metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
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: 0.7842
- Accuracy: 0.6945
Model description
classify text to ["very negative", "negative", "neutral", "positive", "very positive"] if corresponding to labels [0,1,2,3,4]
Intended uses & limitations
More information needed
Training and evaluation data
used dataset from stanford sentiment analysis
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_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8692 | 1.0 | 11962 | 0.7449 | 0.6901 |
| 0.6567 | 2.0 | 23924 | 0.7272 | 0.6992 |
| 0.5388 | 3.0 | 35886 | 0.7842 | 0.6945 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1