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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- f1
model-index:
- name: FIN_BERT_sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
config: sentences_66agree
split: train
args: sentences_66agree
metrics:
- type: f1
value: 0.8890693407692588
name: F1
---
<!-- 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. -->
# FIN_BERT_sentiment
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: 0.4905
- F1: 0.8891
- Acc: 0.8886
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.5295 | 1.0 | 211 | 0.3757 | 0.8731 | 0.8720 |
| 0.2174 | 2.0 | 422 | 0.3117 | 0.8911 | 0.8910 |
| 0.1129 | 3.0 | 633 | 0.4066 | 0.8886 | 0.8874 |
| 0.0459 | 4.0 | 844 | 0.4923 | 0.8896 | 0.8886 |
| 0.0275 | 5.0 | 1055 | 0.4905 | 0.8891 | 0.8886 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
## Code to use model as pipeline classifier
```python
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from IPython.display import display, HTML
import numpy as np
from transformers import pipeline
%matplotlib inline
# Pipelines
classifier = pipeline("text-classification", model="Sharpaxis/Finance_DistilBERT_sentiment", top_k=None)
pipe = pipeline("text-classification", model="Sharpaxis/News_classification_distilbert")
def finance_text_predictor(text):
text = str(text)
out = classifier(text)[0]
type_news = pipe(text)[0]
# Display news type and text in HTML
if type_news['label'] == 'LABEL_1':
display(HTML(f"""
<div style="border: 2px solid red; padding: 10px; margin: 10px; background-color: #ffe6e6; color: black; font-weight: bold;">
IMPORTANT TECH/FIN News<br>
<div style="margin-top: 10px; font-weight: normal; font-size: 14px; color: darkred;">{text}</div>
</div>
"""))
elif type_news['label'] == 'LABEL_0':
display(HTML(f"""
<div style="border: 2px solid green; padding: 10px; margin: 10px; background-color: #e6ffe6; color: black; font-weight: bold;">
NON IMPORTANT NEWS<br>
<div style="margin-top: 10px; font-weight: normal; font-size: 14px; color: darkgreen;">{text}</div>
</div>
"""))
# Sentiment analysis scores
scores = [sample['score'] for sample in out]
labels = [sample['label'] for sample in out]
label_map = {'LABEL_0': "Negative", 'LABEL_1': "Neutral", 'LABEL_2': "Positive"}
sentiments = [label_map[label] for label in labels]
print("SCORES")
for i in range(len(scores)):
print(f"{sentiments[i]} : {scores[i]:.4f}")
print(f"Sentiment of text is {sentiments[np.argmax(scores)]}")
# Bar chart for sentiment scores
fig = go.Figure(
data=[go.Bar(x=sentiments, y=scores, marker=dict(color=["red", "blue", "green"]), width=0.3)]
)
fig.update_layout(
title="Sentiment Analysis Scores",
xaxis_title="Sentiments",
yaxis_title="Scores",
template="plotly_dark"
)
fig.show() |