File size: 2,812 Bytes
bc0e8bc 0d23c5f bc0e8bc 0d23c5f bc0e8bc 0d23c5f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- roc_auc
- f1
model-index:
- name: results_RoBERTa
results: []
datasets:
- alecmontero/dataset_tweetsmx_areasCPC
language:
- es
library_name: transformers
---
<!-- 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. -->
# results_RoBERTa
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- Roc Auc: 0.8669
- Hamming Loss: 0.0454
- F1 Score: 0.7761
- Accuracy: 0.4712
- Precision: 0.7977
- Recall: 0.7665
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc | Hamming Loss | F1 Score | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:--------:|:--------:|:---------:|:------:|
| No log | 1.0 | 374 | 0.1904 | 0.6981 | 0.0674 | 0.4749 | 0.3440 | 0.7840 | 0.4297 |
| 0.2476 | 2.0 | 748 | 0.1674 | 0.7439 | 0.0612 | 0.5672 | 0.3802 | 0.8482 | 0.5228 |
| 0.1597 | 3.0 | 1122 | 0.1512 | 0.7955 | 0.0545 | 0.6516 | 0.4163 | 0.8172 | 0.6218 |
| 0.1597 | 4.0 | 1496 | 0.1414 | 0.8087 | 0.0511 | 0.6736 | 0.4324 | 0.8251 | 0.6535 |
| 0.1222 | 5.0 | 1870 | 0.1395 | 0.8344 | 0.0490 | 0.7153 | 0.4378 | 0.8190 | 0.7038 |
| 0.09 | 6.0 | 2244 | 0.1385 | 0.8485 | 0.0477 | 0.7552 | 0.4645 | 0.8182 | 0.7315 |
| 0.0663 | 7.0 | 2618 | 0.1391 | 0.8544 | 0.0466 | 0.7617 | 0.4712 | 0.7936 | 0.7401 |
| 0.0663 | 8.0 | 2992 | 0.1365 | 0.8669 | 0.0454 | 0.7761 | 0.4712 | 0.7977 | 0.7665 |
| 0.0461 | 9.0 | 3366 | 0.1375 | 0.8617 | 0.0460 | 0.7711 | 0.4699 | 0.7956 | 0.7569 |
| 0.0293 | 10.0 | 3740 | 0.1388 | 0.8636 | 0.0448 | 0.7736 | 0.4926 | 0.7953 | 0.7592 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |