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--- |
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language: |
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- pt |
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license: apache-2.0 |
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library_name: transformers |
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datasets: |
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- wikimedia/wikipedia |
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metrics: |
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- accuracy |
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model-index: |
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- name: periquito-3B |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: ENEM Challenge (No Images) |
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type: eduagarcia/enem_challenge |
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split: train |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc |
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value: 17.98 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BLUEX (No Images) |
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type: eduagarcia-temp/BLUEX_without_images |
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split: train |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc |
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value: 21.14 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: OAB Exams |
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type: eduagarcia/oab_exams |
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split: train |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc |
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value: 22.69 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Assin2 RTE |
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type: assin2 |
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split: test |
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args: |
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num_few_shot: 15 |
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metrics: |
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- type: f1_macro |
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value: 43.01 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Assin2 STS |
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type: eduagarcia/portuguese_benchmark |
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split: test |
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args: |
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num_few_shot: 15 |
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metrics: |
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- type: pearson |
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value: 8.92 |
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name: pearson |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: FaQuAD NLI |
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type: ruanchaves/faquad-nli |
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split: test |
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args: |
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num_few_shot: 15 |
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metrics: |
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- type: f1_macro |
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value: 43.97 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HateBR Binary |
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type: ruanchaves/hatebr |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: f1_macro |
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value: 50.46 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: PT Hate Speech Binary |
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type: hate_speech_portuguese |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: f1_macro |
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value: 41.19 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: tweetSentBR |
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type: eduagarcia-temp/tweetsentbr |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: f1_macro |
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value: 47.96 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=wandgibaut/periquito-3B |
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name: Open Portuguese LLM Leaderboard |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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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. |
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|
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- **Developed by:** Wandemberg Gibaut |
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- **Model type:** Llama |
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- **Language(s) (NLP):** Portuguese |
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- **License:** Apache License 2.0 |
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- **Finetuned from model [optional]:** openlm-research/open_llama_3b |
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### Loading the Weights with Hugging Face Transformers |
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|
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```python |
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import torch |
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from transformers import LlamaTokenizer, LlamaForCausalLM |
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model_path = 'wandgibaut/periquito-3B' |
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tokenizer = LlamaTokenizer.from_pretrained(model_path) |
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model = LlamaForCausalLM.from_pretrained( |
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model_path, torch_dtype=torch.float16, device_map='auto', |
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) |
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prompt = 'Q: Qual o maior animal terrestre?\nA:' |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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generation_output = model.generate( |
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input_ids=input_ids, max_new_tokens=32 |
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) |
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print(tokenizer.decode(generation_output[0])) |
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``` |
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For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama). |
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### Evaluating with LM-Eval-Harness |
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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). |
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## Dataset and Training |
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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/). |
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## Evaluation |
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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/). |
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hf-causal (pretrained=wandgibaut/periquito-3B), limit: None, provide_description: False, num_fewshot: 0, batch_size: None |
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|
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| Task |Version| Metric | Value | |Stderr| |
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|---------|------:|------------|------:|---|-----:| |
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|agnews_pt| 0|acc | 0.6184|± |0.0056| |
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|boolq_pt | 1|acc | 0.6333|± |0.0084| |
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|faquad | 1|exact | 7.9365| | | |
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| | |f1 |45.6971| | | |
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| | |HasAns_exact| 7.9365| | | |
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| | |HasAns_f1 |45.6971| | | |
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| | |NoAns_exact | 0.0000| | | |
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| | |NoAns_f1 | 0.0000| | | |
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| | |best_exact | 7.9365| | | |
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| | |best_f1 |45.6971| | | |
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|imdb_pt | 0|acc | 0.6338|± |0.0068| |
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|sst2_pt | 1|acc | 0.6823|± |0.0158| |
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|toldbr | 0|acc | 0.4629|± |0.0109| |
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| | |f1_macro | 0.3164| | | |
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hf-causal (pretrained=wandgibaut/periquito-3B,dtype=float), limit: None, provide_description: False, num_fewshot: 3, batch_size: None |
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| Task |Version| Metric | Value | |Stderr| |
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|---------|------:|------------|------:|---|-----:| |
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|agnews_pt| 0|acc | 0.6242|± |0.0056| |
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|boolq_pt | 1|acc | 0.6477|± |0.0084| |
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|faquad | 1|exact |34.9206| | | |
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| | |f1 |70.3968| | | |
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| | |HasAns_exact|34.9206| | | |
