Question Answering
Transformers
Safetensors
t5
text2text-generation
flan-t5
fine-tuned
resume
text-generation-inference
Instructions to use vittal147/resumebot-flan-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vittal147/resumebot-flan-t5 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="vittal147/resumebot-flan-t5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vittal147/resumebot-flan-t5") model = AutoModelForSeq2SeqLM.from_pretrained("vittal147/resumebot-flan-t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ResumeBot: FLAN-T5 fine-tuned on Vittal Pujari's resume
A google/flan-t5-base model fine-tuned to answer questions about Vittal Pujari's resume.
It is meant to be used with a retriever that supplies the relevant resume facts.
Prompt format:
Resume information: <retrieved facts>
Question: <your question>
Answer using the resume information:
Files: resume_qa_dataset.csv holds the training question-answer pairs and resume.txt holds the resume text used.
Academic project. Only information present in the resume is used.
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Model tree for vittal147/resumebot-flan-t5
Base model
google/flan-t5-base