metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
results: []
results
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6067
- Accuracy: 0.925
- Precision: 0.8676
- Recall: 0.7415
- F1: 0.7869
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 50 | 0.5663 | 0.905 | 0.7726 | 0.8381 | 0.7997 |
| No log | 2.0 | 100 | 0.4798 | 0.92 | 0.8020 | 0.8646 | 0.8287 |
| No log | 3.0 | 150 | 0.4370 | 0.92 | 0.8424 | 0.7386 | 0.7778 |
| No log | 4.0 | 200 | 0.4873 | 0.93 | 0.85 | 0.7983 | 0.8212 |
| No log | 5.0 | 250 | 0.6067 | 0.925 | 0.8676 | 0.7415 | 0.7869 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1