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:
- e7463c1c6b7c46ae0de79b004f3d7318bf4307ef0bef4de3be2e38ae80fab160
- Size of remote file:
- 792 kB
- SHA256:
- d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
路
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