Create README.md

README.md

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+---
+language:
+- de
+base_model:
+- agne/jobGBERT
+pipeline_tag: text-classification
+---
+# CareerBERT Classifier
+
+A text classification model fine-tuned for career-related text analysis.
+
+## Installation
+
+Install the required dependencies:
+
+```bash
+pip install transformers torch
+```
+
+## Quick Start
+
+Load and use the model in a few lines:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers import pipeline
+
+modelpath = "lwolfrum2/careerbert-classifier"
+model = AutoModelForSequenceClassification.from_pretrained(modelpath)
+tokenizer = AutoTokenizer.from_pretrained(modelpath)
+pipe = pipeline("text-classification", model, tokenizer=tokenizer)
+
+# Classify text
+result = pipe("Your text here")
+print(result)
+```
+
+## Usage
+
+### Simple Classification
+
+```python
+# Single example
+text = "I am looking for a job in software development."
+result = pipe(text)
+print(result)
+# Output: [{'label': 'career_query', 'score': 0.98}]
+```
+
+### Batch Processing
+
+```python
+texts = [
+    "Software engineer with 5 years experience",
+    "Just looking for a new job",
+    "Tell me about this coffee",
+]
+
+results = pipe(texts)
+for text, result in zip(texts, results):
+    print(f"{text} → {result['label']} ({result['score']:.2f})")
+```
+
+## Output Format
+
+Each prediction returns a dictionary with:
+- `label`: The predicted class (0 = not relevant, 1 = relevant)
+- `score`: Confidence score (0–1)
+
+## Notes
+
+- The model runs on CPU by default. For faster inference on large batches, use GPU:
+  ```python
+  pipe = pipeline("text-classification", model, tokenizer=tokenizer, device=0)
+  ```
+- Texts longer than the model's max token length will be truncated.
+
+## Model Details
+
+**Model**: lwolfrum2/careerbert-classifier  
+**Base**: BERT  
+**Task**: Text classification