---
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
```python
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)