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--- |
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license: apache-2.0 |
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base_model: projecte-aina/roberta-base-ca-v2-cased-te |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: 080524_epoch_5 |
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results: [] |
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pipeline_tag: zero-shot-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# 080524_epoch_5 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5972 |
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- Accuracy: 0.8445 |
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- Precision: 0.8448 |
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- Recall: 0.8445 |
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- F1: 0.8445 |
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- Ratio: 0.4874 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 2 |
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- seed: 47 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 20 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.06 |
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- lr_scheduler_warmup_steps: 4 |
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- num_epochs: 1 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| 0.4518 | 0.1626 | 10 | 0.6633 | 0.8361 | 0.8469 | 0.8361 | 0.8348 | 0.4118 | |
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| 0.4418 | 0.3252 | 20 | 0.6798 | 0.8277 | 0.8279 | 0.8277 | 0.8277 | 0.5126 | |
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| 0.5709 | 0.4878 | 30 | 0.7447 | 0.8193 | 0.8367 | 0.8193 | 0.8170 | 0.3866 | |
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| 0.6645 | 0.6504 | 40 | 0.6229 | 0.8487 | 0.8487 | 0.8487 | 0.8487 | 0.5 | |
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| 0.6606 | 0.8130 | 50 | 0.6014 | 0.8445 | 0.8446 | 0.8445 | 0.8445 | 0.5042 | |
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| 0.5763 | 0.9756 | 60 | 0.5972 | 0.8445 | 0.8448 | 0.8445 | 0.8445 | 0.4874 | |
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precision recall f1-score top1-score top2-score top3-score good1-score good2-score support |
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0 Aigua 0.632 0.545 0.585 0.545 0.818 0.955 0.955 0.955 22 |
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1 Consum, comerç i mercats 0.103 0.571 0.174 0.571 0.714 0.857 0.714 0.714 7 |
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2 Cultura 0.500 0.750 0.600 0.750 0.750 0.750 0.750 0.750 8 |
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3 Economia 0.211 0.500 0.296 0.500 0.875 1.000 0.875 0.875 8 |
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4 Educació 0.438 0.636 0.519 0.636 0.818 1.000 1.000 1.000 11 |
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5 Enllumenat públic 0.833 0.851 0.842 0.851 0.936 0.979 0.979 0.979 47 |
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6 Esports 0.562 0.750 0.643 0.750 0.917 1.000 1.000 1.000 12 |
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7 Habitatge 0.208 0.385 0.270 0.385 0.615 0.923 0.692 0.846 13 |
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8 Horta 0.000 0.000 0.000 0.000 0.444 0.556 0.556 0.556 9 |
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9 Medi ambient i jardins 0.429 0.559 0.485 0.559 0.729 0.915 0.915 0.915 59 |
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10 Neteja de la via pública 0.686 0.238 0.353 0.238 0.505 0.772 0.762 0.762 101 |
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11 Salut pública 0.135 0.292 0.184 0.292 0.708 0.792 0.708 0.708 24 |
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12 Seguretat ciutadana i incivisme 0.727 0.471 0.571 0.471 0.588 0.765 0.706 0.706 34 |
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13 Serveis socials 0.333 0.667 0.444 0.667 0.889 0.889 0.889 0.889 9 |
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14 Tràmits 0.395 0.395 0.395 0.395 0.884 0.907 0.907 0.907 43 |
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15 Urbanisme 0.379 0.172 0.237 0.172 0.453 0.641 0.578 0.578 64 |
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16 Via pública i mobilitat 0.778 0.778 0.778 0.778 0.846 0.889 0.864 0.867 279 |
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macro avg 0.432 0.504 0.434 0.504 0.735 0.858 0.815 0.824 750 |
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weighted avg 0.610 0.557 0.559 0.557 0.739 0.853 0.825 0.829 750 |
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accuracy 0.557 |
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error rate 0.443 |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
