🤗 Transformers
To run the 🤗 Transformers examples make sure you have installed the following libraries:
pip install datasets transformers torch evaluate nltk rouge_score
Trainer
The metrics in evaluate can be easily integrated with the Trainer. The Trainer accepts a compute_metrics keyword argument that passes a function to compute metrics. One can specify the evaluation interval with evaluation_strategy in the TrainerArguments, and based on that, the model is evaluated accordingly, and the predictions and labels passed to compute_metrics.
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
import numpy as np
import evaluate
# Prepare and tokenize dataset
dataset = load_dataset("yelp_review_full")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(200))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(200))
# Setup evaluation
metric = evaluate.load("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
# Load pretrained model and evaluate model after each epoch
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
trainer.train()Seq2SeqTrainer
We can use the Seq2SeqTrainer for sequence-to-sequence tasks such as translation or summarization. For such generative tasks usually metrics such as ROUGE or BLEU are evaluated. However, these metrics require that we generate some text with the model rather than a single forward pass as with e.g. classification. The Seq2SeqTrainer allows for the use of the generate method when setting predict_with_generate=True which will generate text for each sample in the evaluation set. That means we evaluate generated text within the compute_metric function. We just need to decode the predictions and labels first.
import nltk
from datasets import load_dataset
import evaluate
import numpy as np
from transformers import AutoTokenizer, DataCollatorForSeq2Seq
from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
# Prepare and tokenize dataset
billsum = load_dataset("billsum", split="ca_test").shuffle(seed=42).select(range(200))
billsum = billsum.train_test_split(test_size=0.2)
tokenizer = AutoTokenizer.from_pretrained("t5-small")
prefix = "summarize: "
def preprocess_function(examples):
inputs = [prefix + doc for doc in examples["text"]]
model_inputs = tokenizer(inputs, max_length=1024, truncation=True)
labels = tokenizer(text_target=examples["summary"], max_length=128, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
tokenized_billsum = billsum.map(preprocess_function, batched=True)
# Setup evaluation
nltk.download("punkt_tab", quiet=True)
metric = evaluate.load("rouge")
def compute_metrics(eval_preds):
preds, labels = eval_preds
# decode preds and labels
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# rougeLSum expects newline after each sentence
decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
return result
# Load pretrained model and evaluate model after each epoch
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
training_args = Seq2SeqTrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=4,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=2,
fp16=True,
predict_with_generate=True
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_billsum["train"],
eval_dataset=tokenized_billsum["test"],
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics
)
trainer.train()You can use any evaluate metric with the Trainer and Seq2SeqTrainer as long as they are compatible with the task and predictions. In case you don’t want to train a model but just evaluate an existing model you can replace trainer.train() with trainer.evaluate() in the above scripts.