"""
This examples loads a pre-trained model and evaluates it on the STSbenchmark dataset

Usage:
python evaluation_stsbenchmark.py
OR
python evaluation_stsbenchmark.py model_name
"""

import logging
import os
import sys

import torch

from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.similarity_functions import SimilarityFunction

script_folder_path = os.path.dirname(os.path.realpath(__file__))

# Limit torch to 4 threads
torch.set_num_threads(4)

# 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)

model_name = sys.argv[1] if len(sys.argv) > 1 else "stsb-distilroberta-base-v2"

# Load a named sentence model (based on BERT). This will download the model from our server.
# Alternatively, you can also pass a filepath to SentenceTransformer()
model = SentenceTransformer(model_name)

stsb_eval_dataset = load_dataset("sentence-transformers/stsb", split="validation")
dev_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=stsb_eval_dataset["sentence1"],
    sentences2=stsb_eval_dataset["sentence2"],
    scores=stsb_eval_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-dev",
)
model.evaluate(dev_evaluator)

test_dataset = load_dataset("sentence-transformers/stsb", split="test")
test_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=test_dataset["sentence1"],
    sentences2=test_dataset["sentence2"],
    scores=test_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-test",
)
model.evaluate(test_evaluator)

"""
This examples loads a pre-trained model and evaluates it on the STSbenchmark dataset

Usage:
python evaluation_stsbenchmark.py
OR
python evaluation_stsbenchmark.py model_name
"""

from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from datasets import load_dataset
import logging
import sys
import torch
import os

from sentence_transformers.similarity_functions import SimilarityFunction

script_folder_path = os.path.dirname(os.path.realpath(__file__))

# Limit torch to 4 threads
torch.set_num_threads(4)

# 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)

model_name = sys.argv[1] if len(sys.argv) > 1 else "stsb-distilroberta-base-v2"

# Load a named sentence model (based on BERT). This will download the model from our server.
# Alternatively, you can also pass a filepath to SentenceTransformer()
model = SentenceTransformer(model_name)

stsb_eval_dataset = load_dataset("sentence-transformers/stsb", split="validation")
dev_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=stsb_eval_dataset["sentence1"],
    sentences2=stsb_eval_dataset["sentence2"],
    scores=stsb_eval_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-dev",
)
model.evaluate(dev_evaluator)

test_dataset = load_dataset("sentence-transformers/stsb", split="test")
test_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=test_dataset["sentence1"],
    sentences2=test_dataset["sentence2"],
    scores=test_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-test",
)
model.evaluate(test_evaluator)
