Comments (2)
It could be a few things. When you first use the worker, a download is issued for the pretrained model. Depending on network speed, that can take a few minutes. Good news is that once it is downloaded, it should be cached in your system.
The next thing could be the model you select. Distill Bert runs much faster than Bert-uncased large, due to the fewer parameters.
GPU is obviously preferable with the size of these models, but can be costly.
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Thank you, Derek.
The download is not the issue here (happens on every run, not only the first);
I tried using Distill Bert with model = Summarizer(model='distilbert-base-uncased')
.
It is indeed much faster, CPU times: user 1min 24s, sys: 13.5 s, total: 1min 37s
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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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