train_script.py

import logging
import traceback

from datasets import load_dataset
from sentence_transformers import (
    SentenceTransformer,
    SentenceTransformerModelCardData,
    SentenceTransformerTrainer,
    SentenceTransformerTrainingArguments,
)
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.losses import CachedMultipleNegativesRankingLoss
from sentence_transformers.training_args import BatchSamplers

# Set the log level to INFO to get more information
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(
    "google/embeddinggemma-300M",
    model_card_data=SentenceTransformerModelCardData(
        language="en",
        license="apache-2.0",
        model_name="EmbeddingGemma-300M trained on the Medical Instruction and RetrIeval Dataset (MIRIAD)",
    ),
)

# 3. Load a dataset to finetune on
train_dataset = load_dataset("tomaarsen/miriad-4.4M-split", split="train").select(range(100_000))
eval_dataset = load_dataset("tomaarsen/miriad-4.4M-split", split="eval").select(range(1_000))
test_dataset = load_dataset("tomaarsen/miriad-4.4M-split", split="test").select(range(1_000))

# 4. Define a loss function. CachedMultipleNegativesRankingLoss (CMNRL) is a special variant of MNRL (a.k.a. InfoNCE),
# which take question-answer pairs (or triplets, etc.) as input. It will take answers from other questions in the batch
# as wrong answers, reducing the distance between the question and the true answer while increasing the distance to the
# wrong answers, in the embedding space.
# The (C)MNRL losses benefit from larger `per_device_train_batch_size` in the Training Arguments, as they can leverage
# more in-batch negative samples. At the same time, the `mini_batch_size` does not affect training performance, but it
# does limit the memory usage. A good trick is setting a high `per_device_train_batch_size` while keeping
# `mini_batch_size` small.
loss = CachedMultipleNegativesRankingLoss(model, mini_batch_size=8)

# 5. (Optional) Specify training arguments
run_name = "embeddinggemma-300M-medical-100k"
args = SentenceTransformerTrainingArguments(
    # Required parameter:
    output_dir=f"models/{run_name}",
    # Optional training parameters:
    num_train_epochs=1,
    per_device_train_batch_size=128,
    per_device_eval_batch_size=128,
    learning_rate=2e-5,
    warmup_ratio=0.1,
    fp16=True,  # Set to False if you get an error that your GPU can't run on FP16
    bf16=False,  # Set to True if you have a GPU that supports BF16
    batch_sampler=BatchSamplers.NO_DUPLICATES,  # (Cached)MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
    prompts={  # Map training column names to model prompts
        "question": model.prompts["query"],
        "passage_text": model.prompts["document"],
    },
    # Optional tracking/debugging parameters:
    eval_strategy="steps",
    eval_steps=100,
    save_strategy="steps",
    save_steps=100,
    save_total_limit=2,
    logging_steps=20,
    run_name=run_name,  # Will be used in W&B if `wandb` is installed
)

# 6. (Optional) Create an evaluator using the evaluation queries and 31k answers & evaluate the base model
queries = dict(enumerate(eval_dataset["question"]))
corpus = dict(enumerate(eval_dataset["passage_text"] + train_dataset["passage_text"][:30_000]))
relevant_docs = {idx: [idx] for idx in queries}
dev_evaluator = InformationRetrievalEvaluator(
    queries=queries,
    corpus=corpus,
    relevant_docs=relevant_docs,
    name="miriad-eval-1kq-31kd",  # 1k questions, 31k passages
    show_progress_bar=True,
)
dev_evaluator(model)

# 7. Create a trainer & train
trainer = SentenceTransformerTrainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss=loss,
    evaluator=dev_evaluator,
)
trainer.train()

# (Optional) Evaluate the trained model on the evaluation set once more, this will also log the results
# and include them in the model card
dev_evaluator(model)

queries = dict(enumerate(test_dataset["question"]))
corpus = dict(enumerate(test_dataset["passage_text"] + train_dataset["passage_text"][:30_000]))
relevant_docs = {idx: [idx] for idx in queries}
test_evaluator = InformationRetrievalEvaluator(
    queries=queries,
    corpus=corpus,
    relevant_docs=relevant_docs,
    name="miriad-test-1kq-31kd",  # 1k questions, 31k passages
    show_progress_bar=True,
)
test_evaluator(model)

# 8. Save the trained model
final_output_dir = f"models/{run_name}/final"
model.save_pretrained(final_output_dir)

# 9. (Optional) Push it to the Hugging Face Hub
# It is recommended to run `huggingface-cli login` to log into your Hugging Face account first
try:
    model.push_to_hub(run_name)
except Exception:
    logging.error(
        f"Error uploading model to the Hugging Face Hub:\n{traceback.format_exc()}To upload it manually, you can run "
        f"`huggingface-cli login`, followed by loading the model using `model = SentenceTransformer({final_output_dir!r})` "
        f"and saving it using `model.push_to_hub('{run_name}')`."
    )