Remove old README for metadata update

README.md

@@ -1,77 +0,0 @@
----
-language: en
-tags:
-- text-classification
-- requirements-engineering
-- bert
-datasets:
-- promise-nfr
-metrics:
-- accuracy
-- f1
-model-index:
-- name: RequirementClassifier
-  results:
-  - task:
-      type: text-classification
-      name: Requirement Classification
-    dataset:
-      name: PROMISE NFR
-      type: promise-nfr
-    metrics:
-    - type: accuracy
-      name: Accuracy
-      value: 0.0
----
-
-# RequirementClassifier
-
-Version: 26
-
-## Model Description
-
-This model is a fine-tuned BERT model for binary classification of software requirements.
-It classifies text as either "requirement" or "non-requirement".
-
-## Intended Uses
-
-- Classify software requirement documents
-- Identify requirement vs non-requirement statements
-- Automated requirement extraction from documents
-
-## Training Data
-
-The model was trained on the PROMISE NFR dataset with additional non-requirement examples.
-
-## Usage
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("rajinikarcg/RequirementClassifier")
-model = AutoModelForSequenceClassification.from_pretrained("rajinikarcg/RequirementClassifier")
-
-# Prepare input
-text = "The system shall respond within 2 seconds"
-inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
-
-# Get prediction
-with torch.no_grad():
-    outputs = model(**inputs)
-    logits = outputs.logits
-    prediction = torch.argmax(logits, dim=-1).item()
-
-# Map to label
-labels = ["non-requirement", "requirement"]
-print(f"Prediction: {labels[prediction]}")
-```
-
-## Version History
-
-- 26: Latest version
-
-## Citation
-
-If you use this model, please cite the PROMISE NFR dataset.