Create train_script.py

train_script.py

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+import logging
+import traceback
+
+import torch
+from datasets import load_dataset
+
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.cross_encoder import CrossEncoder, CrossEncoderModelCardData
+from sentence_transformers.cross_encoder.evaluation import (
+    CrossEncoderNanoBEIREvaluator,
+    CrossEncoderRerankingEvaluator,
+)
+from sentence_transformers.cross_encoder.losses.BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
+from sentence_transformers.cross_encoder.trainer import CrossEncoderTrainer
+from sentence_transformers.cross_encoder.training_args import CrossEncoderTrainingArguments
+from sentence_transformers.evaluation.SequentialEvaluator import SequentialEvaluator
+from sentence_transformers.util import mine_hard_negatives
+
+# 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)
+
+
+def main():
+    model_name = "prajjwal1/bert-tiny"
+
+    train_batch_size = 2048
+    num_epochs = 1
+    num_hard_negatives = 5  # How many hard negatives should be mined for each question-answer pair
+
+    # 1a. Load a model to finetune with 1b. (Optional) model card data
+    model = CrossEncoder(
+        model_name,
+        model_card_data=CrossEncoderModelCardData(
+            language="en",
+            license="apache-2.0",
+            model_name="BERT-tiny trained on GooAQ",
+        ),
+    )
+    print("Model max length:", model.max_length)
+    print("Model num labels:", model.num_labels)
+
+    # 2a. Load the GooAQ dataset: https://huggingface.co/datasets/sentence-transformers/gooaq
+    logging.info("Read the gooaq training dataset")
+    full_dataset = load_dataset("sentence-transformers/gooaq", split="train").select(range(100_000))
+    dataset_dict = full_dataset.train_test_split(test_size=1_000, seed=12)
+    train_dataset = dataset_dict["train"]
+    eval_dataset = dataset_dict["test"]
+    logging.info(train_dataset)
+    logging.info(eval_dataset)
+
+    # 2b. Modify our training dataset to include hard negatives using a very efficient embedding model
+    embedding_model = SentenceTransformer("sentence-transformers/static-retrieval-mrl-en-v1", device="cpu")
+    hard_train_dataset = mine_hard_negatives(
+        train_dataset,
+        embedding_model,
+        num_negatives=num_hard_negatives,  # How many negatives per question-answer pair
+        margin=0,  # Similarity between query and negative samples should be x lower than query-positive similarity
+        range_min=0,  # Skip the x most similar samples
+        range_max=100,  # Consider only the x most similar samples
+        sampling_strategy="top",  # Randomly sample negatives from the range
+        batch_size=4096,  # Use a batch size of 4096 for the embedding model
+        output_format="labeled-pair",  # The output format is (query, passage, label), as required by BinaryCrossEntropyLoss
+        use_faiss=True,
+    )
+    logging.info(hard_train_dataset)
+
+    # 2c. (Optionally) Save the hard training dataset to disk
+    # hard_train_dataset.save_to_disk("gooaq-hard-train")
+    # Load again with:
+    # hard_train_dataset = load_from_disk("gooaq-hard-train")
+
+    # 3. Define our training loss.
+    # pos_weight is recommended to be set as the ratio between positives to negatives, a.k.a. `num_hard_negatives`
+    loss = BinaryCrossEntropyLoss(model=model, pos_weight=torch.tensor(num_hard_negatives))
+
+    # 4a. Define evaluators. We use the CrossEncoderNanoBEIREvaluator, which is a light-weight evaluator for English reranking
+    nano_beir_evaluator = CrossEncoderNanoBEIREvaluator(
+        dataset_names=["msmarco", "nfcorpus", "nq"],
+        batch_size=train_batch_size,
+    )
+
+    # 4b. Define a reranking evaluator by mining hard negatives given query-answer pairs
+    # We include the positive answer in the list of negatives, so the evaluator can use the performance of the
+    # embedding model as a baseline.
+    hard_eval_dataset = mine_hard_negatives(
+        eval_dataset,
+        embedding_model,
+        corpus=full_dataset["answer"],  # Use the full dataset as the corpus
+        num_negatives=30,  # How many documents to rerank
+        batch_size=4096,
+        disqualify_positives=False,
+        output_format="n-tuple",
+        use_faiss=True,
+    )
+    logging.info(hard_eval_dataset)
+    reranking_evaluator = CrossEncoderRerankingEvaluator(
+        samples=[
+            {
+                "query": sample["question"],
+                "positive": [sample["answer"]],
+                "documents": [sample[column_name] for column_name in hard_eval_dataset.column_names[2:]],
+            }
+            for sample in hard_eval_dataset
+        ],
+        batch_size=train_batch_size,
+        name="gooaq-dev",
+    )
+
+    # 4c. Combine the evaluators & run the base model on them
+    evaluator = SequentialEvaluator([reranking_evaluator, nano_beir_evaluator])
+    evaluator(model)
+
+    # 5. Define the training arguments
+    short_model_name = model_name if "/" not in model_name else model_name.split("/")[-1]
+    run_name = f"reranker-{short_model_name}-gooaq-bce"
+    args = CrossEncoderTrainingArguments(
+        # Required parameter:
+        output_dir=f"models/{run_name}",
+        # Optional training parameters:
+        num_train_epochs=num_epochs,
+        per_device_train_batch_size=train_batch_size,
+        per_device_eval_batch_size=train_batch_size,
+        learning_rate=5e-4,
+        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
+        load_best_model_at_end=True,
+        metric_for_best_model="eval_NanoBEIR_R100_mean_ndcg@10",
+        # Optional tracking/debugging parameters:
+        eval_strategy="steps",
+        eval_steps=20,
+        save_strategy="steps",
+        save_steps=20,
+        save_total_limit=2,
+        logging_steps=20,
+        logging_first_step=True,
+        run_name=run_name,  # Will be used in W&B if `wandb` is installed
+        seed=12,
+    )
+
+    # 6. Create the trainer & start training
+    trainer = CrossEncoderTrainer(
+        model=model,
+        args=args,
+        train_dataset=hard_train_dataset,
+        loss=loss,
+        evaluator=evaluator,
+    )
+    trainer.train()
+
+    # 7. Evaluate the final model, useful to include these in the model card
+    evaluator(model)
+
+    # 8. Save the final model
+    final_output_dir = f"models/{run_name}/final"
+    model.save_pretrained(final_output_dir)
+
+    # 9. (Optional) save the model 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(f"cross-encoder-testing/{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 = CrossEncoder({final_output_dir!r})` "
+            f"and saving it using `model.push_to_hub('{run_name}')`."
+        )
+
+
+if __name__ == "__main__":
+    main()