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
Update README.md
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README.md
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# Relevant Model
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This generates the `is_relevant` token as descirbed in Self-RAG.
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---
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license: mit
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datasets:
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- sms1097/self_rag_tokens_train_data
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---
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# Relevant Model
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This generates the `is_relevant` token as descirbed in Self-RAG.
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