metadata
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: []
output
This model is a fine-tuned version of 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