XLMR-sing-ca-fr / README.md
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---
library_name: transformers
license: apache-2.0
base_model: cis-lmu/glot500-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLMR-sing-ca-fr
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. -->
# XLMR-sing-ca-fr
This model is a fine-tuned version of [cis-lmu/glot500-base](https://huggingface.co/cis-lmu/glot500-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1314
- Precision: 0.9641
- Recall: 0.9631
- F1: 0.9636
- Accuracy: 0.9652
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8087 | 1.0 | 625 | 0.1718 | 0.9565 | 0.9557 | 0.9561 | 0.9597 |
| 0.1359 | 2.0 | 1250 | 0.1314 | 0.9641 | 0.9631 | 0.9636 | 0.9652 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0