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---
language:
- pt
license: apache-2.0
library_name: transformers
datasets:
- wikimedia/wikipedia
metrics:
- accuracy
model-index:
- name: periquito-3B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge (No Images)
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 17.98
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX (No Images)
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 21.14
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: OAB Exams
type: eduagarcia/oab_exams
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 22.69
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 RTE
type: assin2
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 43.01
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 STS
type: eduagarcia/portuguese_benchmark
split: test
args:
num_few_shot: 15
metrics:
- type: pearson
value: 8.92
name: pearson
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: FaQuAD NLI
type: ruanchaves/faquad-nli
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 43.97
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HateBR Binary
type: ruanchaves/hatebr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 50.46
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: PT Hate Speech Binary
type: hate_speech_portuguese
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 41.19
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: tweetSentBR
type: eduagarcia-temp/tweetsentbr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 47.96
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B
name: Open Portuguese LLM Leaderboard
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
![periquito](periquito.webp "periquito")
## 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
```python
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](https://huggingface.co/docs/transformers/main/model_doc/llama).
### Evaluating with LM-Eval-Harness
The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-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](https://github.com/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](https://sites.research.google/trc/about/).
## Evaluation
We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/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](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
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](https://sites.research.google/trc/about/) 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}
}
```
# [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/wandgibaut/periquito-3B)
| Metric | Value |
|--------------------------|---------|
|Average |**33.04**|
|ENEM Challenge (No Images)| 17.98|
|BLUEX (No Images) | 21.14|
|OAB Exams | 22.69|
|Assin2 RTE | 43.01|
|Assin2 STS | 8.92|
|FaQuAD NLI | 43.97|
|HateBR Binary | 50.46|
|PT Hate Speech Binary | 41.19|
|tweetSentBR | 47.96|

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