Conversation
* Model artifact for inference or deployment * Eval artifact logging all the metrics * Label artifact logging labels in order of the predictions
* Forces use of padding = "max_length" * `add_special_tokens` and `return_token_type_ids` are set to True
* Use context to load inference hyperparameters
Notes: * Fix argparse batch_size arguments * Add sanity check for iterative models * Log labels and groups separately * Log inference params altogether inside a json file
* Fix MLFlow import related issues
Also fix `python_model` parameter of log_model
* Also read labels and groups
* Do not manually setting `add_special_tokens` and `return_token_type_ids`
Two types of weighting are applied together: * Across-class balancing: each sigmoidal unit loss is multiplied by inverse frequency * In-class balancing: positive loss is weighted by the in-class frequency w.r.t. negatives
Note the model returns pre-sigmoid logits rather than probabilites.
Two types of weighting are applied together: * Across-class balancing: each sigmoidal unit loss is multiplied by inverse frequency * In-class balancing: positive loss is weighted by the in-class frequency w.r.t. negatives
* self-explanatory outputs * two different output formats: flatten vs nested * adjust output style through infer config file
* text dataset only deals with data source and tokenization * target dataset only deals with encoding/decoding of targets and groups
Usage: * pass a list of targets to denote target columns * pass a list of lists as group_names to denote hierarchy groups for each target * pass a list for each group_name instance to denote targets belonging to that group_name i.e. a list of lists of lists * for one-task learning, either provide lists with length-one or provide data without list
… not in same length
Computes stats per-task basis.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Provides an easy-to-use and customizable tree-like multi-task learning model and related training, evaluation processes.