test.py

"""
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from Hugging Face.
It then fine-tunes this model for some epochs on the STS benchmark dataset.

Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like bert-base-uncased, see training_nli.py and training_stsbenchmark.py
"""

import logging
import sys
import traceback
from datetime import datetime

from datasets import load_dataset
from sentence_transformers import SentenceTransformer, losses
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.similarity_functions import SimilarityFunction
from sentence_transformers.trainer import SentenceTransformerTrainer
from sentence_transformers.training_args import SentenceTransformerTrainingArguments

# 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)

# You can specify any Sentence Transformer model here, for example all-mpnet-base-v2, all-MiniLM-L6-v2, mixedbread-ai/mxbai-embed-large-v1
model_name = sys.argv[1] if len(sys.argv) > 1 else "all-MiniLM-L6-v2"
train_batch_size = 16
num_epochs = 4
output_dir = (
    "output/training_stsbenchmark_" + model_name.replace("/", "-") + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)

# 1. Here we define our SentenceTransformer model.
model = SentenceTransformer(model_name)

# 2. Load the STSB dataset: https://huggingface.co/datasets/sentence-transformers/stsb
train_dataset = load_dataset("sentence-transformers/stsb", split="train")
eval_dataset = load_dataset("sentence-transformers/stsb", split="validation")
test_dataset = load_dataset("sentence-transformers/stsb", split="test")
logging.info(train_dataset)

# 3. Define our training loss
# CosineSimilarityLoss (https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) needs two text columns and one
# similarity score column (between 0 and 1)
train_loss = losses.CosineSimilarityLoss(model=model)
# train_loss = losses.CoSENTLoss(model=model)

# 4. Define an evaluator for use during training. This is useful to keep track of alongside the evaluation loss.
dev_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=eval_dataset["sentence1"],
    sentences2=eval_dataset["sentence2"],
    scores=eval_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-dev",
)

# 5. Define the training arguments
args = SentenceTransformerTrainingArguments(
    # Required parameter:
    output_dir=output_dir,
    # Optional training parameters:
    num_train_epochs=num_epochs,
    per_device_train_batch_size=train_batch_size,
    per_device_eval_batch_size=train_batch_size,
    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
    # Optional tracking/debugging parameters:
    eval_strategy="steps",
    eval_steps=100,
    save_strategy="steps",
    save_steps=100,
    save_total_limit=2,
    logging_steps=100,
    run_name="sts",  # Will be used in W&B if `wandb` is installed
)

# 6. Create the trainer & start training
trainer = SentenceTransformerTrainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss=train_loss,
    evaluator=dev_evaluator,
)
trainer.train()


# 7. Evaluate the model performance on the STS Benchmark test dataset
test_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=test_dataset["sentence1"],
    sentences2=test_dataset["sentence2"],
    scores=test_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-test",
)
test_evaluator(model)

# 8. Save the trained & evaluated model locally
final_output_dir = f"{output_dir}/final"
model.save(final_output_dir)