Create train_script.py

train_script.py

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+from datasets import load_dataset
+from sentence_transformers import (
+    SparseEncoder,
+    SparseEncoderTrainer,
+    SparseEncoderTrainingArguments,
+    SparseEncoderModelCardData,
+)
+from sentence_transformers.sparse_encoder.losses import SpladeLoss, SparseMultipleNegativesRankingLoss
+from sentence_transformers.training_args import BatchSamplers
+from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
+from sentence_transformers.sparse_encoder.models import SpladePooling, MLMTransformer, IDF
+from sentence_transformers.models import Asym
+
+import logging
+
+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
+mlm_transformer = MLMTransformer("bert-base-uncased", tokenizer_args={"model_max_length": 512})
+splade_pooling = SpladePooling(pooling_strategy="max", word_embedding_dimension=mlm_transformer.get_sentence_embedding_dimension())
+
+asym = Asym({
+    "query": [IDF(tokenizer=mlm_transformer.tokenizer, frozen=False)],
+    "document": [mlm_transformer, splade_pooling],
+})
+
+model = SparseEncoder(
+    modules=[asym],
+    model_card_data=SparseEncoderModelCardData(
+        language="en",
+        license="apache-2.0",
+        model_name="Inference-free SPLADE bert-base-uncased trained on Natural-Questions tuples",
+    )
+)
+
+# 3. Load a dataset to finetune on
+full_dataset = load_dataset("sentence-transformers/natural-questions", split="train").select(range(100_000))
+# full_dataset = full_dataset.map(lambda sample: {"query": {"query": sample["query"]}, "corpus": {"corpus": sample["answer"]}}, remove_columns=["query", "answer"])
+dataset_dict = full_dataset.train_test_split(test_size=1_000, seed=12)
+train_dataset = dataset_dict["train"]
+eval_dataset = dataset_dict["test"]
+print(train_dataset)
+print(train_dataset[0])
+
+# 4. Define a loss function
+loss = SpladeLoss(
+    model=model,
+    loss=SparseMultipleNegativesRankingLoss(model=model),
+    lambda_query=0,
+    lambda_corpus=3e-3,
+)
+
+# 5. (Optional) Specify training arguments
+run_name = "inference-free-splade-bert-base-uncased-nq-3e-3-lambda-corpus-1e-3-idf-lr-2e-5-lr"
+args = SparseEncoderTrainingArguments(
+    # 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,
+    learning_rate_mapping={"IDF\.weight": 1e-3},  # Set a higher learning rate for the IDF module
+    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,  # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
+    router_mapping=["query", "document"],
+    # Optional tracking/debugging parameters:
+    eval_strategy="steps",
+    eval_steps=400,
+    save_strategy="steps",
+    save_steps=400,
+    save_total_limit=2,
+    logging_steps=200,
+    run_name=run_name,  # Will be used in W&B if `wandb` is installed
+)
+
+# 6. (Optional) Create an evaluator & evaluate the base model
+dev_evaluator = SparseNanoBEIREvaluator(dataset_names=["msmarco", "nfcorpus", "nq"], batch_size=16)
+
+# 7. Create a trainer & train
+trainer = SparseEncoderTrainer(
+    model=model,
+    args=args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    loss=loss,
+    evaluator=dev_evaluator,
+)
+trainer.train()
+
+# 8. Evaluate the model performance again after training
+dev_evaluator(model)
+
+# 9. Save the trained model
+model.save_pretrained(f"models/{run_name}/final")
+
+# 10. (Optional) Push it to the Hugging Face Hub
+model.push_to_hub(run_name)