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import os
import zipfile
import findfile
import requests
import torch
from omnigenbench import (
ClassificationMetric,
OmniTokenizer,
OmniModelForSequenceClassification,
OmniDatasetForSequenceClassification,
Trainer,
)
def download_te_dataset(local_dir):
if not findfile.find_cwd_dir(local_dir, disable_alert=True):
os.makedirs(local_dir, exist_ok=True)
url_to_download = "https://huggingface.co/datasets/yangheng/translation_efficiency_prediction/resolve/main/translation_efficiency_prediction.zip"
zip_path = os.path.join(local_dir, "te_rice_dataset.zip")
if not os.path.exists(zip_path):
print(f"Downloading te_rice_dataset.zip from {url_to_download}...")
response = requests.get(url_to_download, stream=True)
response.raise_for_status()
with open(zip_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
print(f"Downloaded {zip_path}")
# Unzip the dataset if the zip file exists
ZIP_DATASET = findfile.find_cwd_file("te_rice_dataset.zip")
if ZIP_DATASET:
with zipfile.ZipFile(ZIP_DATASET, 'r') as zip_ref:
zip_ref.extractall(local_dir)
print(f"Extracted te_rice_dataset.zip into {local_dir}")
os.remove(ZIP_DATASET)
else:
print("te_rice_dataset.zip not found. Skipping extraction.")
class TEClassificationDataset(OmniDatasetForSequenceClassification):
def __init__(self, data_source, tokenizer, max_length, **kwargs):
super().__init__(data_source, tokenizer, max_length, **kwargs)
def prepare_input(self, instance, **kwargs):
sequence, labels = instance["sequence"], instance["label"]
tokenized_inputs = self.tokenizer(
sequence,
padding=True,
truncation=True,
max_length=self.max_length,
return_tensors="pt",
**kwargs
)
tokenized_inputs["labels"] = torch.tensor(int(labels), dtype=torch.long)
# Remove the batch dimension that gets added by return_tensors="pt"
for col in tokenized_inputs:
tokenized_inputs[col] = tokenized_inputs[col].squeeze(0)
if labels is not None:
label_id = self.label2id.get(str(labels), -100)
tokenized_inputs["labels"] = torch.tensor(label_id, dtype=torch.long)
return tokenized_inputs
def run_finetuning(
model_name,
train_file,
valid_file,
test_file,
label2id,
epochs,
learning_rate,
weight_decay,
batch_size,
max_length,
seed,
):
"""
Runs the full TE classification analysis pipeline.
"""
# 1. Model & Tokenizer Initialization
tokenizer = OmniTokenizer.from_pretrained(model_name, trust_remote_code=True)
ssp_model = OmniModelForSequenceClassification(
model_name,
tokenizer=tokenizer,
label2id=label2id,
trust_remote_code=True,
)
print(f"Model '{model_name}' and tokenizer loaded successfully.")
# 2. Data Loading & Preparation
train_set = TEClassificationDataset(data_source=train_file, tokenizer=tokenizer, label2id=label2id, max_length=max_length)
valid_set = TEClassificationDataset(data_source=valid_file, tokenizer=tokenizer, label2id=label2id, max_length=max_length)
test_set = TEClassificationDataset(data_source=test_file, tokenizer=tokenizer, label2id=label2id, max_length=max_length)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size)
print("Datasets and DataLoaders created.")
# 3. Training & Evaluation Setup
compute_metrics = [ClassificationMetric(ignore_y=-100, average="macro").f1_score]
optimizer = torch.optim.AdamW(ssp_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
trainer = Trainer(
model=ssp_model,
train_loader=train_loader,
eval_loader=valid_loader,
test_loader=test_loader,
batch_size=batch_size,
epochs=epochs,
optimizer=optimizer,
compute_metrics=compute_metrics,
seeds=seed,
)
# 4. Run Training
metrics = trainer.train()
trainer.save_model("finetuned_te_model")
print("Training completed!")
return metrics
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