1 简介 transformers库是一个用于自然语言处理(NLP)的机器学习库,提供了近几年在NLP领域取得巨大成功的预训练模型,例如BERT、GPT、RoBERTa、T5等。 该库由Hugging Face公司开发,是目前最流行的NLP预训练模型库之一。在实际应用中,使用已经训练好的模型可以显著提高模型的效果和速度。 同时,该库还提供了丰富的工具和A...
首先,用你打算使用的版本的 Python 创建一个虚拟环境并激活。 然后,你需要安装 Flax、PyTorch 或 TensorFlow 其中之一。关于在你使用的平台上安装这些框架,请参阅TensorFlow 安装页,PyTorch 安装页或Flax 安装页。 当这些后端之一安装成功后, 🤗 Transformers 可依此安装: ...
我们可以看到数据是推文文本和情感标签,这表明了数据集基于 Apache Arrow 构建(Arrow定义了一种比原生 Python 内存效率更高的类型化列格式)。 我们可以通过访问 Dataset 对象的 features 属性来查看背后使用的数据类型: print(train_ds.features) 复制 {'text':Value(dtype='string',id=None),'label':ClassLabel(...
Transformers.js is designed to be functionally equivalent to Hugging Face'stransformerspython library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as: ...
git pull pip install --upgrade . Run the examples Examples are included in the repository but are not shipped with the library. Therefore, in order to run the latest versions of the examples, you need to install from source, as described above. ...
首先下载 GLUE 数据集,并安装额外依赖: pip install -r ./examples/requirements.txt 然后可进行微调: export GLUE_DIR=/path/to/glueexport TASK_NAME=MRPCpython ./examples/run_glue.py \ --model_type bert \ --model_name_or_path bert-base-uncased \ --task_name $TASK_NAME \ --do_train \ ...
# venvpython-mvenv.my-envsource.my-env/bin/activate# uvuvvenv.my-envsource.my-env/bin/activate Install Transformers in your virtual environment. # pippipinstalltransformers# uvuvpipinstalltransformers Install Transformers from source if you want the latest changes in the library or are interested...
optimum-tpucomes with an handy PyPi released package compatible with your classical python dependency management tool. pip install optimum-tpu -f https://storage.googleapis.com/libtpu-releases/index.html export PJRT_DEVICE=TPU Inference optimum-tpuprovides a set of dedicated tools and integrations in...
pt_batch = tokenizer( ["We are very happy to show you the Transformers library.", "We hope you don't hate it."], padding=True, truncation=True, max_length=512, return_tensors="pt", ) 直接将pt_batch输入到模型中: from transformers import AutoModelForSequenceClassification model_name = ...
pipeline是使用Transformers库最简单直接的方法,它会自动下载并缓存默认的pre-trained model和tokenizer以进行相应的任务推理。 from transformers import pipeline classifier = pipeline("sentiment-analysis") # Inference classifier("We are very happy to show you the Transformers library.") # Output: [{'label':...