metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_financial_phrasebank
results: []
datasets:
- financial_phrasebank
library_name: transformers
tps_sentimental_analysis
This model is a fine-tuned version of bert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.2586
- Accuracy: 0.9604
Model description
A fine-tuned version of bert-base-uncased
Intended uses & limitations
Sentimental Analysis
| Lines | Emotions |
|---|---|
| Hi, Harper. I’m really happy you came. | Positive |
| Happy Father’s Day. | Positive |
| It was Christmas. | Neutral |
| HARPER sits at a table alone in a room. | Neutral |
| I am mad at you badly. | Negative |
Training and evaluation data
financial_phrasebank
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 114 | 0.5293 | 0.8230 |
| No log | 2.0 | 228 | 0.0804 | 0.9779 |
| No log | 3.0 | 342 | 0.0367 | 0.9867 |
| No log | 4.0 | 456 | 0.1544 | 0.9646 |
| 0.3241 | 5.0 | 570 | 0.0497 | 0.9912 |
| 0.3241 | 6.0 | 684 | 0.0520 | 0.9912 |
| 0.3241 | 7.0 | 798 | 0.0318 | 0.9912 |
| 0.3241 | 8.0 | 912 | 0.0628 | 0.9912 |
| 0.0218 | 9.0 | 1026 | 0.0777 | 0.9867 |
| 0.0218 | 10.0 | 1140 | 0.0866 | 0.9867 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.1.0+cu118
- Tokenizers 0.13.3