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---
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: bert-xnli-es-classifier
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli
type: xnli
config: es
split: validation
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.827710843373494
---
<!-- 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. -->
# bert-xnli-es-classifier
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5109
- Accuracy: 0.8277
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4401 | 1.0 | 6136 | 0.4733 | 0.8116 |
| 0.4245 | 2.0 | 12272 | 0.4667 | 0.8309 |
| 0.29 | 3.0 | 18408 | 0.5109 | 0.8277 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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