TeenyTinyLlama-160m-FaQuAD-NLI
TeenyTinyLlama is a pair of small foundational models trained in Brazilian Portuguese.
This repository contains a version of TeenyTinyLlama-160m (TeenyTinyLlama-160m-FaQuAD-NLI
) fine-tuned on the FaQuAD-NLI dataset.
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
:
from transformers import pipeline
text = "<s>Qual a capital do Brasil?<s>A capital do Brasil é Brasília!</s>"
classifier = pipeline("text-classification", model="nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI")
classifier(text)
# >>> [{'label': 'SUITABLE', 'score': 0.9774010181427002}]
Reproducing
To reproduce the fine-tuning process, use the following code snippet:
# 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-160m",
num_labels=2,
id2label={0: "UNSUITABLE", 1: "SUITABLE"},
label2id={"UNSUITABLE": 0, "SUITABLE": 1}
)
tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-160m")
# 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="epoch",
save_strategy="epoch",
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()
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 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 of our fine-tuned models.
Cite as 🤗
@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}
}
@misc{correa24ttllama,
doi = {10.1016/j.mlwa.2024.100558},
url = {https://www.sciencedirect.com/science/article/pii/S2666827024000343},
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={Machine Learning With Applications},
publisher = {Springer},
year={2024}
}
Funding
This repository was built as part of the RAIES (Rede de Inteligência Artificial Ética e Segura) initiative, a project supported by FAPERGS - (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), Brazil.
License
TeenyTinyLlama-160m-FaQuAD-NLI is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.
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