#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/Zh0FfmVrKX0?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

! pip install datasets transformers[sentencepiece]

from datasets import load_dataset, load_metric

raw_datasets = load_dataset("glue", "cola")

raw_datasets

from transformers import AutoTokenizer

model_checkpoint = "bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

def preprocess_function(examples):
    return tokenizer(examples["sentence"], truncation=True)

tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)

import numpy as np
from datasets import load_metric

metric = load_metric("glue", "cola")

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

from transformers import TrainingArguments

args = TrainingArguments(
    "bert-fine-tuned-cola",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    num_train_epochs=3,
    weight_decay=0.01,
    push_to_hub=True,
)

from transformers import Trainer

trainer = Trainer(
    model,
    args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
)
trainer.train()

trainer.push_to_hub("End of training")



repo_name = "bert-fine-tuned-cola"

model.push_to_hub(repo_name)
tokenizer.push_to_hub(repo_name)



label_names = raw_datasets["train"].features["label"].names
label_names

model.config.id2label = {str(i): lbl for i, lbl in enumerate(label_names)}
model.config.label2id = {lbl: str(i) for i, lbl in enumerate(label_names)}

repo_name = "bert-fine-tuned-cola"
model.config.push_to_hub(repo_name)