metadata
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: 0.575
name: Validation Accuracy
- type: yes_ratio
value: 0.475
name: Yes Prediction Ratio
- type: no_ratio
value: 0.525
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
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