metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: bert-base-uncased
model-index:
- name: finetuning-sentiment-analysis-en
results: []
finetuning-sentiment-analysis-en
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: 0.0792
- Accuracy: 0.9803
- F1: 0.9856
- Precision: 0.9875
- Recall: 0.9837
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: 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.426 | 1.0 | 1408 | 0.2718 | 0.8910 | 0.9201 | 0.9251 | 0.9151 |
| 0.3247 | 2.0 | 2816 | 0.1552 | 0.9540 | 0.9665 | 0.9656 | 0.9674 |
| 0.1582 | 3.0 | 4224 | 0.0792 | 0.9803 | 0.9856 | 0.9875 | 0.9837 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1