Create train_script.py

train_script.py

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+import random
+import logging
+from datasets import load_dataset, Dataset
+from sentence_transformers import (
+    SentenceTransformer,
+    SentenceTransformerTrainer,
+    SentenceTransformerTrainingArguments,
+    SentenceTransformerModelCardData,
+)
+from typing import Any, Dict, Iterable
+import torch
+from torch import nn
+from sentence_transformers.losses import MultipleNegativesRankingLoss, MultipleNegativesSymmetricRankingLoss
+from sentence_transformers import util
+from sentence_transformers.training_args import BatchSamplers
+from sentence_transformers.evaluation import InformationRetrievalEvaluator
+
+
+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(
+    "microsoft/mpnet-base",
+    model_card_data=SentenceTransformerModelCardData(
+        language="en",
+        license="apache-2.0",
+        model_name="MPNet base trained on Natural Questions pairs",
+    ),
+)
+model_name = "mpnet-base-natural-questions-mnsrl"
+
+# 3. Load a dataset to finetune on
+dataset = load_dataset("sentence-transformers/natural-questions", split="train")
+dataset = dataset.add_column("id", range(len(dataset)))
+train_dataset: Dataset = dataset.select(range(90_000))
+eval_dataset: Dataset = dataset.select(range(90_000, len(dataset)))
+
+# 4. Define a loss function
+class ImprovedContrastiveLoss(nn.Module):
+    def __init__(self, model: SentenceTransformer, temperature: float = 0.01):
+        super(ImprovedContrastiveLoss, self).__init__()
+        self.model = model
+        self.temperature = temperature
+
+    def forward(self, sentence_features: Iterable[Dict[str, torch.Tensor]], labels: torch.Tensor = None) -> torch.Tensor:
+        # Get the embeddings for each sentence in the batch
+        embeddings = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]
+        query_embeddings = embeddings[0]
+        doc_embeddings = embeddings[1]
+
+        # Compute similarity scores
+        similarity_q_d = util.cos_sim(query_embeddings, doc_embeddings)
+        similarity_q_q = util.cos_sim(query_embeddings, query_embeddings)
+        similarity_d_d = util.cos_sim(doc_embeddings, doc_embeddings)
+
+        # Move the similarity range from [-1, 1] to [-2, 0] to avoid overflow
+        similarity_q_d = similarity_q_d - 1
+        similarity_q_q = similarity_q_q - 1
+        similarity_d_d = similarity_d_d - 1
+
+        # Compute the partition function
+        exp_sim_q_d = torch.exp(similarity_q_d / self.temperature)
+        exp_sim_q_q = torch.exp(similarity_q_q / self.temperature)
+        exp_sim_d_d = torch.exp(similarity_d_d / self.temperature)
+        
+        # Ensure the diagonal is not considered in negative samples
+        mask = torch.eye(similarity_q_d.size(0), device=similarity_q_d.device).bool()
+        exp_sim_q_q = exp_sim_q_q.masked_fill(mask, 0)
+        exp_sim_d_d = exp_sim_d_d.masked_fill(mask, 0)
+
+        partition_function = exp_sim_q_d.sum(dim=1) + exp_sim_q_d.sum(dim=0) + exp_sim_q_q.sum(dim=1) + exp_sim_d_d.sum(dim=0)
+
+        # Compute the loss
+        loss = -torch.log(exp_sim_q_d.diag() / partition_function).mean()
+        return loss
+
+    def get_config_dict(self) -> Dict[str, Any]:
+        return {"temperature": self.temperature}
+
+# loss = ImprovedContrastiveLoss(model)
+loss = MultipleNegativesSymmetricRankingLoss(model)
+
+
+# 5. (Optional) Specify training arguments
+args = SentenceTransformerTrainingArguments(
+    # Required parameter:
+    output_dir=f"models/{model_name}",
+    # Optional training parameters:
+    num_train_epochs=1,
+    per_device_train_batch_size=32,
+    per_device_eval_batch_size=32,
+    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=100,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=2,
+    logging_steps=100,
+    logging_first_step=True,
+    run_name=model_name,  # Will be used in W&B if `wandb` is installed
+)
+
+# 6. (Optional) Create an evaluator & evaluate the base model
+# The full corpus, but only the evaluation queries
+queries = dict(zip(eval_dataset["id"], eval_dataset["query"]))
+corpus = {cid: dataset[cid]["answer"] for cid in range(20_000)} | {cid: dataset[cid]["answer"] for cid in eval_dataset["id"]}
+relevant_docs = {qid: {qid} for qid in eval_dataset["id"]}
+dev_evaluator = InformationRetrievalEvaluator(
+    corpus=corpus,
+    queries=queries,
+    relevant_docs=relevant_docs,
+    show_progress_bar=True,
+    name="natural-questions-dev",
+)
+dev_evaluator(model)
+
+# 7. Create a trainer & train
+trainer = SentenceTransformerTrainer(
+    model=model,
+    args=args,
+    train_dataset=train_dataset.remove_columns("id"),
+    eval_dataset=eval_dataset.remove_columns("id"),
+    loss=loss,
+    evaluator=dev_evaluator,
+)
+trainer.train()
+
+# (Optional) Evaluate the trained model on the evaluator after training
+dev_evaluator(model)
+
+# 8. Save the trained model
+model.save_pretrained(f"models/{model_name}/final")
+
+# 9. (Optional) Push it to the Hugging Face Hub
+model.push_to_hub(f"{model_name}")