Librarian Bot: Add base_model information to model

#1
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  1. README.md +16 -15
README.md CHANGED
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  ---
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- license: apache-2.0
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  language: en
 
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  tags:
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  - generated_from_keras_callback
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- model-index:
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- - name: pull_request_comments_model
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- results: []
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  widget:
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- - text: >-
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- Please add a detailed comment explaining what this config parameter means.
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  example_title: Code example
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- - text: >-
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- Because if non-chief workers don’t restore the model from the checkpoint, training can‘t continue.
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- For example, I am using tf.estimator.Estimator API and tf.distribute.CollectiveAllReduceStrategy for distributed training.
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- Firstly, I train for 1000 rounds(train_stpes=1000) and save the checkpoint, it works normally.
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- Then I set the train_steps to 2000, only the is_chief role can restore the model from the checkpoint without any error.
 
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  example_title: ML example
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- - text: >-
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- Hi, Could you please review this PR when you have some time? Thank you very much!
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  example_title: Management example
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- - text: >-
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- Sorry, i was on holiday all the month and i didn’t have so much time to review this PR. I also had a lot of problems with my computer, so i couldn’t work...
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  example_title: Other example
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  pipeline_tag: text-classification
 
 
 
 
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  ---
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  # pull_request_comments_model
 
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  ---
 
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  language: en
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+ license: apache-2.0
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  tags:
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  - generated_from_keras_callback
 
 
 
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  widget:
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+ - text: Please add a detailed comment explaining what this config parameter means.
 
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  example_title: Code example
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+ - text: Because if non-chief workers don’t restore the model from the checkpoint,
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+ training can‘t continue. For example, I am using tf.estimator.Estimator API and
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+ tf.distribute.CollectiveAllReduceStrategy for distributed training. Firstly, I
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+ train for 1000 rounds(train_stpes=1000) and save the checkpoint, it works normally.
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+ Then I set the train_steps to 2000, only the is_chief role can restore the model
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+ from the checkpoint without any error.
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  example_title: ML example
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+ - text: Hi, Could you please review this PR when you have some time? Thank you very
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+ much!
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  example_title: Management example
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+ - text: Sorry, i was on holiday all the month and i didn’t have so much time to review
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+ this PR. I also had a lot of problems with my computer, so i couldn’t work...
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  example_title: Other example
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  pipeline_tag: text-classification
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+ base_model: distilbert-base-uncased
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+ model-index:
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+ - name: pull_request_comments_model
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+ results: []
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  ---
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  # pull_request_comments_model