Instructions to use kamel-usp/jbcs2025_Phi-3.5-mini-instruct-phi35_classification_lora-C1-full_context-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-full_context-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-full_context-r16") - Notebooks
- Google Colab
- Kaggle
| 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-full_context-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: 0.1697680097680097 | |
| - name: QWK | |
| type: qwk | |
| value: 0.2635988065182465 | |
| - name: Weighted Macro F1 | |
| type: f1 | |
| value: 0.2978287413070022 | |
| # Model ID: Phi-3.5-mini-instruct-phi35_classification_lora-C1-full_context-r16 | |
| ## Results | |
| | | test_data | | |
| |:-----------------|------------:| | |
| | eval_accuracy | 0.275362 | | |
| | eval_RMSE | 46.4384 | | |
| | eval_QWK | 0.263599 | | |
| | eval_Macro_F1 | 0.169768 | | |
| | eval_Weighted_F1 | 0.297829 | | |
| | eval_Micro_F1 | 0.275362 | | |
| | eval_HDIV | 0.00724638 | | |