Instructions to use kamel-usp/jbcs2025_Tucano-2b4-Instruct-tucano_classification_lora-C4-full_context-r16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kamel-usp/jbcs2025_Tucano-2b4-Instruct-tucano_classification_lora-C4-full_context-r16 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("TucanoBR/Tucano-2b4-Instruct") model = PeftModel.from_pretrained(base_model, "kamel-usp/jbcs2025_Tucano-2b4-Instruct-tucano_classification_lora-C4-full_context-r16") - Notebooks
- Google Colab
- Kaggle
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---
language:
- pt
- en
tags:
- aes
datasets:
- kamel-usp/aes_enem_dataset
base_model: TucanoBR/Tucano-2b4-Instruct
metrics:
- accuracy
- qwk
library_name: peft
model-index:
- name: Tucano-2b4-Instruct-tucano_classification_lora-C4-full_context-r16
results:
- task:
type: text-classification
name: Automated Essay Score
dataset:
name: Automated Essay Score ENEM Dataset
type: kamel-usp/aes_enem_dataset
config: JBCS2025
split: test
metrics:
- name: Macro F1
type: f1
value: 0.3983903420523138
- name: QWK
type: qwk
value: 0.5176260597947346
- name: Weighted Macro F1
type: f1
value: 0.5923346844351132
---
# Model ID: Tucano-2b4-Instruct-tucano_classification_lora-C4-full_context-r16
## Results
| | test_data |
|:-----------------|------------:|
| eval_accuracy | 0.57971 |
| eval_RMSE | 33.013 |
| eval_QWK | 0.517626 |
| eval_Macro_F1 | 0.39839 |
| eval_Weighted_F1 | 0.592335 |
| eval_Micro_F1 | 0.57971 |
| eval_HDIV | 0.0217391 |
|