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@simonmesmith
Last active Sep 9, 2022
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Fine-tuning T5 with Hugging Face

Fine-tuning T5 with Hugging Face

Recently, I had to fine-tune a T5 model using Hugging Face's libraries.

Unfortunately, there was a lot of outdated information and many conflicting examples online.

If you just want to get going quickly, you can:

  1. Copy this code
  2. Swap in your own dataframe (ensure it has "source_text" and "target_text" columns, or modify parts of the code that depend on them accordingly)
  3. Update the "output_directory" to your desired destination

And that should do it. Feedback welcome!

# 1. Install dependencies
# ------------------------------------------------------------------------------
# pip install datasets pandas transformers
# 2. Import libraries and modules
# ------------------------------------------------------------------------------
from datasets import Dataset, DatasetDict
import pandas as pd
from transformers import T5Tokenizer, T5ForConditionalGeneration, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
# 3. Set model, tokenizer, and data_collator variables
# ------------------------------------------------------------------------------
# See options besides t5-base here:
# https://huggingface.co/docs/transformers/model_doc/t5
# ------------------------------------------------------------------------------
tokenizer = T5Tokenizer.from_pretrained("t5-base")
model = T5ForConditionalGeneration.from_pretrained("t5-base")
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
# 4. Get data and divide into train, eval, and test sets
# ------------------------------------------------------------------------------
# Replace the dataframe with your own data. Ensure you use "source_text" and
# "target_text" as column names or you'll need to change the code below.
# Also note that we we use 80% of the data for training, 10% for evaluation,
# and 10% for testing. You can modify this as well.
# ------------------------------------------------------------------------------
df = pd.DataFrame({"source_text": [], "target_text": []})
train_df = df.sample(frac = 0.8)
eval_df = df.drop(train_df.index).sample(frac = 0.5)
test_df = df.drop(train_df.index).drop(eval_df.index)
# 5. Create a dataset dict from the dataframes
# ------------------------------------------------------------------------------
dataset = DatasetDict({
"train": Dataset.from_pandas(train_df),
"eval": Dataset.from_pandas(eval_df),
"test": Dataset.from_pandas(test_df),
})
# 6. Tokenize the dataset
# ------------------------------------------------------------------------------
# You can change the max_length to whatever makes sense for your data.
# ------------------------------------------------------------------------------
def tokenize(source_texts, target_texts):
model_inputs = tokenizer(source_texts, max_length=512, truncation=True)
with tokenizer.as_target_tokenizer():
labels = tokenizer(target_texts, max_length=512, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
tokenized_dataset = dataset.map(tokenize, input_columns=["source_text", "target_text"], remove_columns=["source_text", "target_text"])
# 7. Set training arguments
# ------------------------------------------------------------------------------
# Change "output_directory" to your desired output directory. You can also change the
# batch_size, learning_rate, num_train_epochs and other parameters here. See the
# documentation for more details:
# https://huggingface.co/docs/transformers/v4.21.3/en/main_classes/trainer#transformers.TrainingArguments
# ------------------------------------------------------------------------------
training_arguments = Seq2SeqTrainingArguments(
"output_directory",
learning_rate=0.0001,
weight_decay=0.01,
fp16=True,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=2,
num_train_epochs=20,
evaluation_strategy="epoch",
report_to="all"
)
# 8. Create a trainer
# ------------------------------------------------------------------------------
trainer = Seq2SeqTrainer(
model,
training_arguments,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["eval"],
data_collator=data_collator,
tokenizer=tokenizer
)
# 9. Train the model
# ------------------------------------------------------------------------------
trainer.train()
# 10. Save the tokenizer and model
# ------------------------------------------------------------------------------
# Change "output_directory" to your desired output directory. Note that you can
# also run evaluation before or after saving the tokenizer and model.
# ------------------------------------------------------------------------------
tokenizer.save_pretrained("output_directory")
model.save_pretrained("output_directory")
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