Instructions to use Saravananofficial/Text_Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saravananofficial/Text_Summarizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Saravananofficial/Text_Summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("Saravananofficial/Text_Summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Saravananofficial/Text_Summarizer: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/Saravananofficial/Text_Summarizer/resolve/main/tf_model.h5
- Command line
-
hf download hf://Saravananofficial/Text_Summarizer/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Saravananofficial/Text_Summarizer/resolve/main/tf_model.h5
1.63 GB
- Xet hash:
- d4c5e54c3a43986b607241667aac1176fa657d2890f2ea7612b3b7123ead077a
- Size of remote file:
- 1.63 GB
- SHA256:
- 8d845bfe2bbbc0ea00ee11b6e032aa285a008caef008cbbac975454c24a4415f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.