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prepare_training_data.py train_xlm_roberta.py 41 changes: 36 additions & 5 deletions 41 prepare_training_data.py Original file line numberDiff line numberDiff line change @@ -1,6 +1,8 @@ import fileinput import os import logging import re import random...
Parent Model:XLM-RoBERTa-large Resources for more information:-GitHub Repo-Associated Paper Uses Direct Use The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. ...
as is now, here: https://github.com/eole-nlp/eole/blob/main/eole/inputters/text_utils.py#L86-L102 I have handled only the xlm-roberta-xl case in the context of COMETKIWI. for training, numericalize will take SRC and TGT and encode BOS + TGT + EOS + EOS + SRC + EOS for infere...
The problem arises when using XLMRobertaTokenizerFast tokenizer. The tasks I am working on is token-classification. In order to align the labels with the sub-word units I have used the code snippet provided here:https://huggingface.co/transformers/custom_datasets.html[ Fine-tuning with custom ...
XLM模型的代码开源在https://github.com/facebookresearch/XLM XLM-R (Unsupervised Cross-lingual Representation Learning at Scale) XLM-R全称叫做XLM-RoBERTa,为了更好的理解这个模型,请先花5分钟阅读一下RoBERTa概述这篇文章 XLM-R的改进 在XLM和RoBERTa中使用的跨语言方法的基础上(所以,本质就是XLM+RoBERTa,没...
This PR, Add a new model XLMRobertaForSequenceClassification (for RAG scenario) https://github.com/huggingface/transformers/blob/v4.42.3/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py...
XLM-RoBERTa (large-sized model) XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository. Disclaimer: The team ...
https://github.com/pytorch/fairseq 摘要 本文的XLM-R(XLM-RoBERTa)证明了使用大规模多语言预训练的模型可以显著提高跨语言迁移任务的性能。XLM-R在技术上等同于XLM+RoBERTa,在数据上使用100种语言、2.5TB文本数(CommonCrawl项目爬取的)进行训练。 战绩如下: ...
在XLM和RoBERTa中使用的跨语言方法的基础上,我们增加了新模型的语言数量和训练示例的数量,用超过2TB的已经过清理和过滤的CommonCrawl 数据以自我监督的方式训练跨语言表示。这包括为低资源语言生成新的未标记语料库,并将用于这些语言的训练数据量扩大两个数量级。