license: apache-2.0
datasets:
- wikimedia/wikipedia
language:
- pt
metrics:
- accuracy
library_name: transformers
Model Card for Model ID
Model Details
Model Description
Periquito-3B is a large language model (LLM) trained by Wandgibaut. It is built upon the OpenLlama-3B architecture and specifically fine-tuned using Portuguese Wikipedia (pt-br) data. This specialization makes it particularly adept at understanding and generating text in Brazilian Portuguese.
- Developed by: Wandemberg Gibaut
- Model type: Llama
- Language(s) (NLP): Portuguese
- License: Apache License 2.0
- Finetuned from model [optional]: openlm-research/open_llama_3b
Loading the Weights with Hugging Face Transformers
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
model_path = 'wandgibaut/periquito-3B'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map='auto',
)
prompt = 'Q: Qual o maior animal terrestre?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))
For more advanced usage, please follow the transformers LLaMA documentation.
Evaluating with LM-Eval-Harness
The model can be evaluated with lm-eval-harness. However, we used a custom version, that has some translated tasks and the ENEM suit. This can be found in wandgibaut/lm-evaluation-harness-PTBR.
Dataset and Training
We finetunned the model on Wikipedia-pt dataset with LoRA, in Google's TPU-v3 in the Google's TPU Research program.
Evaluation
We evaluated OpenLLaMA on a wide range of tasks using lm-evaluation-harness. The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in this issue of lm-evaluation-harness. Additionally, we present the results of GPT-J, a 6B parameter model trained on the Pile dataset by EleutherAI.
hf-causal (pretrained=wandgibaut/periquito-3B), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| agnews_pt | 0 | acc | 0.6184 | ± | 0.0056 |
| boolq_pt | 1 | acc | 0.6333 | ± | 0.0084 |
| faquad | 1 | exact | 7.9365 | ||
| f1 | 45.6971 | ||||
| HasAns_exact | 7.9365 | ||||
| HasAns_f1 | 45.6971 | ||||
| NoAns_exact | 0.0000 | ||||
| NoAns_f1 | 0.0000 | ||||
| best_exact | 7.9365 | ||||
| best_f1 | 45.6971 | ||||
| imdb_pt | 0 | acc | 0.6338 | ± | 0.0068 |
| sst2_pt | 1 | acc | 0.6823 | ± | 0.0158 |
| toldbr | 0 | acc | 0.4629 | ± | 0.0109 |
| f1_macro | 0.3164 |
hf-causal (pretrained=wandgibaut/periquito-3B,dtype=float), limit: None, provide_description: False, num_fewshot: 3, batch_size: None
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| agnews_pt | 0 | acc | 0.6242 | ± | 0.0056 |
| boolq_pt | 1 | acc | 0.6477 | ± | 0.0084 |
| faquad | 1 | exact | 34.9206 | ||
| f1 | 70.3968 | ||||
| HasAns_exact | 34.9206 | ||||
| HasAns_f1 | 70.3968 | ||||
| NoAns_exact | 0.0000 | ||||
| NoAns_f1 | 0.0000 | ||||
| best_exact | 34.9206 | ||||
| best_f1 | 70.3968 | ||||
| imdb_pt | 0 | acc | 0.8408 | ± | 0.0052 |
| sst2_pt | 1 | acc | 0.7775 | ± | 0.0141 |
| toldbr | 0 | acc | 0.5143 | ± | 0.0109 |
| f1_macro | 0.5127 |
hf-causal (pretrained=wandgibaut/periquito-3B), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| enem | 0 | acc | 0.1976 | ± | 0.0132 |
| 2009 | 0.2022 | ± | 0.0428 | ||
| 2016 | 0.1809 | ± | 0.0399 | ||
| 2015 | 0.1348 | ± | 0.0364 | ||
| 2016_2_ | 0.2366 | ± | 0.0443 | ||
| 2017 | 0.2022 | ± | 0.0428 | ||
| 2013 | 0.1647 | ± | 0.0405 | ||
| 2012 | 0.2174 | ± | 0.0432 | ||
| 2011 | 0.2292 | ± | 0.0431 | ||
