Instructions to use sms1097/utility_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/utility_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/utility_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/utility_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/utility_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- sms1097/self_rag_tokens_train_data
Utility Model
This generates the IsUseful token as descirbed in Self-RAG.
We are testing to see if an answer is useful to the given user question. We output a score from 1-5 based on how useful the answer is.
The expected input to the model is:
Instruction: {instruction}\nAnswer: {answer}",
Training Results
{'eval_loss': 0.4719298779964447,
'eval_mse': 0.4719298183917999,
'eval_mae': 0.25655537843704224,
'eval_r2': 0.5200293292355334,
'eval_accuracy': 0.9001516683518705}