Comments (1)
Hello, the models do not need to be purchased. This library wraps around the huggingface transformers library. You can either provide your own custom model or use one of the existing BERT models from huggingface. When running the summarizer, the model is automatically downloaded and cached in your file system.
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Related Issues (20)
- unable to build on Mac m1 - Big Sur HOT 1
- ValueError: n_samples=4 should be >= n_clusters=40 HOT 1
- how to save model as pkl.file for deploying
- "from summarizer import Summaizer" HOT 1
- training custom model HOT 1
- can you please provide english.json file ,i was having issue that trainer folder is not there
- AWS Lambda + Container issue with model loading as /home is read only HOT 1
- Error when running xlnet for individual paragraphs on linux using gpu
- Reproducibility bug on run_embeddings method
- Don't load the SBERT model twice
- Which kind of model should I choose?
- How to use cached sentence embedding vector as the input instead of text?
- How to support Japaneses
- tensor size mismatch for specific input text
- TypeError: 'Summarizer' object is not callable
- Run Summarizer model on array of strings HOT 2
- Trying to mimic the API's result
- [News API] Summarization returns empty string HOT 2
- cannot import name summarizer HOT 1
- Need a way to force load on CPU when an unsupported GPU throws a pytorch error.
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from bert-extractive-summarizer.