-base', 'uncased', 'asr', 'speech', 'oral', 'natural language understanding', 'NLU', 'spoken language understanding', 'SLU', 'understanding'] | false | Available FlauBERT-Oral models
- `flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased
- `flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus
- `flaubert-oral-mixed` : trained from scratch on... | 43ec59605285af382fb47e27e5463ee2 |
mit | ['bert', 'language-model', 'flaubert', 'french', 'flaubert-base', 'uncased', 'asr', 'speech', 'oral', 'natural language understanding', 'NLU', 'spoken language understanding', 'SLU', 'understanding'] | false | Usage for sequence classification
```python
flaubert_tokenizer = FlaubertTokenizer.from_pretrained("nherve/flaubert-oral-asr")
flaubert_classif = FlaubertForSequenceClassification.from_pretrained("nherve/flaubert-oral-asr", num_labels=14)
flaubert_classif.sequence_summary.summary_type = 'mean'
| 009da42b2c78f0db842ad7b4b9fdb7c9 |
mit | ['bert', 'language-model', 'flaubert', 'french', 'flaubert-base', 'uncased', 'asr', 'speech', 'oral', 'natural language understanding', 'NLU', 'spoken language understanding', 'SLU', 'understanding'] | false | References
If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:
```
@InProceedings{herve2022flaubertoral,
author = {Herv\'{e}, Nicolas and Pelloin, Valentin and Favre, Benoit and Dary, Franck and Laurent, Ant... | fd6c5278d9abcec84357e24b6e76235b |
apache-2.0 | ['generated_from_trainer'] | false | nubes_test This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1333 - Precision: 0.8783 - Recall: 0.9154 - F1: 0.8965 - Accuracy: 0.9717 | 064993fd48ffbc04e08c2df39e4adf89 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1266 | 1.0 | 1726 | 0.1112 | 0.8348 | 0.8856 | 0.8594 | 0.9676 | | 0.0731 | 2.0 |... | d04533a9295a3a8915f9ec0c37a595af |
mit | [] | false | Example
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/maptask-deberta-pair")
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label = lambda ... | f54c67848fc99fca6c2d9f68348164f2 |
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