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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)
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