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---
license: apache-2.0
datasets:
- christykoh/imdb_pt
language:
- pt
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- sentiment-analysis
widget:
- text: "Esqueceram de mim 2 é um dos melhores filmes de natal de todos os tempos."
example_title: Exemplo
- text: "Esqueceram de mim 2 é o pior filme da franquia inteira."
example_title: Exemplo
---
# TeenyTinyLlama-460m-IMDB
TeenyTinyLlama is a pair of small foundational models trained in Brazilian Portuguese.
This repository contains a version of [TeenyTinyLlama-460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m) (`TeenyTinyLlama-460m-IMDB`) fine-tuned on the the [IMDB dataset](https://huggingface.co/datasets/christykoh/imdb_pt).
## Details
- **Number of Epochs:** 3
- **Batch size:** 16
- **Optimizer:** `torch.optim.AdamW` (learning_rate = 4e-5, epsilon = 1e-8)
- **GPU:** 1 NVIDIA A100-SXM4-40GB
## Usage
Using `transformers.pipeline`:
```python
from transformers import pipeline
text = "Esqueceram de mim 2 é um dos melhores filmes de natal de todos os tempos."
classifier = pipeline("text-classification", model="nicholasKluge/TeenyTinyLlama-460m-IMDB")
classifier(text)
# >>> [{'label': 'POSITIVE', 'score': 0.9971244931221008}]
```
## Reproducing
To reproduce the fine-tuning process, use the following code snippet:
```python
# IMDB
! pip install transformers datasets evaluate accelerate -q
import evaluate
import numpy as np
from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorWithPadding
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
# Load the task
dataset = load_dataset("christykoh/imdb_pt")
# Create a `ModelForSequenceClassification`
model = AutoModelForSequenceClassification.from_pretrained(
"nicholasKluge/TeenyTinyLlama-460m",
num_labels=2,
id2label={0: "NEGATIVE", 1: "POSITIVE"},
label2id={"NEGATIVE": 0, "POSITIVE": 1}
)
tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-460m")
# Preprocess the dataset
def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True, max_length=256)
dataset_tokenized = dataset.map(preprocess_function, batched=True)
# Create a simple data collactor
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# Use accuracy as an 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="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
push_to_hub=True,
hub_token="your_token_here",
hub_model_id="username/model-name-imdb"
)
# 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()
```
## Fine-Tuning Comparisons
To further evaluate the downstream capabilities of our models, we decided to employ a basic fine-tuning procedure for our TTL pair on a subset of tasks from the Poeta benchmark. We apply the same procedure for comparison purposes on both [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) models, given that they are also LLM trained from scratch in Brazilian Portuguese and have a similar size range to our models. We used these comparisons to assess if our pre-training runs produced LLM capable of producing good results ("good" here means "close to BERTimbau") when utilized for downstream applications.
| Models | IMDB | FaQuAD-NLI | HateBr | Assin2 | AgNews | Average |
|-----------------|-----------|------------|-----------|-----------|-----------|---------|
| BERTimbau-large | **93.58** | 92.26 | 91.57 | **88.97** | 94.11 | 92.10 |
| BERTimbau-small | 92.22 | **93.07** | 91.28 | 87.45 | 94.19 | 91.64 |
| **TTL-460m** | 91.64 | 91.18 | **92.28** | 86.43 | **94.42** | 91.19 |
| **TTL-160m** | 91.14 | 90.00 | 90.71 | 85.78 | 94.05 | 90.34 |
All the shown results are the higher accuracy scores achieved on the respective task test sets after fine-tuning the models on the training sets. All fine-tuning runs used the same hyperparameters, and the code implementation can be found in the [model cards](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m-HateBR) of our fine-tuned models.
## Cite as 🤗
```latex
@misc{correa24ttllama,
title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese},
author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar},
journal={arXiv preprint arXiv:2401.16640},
year={2024}
}
```
## Funding
This repository was built as part of the RAIES ([Rede de Inteligência Artificial Ética e Segura](https://www.raies.org/)) initiative, a project supported by FAPERGS - ([Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul](https://fapergs.rs.gov.br/inicial)), Brazil.
## License
TeenyTinyLlama-460m-IMDB is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
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