Create train_script.py

train_script.py

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+# See https://huggingface.co/collections/tomaarsen/training-with-prompts-672ce423c85b4d39aed52853 for some already trained models
+
+import logging
+import random
+
+import numpy
+import torch
+from datasets import Dataset, load_dataset
+
+from sentence_transformers import (
+    SentenceTransformer,
+    SentenceTransformerModelCardData,
+    SentenceTransformerTrainer,
+    SentenceTransformerTrainingArguments,
+)
+from sentence_transformers.evaluation import NanoBEIREvaluator
+from sentence_transformers.losses import CachedMultipleNegativesRankingLoss
+from sentence_transformers.training_args import BatchSamplers
+from sentence_transformers.models import Router, Transformer, Pooling
+
+logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
+random.seed(12)
+torch.manual_seed(12)
+numpy.random.seed(12)
+
+# Feel free to adjust these variables:
+# use_prompts = True
+# include_prompts_in_pooling = True
+
+# 1. Load a model to finetune with 2. (Optional) model card data
+model = SentenceTransformer(
+    modules=[
+        Transformer("microsoft/mpnet-base"),
+        Router.for_query_document(
+            query_modules=[Pooling(768, pooling_mode="cls")],
+            document_modules=[Pooling(768, pooling_mode="lasttoken")],
+        ),
+    ],
+    model_card_data=SentenceTransformerModelCardData(
+        language="en",
+        license="apache-2.0",
+        model_name="MPNet base trained on Natural Questions pairs",
+    ),
+)
+
+# 2. (Optional) Define prompts
+# if use_prompts:
+#     query_prompt = "query: "
+#     corpus_prompt = "document: "
+#     prompts = {
+#         "query": query_prompt,
+#         "answer": corpus_prompt,
+#     }
+
+# 3. Load a dataset to finetune on
+dataset = load_dataset("sentence-transformers/natural-questions", split="train")
+dataset_dict = dataset.train_test_split(test_size=1_000, seed=12)
+train_dataset: Dataset = dataset_dict["train"]
+eval_dataset: Dataset = dataset_dict["test"]
+
+# 4. Define a loss function
+loss = CachedMultipleNegativesRankingLoss(model, mini_batch_size=16)
+
+# 5. (Optional) Specify training arguments
+run_name = "mpnet-base-nq"
+# if use_prompts:
+#     run_name += "-prompts"
+# if not include_prompts_in_pooling:
+#     run_name += "-exclude-pooling-prompts"
+run_name += "cls-last-split-pooling"
+args = SentenceTransformerTrainingArguments(
+    # Required parameter:
+    output_dir=f"models/{run_name}",
+    # Optional training parameters:
+    num_train_epochs=1,
+    per_device_train_batch_size=256,
+    per_device_eval_batch_size=256,
+    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
+    router_mapping={
+        "query": "query",
+        "answer": "document",
+    },
+    # Optional tracking/debugging parameters:
+    eval_strategy="steps",
+    eval_steps=50,
+    save_strategy="steps",
+    save_steps=50,
+    save_total_limit=2,
+    logging_steps=5,
+    logging_first_step=True,
+    run_name=run_name,  # Will be used in W&B if `wandb` is installed
+    seed=12,
+    # prompts=prompts if use_prompts else None,
+)
+
+# 6. (Optional) Create an evaluator & evaluate the base model
+dev_evaluator = NanoBEIREvaluator(
+    # query_prompts=query_prompt if use_prompts else None,
+    # corpus_prompts=corpus_prompt if use_prompts else None,
+)
+dev_evaluator(model)
+
+# 7. Create a trainer & train
+trainer = SentenceTransformerTrainer(
+    model=model,
+    args=args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    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/{run_name}/final")
+
+# 9. (Optional) Push it to the Hugging Face Hub
+model.push_to_hub(run_name)