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import logging
from datasets import load_dataset
from sentence_transformers import (
SentenceTransformer,
SentenceTransformerModelCardData,
SentenceTransformerTrainer,
SentenceTransformerTrainingArguments,
)
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.losses import MultipleNegativesRankingLoss
from sentence_transformers.training_args import BatchSamplers
import logging
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
logging.getLogger("httpx").setLevel(logging.WARNING)
# 1. Load a model to finetune with 2. (Optional) model card data
model = SentenceTransformer(
"google/siglip-base-patch16-512",
model_card_data=SentenceTransformerModelCardData(
language="en",
license="apache-2.0",
model_name="Google SigLIP (512x512 resolution) model trained on COCO Captions",
),
tokenizer_kwargs={"do_convert_rgb": True}
)
# 3. Load a dataset to finetune on
dataset = load_dataset("jxie/coco_captions")
train_dataset = dataset["train"].select(range(10_000))
eval_dataset = dataset["validation"].select(range(1_000))
test_dataset = dataset["test"].select(range(1_000))
# 4. Define a loss function
loss = MultipleNegativesRankingLoss(model)
# 5. (Optional) Specify training arguments
run_name = "google-siglip-base-coco"
args = SentenceTransformerTrainingArguments(
# Required parameter:
output_dir=f"models/{run_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=0.1,
save_strategy="steps",
save_steps=0.1,
save_total_limit=2,
logging_steps=0.01,
run_name=run_name, # Will be used in W&B if `wandb` is installed
)
# 6. (Optional) Create an evaluator & evaluate the base model
eval_queries = {qid: sample["caption"] for qid, sample in enumerate(eval_dataset)}
eval_corpus = {sample["cocoid"]: sample["image"] for sample in eval_dataset}
eval_relevant_docs = {qid: [sample["cocoid"]] for qid, sample in enumerate(eval_dataset)}
eval_evaluator = InformationRetrievalEvaluator(
queries=eval_queries,
corpus=eval_corpus,
relevant_docs=eval_relevant_docs,
name="coco-eval",
)
eval_evaluator(model)
# 7. Create a trainer & train
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=train_dataset.select_columns(["image", "caption"]),
eval_dataset=eval_dataset.select_columns(["image", "caption"]),
loss=loss,
evaluator=eval_evaluator,
)
trainer.train()
# (Optional) Evaluate the trained model on the test set
test_queries = {qid: sample["caption"] for qid, sample in enumerate(test_dataset)}
test_corpus = {sample["cocoid"]: sample["image"] for sample in test_dataset}
test_relevant_docs = {qid: [sample["cocoid"]] for qid, sample in enumerate(test_dataset)}
test_evaluator = InformationRetrievalEvaluator(
queries=test_queries,
corpus=test_corpus,
relevant_docs=test_relevant_docs,
name="coco-test",
)
test_evaluator(model)
# 8. Save the trained model
model.save_pretrained(f"models/{run_name}/final")
# 9. (Optional) Push it to the Hugging Face Hub
model.push_to_hub(run_name, private=True)
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