Question Answering
Transformers
Safetensors
English
t5
text2text-generation
RAG
FAISS
Telecom
Question-Answering
Flan-T5
Sentence-Transformers
text-generation-inference
Instructions to use Sathya77/Telecom_Plan_RAG_based with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sathya77/Telecom_Plan_RAG_based with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Sathya77/Telecom_Plan_RAG_based")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Sathya77/Telecom_Plan_RAG_based") model = AutoModelForSeq2SeqLM.from_pretrained("Sathya77/Telecom_Plan_RAG_based", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 131 Bytes
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