Create train_script.py

train_script.py

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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)