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sms1097
/
relevant_model

Text Classification
Transformers
Safetensors
distilbert
text-embeddings-inference
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xet
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How to use from the
Use from the
Transformers library
# 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")
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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}
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11
Safetensors
Model size
67M params
Tensor type
F32
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Dataset used to train sms1097/relevant_model

Collection including sms1097/relevant_model