File size: 1,491 Bytes
3da7b20 | 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 | from datasets import load_dataset
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
Trainer,
TrainingArguments
)
import pandas as pd
# Load dataset
df = pd.read_csv("data/vibes.csv")
dataset = load_dataset("csv", data_files="data/vibes.csv")
labels = ["negative", "neutral", "positive"]
label2id = {l: i for i, l in enumerate(labels)}
id2label = {i: l for l, i in label2id.items()}
def encode_labels(example):
example["label"] = label2id[example["label"]]
return example
dataset = dataset.map(encode_labels)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
def tokenize(batch):
return tokenizer(batch["text"], truncation=True, padding=True)
dataset = dataset.map(tokenize, batched=True)
dataset = dataset["train"].train_test_split(test_size=0.2)
model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=3,
id2label=id2label,
label2id=label2id
)
training_args = TrainingArguments(
output_dir="./model",
evaluation_strategy="epoch",
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=5,
save_strategy="epoch",
logging_dir="./logs",
logging_steps=10
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
tokenizer=tokenizer
)
trainer.train()
trainer.save_model("./model")
tokenizer.save_pretrained("./model")
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