---
library_name: transformers
license: mit
base_model: jhu-clsp/mmBERT-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: mmbert-xnli
  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. -->

# mmbert-xnli

This model is a fine-tuned version of [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3736
- Accuracy: 0.8863
- F1: 0.8863

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 337  | 0.4502          | 0.8600   | 0.8587 |
| 0.6169        | 2.0   | 674  | 0.3983          | 0.8796   | 0.8786 |
| 0.2926        | 3.0   | 1011 | 0.3693          | 0.8904   | 0.8907 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2