Python Transformer library installation configuration and usage method

Transformer library and usage methods in Python

1. Overview of the library

Hugging Face TransformersIt is one of the most popular open source libraries in the field of natural language processing (NLP), and supports pre-trained models based on Transformer architecture (such as BERT, GPT, T5, etc.), covering multimodal tasks such as text, images, and audio. Its core functions include:

Pre-trained model: Supports hundreds of models, adapts to text classification, generation, translation, question and answer tasks.
Word Participle and Tool Chain: Provides efficient word segmentation (Tokenizer) and data processing tools.
Cross-frame support: Compatible with deep learning frameworks such as PyTorch, TensorFlow and JAX.

2. Installation and configuration

Install the library

pip install transformers
# Install full dependencies (recommended)pip install transformers[sentencepiece]

Domestic mirror acceleration
If the download model is slow, you can set the mirror source:

import os
["HF_ENDPOINT"] = ""

3. Basic use: Pipeline Quick Reasoning

pipeline()It is the core interface of the Transformers library, which supports one-click call to pre-trained models to complete common tasks.

Sentiment analysis example

from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("I love using transformers library!")
print(result)  # Output：[{'label': 'POSITIVE', 'score': 0.9998}]

Text generation example

generator = pipeline("text-generation", model="gpt2")
text = generator("The future of AI is", max_length=50)
print(text[0]["generated_text"])

Supported task types

Text classification, named entity recognition (NER), translation, summary, question and answer, etc.
Multimodal tasks: image classification, speech recognition, visual question and answer, etc.

4. Advanced use: Custom model and word participle

Loading model and word participle
useAutoModelandAutoTokenizerLoading the model on demand:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the translation model (English → French)model_name = "Helsinki-NLP/opus-mt-en-fr"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Write translation functions

def translate(text, tokenizer, model):
    inputs = (text, return_tensors="pt", truncation=True)
    outputs = (inputs, max_length=50, num_beams=4)
    return (outputs[0], skip_special_tokens=True)
english_text = "Hello, how are you?"
print(translate(english_text, tokenizer, model))  # Output French translation

Batch translation and parameter optimization

def batch_translate(texts, tokenizer, model):
    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
    outputs = (**inputs, max_length=50, num_beams=8)
    return [(output, skip_special_tokens=True) for output in outputs]

5. Model fine-tuning and multimodal application

Fine-tune pretrained model

useTrainerClass andTrainingArgumentsConfigure training parameters:

from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16
)
trainer = Trainer(model=model, args=training_args, train_dataset=train_data)
()

Image classification task (Vision Transformer)

from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16
)
trainer = Trainer(model=model, args=training_args, train_dataset=train_data)
()

6. Frequently Asked Questions and Optimizations

Insufficient video memory

Using low-precision quantization (e.g.()) Reduce video memory usage.
Batch reasoning or enable Gradient Checkpointing.

Equipment adaptation

Specify GPU acceleration:("cuda")
Doka training: ByaccelerateThe library implements distributed training.

7. Learning resources and summary

Official Documentation：/docs/transformers
Model warehouse：/models
Core advantages: Simplify the NLP task development process and support rapid prototype to industrial-grade deployment.

Applicable scenarios：

Text Tasks: Customer service dialogue, news generation, legal document analysis.
Multimodal tasks: Medical image recognition, video content understanding.

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