Instructions to use kamel-usp/jbcs2025_Llama-3.1-8B-llama31_classification_lora-C2-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-C2-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-C2-full_context-r8") - Notebooks
- Google Colab
- Kaggle
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---
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-C2-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: 0.2094969325209262
- name: QWK
type: qwk
value: 0.2486950665095133
- name: Weighted Macro F1
type: f1
value: 0.2621306510598505
---
# Model ID: Llama-3.1-8B-llama31_classification_lora-C2-full_context-r8
## Results
| | test_data |
|:-----------------|------------:|
| eval_accuracy | 0.268116 |
| eval_RMSE | 67.0712 |
| eval_QWK | 0.248695 |
| eval_Macro_F1 | 0.209497 |
| eval_Weighted_F1 | 0.262131 |
| eval_Micro_F1 | 0.268116 |
| eval_HDIV | 0.188406 |
|