Create README.md

README.md

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+---
+tags:
+- crypto-prediction
+- time-series
+- bert
+license: apache-2.0
+datasets:
+- custom-crypto-news
+metrics:
+- f1-score
+model-index:
+- name: crypto-trend-predictor
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: custom-crypto-news
+      type: custom
+    metrics:
+    - name: F1-Score
+      type: f1
+      value: 0.85
+---
+
+# Crypto Trend Predictor
+
+## Overview
+
+This BERT-based model predicts cryptocurrency market trends (bearish, bullish, or neutral) based on news articles, tweets, or market summaries. It was fine-tuned on a dataset of historical crypto news and price movements.
+
+## Model Architecture
+
+- Base Model: BERT-base-uncased
+- Layers: 12
+- Hidden Size: 768
+- Attention Heads: 12
+- Fine-tuned for multi-class classification (bearish/bullish/neutral)
+
+## Intended Use
+
+Ideal for analyzing crypto-related text to forecast short-term market trends, assisting traders or analysts in decision-making.
+
+## Limitations
+
+- Predictions are based on text sentiment and may not account for external factors like regulations or economic events.
+- Performance degrades on non-English text or highly technical jargon not seen in training.
+- Not financial advice; use at your own risk.
+
+## Example Code
+
+```python
+from transformers import pipeline
+
+predictor = pipeline("text-classification", model="user/crypto-trend-predictor")
+result = predictor("Bitcoin surges after ETF approval.")
+print(result)
+# [{'label': 'BULLISH', 'score': 0.95}]