README.md

metadata

license: apache-2.0
language:
  - en
metrics:
  - accuracy
  - precision
  - recall
base_model:
  - microsoft/deberta-v3-base
pipeline_tag: text-classification

Stock Sentiment Analysis

This model is a fine-tuned version of microsoft/deberta-v3-base for stock sentiment analysis.

Model Details

• Language: English
• Task: Text Classification (Sentiment Analysis)
• Classes: Positive, Neutral, Negative

Training

• Evaluation Metric: F1 Score
• Training Args: See training_args.bin for details.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("vinD27/stock_sentiment_analysis")
model = AutoModelForSequenceClassification.from_pretrained("vinD27/stock_sentiment_analysis")

text = "The stock market is performing well today."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits)