Machine learning use cases can involve a lot of input data and compute-heavy thus expensive model training. It is common to downloadpre-trained modelsfrom remote repositories and use them instead. Hugging Face hosts a well-knownonewith models for image and text processing. In this tutori...
我们可以将模型保存下来以用于后面的推理和评估。我们暂时将其保存到磁盘，但你也可以使用model.push_to_hub方法将其上传到 Hugging Face Hub。https://hf.co/docs/hub/main # Save our LoRA model & tokenizer resultspeft_model_id="results"trainer.model.save_pretrained(peft_model_id)tokenizer.save_...
Imagine dealing with a report much longer than the one about Apple. And, all you are interested in is the date of the event being mentioned. Instead of reading the whole report to find the key information, we can use a question-answering model from Hugging Face that will provide the answe...
I ran this notebook across all the pretrained models found on Hugging Face Transformer. This way you know ahead of time if the model you plan to use works with this code without any modifications. The list of pretrained transformers models that work with this notebook can be foundhere. Ther...
2. Prepare your input: Load a tokenizer for our model, in this case, the transformers library facilitates the process as it inferes the tokenizer to be used from the name of the model that we have chosen. #We call the tokenizer class tokenizer = AutoTokenizer.from_pretrained(model_name) ...
model = AutoModelForCausalLM.from_pretrained(model_path) Then, we can try to evaluate our model based on the training input we have given. For example, we use the "Health benefits of regular exercise" as the input. input_text = "Health benefits of regular exercise" ...
Hugging Face 是一家为自然语言处理(NLP) 模型训练和部署提供平台的公司。该平台拥有适用于各种 NLP 任务的模型库,包括语言翻译、文本生成和问答。这些模型在广泛的数据集上接受训练,旨在在广泛的自然语言处理 (NLP) 活动中表现出色。 Hugging Face 平台还包括用于在特定数据集上微调预训练模型的工具,这有助于使算法...
save_pretrained("tokenizer")We push the tokenizer to the Hugging Face Hub for later training our model.# you need to be logged in to push the tokenizer bert_tokenizer.push_to_hub(tokenizer_id)3. Preprocess the datasetBefore we can get started with training our model, the last step...
本章主要介绍Hugging Face下的另外一个重要库:Datasets库,用来处理数据集的一个python库。当微调一个模型时候,需要在以下三个方面使用该库,如下。 从Huggingface Hub上下载和缓冲数据集(也可以本地哟!) 使用Dataset.map()预处理数据 加载和计算指标 ...
This tutorial demonstrates fine-tuning a GPT-2* model on Intel® Gaudi® AI processors using Hugging Face* Optimum for Intel library with DeepSpeed*.