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import logging
from datasets import load_dataset, Dataset
from sentence_transformers import (
SentenceTransformer,
SentenceTransformerTrainer,
SentenceTransformerTrainingArguments,
SentenceTransformerModelCardData,
)
from sentence_transformers.losses import MultipleNegativesRankingLoss
from sentence_transformers.training_args import BatchSamplers
from sentence_transformers.evaluation import InformationRetrievalEvaluator
logging.basicConfig(
format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO
)
# 1. Load a model to finetune with 2. (Optional) model card data
model = SentenceTransformer(
"microsoft/mpnet-base",
model_card_data=SentenceTransformerModelCardData(
language="en",
license="apache-2.0",
model_name="MPNet base trained on Natural Questions pairs",
),
)
model_name = "mpnet-base-natural-questions-mnrl"
# 3. Load a dataset to finetune on
dataset = load_dataset("sentence-transformers/natural-questions", split="train")
dataset = dataset.add_column("id", range(len(dataset)))
train_dataset: Dataset = dataset.select(range(90_000))
eval_dataset: Dataset = dataset.select(range(90_000, len(dataset)))
# 4. Define a loss function
loss = MultipleNegativesRankingLoss(model)
# 5. (Optional) Specify training arguments
args = SentenceTransformerTrainingArguments(
# Required parameter:
output_dir=f"models/{model_name}",
# Optional training parameters:
num_train_epochs=1,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
learning_rate=2e-5,
warmup_ratio=0.1,
fp16=False, # Set to False if you get an error that your GPU can't run on FP16
bf16=True, # Set to True if you have a GPU that supports BF16
batch_sampler=BatchSamplers.NO_DUPLICATES, # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
# Optional tracking/debugging parameters:
eval_strategy="steps",
eval_steps=200,
save_strategy="steps",
save_steps=200,
save_total_limit=2,
logging_steps=200,
logging_first_step=True,
run_name=model_name, # Will be used in W&B if `wandb` is installed
)
# 6. (Optional) Create an evaluator & evaluate the base model
# The full corpus, but only the evaluation queries
queries = dict(zip(eval_dataset["id"], eval_dataset["query"]))
corpus = {cid: dataset[cid]["answer"] for cid in range(10_000)} | {cid: dataset[cid]["answer"] for cid in eval_dataset["id"]}
relevant_docs = {qid: {qid} for qid in eval_dataset["id"]}
dev_evaluator = InformationRetrievalEvaluator(
corpus=corpus,
queries=queries,
relevant_docs=relevant_docs,
show_progress_bar=True,
name="natural-questions-dev",
batch_size=8,
)
dev_evaluator(model)
# 7. Create a trainer & train
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=train_dataset.remove_columns("id"),
eval_dataset=eval_dataset.remove_columns("id"),
loss=loss,
evaluator=dev_evaluator,
)
trainer.train()
# (Optional) Evaluate the trained model on the evaluator after training
dev_evaluator(model)
# 8. Save the trained model
model.save_pretrained(f"models/{model_name}/final")
# 9. (Optional) Push it to the Hugging Face Hub
model.push_to_hub(f"{model_name}")
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