Instructions to use sms1097/relevant_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/relevant_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/relevant_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/relevant_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/relevant_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 343 Bytes
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license: mit
datasets:
- sms1097/self_rag_tokens_train_data
---
# Relevant Model
This generates the `is_relevant` token as descirbed in Self-RAG.
We are testing to see if a retrieved document is relevant to the user input of our language model.
The expected input to the model is:
```
Instruction:\n{instruction}\nContext:\n{doc}
``` |