---
tags:
- document-relevance
- dpo
- gpt-oss-20b
datasets:
- custom-relevance-dataset
metrics:
- accuracy
model-index:
- name: gpt-oss-20b-relevance-ft-20250811_213108
  results:
  - task:
      type: text-classification
      name: Document Relevance Classification
    metrics:
    - type: accuracy
      value: 0.5750
      name: Validation Accuracy
    - type: yes_ratio
      value: 0.4750
      name: Yes Prediction Ratio
    - type: no_ratio  
      value: 0.5250
      name: No Prediction Ratio
---

# gpt-oss-20b Document Relevance Classifier

This model was trained using standard fine-tuning for document relevance classification.

## Training Configuration
- Base Model: openai/gpt-oss-20b
- Training Type: Standard Fine-tuning
- Learning Rate: 5e-06
- Batch Size: 32
- Epochs: 5
- Training Samples: 2000
- Validation Samples: 400

## Performance Metrics
- **Accuracy**: 57.50%
- **Yes Predictions**: 47.5%
- **No Predictions**: 52.5%

## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b")
tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")

# Load adapter
model = PeftModel.from_pretrained(model, "amos1088/gpt-oss-20b-relevance-ft-20250811_213108")
```

## Training Date
2025-08-11 21:31:08 UTC