FIN_BERT_sentiment

This model is a fine-tuned version of 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

import plotly.graph_objects as go
%matplotlib inline
from transformers import pipeline

# Load the sentiment analysis pipeline
classifier = pipeline("text-classification", model="Sharpaxis/FIN_BERT_sentiment", top_k=None)

def finance_sentiment_predictor(text):
    text = str(text)
    out = classifier(text)[0]
    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]
    for i in range(len(scores)):
        print(f"{sentiments[i]} : {scores[i]}")
    print(f"Sentiment of text is {sentiments[np.argmax(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()