Update README.md

README.md

@@ -1,13 +1,25 @@
----
-language:
-- en
-- id
-metrics:
-- accuracy
-- precision
-- recall
-- roc_auc
-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
+- Dataset: [Your Dataset Name or Description]
+- 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)