405 lines
13 KiB
Markdown
405 lines
13 KiB
Markdown
---
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license: mit
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-0.5B-Instruct
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pipeline_tag: text-generation
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datasets:
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- adalbertojunior/openHermes_portuguese
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- cnmoro/smoltalk-555k-ptbr
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- cnmoro/RagMixPTBR-Legal-Alpaca-2M
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model-index:
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- name: Qwen2.5-0.5B-Portuguese-v1
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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: 37.86
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 34.63
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 33.12
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 86.3
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 54.3
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 65.33
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 44.06
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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: 55.1
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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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/tweetsentbr_fewshot
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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: 45.96
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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=cnmoro/Qwen2.5-0.5B-Portuguese-v1
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name: Open Portuguese LLM Leaderboard
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---
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Qwen2.5-0.5B finetuned for proficiency in Portuguese language and increased intelligence.
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```text
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https://ollama.com/cnmoro/Qwen2.5-0.5B-Portuguese-v1
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```
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "cnmoro/Qwen2.5-0.5B-Portuguese-v1"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Escreva uma breve introdução sobre LLMs (Large Language Models) e suas aplicações."
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# System prompt is always injected and hardcoded automatically
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# for ideal performance in portuguese language.
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# No need to write it again.
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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response
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# LLM significa Large Language Models, que são modelos de linguagem computacional
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# projetados para simular a inteligência humana no processamento e geração de texto.
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# Esses modelos usam técnicas avançadas de aprendizado de máquina e redes neurais para
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# compreender e gerar texto com base em dados de entrada. As aplicações de LLM incluem
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# tradução automática, análise de sentimento, modelagem de tópicos e resposta a perguntas
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# automatizadas. Eles estão sendo cada vez mais utilizados em diversas áreas, como
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# saúde, educação e finanças, para melhorar a comunicação, as experiências dos clientes
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# e os resultados da pesquisa.
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```
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## Overall Results
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| Task | Metric | Value | Stdev |
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| ------------------------ | --------------- | ------- | ------- |
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| assin2_rte | f1_macro | 0.391 | 0.006 |
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| assin2_rte | acc | 0.527 | 0.007 |
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| assin2_sts | pearson | 0.115 | 0.014 |
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| assin2_sts | mse | 1.011 | N/A |
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| bluex | acc | 0.349 | 0.010 |
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| enem_challenge | acc | 0.363 | 0.007 |
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| faquad_nli | f1_macro | 0.595 | 0.017 |
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| faquad_nli | acc | 0.791 | 0.011 |
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| hatebr_offensive | f1_macro | 0.338 | 0.005 |
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| hatebr_offensive | acc | 0.502 | 0.009 |
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| oab_exams | acc | 0.326 | 0.006 |
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| portuguese_hate_speech | f1_macro | 0.412 | 0.004 |
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| portuguese_hate_speech | acc | 0.702 | 0.011 |
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| tweetsentbr | f1_macro | 0.455 | 0.005 |
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| tweetsentbr | acc | 0.594 | 0.008 |
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## Detailed Results
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### assin2_rte
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| Metric | Value | Stdev |
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| --------- | ----- | ----- |
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| f1_macro | 0.391 | 0.006 |
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| acc | 0.527 | 0.007 |
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### assin2_sts
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| Metric | Value | Stdev |
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| --------- | ----- | ----- |
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| pearson | 0.115 | 0.014 |
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| mse | 1.011 | N/A |
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### bluex
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| Exam ID | Metric | Value | Stdev |
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| ----------------------- | ------ | -------- | -------- |
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| all | acc | 0.349 | 0.010 |
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| USP_2019 | acc | 0.225 | 0.038 |
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| USP_2024 | acc | 0.293 | 0.041 |
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| USP_2021 | acc | 0.423 | 0.040 |
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| UNICAMP_2018 | acc | 0.241 | 0.034 |
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| UNICAMP_2024 | acc | 0.444 | 0.043 |
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| USP_2020 | acc | 0.393 | 0.038 |
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| UNICAMP_2020 | acc | 0.291 | 0.035 |
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| UNICAMP_2021_1 | acc | 0.326 | 0.040 |
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| UNICAMP_2022 | acc | 0.487 | 0.046 |
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| USP_2022 | acc | 0.388 | 0.040 |
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| UNICAMP_2019 | acc | 0.280 | 0.037 |
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| UNICAMP_2021_2 | acc | 0.294 | 0.037 |
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| UNICAMP_2023 | acc | 0.558 | 0.044 |
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| USP_2023 | acc | 0.364 | 0.042 |
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| USP_2018 | acc | 0.278 | 0.035 |
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### enem_challenge
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| Exam ID | Metric | Value | Stdev |
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| --------- | ------ | ----- | ----- |
