testo

from datasets import load_dataset

dataset = load_dataset("imdb") # Example with IMDB dataset from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments

Load pre-trained model and tokenizer

model_name = "bert-base-uncased" tokenizer = BertTokenizer.from_pretrained(model_name) model = BertForSequenceClassification.from_pretrained(model_name)

Tokenize the dataset

def tokenize_function(examples): return tokenizer(examples['text'], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

Define training arguments

training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, num_train_epochs=3, )

Create Trainer instance

trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], )

Train the model

trainer.train() model.save_pretrained("./my_model") tokenizer.save_pretrained("./my_model")

Upload to Hugging Face

from huggingface_hub import HfApi, HfFolder

Log in to your Hugging Face account

HfFolder.save_token("YOUR_HUGGINGFACE_TOKEN")

api = HfApi() api.upload_folder( folder_path="./my_model", path_in_repo="my_model", repo_id="your_username/my_model", repo_type="model", ) from transformers import pipeline

classifier = pipeline("text-classification", model="your_username/my_model") result = classifier("This is an example sentence.") print(result)