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e0cfb62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | import numpy as np
from datasets import load_dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainingArguments,
)
MODEL_NAME = "distilbert-base-uncased"
LABELS = [
"1.3.1 Info and Relationships",
"2.1.1 Keyboard",
"2.4.3 Focus Order",
"2.4.7 Focus Visible",
"4.1.2 Name, Role, Value",
]
label2id = {label: i for i, label in enumerate(LABELS)}
id2label = {i: label for i, label in enumerate(LABELS)}
# Load our CSV files
dataset = load_dataset(
"csv",
data_files={
"train": "train.csv",
"validation": "validation.csv",
"test": "test.csv",
},
)
# Load DistilBERT's tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
def preprocess(example):
encoded = tokenizer(
example["text"],
truncation=True,
)
encoded["label_id"] = label2id[example["label"]]
return encoded
tokenized_dataset = dataset.map(
preprocess,
remove_columns=["text", "label"],
)
tokenized_dataset = tokenized_dataset.rename_column(
"label_id",
"labels",
)
# Pads each batch to the longest sentence in that batch
data_collator = DataCollatorWithPadding(
tokenizer=tokenizer
)
# Load pretrained DistilBERT with our new 5-class head
model = AutoModelForSequenceClassification.from_pretrained(
MODEL_NAME,
num_labels=len(LABELS),
id2label=id2label,
label2id=label2id,
)
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
precision, recall, f1, _ = precision_recall_fscore_support(
labels,
predictions,
average="weighted",
zero_division=0,
)
accuracy = accuracy_score(
labels,
predictions,
)
return {
"accuracy": accuracy,
"precision": precision,
"recall": recall,
"f1": f1,
}
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
learning_rate=2e-5,
eval_strategy="epoch",
save_strategy="epoch",
logging_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="f1",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
data_collator=data_collator,
compute_metrics=compute_metrics,
)
print("\nStarting training...\n")
trainer.train()
print("\nEvaluating final model on test data...\n")
test_results = trainer.evaluate(
tokenized_dataset["test"]
)
print(test_results)
# Save our finished model locally
trainer.save_model("./wcag-classifier")
tokenizer.save_pretrained("./wcag-classifier") |