--- license: apache-2.0 base_model: projecte-aina/roberta-base-ca-v2-cased-te tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: 080524_epoch_5 results: [] pipeline_tag: zero-shot-classification --- # 080524_epoch_5 This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5972 - Accuracy: 0.8445 - Precision: 0.8448 - Recall: 0.8445 - F1: 0.8445 - Ratio: 0.4874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 2 - seed: 47 - gradient_accumulation_steps: 2 - total_train_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - lr_scheduler_warmup_steps: 4 - num_epochs: 1 - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| | 0.4518 | 0.1626 | 10 | 0.6633 | 0.8361 | 0.8469 | 0.8361 | 0.8348 | 0.4118 | | 0.4418 | 0.3252 | 20 | 0.6798 | 0.8277 | 0.8279 | 0.8277 | 0.8277 | 0.5126 | | 0.5709 | 0.4878 | 30 | 0.7447 | 0.8193 | 0.8367 | 0.8193 | 0.8170 | 0.3866 | | 0.6645 | 0.6504 | 40 | 0.6229 | 0.8487 | 0.8487 | 0.8487 | 0.8487 | 0.5 | | 0.6606 | 0.8130 | 50 | 0.6014 | 0.8445 | 0.8446 | 0.8445 | 0.8445 | 0.5042 | | 0.5763 | 0.9756 | 60 | 0.5972 | 0.8445 | 0.8448 | 0.8445 | 0.8445 | 0.4874 | precision recall f1-score top1-score top2-score top3-score good1-score good2-score support 0 Aigua 0.632 0.545 0.585 0.545 0.818 0.955 0.955 0.955 22 1 Consum, comerç i mercats 0.103 0.571 0.174 0.571 0.714 0.857 0.714 0.714 7 2 Cultura 0.500 0.750 0.600 0.750 0.750 0.750 0.750 0.750 8 3 Economia 0.211 0.500 0.296 0.500 0.875 1.000 0.875 0.875 8 4 Educació 0.438 0.636 0.519 0.636 0.818 1.000 1.000 1.000 11 5 Enllumenat públic 0.833 0.851 0.842 0.851 0.936 0.979 0.979 0.979 47 6 Esports 0.562 0.750 0.643 0.750 0.917 1.000 1.000 1.000 12 7 Habitatge 0.208 0.385 0.270 0.385 0.615 0.923 0.692 0.846 13 8 Horta 0.000 0.000 0.000 0.000 0.444 0.556 0.556 0.556 9 9 Medi ambient i jardins 0.429 0.559 0.485 0.559 0.729 0.915 0.915 0.915 59 10 Neteja de la via pública 0.686 0.238 0.353 0.238 0.505 0.772 0.762 0.762 101 11 Salut pública 0.135 0.292 0.184 0.292 0.708 0.792 0.708 0.708 24 12 Seguretat ciutadana i incivisme 0.727 0.471 0.571 0.471 0.588 0.765 0.706 0.706 34 13 Serveis socials 0.333 0.667 0.444 0.667 0.889 0.889 0.889 0.889 9 14 Tràmits 0.395 0.395 0.395 0.395 0.884 0.907 0.907 0.907 43 15 Urbanisme 0.379 0.172 0.237 0.172 0.453 0.641 0.578 0.578 64 16 Via pública i mobilitat 0.778 0.778 0.778 0.778 0.846 0.889 0.864 0.867 279 macro avg 0.432 0.504 0.434 0.504 0.735 0.858 0.815 0.824 750 weighted avg 0.610 0.557 0.559 0.557 0.739 0.853 0.825 0.829 750 accuracy 0.557 error rate 0.443 ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1