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---
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: hib30_0524_epoch_4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hib30_0524_epoch_4

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: 0.3876
- Accuracy: 0.955
- Precision: 0.9553
- Recall: 0.955
- F1: 0.9550
- Ratio: 0.487

## 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: 16
- eval_batch_size: 16
- seed: 47
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----:|
| 0.3491        | 0.04  | 10   | 0.3923          | 0.951    | 0.9510    | 0.951  | 0.9510 | 0.495 |
| 0.3703        | 0.08  | 20   | 0.3979          | 0.954    | 0.9550    | 0.954  | 0.9540 | 0.476 |
| 0.3298        | 0.12  | 30   | 0.4131          | 0.95     | 0.9500    | 0.95   | 0.9500 | 0.498 |
| 0.3453        | 0.16  | 40   | 0.4259          | 0.948    | 0.9489    | 0.948  | 0.9480 | 0.478 |
| 0.3714        | 0.2   | 50   | 0.4134          | 0.951    | 0.9523    | 0.9510 | 0.9510 | 0.473 |
| 0.3345        | 0.24  | 60   | 0.4098          | 0.949    | 0.9490    | 0.949  | 0.9490 | 0.495 |
| 0.3626        | 0.28  | 70   | 0.3956          | 0.949    | 0.9490    | 0.949  | 0.9490 | 0.503 |
| 0.3712        | 0.32  | 80   | 0.3853          | 0.958    | 0.9587    | 0.958  | 0.9580 | 0.48  |
| 0.3403        | 0.36  | 90   | 0.3945          | 0.954    | 0.9542    | 0.954  | 0.9540 | 0.49  |
| 0.3592        | 0.4   | 100  | 0.4063          | 0.951    | 0.9510    | 0.951  | 0.9510 | 0.505 |
| 0.3839        | 0.44  | 110  | 0.3904          | 0.954    | 0.9552    | 0.954  | 0.9540 | 0.474 |
| 0.3685        | 0.48  | 120  | 0.3999          | 0.949    | 0.9512    | 0.9490 | 0.9489 | 0.465 |
| 0.368         | 0.52  | 130  | 0.3817          | 0.958    | 0.9583    | 0.958  | 0.9580 | 0.488 |
| 0.3658        | 0.56  | 140  | 0.3862          | 0.957    | 0.9572    | 0.957  | 0.9570 | 0.489 |
| 0.3752        | 0.6   | 150  | 0.4040          | 0.954    | 0.9561    | 0.954  | 0.9539 | 0.466 |
| 0.3376        | 0.64  | 160  | 0.3977          | 0.956    | 0.9572    | 0.956  | 0.9560 | 0.474 |
| 0.3531        | 0.68  | 170  | 0.3943          | 0.958    | 0.9587    | 0.958  | 0.9580 | 0.48  |
| 0.3433        | 0.72  | 180  | 0.4013          | 0.956    | 0.9576    | 0.956  | 0.9560 | 0.47  |
| 0.396         | 0.76  | 190  | 0.3928          | 0.955    | 0.9557    | 0.9550 | 0.9550 | 0.481 |
| 0.3993        | 0.8   | 200  | 0.3895          | 0.955    | 0.9555    | 0.955  | 0.9550 | 0.483 |
| 0.3738        | 0.84  | 210  | 0.3865          | 0.955    | 0.9553    | 0.955  | 0.9550 | 0.487 |
| 0.334         | 0.88  | 220  | 0.3872          | 0.954    | 0.9544    | 0.954  | 0.9540 | 0.486 |
| 0.4014        | 0.92  | 230  | 0.3880          | 0.955    | 0.9553    | 0.955  | 0.9550 | 0.487 |
| 0.4279        | 0.96  | 240  | 0.3878          | 0.955    | 0.9553    | 0.955  | 0.9550 | 0.487 |
| 0.358         | 1.0   | 250  | 0.3876          | 0.955    | 0.9553    | 0.955  | 0.9550 | 0.487 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

METRICS REPORT                         precision      recall    f1-score  top1-score  top2-score  top3-score good1-score good2-score     support
  0                           Aigües       1.000       0.960       0.980       0.960       0.960       1.000       0.960       0.960          25
  1         Consum, comerç i mercats       0.852       0.920       0.885       0.920       1.000       1.000       1.000       1.000          25
  2                          Cultura       0.917       0.880       0.898       0.880       0.960       1.000       0.960       0.960          25
  3                         Economia       0.792       0.760       0.776       0.760       0.920       0.960       0.920       0.920          25
  4                         Educació       0.852       0.920       0.885       0.920       1.000       1.000       1.000       1.000          25
  5                Enllumenat públic       0.920       0.920       0.920       0.920       1.000       1.000       1.000       1.000          25
  6                          Esports       1.000       1.000       1.000       1.000       1.000       1.000       1.000       1.000          25
  7                        Habitatge       0.667       0.800       0.727       0.800       0.840       0.880       0.840       0.840          25
  8                            Horta       0.913       0.840       0.875       0.840       0.960       1.000       0.920       0.920          25
  9               Informació general       0.750       0.600       0.667       0.600       0.960       1.000       0.920       0.960          25
 10                      Informàtica       0.947       0.720       0.818       0.720       0.960       0.960       0.960       0.960          25
 11                         Joventut       0.913       0.840       0.875       0.840       1.000       1.000       1.000       1.000          25
 12                     Medi ambient       0.882       0.600       0.714       0.600       0.960       0.960       0.920       0.920          25
 13         Neteja de la via pública       0.792       0.760       0.776       0.760       0.960       1.000       1.000       1.000          25
 14        Salut pública i Cementiri       0.880       0.880       0.880       0.880       1.000       1.000       1.000       1.000          25
 15                        Seguretat       0.909       0.800       0.851       0.800       1.000       1.000       1.000       1.000          25
 16                  Serveis socials       0.857       0.960       0.906       0.960       1.000       1.000       1.000       1.000          25
 17                     Tramitacions       0.677       0.840       0.750       0.840       1.000       1.000       0.960       0.960          25
 18                        Urbanisme       0.864       0.760       0.809       0.760       0.880       0.920       0.920       0.920          25
 19          Via pública i mobilitat       0.575       0.920       0.708       0.920       0.960       1.000       1.000       1.000          25
                           macro avg       0.848       0.834       0.835       0.834       0.966       0.984       0.964       0.966         500
                        weighted avg       0.848       0.834       0.835       0.834       0.966       0.984       0.964       0.966         500
                            accuracy       0.834
                          error rate       0.166