Transformers

Overview

The Transformers library provides state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks. Apply this skill for quick inference through pipelines, comprehensive training via the Trainer API, and flexible text generation with various decoding strategies.

Core Capabilities

1. Quick Inference with Pipelines

For rapid inference without complex setup, use the pipeline() API. Pipelines abstract away tokenization, model invocation, and post-processing.

from transformers import pipeline

Text classification

classifier = pipeline("text-classification") result = classifier("This product is amazing!")

Named entity recognition

ner = pipeline("token-classification") entities = ner("Sarah works at Microsoft in Seattle")

Question answering

qa = pipeline("question-answering") answer = qa(question="What is the capital?", context="Paris is the capital of France.")

Text generation

generator = pipeline("text-generation", model="gpt2") text = generator("Once upon a time", max_length=50)

Image classification

image_classifier = pipeline("image-classification") predictions = image_classifier("image.jpg")

When to use pipelines:

• Quick prototyping and testing

• Simple inference tasks without custom logic

• Demonstrations and examples

• Production inference for standard tasks

Available pipeline tasks:

• NLP: text-classification, token-classification, question-answering, summarization, translation, text-generation, fill-mask, zero-shot-classification

• Vision: image-classification, object-detection, image-segmentation, depth-estimation, zero-shot-image-classification

• Audio: automatic-speech-recognition, audio-classification, text-to-audio

• Multimodal: image-to-text, visual-question-answering, image-text-to-text

For comprehensive pipeline documentation, see references/pipelines.md .

2. Model Training and Fine-Tuning

Use the Trainer API for comprehensive model training with support for distributed training, mixed precision, and advanced optimization.

Basic training workflow:

from transformers import ( AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer ) from datasets import load_dataset

1. Load and tokenize data

dataset = load_dataset("imdb") tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

2. Load model

model = AutoModelForSequenceClassification.from_pretrained( "bert-base-uncased", num_labels=2 )

3. Configure training

training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=16, eval_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, )

4. Create trainer and train

trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], )

trainer.train()

Key training features:

• Mixed precision training (fp16/bf16)

• Distributed training (multi-GPU, multi-node)

• Gradient accumulation

• Learning rate scheduling with warmup

• Checkpoint management

• Hyperparameter search

• Push to Hugging Face Hub

For detailed training documentation, see references/training.md .

3. Text Generation

Generate text using various decoding strategies including greedy decoding, beam search, sampling, and more.

Generation strategies:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("gpt2") inputs = tokenizer("Once upon a time", return_tensors="pt")

Greedy decoding (deterministic)

outputs = model.generate(**inputs, max_new_tokens=50)

Beam search (explores multiple hypotheses)

outputs = model.generate( **inputs, max_new_tokens=50, num_beams=5, early_stopping=True )

Sampling (creative, diverse)

outputs = model.generate( **inputs, max_new_tokens=50, do_sample=True, temperature=0.7, top_p=0.9, top_k=50 )

Generation parameters:

• temperature : Controls randomness (0.1-2.0)

• top_k : Sample from top-k tokens

• top_p : Nucleus sampling threshold

• num_beams : Number of beams for beam search

• repetition_penalty : Discourage repetition

• no_repeat_ngram_size : Prevent repeating n-grams

For comprehensive generation documentation, see references/generation_strategies.md .

4. Task-Specific Patterns

Common task patterns with appropriate model classes:

Text Classification:

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained( "bert-base-uncased", num_labels=3, id2label={0: "negative", 1: "neutral", 2: "positive"} )

Named Entity Recognition (Token Classification):

from transformers import AutoModelForTokenClassification

model = AutoModelForTokenClassification.from_pretrained( "bert-base-uncased", num_labels=9 # Number of entity types )

Question Answering:

from transformers import AutoModelForQuestionAnswering

model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")

Summarization and Translation (Seq2Seq):

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

Image Classification:

from transformers import AutoModelForImageClassification

model = AutoModelForImageClassification.from_pretrained( "google/vit-base-patch16-224", num_labels=num_classes )

For detailed task-specific workflows including data preprocessing, training, and evaluation, see references/task_patterns.md .

