Instructions to use kamel-usp/jbcs2025_Llama-3.1-8B-llama31_classification_lora-C1-full_context-r8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kamel-usp/jbcs2025_Llama-3.1-8B-llama31_classification_lora-C1-full_context-r8 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "kamel-usp/jbcs2025_Llama-3.1-8B-llama31_classification_lora-C1-full_context-r8") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- pt
- en
tags:
- aes
datasets:
- kamel-usp/aes_enem_dataset
base_model: meta-llama/Llama-3.1-8B
metrics:
- accuracy
- qwk
library_name: peft
model-index:
- name: Llama-3.1-8B-llama31_classification_lora-C1-full_context-r8
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: 0.3562924218759459
- name: QWK
type: qwk
value: 0.5690952762209768
- name: Weighted Macro F1
type: f1
value: 0.5532273763594029
Model ID: Llama-3.1-8B-llama31_classification_lora-C1-full_context-r8
Results
| test_data | |
|---|---|
| eval_accuracy | 0.557971 |
| eval_RMSE | 30.0724 |
| eval_QWK | 0.569095 |
| eval_Macro_F1 | 0.356292 |
| eval_Weighted_F1 | 0.553227 |
| eval_Micro_F1 | 0.557971 |
| eval_HDIV | 0.00724638 |