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| | |HasAns_f1 |70.3968| | | |
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| | |NoAns_exact | 0.0000| | | |
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| | |NoAns_f1 | 0.0000| | | |
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| | |best_exact |34.9206| | | |
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| | |best_f1 |70.3968| | | |
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|imdb_pt | 0|acc | 0.8408|± |0.0052| |
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|sst2_pt | 1|acc | 0.7775|± |0.0141| |
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|toldbr | 0|acc | 0.5143|± |0.0109| |
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| | |f1_macro | 0.5127| | | |
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hf-causal (pretrained=wandgibaut/periquito-3B), limit: None, provide_description: False, num_fewshot: 0, batch_size: None |
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|
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| Task |Version| Metric |Value | |Stderr| |
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|-------------|------:|----------------|-----:|---|-----:| |
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|enem | 0|acc |0.1976|± |0.0132| |
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| | |2009 |0.2022|± |0.0428| |
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| | |2016 |0.1809|± |0.0399| |
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| | |2015 |0.1348|± |0.0364| |
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| | |2016_2_ |0.2366|± |0.0443| |
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| | |2017 |0.2022|± |0.0428| |
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| | |2013 |0.1647|± |0.0405| |
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| | |2012 |0.2174|± |0.0432| |
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| | |2011 |0.2292|± |0.0431| |
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| | |2010 |0.2157|± |0.0409| |
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| | |2014 |0.1839|± |0.0418| |
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|enem_2022 | 0|acc |0.2373|± |0.0393| |
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| | |2022 |0.2373|± |0.0393| |
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| | |human-sciences |0.2703|± |0.0740| |
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| | |mathematics |0.1818|± |0.0842| |
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| | |natural-sciences|0.1538|± |0.0722| |
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| | |languages |0.3030|± |0.0812| |
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|enem_CoT | 0|acc |0.1812|± |0.0127| |
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| | |2009 |0.1348|± |0.0364| |
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| | |2016 |0.1596|± |0.0380| |
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| | |2015 |0.1124|± |0.0337| |
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| | |2016_2_ |0.1290|± |0.0350| |
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| | |2017 |0.2247|± |0.0445| |
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| | |2013 |0.1765|± |0.0416| |
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| | |2012 |0.2391|± |0.0447| |
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| | |2011 |0.1979|± |0.0409| |
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| | |2010 |0.2451|± |0.0428| |
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| | |2014 |0.1839|± |0.0418| |
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|enem_CoT_2022| 0|acc |0.2119|± |0.0378| |
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| | |2022 |0.2119|± |0.0378| |
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| | |human-sciences |0.2703|± |0.0740| |
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| | |mathematics |0.1818|± |0.0842| |
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| | |natural-sciences|0.2308|± |0.0843| |
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| | |languages |0.1515|± |0.0634| |
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hf-causal (pretrained=wandgibaut/periquito-3B,dtype=float), limit: None, provide_description: False, num_fewshot: 1, batch_size: None |
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|
|
| Task |Version| Metric |Value | |Stderr| |
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|-------------|------:|----------------|-----:|---|-----:| |
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|enem | 0|acc |0.1790|± |0.0127| |
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| | |2009 |0.1573|± |0.0388| |
|
| | |2016 |0.2021|± |0.0416| |
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| | |2015 |0.1573|± |0.0388| |
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| | |2016_2_ |0.1935|± |0.0412| |
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| | |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| |
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| | |2014 |0.1379|± |0.0372| |
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|enem_2022 | 0|acc |0.1864|± |0.0360| |
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| | |2022 |0.1864|± |0.0360| |
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| | |human-sciences |0.2432|± |0.0715| |
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| | |mathematics |0.1364|± |0.0749| |
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| | |natural-sciences|0.1154|± |0.0639| |
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| | |languages |0.2121|± |0.0723| |
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|enem_CoT | 0|acc |0.2009|± |0.0132| |
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| | |2009 |0.2135|± |0.0437| |
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| | |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| |
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| | |2010 |0.1667|± |0.0371| |
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| | |2014 |0.1954|± |0.0428| |
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|enem_CoT_2022| 0|acc |0.2542|± |0.0403| |
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| | |2022 |0.2542|± |0.0403| |
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| | |human-sciences |0.2703|± |0.0740| |
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| | |mathematics |0.2273|± |0.0914| |
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| | |natural-sciences|0.3846|± |0.0973| |
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| | |languages |0.1515|± |0.0634| |
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## Use Cases: |
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The model is suitable for text generation, language understanding, and various natural language processing tasks in Brazilian Portuguese. |
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## Limitations: |
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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. |
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## Ethical Considerations: |
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Users are encouraged to use the model ethically, particularly by avoiding the generation of harmful or biased content. |
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## Acknowledgment |
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We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. |
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## Citation [optional] |
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If you found periquito-3B useful in your research or applications, please cite using the following BibTeX: |
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**BibTeX:** |
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``` |
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@software{wandgibautperiquito3B, |
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author = {Gibaut, Wandemberg}, |
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title = {Periquito-3B}, |
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month = Sep, |
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year = 2023, |
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url = {https://huggingface.co/wandgibaut/periquito-3B} |
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} |
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``` |
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|
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# [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/wandgibaut/periquito-3B) |
|
|
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| Metric | Value | |
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|--------------------------|---------| |
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|Average |**33.04**| |
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|ENEM Challenge (No Images)| 17.98| |
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|BLUEX (No Images) | 21.14| |
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|OAB Exams | 22.69| |
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|Assin2 RTE | 43.01| |
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|Assin2 STS | 8.92| |
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|FaQuAD NLI | 43.97| |
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|HateBR Binary | 50.46| |
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|PT Hate Speech Binary | 41.19| |
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|tweetSentBR | 47.96| |
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|