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| all | acc | 0.363 | 0.007 |
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| 2016_2 | acc | 0.390 | 0.025 |
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| 2015 | acc | 0.319 | 0.025 |
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| 2011 | acc | 0.410 | 0.026 |
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| 2013 | acc | 0.398 | 0.027 |
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| 2017 | acc | 0.319 | 0.025 |
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| 2022 | acc | 0.376 | 0.024 |
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| 2009 | acc | 0.226 | 0.023 |
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| 2010 | acc | 0.444 | 0.026 |
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| 2012 | acc | 0.345 | 0.025 |
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| 2014 | acc | 0.339 | 0.026 |
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| 2016 | acc | 0.397 | 0.026 |
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| 2023 | acc | 0.385 | 0.024 |
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### faquad_nli
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| Metric | Value | Stdev |
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| --------- | ----- | ----- |
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| f1_macro | 0.595 | 0.017 |
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| acc | 0.791 | 0.011 |
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### hatebr_offensive
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| Metric | Value | Stdev |
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| --------- | ----- | ----- |
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| f1_macro | 0.338 | 0.005 |
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| acc | 0.502 | 0.009 |
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### oab_exams
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| Exam ID | Metric | Value | Stdev |
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| ------------- | ------ | ----- | ----- |
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| all | acc | 0.326 | 0.006 |
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| 2018-25 | acc | 0.400 | 0.032 |
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| 2016-20a | acc | 0.238 | 0.027 |
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| 2011-05 | acc | 0.400 | 0.032 |
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| 2012-08 | acc | 0.325 | 0.030 |
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| 2012-09 | acc | 0.260 | 0.029 |
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| 2014-13 | acc | 0.325 | 0.030 |
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| 2011-03 | acc | 0.313 | 0.027 |
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| 2016-20 | acc | 0.275 | 0.029 |
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| 2012-06a | acc | 0.325 | 0.030 |
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| 2017-22 | acc | 0.338 | 0.031 |
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| 2015-16 | acc | 0.325 | 0.030 |
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| 2013-12 | acc | 0.300 | 0.030 |
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| 2017-24 | acc | 0.250 | 0.028 |
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| 2012-06 | acc | 0.238 | 0.027 |
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| 2014-14 | acc | 0.325 | 0.030 |
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| 2013-11 | acc | 0.325 | 0.030 |
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| 2013-10 | acc | 0.413 | 0.032 |
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| 2010-02 | acc | 0.390 | 0.028 |
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| 2016-21 | acc | 0.375 | 0.031 |
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| 2015-18 | acc | 0.300 | 0.030 |
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| 2015-17 | acc | 0.282 | 0.029 |
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| 2016-19 | acc | 0.333 | 0.031 |
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| 2012-07 | acc | 0.388 | 0.031 |
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| 2017-23 | acc | 0.325 | 0.030 |
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| 2011-04 | acc | 0.350 | 0.031 |
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| 2010-01 | acc | 0.282 | 0.028 |
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| 2014-15 | acc | 0.385 | 0.032 |
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### portuguese_hate_speech
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| Metric | Value | Stdev |
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| --------- | ----- | ----- |
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| f1_macro | 0.412 | 0.004 |
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| acc | 0.702 | 0.011 |
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### tweetsentbr
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| Metric | Value | Stdev |
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| --------- | ----- | ----- |
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| f1_macro | 0.455 | 0.005 |
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| acc | 0.594 | 0.008 |
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## Model Meta Information
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* **Truncated Samples:** 3863
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* **Non-Truncated Samples:** 10287
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* **Padded Samples:** 0
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* **Non-Padded Samples:** 14150
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* **Fewshots Truncated:** 3863
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* **Has Chat Template:** True
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* **Chat Type:** system\_user\_assistant
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* **Number of GPUs:** 1
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* **Accelerate Number of Processes:** N/A
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* **Model SHA:** None
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* **Model Data Type:** torch.bfloat16
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* **Model Memory Footprint:** 988065664 bytes
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* **Model Number of Parameters:** 494032768
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* **Model is Loaded in 4bit:** N/A
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* **Model is Loaded in 8bit:** N/A
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* **Model is Quantized:** N/A
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* **Model Device:** cuda:0
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* **Batch Size:** 1
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* **Max Length:** 512
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* **Max Context Length** 480
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* **Max Generation Tokens:** 32
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* **Effective Batch Size:** 1.0
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# Open Portuguese LLM Leaderboard Evaluation Results
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Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/cnmoro/Qwen2.5-0.5B-Portuguese-v1) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
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| Metric | Value |
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|--------------------------|---------|
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|Average |**50.74**|
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|ENEM Challenge (No Images)| 37.86|
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|BLUEX (No Images) | 34.63|
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|OAB Exams | 33.12|
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|Assin2 RTE | 86.30|
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|Assin2 STS | 54.30|
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|FaQuAD NLI | 65.33|
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|HateBR Binary | 44.06|
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|PT Hate Speech Binary | 55.10|
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|tweetSentBR | 45.96|
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