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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_financial_phrasebank
results: []
datasets:
- financial_phrasebank
library_name: transformers
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tps_sentimental_analysis
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./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](https://huggingface.co./bert-base-uncased)
## Intended uses & limitations
Sentimental Analysis
| Lines | Emotions |
|:-----:|:--------:|
|HARPER sits at a table alone in a room.| Neutral |
|Hi, Harper. I’m really happy you came.| Positive |
|Happy Father’s Day.| Positive |
|It was Christmas.| Neutral |
## 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 |