news_sentiment_classifier

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news_sentiment_classifier

This model is a fine-tuned version of distilbert-base-uncased on the Sentiment analysis for financial news dataset. It achieves the following results on the evaluation set:

• Accuracy: 0.8201
• F1: 0.8205
• Loss: 0.4548

Model Description

• Base Architecture: DistilBERT (distilbert-base-uncased) – a lightweight transformer architecture distilled from BERT, optimized for efficiency while maintaining strong performance in NLP tasks.
• Task: Multiclass sentiment classification of financial news headlines
• Output Classes:
  • 0 – Negative
  • 1 – Neutral
  • 2 – Positive
• Input Format: Raw English text (financial/news headlines), tokenized using the DistilBERT WordPiece tokenizer with:
  • padding=True (dynamic batch padding)
  • truncation=True (truncate sequences longer than the model max length, 512 tokens)

Intended Uses & Limitations

Intended Uses:

• Sentiment classification of short text in financial and economic contexts
• Integration into news monitoring pipelines or financial market analysis tools
• Batch or streaming inference for classifying sentiment of headlines

Limitations:

• Model performance may degrade on long-form text, as it is optimized for short inputs like headlines
• Domain-specific bias: Training data is financial news; model may not generalize to unrelated topics (sports, social media)
• Language limitation: English only (based on distilbert-base-uncased)
• Predictions are class labels and probabilities only; the model does not provide reasoning or explanation

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Training and Evaluation Data

• Training Dataset: Custom dataset of financial news headlines, labeled with sentiment (negative, neutral, positive)
• Dataset Size: 4,838 total examples
  • Training Set: 70% (~3,386 examples)
  • Validation Set: 15% (~726 examples)
  • Test Set: 15% (~726 examples)
• Evaluation Metrics:
  • Accuracy
  • Weighted F1-score (preferred for multiclass balance)
• Tokenizer: DistilBERT WordPiece tokenizer with padding=True (dynamic padding) and truncation=True

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Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Dugerij/news_sentiment_classifier")
model = AutoModelForSequenceClassification.from_pretrained("Dugerij/news_sentiment_classifier")

text = ["Stocks plunge after weak earnings report"]
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
print(predictions)  # 0=Negative, 1=Neutral, 2=Positive

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-06
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.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: 20

Training results

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.2