Data prepared and loaded for fine-tuning a model with transformers. Tokenize a Hugging Face dataset Hugging Face Transformers models expect tokenized input, rather than the text in the downloaded data. To ensure compatibility with the base model, use anAutoTokenizerloaded from the base model. Hu...
Data prepared and loaded for fine-tuning a model with transformers.Tokenize a Hugging Face dataset Hugging Face Transformers models expect tokenized input, rather than the text in the downloaded data. To ensure compatibility with the base model, use an AutoTokenizer loaded from the base model. Hug...
本节使用 LoRA 微调了一个 67M 的 Bert的蒸馏模型,distilbert/distilbert-base-uncased,实现对电影的评论进行分类的功能,用于是正面还是负面的评论,微调使用的数据为 stanfordnlp/imdb,相关资源地址:初始模型:https://huggingface.co/distilbert/distilbert-base-uncased微调数据:https://huggingface.co/datasets/s...
huggingface很贴心的把常见的fine-Tuning方法都做了集成,只用几行代码就可添加和修改,十分方便[7] from transformers import AutoModelForSeq2SeqLM from peft import get_peft_config, get_peft_model, LoraConfig, TaskType model_name_or_path = "bigscience/mt0-large" tokenizer_name_or_path = "bigscience...
微调(finetuning)对人的作用包括行为改变和知识获取。行为改变方面,包括学习更一致地回应、学会专注(如适度)以及发挥能力(如更擅长对话);知识获取方面,包括增加对新特定概念的了解、纠正旧的不正确信息。总的来说,微调既能带来行为改变,也能实现知识获取。
https://huggingface.co/course/chapter7/3?fw=pt 这次数据集是自己造的,不过提供 text 和 label(其实 label 没什么用,只是我生成数据时确定 mask 位置用了) 就好,问题不大。官方例子是随机 mask 的,我是根据一定规则确定位置的。官网例子中只 mask 了一个位置...
本文将介绍如何使用Huggingface工具库对BERT模型进行fine-tuning,以便进行文本分类任务。我们将首先加载数据,然后对文本进行编码,并建立模型。最后,我们将对模型进行训练和评估。
If you were curious how to upload your own dataset to Huggingface # Here is how we did it #!pip install huggingface_hub # !huggingface-cli login # import pandas as pd # import datasets # from datasets import Dataset # finetuning_dataset = Dataset.from_pandas(pd.DataFrame(data=finetuning_...
BTW, we're working on making this very easy intransformers. You can check: https://huggingface.co/docs/transformers/main/en/model_doc/mms [MMS] Scaling Speech Technology to 1,000+ Languages | Add attention adapter to Wav2Vec2huggingface/transformers#23813 ...
Motivation: While working on a data science competition, I was fine-tuning a pre-trained model and realised how tedious it was to fine-tune a model using native PyTorch or Tensorflow. I experimented…