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import torch
import argparse
import json
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
TrainingArguments,
Trainer,
EarlyStoppingCallback,
)
import evaluate
from datasets import Dataset
# the LLM model we are going to be using:
# google's BERT model
MODEL = "bert-base-uncased"
ACCURACY_METRIC = evaluate.load("accuracy")
F1_METRIC = evaluate.load("f1")
PRECISION_METRIC = evaluate.load("precision")
RECALL_METRIC = evaluate.load("recall")
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = logits.argmax(axis=-1)
# weighted averages
f1_w = F1_METRIC.compute(
predictions=preds, references=labels, average="weighted"
)["f1"]
prec_w = PRECISION_METRIC.compute(
predictions=preds, references=labels, average="weighted"
)["precision"]
rec_w = RECALL_METRIC.compute(
predictions=preds, references=labels, average="weighted"
)["recall"]
# macro averages
f1_m = F1_METRIC.compute(
predictions=preds, references=labels, average="macro"
)["f1"]
prec_m = PRECISION_METRIC.compute(
predictions=preds, references=labels, average="macro"
)["precision"]
rec_m = RECALL_METRIC.compute(
predictions=preds, references=labels, average="macro"
)["recall"]
return {
"accuracy": ACCURACY_METRIC.compute(
predictions=preds, references=labels
)["accuracy"],
"f1_weighted": f1_w,
"precision_weighted": prec_w,
"recall_weighted": rec_w,
"f1_macro": f1_m,
"precision_macro": prec_m,
"recall_macro": rec_m,
}
# creates a dataset object from the training data
def main() -> None:
data = None
aggregate_data = None
context = None
flat_source = "./flattened_data_new.json"
aggregate_source = "./aggregate_data_new.json"
with open(flat_source, "r", encoding="utf-8") as f:
data = json.load(f)
with open(aggregate_source, "r", encoding="utf-8") as f:
aggregate_data = json.load(f)
try:
for rec in data:
rec["context"] = " ".join(
str(v) for k, v in rec.items() if k not in ("text", "label")
).strip()
ds = Dataset.from_list(data)
except:
raise (Exception("Error creating dataset from list"))
labels = list(aggregate_data.keys())
label2id = {l: i for i, l in enumerate(labels)}
id2label = {i: l for i, l in enumerate(labels)}
if context and "context" in data[0]:
ds = ds.map(
lambda x: {"input_text": x["context"] + " " + x["text"]},
batched=False,
)
text_field = "input_text"
else:
ds = ds.map(lambda x: {"input_text": x["text"]}, batched=False)
text_field = "input_text"
# maps labels to integers
ds = ds.map(
lambda x: {"labels": label2id[x["label"]]},
remove_columns=(
["label", "text", "context"]
if "context" in data[0]
else ["label", "text"]
),
)
# quickly write the label/id mappings to files
with open("label2id.json", "w", encoding="utf-8") as f:
json.dump(label2id, f, indent=2)
with open("id2label.json", "w", encoding="utf-8") as f:
json.dump(id2label, f, indent=2)
# this creates a datadict with two keys, "train" and "test"
# each has a subset of data, one for testing and one for training
# ratio of 80/20 train/test
split = ds.train_test_split(0.2)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(
MODEL,
num_labels=len(labels),
id2label=id2label,
label2id=label2id,
)
tokenized = split.map(
lambda x: tokenizer(
x[text_field], padding="max_length", truncation=True
),
batched=True,
)
tokenized.set_format(
"torch", columns=["input_ids", "attention_mask", "labels"]
)
# these are the training arguments. these should be ok for testing
# but not a full fledged run. once dataset is larger, num_train_epochs should be raised
training_args = TrainingArguments(
output_dir="./BERTley",
learning_rate=2e-5,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
gradient_accumulation_steps=2, # simulate a 64‑batch without OOM
num_train_epochs=5, # for a full run, more epochs may be needed
weight_decay=0.01,
dataloader_num_workers=4,
eval_strategy="epoch", # evaluate every few steps instead of per epoch
fp16=True,
logging_strategy="epoch", # log based on epoch
logging_dir="./logs",
save_strategy="epoch",
save_total_limit=1, # save checkpoints based on steps
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
report_to=[
"tensorboard"
], # report metrics to TensorBoard, for example
)
# arguments for training the model
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized["train"],
eval_dataset=tokenized["test"],
compute_metrics=compute_metrics,
callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],
)
# training the model...
trainer.train()
# evaluate after training
evals = trainer.evaluate()
with open("evals.json", "w", encoding="utf-8") as f:
json.dump(evals, f, indent=2)
print("Evaluation results: ")
print(evals)
print("Accuracy, F1, Precision, and Recall metrics: ")
for key, value in evals.items():
print(f"{key}: {value}")
if __name__ == "__main__":
main()
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