Instructions to use kamel-usp/jbcs2025_Phi-3.5-mini-instruct-phi35_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_Phi-3.5-mini-instruct-phi35_classification_lora-C2-full_context-r8 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/Phi-3.5-mini-instruct") model = PeftModel.from_pretrained(base_model, "kamel-usp/jbcs2025_Phi-3.5-mini-instruct-phi35_classification_lora-C2-full_context-r8") - Notebooks
- Google Colab
- Kaggle
File size: 1,213 Bytes
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---
language:
- pt
- en
tags:
- aes
datasets:
- kamel-usp/aes_enem_dataset
base_model: microsoft/Phi-3.5-mini-instruct
metrics:
- accuracy
- qwk
library_name: peft
model-index:
- name: Phi-3.5-mini-instruct-phi35_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: 0.1469089842699924
- name: QWK
type: qwk
value: 0.0458698830409355
- name: Weighted Macro F1
type: f1
value: 0.2236594123209007
---
# Model ID: Phi-3.5-mini-instruct-phi35_classification_lora-C2-full_context-r8
## Results
| | test_data |
|:-----------------|------------:|
| eval_accuracy | 0.224638 |
| eval_RMSE | 72.5518 |
| eval_QWK | 0.0458699 |
| eval_Macro_F1 | 0.146909 |
| eval_Weighted_F1 | 0.223659 |
| eval_Micro_F1 | 0.224638 |
| eval_HDIV | 0.15942 |
|