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
| 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} | |
| ``` |