Auto Classes

Use Auto classes for automatic architecture selection based on model checkpoints:

from transformers import ( AutoTokenizer, # Tokenization AutoModel, # Base model (hidden states) AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoModelForQuestionAnswering, AutoModelForCausalLM, # GPT-style AutoModelForMaskedLM, # BERT-style AutoModelForSeq2SeqLM, # T5, BART AutoProcessor, # For multimodal models AutoImageProcessor, # For vision models )

Load any model by name

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")

For comprehensive API documentation, see references/api_reference.md .

Model Loading and Optimization

Device placement:

model = AutoModel.from_pretrained("bert-base-uncased", device_map="auto")

Mixed precision:

model = AutoModel.from_pretrained( "model-name", torch_dtype=torch.float16 # or torch.bfloat16 )

Quantization:

from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16 )

model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-hf", quantization_config=quantization_config, device_map="auto" )

Common Workflows

Quick Inference Workflow

• Choose appropriate pipeline for task

• Load pipeline with optional model specification

• Pass inputs and get results

• For batch processing, pass list of inputs

See: scripts/quick_inference.py for comprehensive pipeline examples

Training Workflow

• Load and preprocess dataset using 🤗 Datasets

• Tokenize data with appropriate tokenizer

• Load pre-trained model for specific task

• Configure TrainingArguments

• Create Trainer with model, data, and compute_metrics

• Train with trainer.train()

• Evaluate with trainer.evaluate()

• Save model and optionally push to Hub

See: scripts/fine_tune_classifier.py for complete training example

Text Generation Workflow

• Load causal or seq2seq language model

• Load tokenizer and tokenize prompt

• Choose generation strategy (greedy, beam search, sampling)

• Configure generation parameters

• Generate with model.generate()

• Decode output tokens to text

See: scripts/generate_text.py for generation strategy examples

Best Practices

• Use Auto classes for flexibility across different model architectures

• Batch processing for efficiency - process multiple inputs at once

• Device management - use device_map="auto" for automatic placement

• Memory optimization - enable fp16/bf16 or quantization for large models

• Checkpoint management - save checkpoints regularly and load best model

• Pipeline for quick tasks - use pipelines for standard inference tasks

• Custom metrics - define compute_metrics for task-specific evaluation

• Gradient accumulation - use for large effective batch sizes on limited memory

• Learning rate warmup - typically 5-10% of total training steps

• Hub integration - push trained models to Hub for sharing and versioning

Resources

scripts/

Executable Python scripts demonstrating common Transformers workflows:

• quick_inference.py

• Pipeline examples for NLP, vision, audio, and multimodal tasks

• fine_tune_classifier.py

• Complete fine-tuning workflow with Trainer API

• generate_text.py

• Text generation with various decoding strategies

Run scripts directly to see examples in action:

python scripts/quick_inference.py python scripts/fine_tune_classifier.py python scripts/generate_text.py

references/

Comprehensive reference documentation loaded into context as needed:

• api_reference.md

• Core classes and APIs (Auto classes, Trainer, GenerationConfig, etc.)

• pipelines.md

• All available pipelines organized by modality with examples

• training.md

• Training patterns, TrainingArguments, distributed training, callbacks

• generation_strategies.md

• Text generation methods, decoding strategies, parameters

• task_patterns.md

• Complete workflows for common tasks (classification, NER, QA, summarization, etc.)

When working on specific tasks or features, load the relevant reference file for detailed guidance.

Additional Information

• Official Documentation: https://huggingface.co/docs/transformers/index

• Model Hub: https://huggingface.co/models (1M+ pre-trained models)

• Datasets Hub: https://huggingface.co/datasets

• Installation: pip install transformers datasets evaluate accelerate

• GPU Support: Requires PyTorch or TensorFlow with CUDA

• Framework Support: PyTorch (primary), TensorFlow, JAX/Flax