---
library_name: transformers
license: mit
base_model: jhu-clsp/mmBERT-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  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. -->

# output

This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6104
- Precision: 0.6530
- Recall: 0.6880
- F1: 0.6700
- Accuracy: 0.7837

## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6422        | 1.0   | 971  | 0.6301          | 0.6543    | 0.6677 | 0.6609 | 0.7806   |
| 0.6043        | 2.0   | 1942 | 0.6104          | 0.6530    | 0.6880 | 0.6700 | 0.7837   |
| 0.5953        | 3.0   | 2913 | 0.6006          | 0.6385    | 0.6841 | 0.6605 | 0.7800   |
| 0.5578        | 4.0   | 3884 | 0.6148          | 0.6517    | 0.6811 | 0.6661 | 0.7831   |
| 0.4908        | 5.0   | 4855 | 0.6539          | 0.6385    | 0.6655 | 0.6518 | 0.7723   |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.2