Instructions to use kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C2-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-4-phi4_classification_lora-C2-full_context-r16 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-C2-full_context-r16") - Notebooks
- Google Colab
- Kaggle
Download evaluation_results.csv from kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C2-full_context-r16: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C2-full_context-r16/resolve/main/evaluation_results.csv
- Command line
-
hf download hf://kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C2-full_context-r16/evaluation_results.csv
-
curl -L -o evaluation_results.csv https://huggingface.co/kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C2-full_context-r16/resolve/main/evaluation_results.csv
1.47 kB
| eval_loss,eval_model_preparation_time,eval_accuracy,eval_RMSE,eval_QWK,eval_HDIV,eval_Macro_F1,eval_Micro_F1,eval_Weighted_F1,eval_TP_0,eval_TN_0,eval_FP_0,eval_FN_0,eval_TP_1,eval_TN_1,eval_FP_1,eval_FN_1,eval_TP_2,eval_TN_2,eval_FP_2,eval_FN_2,eval_TP_3,eval_TN_3,eval_FP_3,eval_FN_3,eval_TP_4,eval_TN_4,eval_FP_4,eval_FN_4,eval_TP_5,eval_TN_5,eval_FP_5,eval_FN_5,eval_runtime,eval_samples_per_second,eval_steps_per_second,epoch,reference,timestamp,id | |
| 4.016066074371338,0.0091,0.22727272727272727,61.987290975039734,0.0,0.19696969696969702,0.07407407407407407,0.22727272727272727,0.08417508417508417,0,131,0,1,0,107,0,25,0,132,0,0,0,71,0,61,30,0,102,0,0,117,0,15,47.2806,2.792,0.698,-1,validation_before_training,2025-07-06 05:43:21,phi-4-phi4_classification_lora-C2-full_context-r16 | |
| 1.3566747903823853,0.0091,0.4318181818181818,53.59782899266791,0.577060350421804,0.030303030303030276,0.2910434735561938,0.4318181818181818,0.3887009869910726,0,131,0,1,20,83,24,5,0,132,0,0,26,57,14,35,0,100,2,30,11,82,35,4,46.9596,2.811,0.703,13.0,validation_after_training,2025-07-06 05:43:21,phi-4-phi4_classification_lora-C2-full_context-r16 | |
| 1.3918403387069702,0.0091,0.5144927536231884,52.08688393694587,0.5905356055995131,0.050724637681159424,0.36549678320993584,0.5144927536231884,0.45549927273540014,0,137,0,1,29,78,25,6,4,125,8,1,26,65,22,25,0,112,0,26,12,106,12,8,49.6901,2.777,0.704,13.0,test_results,2025-07-06 05:43:21,phi-4-phi4_classification_lora-C2-full_context-r16 | |