README.md

metadata

library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - f1
model-index:
  - name: Finance_DistilBERT_sentiment
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: financial_phrasebank
          type: financial_phrasebank
          config: sentences_75agree
          split: train
          args: sentences_75agree
        metrics:
          - type: f1
            value: 0.9101001493367561
            name: F1

Finance_DistilBERT_sentiment

This model is a fine-tuned version of distilbert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

• Loss: 0.2763
• F1: 0.9101
• Acc: 0.9088

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: 4e-06
• train_batch_size: 32
• eval_batch_size: 32
• 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
• lr_scheduler_warmup_steps: 600
• num_epochs: 12

Training results (Final epoch)

Training Loss	Epoch	Step	Validation Loss	F1	Acc
0.0975	1.0	87	0.2763	0.9101	0.9088

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1
• Datasets 3.1.0
• Tokenizers 0.20.3

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()