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Sathya77
/
Telecom_Plan_RAG_based

Question Answering
Transformers
Safetensors
English
t5
text2text-generation
RAG
FAISS
Telecom
Question-Answering
Flan-T5
Sentence-Transformers
text-generation-inference
Model card Files Files and versions
xet
Community

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")
  • Notebooks
  • Google Colab
  • Kaggle
Telecom_Plan_RAG_based
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  • 1 contributor
History: 4 commits
Sathya77's picture
Sathya77
Update README.md
dd8f968 verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 9 months ago
  • Plan_Advisor_RAG.ipynb
    24.9 kB
    Upload 9 files 9 months ago
  • README.md
    2.23 kB
    Update README.md 3 months ago
  • config.json
    805 Bytes
    Upload 9 files 9 months ago
  • generation_config.json
    149 Bytes
    Upload 9 files 9 months ago
  • model.safetensors
    3.13 GB
    xet
    Upload 9 files 9 months ago
  • special_tokens_map.json
    2.67 kB
    Upload 9 files 9 months ago
  • spiece.model
    792 kB
    xet
    Upload 9 files 9 months ago
  • telecom_plans.csv
    12 kB
    Upload 9 files 9 months ago
  • tokenizer.json
    2.42 MB
    Upload 9 files 9 months ago
  • tokenizer_config.json
    21.8 kB
    Upload 9 files 9 months ago