Instructions to use sms1097/support_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/support_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/support_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/support_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/support_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- sms1097/self_rag_tokens_train_data
Support Model
This generates the IsSupported token as descirbed in Self-RAG.
We are testing to see if a generated LLM answer is supported by the document. This is similar to testing for a hallucination in the model result.
The expected input to the model is shown here:
Context: {'doc'}\nAnswer: {answer}"
Training results:
{'eval_loss': 0.11030498147010803,
'eval_mse': 0.11030498147010803,
'eval_mae': 0.14249496161937714,
'eval_r2': 0.6906673524053266,
'eval_accuracy': 0.9117161716171617}