metadata
library_name: transformers
license: mit
base_model: jhu-clsp/mmBERT-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: mmbert-xnli
results: []
mmbert-xnli
This model is a fine-tuned version of 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