--- license: apache-2.0 datasets: - maritaca-ai/ag_news_pt language: - pt metrics: - accuracy library_name: transformers pipeline_tag: text-classification tags: - hate-speech widget: - text: >- Os preços do petróleo do Tearway, derrubar registros e forçar carteiras, apresentam uma nova ameaça econômica apenas três meses antes das eleições presidenciais dos EUA. example_title: Exemplo - text: >- Quando falamos de Inteligência Artificial, hoje em dia existem já vários sistemas que começam a ganhar popularidade, embora nenhum seja ainda tão conhecido como o ChatGPT da OpenAI. example_title: Exemplo - text: >- O Sport Club Internacional comunica a permanência do seu Diretor Esportivo, Márcio Rodrigues, o Magrão. Com novas atribuições, liderando os setores de trabalho do Departamento de Futebol, e a interligação com as categorias de base, exercendo uma função de gestão e suporte ao trabalho desempenhado pela comissão técnica, grupo de jogadores e staff do Clube. example_title: Exemplo - text: >- Uma conferência da ONU terminou no início do sábado com um plano vago para novas negociações informais sobre como diminuir o aquecimento global, mas sem um compromisso dos EUA com negociações multilaterais sobre os próximos passos, incluindo controles de emissões. example_title: Exemplo base_model: - nicholasKluge/TeenyTinyLlama-160m --- # TeenyTinyLlama-160m-AgNews TeenyTinyLlama is a pair of small foundational models trained in Brazilian Portuguese. This repository contains a version of [TeenyTinyLlama-160m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m) (`TeenyTinyLlama-160m-AgNews`) fine-tuned on the [AgNews dataset](https://huggingface.co/datasets/maritaca-ai/ag_news_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 = "Quando falamos de Inteligência Artificial, hoje em dia existem já vários sistemas que começam a ganhar popularidade,\ embora nenhum seja ainda tão conhecido como o ChatGPT da OpenAI." classifier = pipeline("text-classification", model="nicholasKluge/TeenyTinyLlama-160m-AgNews") classifier(text) # >>> [{'label': 'TECNOLOGIA', 'score': 0.9997298121452332}] ``` ## Reproducing To reproduce the fine-tuning process, use the following code snippet: ```python # AgNews !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("maritaca-ai/ag_news_pt") # Create a `ModelForSequenceClassification` model = AutoModelForSequenceClassification.from_pretrained( "nicholasKluge/TeenyTinyLlama-160m", num_labels=4, id2label={0: "MUNDO", 1: "ESPORTES", 2: "NEGÓCIOS", 3: "TECNOLOGIA"}, label2id={"MUNDO": 0, "ESPORTES": 1, "NEGÓCIOS": 2, "TECNOLOGIA": 3} ) tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-160m") # Format the dataset train = dataset['train'].to_pandas() train["text"] = train["title"] + "\n\n" + train["text"] train.label = train.label.astype(int) train = Dataset.from_pandas(train) test = dataset['test'].to_pandas() test["text"] = test["title"] + "\n\n" + test["text"] test.label = 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, 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-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](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} } @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](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-160m-AgNews is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.