README.md

---
language: 
  - en
thumbnail:  https://cdn.pixabay.com/photo/2017/09/07/08/54/money-2724241__340.jpg
tags:
- text-classification
- sentiment-analysis
- finance-sentiment-detection
- finance-sentiment
license: apache-2.0
datasets:
- cyrilzhang/financial_phrasebank_split
metrics:
- Accuracy, F1 score
widget:
- text: "HK stocks open lower after Fed rate comments"
  example_title: "HK stocks open lower"
- text: "US stocks end lower on earnings worries"
  example_title: "US stocks end lower"
- text: "Muted Fed, AI hopes send Wall Street higher"  
  example_title: "Muted Fed"

---
## nickwong64/bert-base-uncased-finance-sentiment
Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective.
[bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the [cyrilzhang/financial_phrasebank_split](https://huggingface.co/datasets/cyrilzhang/financial_phrasebank_split) dataset using HuggingFace Trainer with below training parameters.
```
 learning rate 2e-5, 
 batch size 8,
 num_train_epochs=6,
```

 ## Model Performance
| Epoch | Training Loss | Validation Loss | Accuracy | F1 |
| --- | --- | --- | --- | --- |
| 6 | 0.034100	| 0.954745 | 0.853608 | 0.854358 |



 ## How to Use the Model
```python 
from transformers import pipeline
nlp = pipeline(task='text-classification', 
               model='nickwong64/bert-base-uncased-finance-sentiment')
p1 = "HK stocks open lower after Fed rate comments"
p2 = "US stocks end lower on earnings worries"
p3 = "Muted Fed, AI hopes send Wall Street higher"
print(nlp(p1))
print(nlp(p2))
print(nlp(p3))
"""
output:
[{'label': 'negative', 'score': 0.9991507530212402}]
[{'label': 'negative', 'score': 0.9997240900993347}]
[{'label': 'neutral', 'score': 0.9834381937980652}]
"""
```

## Dataset
  [cyrilzhang/financial_phrasebank_split](https://huggingface.co/datasets/cyrilzhang/financial_phrasebank_split) 

## Labels
```
{0: 'negative', 1: 'neutral', 2: 'positive'}
```

## Evaluation
```
{'test_loss': 0.9547446370124817,
 'test_accuracy': 0.8536082474226804,
 'test_f1': 0.8543579048224414,
 'test_runtime': 4.9865,
 'test_samples_per_second': 97.263,
 'test_steps_per_second': 12.233}
```