Instructions to use hariharan9710/Text-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hariharan9710/Text-Summarizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hariharan9710/Text-Summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("hariharan9710/Text-Summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hariharan9710/Text-Summarizer: direct link, hf CLI and curl.
- Browser
- Download file 866 Bytes
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https://huggingface.co/hariharan9710/Text-Summarizer/resolve/main/README.md
- Command line
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hf download hf://hariharan9710/Text-Summarizer/README.md
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curl -L -o README.md https://huggingface.co/hariharan9710/Text-Summarizer/resolve/main/README.md
866 Bytes
metadata
license: apache-2.0
Text-Summarizer
About
An Abstractive text summarizer trained using lstm based sequence to sequence model with attention mechanisim. The attention model is used for generating each word of the summary conditioned on the input sentence.
Used CNN_DailyMail dataset.
Training Model Overview
loss graph
encoder-decoder overview
Conclusion
🫶 The machine learning model to convert a text document to abstract is done successfully.
🫶 Created a Flask app using an api call from this repository & deployed the app in heroku app.

