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

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()