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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
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