[Not for merge] For benchmarking training time numbers on 2.8.x

[dakshvar22]

Status (please check what you already did):

• added some tests for the functionality
• updated the documentation
• updated the changelog (please check changelog for instructions)
• reformat files using black (please check Readme for instructions)

[github-actions]

Hey @dakshvar22! 👋 To run model regression tests, comment with the /modeltest command and a configuration.

Tips 💡: The model regression test will be run on push events. You can re-run the tests by re-add status:model-regression-tests label or use a Re-run jobs button in Github Actions workflow.

Tips 💡: Every time when you want to change a configuration you should edit the comment with the previous configuration.

You can copy this in your comment and customize:

/modeltest

```yml
##########
## Available datasets
##########
# - "Carbon Bot" (NLU)
# - "Hermit" (NLU)
# - "Private 1" (NLU)
# - "Private 2" (NLU)
# - "Private 3" (NLU)
# - "Sara" (NLU, Core)
# - "financial-demo" (NLU, Core)
# - "helpdesk-assistant" (NLU, Core)
# - "insurance-demo" (NLU, Core)
# - "retail-demo" (NLU, Core)

##########
## Available NLU configurations
##########
# - "BERT + DIET(bow) + ResponseSelector(bow)"
# - "BERT + DIET(seq) + ResponseSelector(t2t)"
# - "Spacy + DIET(bow) + ResponseSelector(bow)"
# - "Spacy + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + BERT + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + BERT + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + Spacy + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)"

##########
## Available Core configurations
##########
# - "Rules"
# - "Rules + AugMemo"
# - "Rules + AugMemo + TED"
# - "Rules + Memo"
# - "Rules + Memo + TED"
# - "Rules + TED"

## Example configuration
#################### syntax #################
## include:
##   - dataset: ["<dataset_name>"]
##     config: ["<configuration_name>"]
#
## Example:
## include:
##  - dataset: ["Carbon Bot"]
##    config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]
#
## Shortcut:
## You can use the "all" shortcut to include all available configurations or datasets
#
## Example: Use the "Sparse + EmbeddingIntent + ResponseSelector(bow)" configuration
## for all available datasets
## include:
##  - dataset: ["all"]
##    config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]
#
## Example: Use all available configurations for the "Carbon Bot" and "Sara" datasets
## and for the "Hermit" dataset use the "Sparse + DIET + ResponseSelector(T2T)" and
## "BERT + DIET + ResponseSelector(T2T)" configurations:
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["all"]
##  - dataset: ["Hermit"]
##    config: ["Sparse + DIET(seq) + ResponseSelector(t2t)", "BERT + DIET(seq) + ResponseSelector(t2t)"]
#
## Example: Define a branch name to check-out for a dataset repository. Default branch is 'main'
## dataset_branch: "test-branch"
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["all"]
##
## Shortcuts:
## You can use the "all" shortcut to include all available configurations or datasets.
## You can use the "all-nlu" shortcut to include all available NLU configurations or datasets.
## You can use the "all-core" shortcut to include all available core configurations or datasets.

include:
 - dataset: ["Carbon Bot"]
   config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]

```

[github-actions]

/modeltest

include:
 - dataset: ["Carbon Bot"]
   config: ["Spacy + DIET(seq) + ResponseSelector(t2t)", "Sparse + DIET(seq) + ResponseSelector(t2t)", "Sparse + BERT + DIET(seq) + ResponseSelector(t2t)"]

[github-actions]

The model regression tests have started. It might take a while, please be patient.
As soon as results are ready you'll see a new comment with the results.

Used configuration can be found in the comment.

[github-actions]

Commit: 90a4d73, The full report is available as an artifact.

Dataset: Carbon Bot, Dataset repository branch: main, commit: 15f3a3ed0e8f9d592c47b3f6e5f927eb5b891854

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
Sparse + BERT + DIET(seq) + ResponseSelector(t2t) test: 5m7s, train: 21m37s, total: 26m44s	0.7806 (0.00)	0.7935 (0.01)	0.5695 (0.01)
Sparse + DIET(seq) + ResponseSelector(t2t) test: 2m20s, train: 12m18s, total: 14m37s	0.7398 (0.00)	0.6819 (-0.02)	0.5430 (0.03)

[stale]

This PR has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

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