Instructions to use kamel-usp/jbcs2025_Phi-3.5-mini-instruct-phi35_classification_lora-C1-essay_only-r16 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-C1-essay_only-r16 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-C1-essay_only-r16") - Notebooks
- Google Colab
- Kaggle
File size: 1,210 Bytes
e78373e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
---
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-C1-essay_only-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: 0.307120596205962
- name: QWK
type: qwk
value: 0.4240400667779632
- name: Weighted Macro F1
type: f1
value: 0.3921129374337222
---
# Model ID: Phi-3.5-mini-instruct-phi35_classification_lora-C1-essay_only-r16
## Results
| | test_data |
|:-----------------|------------:|
| eval_accuracy | 0.413043 |
| eval_RMSE | 37.3002 |
| eval_QWK | 0.42404 |
| eval_Macro_F1 | 0.307121 |
| eval_Weighted_F1 | 0.392113 |
| eval_Micro_F1 | 0.413043 |
| eval_HDIV | 0.00724638 |
|