metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 10k_test3_nli_finetuned_bert
results: []
10k_test3_nli_finetuned_bert
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9076
- Accuracy: 0.6693
- F1 Weighted: 0.6690
- Precision Weighted: 0.6889
- Recall Weighted: 0.6693
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 | F1 Weighted | Precision Weighted | Recall Weighted |
|---|---|---|---|---|---|---|---|
| 0.8753 | 1.0 | 312 | 0.9773 | 0.5313 | 0.5138 | 0.6276 | 0.5313 |
| 0.6746 | 2.0 | 625 | 0.7384 | 0.692 | 0.6927 | 0.6954 | 0.692 |
| 0.4239 | 3.0 | 936 | 0.9076 | 0.6693 | 0.6690 | 0.6889 | 0.6693 |
Framework versions
- Transformers 4.27.2
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2