README.md

metadata

license: apache-2.0
datasets:
  - ruanchaves/faquad-nli
language:
  - pt
metrics:
  - accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
  - textual-entailment
widget:
  - text: Qual a capital do Brasil?<s>A capital do Brasil é Brasília!</>
    example_title: Exemplo
  - text: Qual a capital do Brasil?<s>Anões são muito mais legais do que elfos!</s>
    example_title: Exemplo

TeenyTinyLlama-162m-FAQUAD

TeenyTinyLlama is a series of small foundational models trained on Portuguese.

This repository contains a version of TeenyTinyLlama-162m fine-tuned on the FaQuAD-NLI dataset.

Reproducing

# Faquad-nli
! pip install transformers datasets evaluate accelerate -q

import evaluate
import numpy as np
from datasets import load_dataset, Dataset, DatasetDict
from transformers import AutoTokenizer, DataCollatorWithPadding
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

# Load the task
dataset = load_dataset("ruanchaves/faquad-nli")

# Create a `ModelForSequenceClassification`
model = AutoModelForSequenceClassification.from_pretrained(
    "nicholasKluge/TeenyTinyLlama-162m", 
    num_labels=2, 
    id2label={0: "UNSUITABLE", 1: "SUITABLE"}, 
    label2id={"UNSUITABLE": 0, "SUITABLE": 1}
)

tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-162m")

# Format the dataset
train = dataset['train'].to_pandas()
train['text'] = train['question'] + tokenizer.bos_token + train['answer'] + tokenizer.eos_token
train = train[['text', 'label']]
train.labels = train.label.astype(int)
train = Dataset.from_pandas(train)

test = dataset['test'].to_pandas()
test['text'] = test['question'] + tokenizer.bos_token + test['answer'] + tokenizer.eos_token
test = test[['text', 'label']]
test.labels = test.label.astype(int)
test = Dataset.from_pandas(test)

dataset = DatasetDict({
    "train": train,  
    "test": test                  
})

# Preprocess the dataset
def preprocess_function(examples):
    return tokenizer(examples["text"], truncation=True)

dataset_tokenized = dataset.map(preprocess_function, batched=True)

# Create a simple data collactor
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

# Use accuracy as evaluation metric
accuracy = evaluate.load("accuracy")

# Function to compute accuracy
def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=labels)

# Define training arguments
training_args = TrainingArguments(
    output_dir="checkpoints",
    learning_rate=4e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
    evaluation_strategy="epochs",
    save_strategy="epochs",
    load_best_model_at_end=True,
    push_to_hub=True,
    hub_token="your_token_here",
    hub_model_id="username/model-ID"
)

# Define the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset_tokenized["train"],
    eval_dataset=dataset_tokenized["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

# Train!
trainer.train()

Results

Models	FaQuAD-NLI
Teeny Tiny Llama 162m	90.00
Bert-base-portuguese-cased	93.07
Gpt2-small-portuguese	86.46