Instructions to use kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/phi-4") model = PeftModel.from_pretrained(base_model, "kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- pt
- en
tags:
- aes
datasets:
- kamel-usp/aes_enem_dataset
base_model: microsoft/phi-4
metrics:
- accuracy
- qwk
library_name: peft
model-index:
- name: phi-4-phi4_classification_lora-C4-full_context
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: 0.2566666666666666
- name: QWK
type: qwk
value: 0.5601593625498008
- name: Weighted Macro F1
type: f1
value: 0.6872463768115942
Model ID: phi-4-phi4_classification_lora-C4-full_context
Results
| test_data | |
|---|---|
| eval_accuracy | 0.731884 |
| eval_RMSE | 23.5907 |
| eval_QWK | 0.560159 |
| eval_Macro_F1 | 0.256667 |
| eval_Weighted_F1 | 0.687246 |
| eval_Micro_F1 | 0.731884 |
| eval_HDIV | 0.00724638 |