File size: 1,789 Bytes
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"""train_model.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1BMInZz4vjJ1PfgTbbqIknpJYcbM5cwV0
"""
import torch
import numpy as np
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
print("Downloading dataset...")
dataset = load_dataset("papluca/language-identification", split="train")
target_langs = {'en', 'fr', 'es', 'de'}
filtered_dataset = dataset.filter(lambda example: example['labels'] in target_langs)
label2id = {"en": 0, "fr": 1, "es": 2, "de": 3}
id2label = {0: "en", 1: "fr", 2: "es", 3: "de"}
model_ckpt = "distilbert-base-multilingual-cased"
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
def preprocess(examples):
tokenized = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=64)
tokenized["labels"] = [label2id[lang] for lang in examples["labels"]]
return tokenized
print("Preprocessing data...")
train_subset = filtered_dataset.shuffle(seed=42).select(range(1500))
tokenized_data = train_subset.map(preprocess, batched=True)
model = AutoModelForSequenceClassification.from_pretrained(
model_ckpt,
num_labels=4,
id2label=id2label,
label2id=label2id
)
args = TrainingArguments(
output_dir="my_real_model",
learning_rate=2e-5,
per_device_train_batch_size=16,
num_train_epochs=2,
weight_decay=0.01,
save_strategy="no",
use_cpu=True
trainer = Trainer(
model=model,
args=args,
train_dataset=tokenized_data,
tokenizer=tokenizer,
)
print("Starting training...")
trainer.train()
print("Saving model to './production_model'...")
trainer.save_model("production_model")
print("Done!") |