| 2010 | 0.2157 | ± | 0.0409 | ||
| 2014 | 0.1839 | ± | 0.0418 | ||
| enem_2022 | 0 | acc | 0.2373 | ± | 0.0393 |
| 2022 | 0.2373 | ± | 0.0393 | ||
| human-sciences | 0.2703 | ± | 0.0740 | ||
| mathematics | 0.1818 | ± | 0.0842 | ||
| natural-sciences | 0.1538 | ± | 0.0722 | ||
| languages | 0.3030 | ± | 0.0812 | ||
| enem_CoT | 0 | acc | 0.1812 | ± | 0.0127 |
| 2009 | 0.1348 | ± | 0.0364 | ||
| 2016 | 0.1596 | ± | 0.0380 | ||
| 2015 | 0.1124 | ± | 0.0337 | ||
| 2016_2_ | 0.1290 | ± | 0.0350 | ||
| 2017 | 0.2247 | ± | 0.0445 | ||
| 2013 | 0.1765 | ± | 0.0416 | ||
| 2012 | 0.2391 | ± | 0.0447 | ||
| 2011 | 0.1979 | ± | 0.0409 | ||
| 2010 | 0.2451 | ± | 0.0428 | ||
| 2014 | 0.1839 | ± | 0.0418 | ||
| enem_CoT_2022 | 0 | acc | 0.2119 | ± | 0.0378 |
| 2022 | 0.2119 | ± | 0.0378 | ||
| human-sciences | 0.2703 | ± | 0.0740 | ||
| mathematics | 0.1818 | ± | 0.0842 | ||
| natural-sciences | 0.2308 | ± | 0.0843 | ||
| languages | 0.1515 | ± | 0.0634 |
hf-causal (pretrained=wandgibaut/periquito-3B,dtype=float), limit: None, provide_description: False, num_fewshot: 1, batch_size: None
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| enem | 0 | acc | 0.1790 | ± | 0.0127 |
| 2009 | 0.1573 | ± | 0.0388 | ||
| 2016 | 0.2021 | ± | 0.0416 | ||
| 2015 | 0.1573 | ± | 0.0388 | ||
| 2016_2_ | 0.1935 | ± | 0.0412 | ||
| 2017 | 0.2247 | ± | 0.0445 | ||
| 2013 | 0.1412 | ± | 0.0380 | ||
| 2012 | 0.1739 | ± | 0.0397 | ||
| 2011 | 0.1979 | ± | 0.0409 | ||
| 2010 | 0.1961 | ± | 0.0395 | ||
| 2014 | 0.1379 | ± | 0.0372 | ||
| enem_2022 | 0 | acc | 0.1864 | ± | 0.0360 |
| 2022 | 0.1864 | ± | 0.0360 | ||
| human-sciences | 0.2432 | ± | 0.0715 | ||
| mathematics | 0.1364 | ± | 0.0749 | ||
| natural-sciences | 0.1154 | ± | 0.0639 | ||
| languages | 0.2121 | ± | 0.0723 | ||
| enem_CoT | 0 | acc | 0.2009 | ± | 0.0132 |
| 2009 | 0.2135 | ± | 0.0437 | ||
| 2016 | 0.2340 | ± | 0.0439 | ||
| 2015 | 0.1348 | ± | 0.0364 | ||
| 2016_2_ | 0.2258 | ± | 0.0436 | ||
| 2017 | 0.2360 | ± | 0.0453 | ||
| 2013 | 0.1529 | ± | 0.0393 | ||
| 2012 | 0.1957 | ± | 0.0416 | ||
| 2011 | 0.2500 | ± | 0.0444 | ||
| 2010 | 0.1667 | ± | 0.0371 | ||
| 2014 | 0.1954 | ± | 0.0428 | ||
| enem_CoT_2022 | 0 | acc | 0.2542 | ± | 0.0403 |
| 2022 | 0.2542 | ± | 0.0403 | ||
| human-sciences | 0.2703 | ± | 0.0740 | ||
| mathematics | 0.2273 | ± | 0.0914 | ||
| natural-sciences | 0.3846 | ± | 0.0973 | ||
| languages | 0.1515 | ± | 0.0634 |
Use Cases:
The model is suitable for text generation, language understanding, and various natural language processing tasks in Brazilian Portuguese.
Limitations:
Like many language models, Periquito-3B might exhibit biases present in its training data. Additionally, its performance is primarily optimized for Portuguese, potentially limiting its effectiveness with other languages.
Ethical Considerations:
Users are encouraged to use the model ethically, particularly by avoiding the generation of harmful or biased content.
Acknowledgment
We thank the Google TPU Research Cloud program for providing part of the computation resources.
Citation [optional]
If you found periquito-3B useful in your research or applications, please cite using the following BibTeX:
BibTeX:
@software{wandgibautperiquito3B,
author = {Gibaut, Wandemberg},
title = {Periquito-3B},
month = Sep,
year = 2023,
url = {https://huggingface.co/wandgibaut/periquito-3B}
}