File size: 2,809 Bytes
ca03f8d e6912c0 ca03f8d e6912c0 ca03f8d e6912c0 ca03f8d e6912c0 ca03f8d e6912c0 ea4cff4 ca03f8d e6912c0 ea4cff4 e6912c0 ca03f8d |
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 |
---
library_name: transformers
license: mit
base_model: xaviergillard/xlm-roberta-large-vieille-france
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: xlm-roberta-large-vieille-france-v2
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. -->
# xlm-roberta-large-vieille-france-v2
This model is a fine-tuned version of [xaviergillard/xlm-roberta-large-vieille-france](https://huggingface.co/xaviergillard/xlm-roberta-large-vieille-france) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0960
- Precision: 0.7626
- Recall: 0.8061
- F1: 0.7838
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 54 | 0.0609 | 0.7207 | 0.7966 | 0.7568 |
| No log | 2.0 | 108 | 0.0541 | 0.7140 | 0.8054 | 0.7570 |
| No log | 3.0 | 162 | 0.0585 | 0.7595 | 0.8083 | 0.7831 |
| No log | 4.0 | 216 | 0.0592 | 0.7384 | 0.8361 | 0.7842 |
| No log | 5.0 | 270 | 0.0684 | 0.7379 | 0.7827 | 0.7597 |
| No log | 6.0 | 324 | 0.0649 | 0.7568 | 0.8193 | 0.7868 |
| No log | 7.0 | 378 | 0.0668 | 0.7585 | 0.8113 | 0.7840 |
| No log | 8.0 | 432 | 0.0825 | 0.7094 | 0.8142 | 0.7582 |
| No log | 9.0 | 486 | 0.0767 | 0.7653 | 0.8252 | 0.7941 |
| 0.0406 | 10.0 | 540 | 0.0841 | 0.7621 | 0.8040 | 0.7825 |
| 0.0406 | 11.0 | 594 | 0.0850 | 0.7678 | 0.8032 | 0.7851 |
| 0.0406 | 12.0 | 648 | 0.0880 | 0.7579 | 0.7944 | 0.7757 |
| 0.0406 | 13.0 | 702 | 0.0914 | 0.7619 | 0.8054 | 0.7831 |
| 0.0406 | 14.0 | 756 | 0.0950 | 0.7613 | 0.8003 | 0.7803 |
| 0.0406 | 15.0 | 810 | 0.0960 | 0.7626 | 0.8061 | 0.7838 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.2
- Datasets 3.3.0
- Tokenizers 0.21.0
|