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
| 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} | |
| ``` |