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
- Xet hash:
- 3b8774e96157c124e34cf8f2c1c3054bee9bb2e0d11ec17e252111b4a6446eba
- Size of remote file:
- 3.13 GB
- SHA256:
- 7d4181c8ae69d0c128880b013340b85c579256adbea4572f9c4b97bd1db40650
路
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