metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: anti_semic_test_trainer
results: []
anti_semic_test_trainer
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6848
- Accuracy: 0.8214
- F1: 0.8148
- Precision: 0.825
- Recall: 0.8049
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 68 | 0.4601 | 0.8370 | 0.7963 | 0.8776 | 0.7288 |
| No log | 2.0 | 136 | 0.4343 | 0.7926 | 0.8056 | 0.6824 | 0.9831 |
| No log | 3.0 | 204 | 0.4401 | 0.8370 | 0.8333 | 0.7534 | 0.9322 |
| No log | 4.0 | 272 | 0.4575 | 0.8889 | 0.8780 | 0.8438 | 0.9153 |
| No log | 5.0 | 340 | 0.5199 | 0.8444 | 0.8108 | 0.8654 | 0.7627 |
| No log | 6.0 | 408 | 0.5788 | 0.8222 | 0.7692 | 0.8889 | 0.6780 |
| No log | 7.0 | 476 | 0.5212 | 0.8519 | 0.8214 | 0.8679 | 0.7797 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cpu
- Datasets 2.16.1
- Tokenizers 0.14.1