Update TensorFlow to 2.5 #9123

samsucik · 2021-07-14T10:16:25Z

Proposed changes:

...

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)

….toml.

…n TF2.4 and 2.5.

…ment for previously used `to_numpy_or_python_type`.

…ackage as opposed to tensorflow.python.keras. We don't want to use or rely on standalone keras.

…rker` - the decorator was just copy-pasted from TF code and has now been removed.

… `fit` in TF 2.5.

…ining._keras_api_gauge`.

github-actions · 2021-07-31T10:04:37Z

Commit: b8d42a6, The full report is available as an artifact.

Dataset: Carbon Bot, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `2m34s`, train: `19m45s`, total: `22m18s`	0.7942 (0.00)	0.7529 (0.00)	0.5316 (-0.01)
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `3m25s`, train: `27m32s`, total: `30m56s`	0.8000 (0.00)	0.7766 (0.00)	0.5298 (-0.01)
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `2m36s`, train: `29m54s`, total: `32m29s`	0.7864 (0.01)	0.7529 (0.00)	0.5828 (0.01)
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `3m27s`, train: `30m5s`, total: `33m32s`	0.7806 (0.00)	0.7935 (0.00)	0.5695 (0.04)
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `36s`, train: `15m40s`, total: `16m16s`	0.7476 (0.01)	0.7529 (0.00)	0.5249 (0.03)
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m32s`, train: `22m45s`, total: `24m17s`	0.7398 (0.00)	0.6819 (0.00)	0.5232 (0.02)

Dataset: Hermit, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `4m47s`, train: `57m48s`, total: `1h2m34s`	0.8987 (0.00)	0.7504 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `4m59s`, train: `1h34m6s`, total: `1h39m4s`	0.8717 (0.00)	0.7504 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `1m2s`, train: `1h6m47s`, total: `1h7m48s`	0.8290 (-0.01)	0.7504 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m42s`, train: `4h6m47s`, total: `4h8m29s`	0.8346 (0.00)	0.7520 (-0.00)	`no data`

Dataset: Private 1, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `3m55s`, train: `19m55s`, total: `23m49s`	0.9096 (0.00)	0.9612 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `4m42s`, train: `24m46s`, total: `29m28s`	0.9137 (0.00)	0.9709 (0.00)	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `31s`, train: `11m54s`, total: `12m24s`	0.8420 (0.00)	0.9574 (0.00)	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `1m0s`, train: `21m26s`, total: `22m26s`	0.8555 (0.00)	0.9402 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `23s`, train: `14m47s`, total: `15m10s`	0.8960 (-0.00)	0.9612 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m0s`, train: `18m55s`, total: `19m55s`	0.8992 (-0.00)	0.9672 (-0.00)	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `33s`, train: `17m48s`, total: `18m21s`	0.8940 (0.00)	0.9574 (0.00)	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `1m12s`, train: `22m16s`, total: `23m27s`	0.8971 (0.00)	0.9718 (0.00)	`no data`

Dataset: Private 2, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `4m19s`, train: `1h29m45s`, total: `1h34m4s`	0.8745 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `4m44s`, train: `1h54m39s`, total: `1h59m23s`	0.8830 (0.00)	`no data`	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `34s`, train: `54m51s`, total: `55m25s`	0.7253 (0.00)	`no data`	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `49s`, train: `1h53m40s`, total: `1h54m28s`	0.7811 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `29s`, train: `42m31s`, total: `43m0s`	0.8509 (-0.00)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `36s`, train: `1h31m40s`, total: `1h32m15s`	0.8541 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `40s`, train: `1h4m1s`, total: `1h4m40s`	0.8702 (0.01)	`no data`	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `53s`, train: `1h54m6s`, total: `1h54m59s`	0.8530 (0.00)	`no data`	`no data`

Dataset: Private 3, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `1m44s`, train: `3m30s`, total: `5m14s`	0.9177 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m47s`, train: `4m33s`, total: `6m20s`	0.8436 (0.00)	`no data`	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `31s`, train: `2m7s`, total: `2m38s`	0.6173 (0.00)	`no data`	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `37s`, train: `3m23s`, total: `4m0s`	0.6214 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `28s`, train: `2m47s`, total: `3m14s`	0.8683 (0.00)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `34s`, train: `3m38s`, total: `4m11s`	0.8642 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `34s`, train: `3m14s`, total: `3m47s`	0.8477 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `39s`, train: `3m42s`, total: `4m21s`	0.8601 (0.00)	`no data`	`no data`

Dataset: Sara, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `8m0s`, train: `29m3s`, total: `37m3s`	0.7126 (-0.00)	0.7895 (0.00)	0.7938 (0.00)
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `8m48s`, train: `34m57s`, total: `43m45s`	0.7087 (0.00)	0.7903 (0.00)	0.7736 (-0.00)
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `8m3s`, train: `56m14s`, total: `1h4m16s`	0.6880 (-0.00)	0.7895 (0.00)	0.7938 (-0.00)
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `8m59s`, train: `43m2s`, total: `52m0s`	0.6919 (-0.00)	0.7823 (-0.00)	0.7891 (0.00)
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `1m27s`, train: `33m44s`, total: `35m11s`	0.6577 (-0.00)	0.7895 (0.00)	0.7758 (-0.01)
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m55s`, train: `31m27s`, total: `33m21s`	0.6726 (0.00)	0.7743 (-0.00)	0.8016 (0.00)

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.1266 (0.00)	0.0000 (0.00)	`2m44s`	`1m21s`
`Rules + AugMemo`	0.9229 (0.00)	0.6644 (0.00)	`2m42s`	`3m15s`
`Rules + AugMemo + TED`	0.9733 (0.00)	0.7603 (0.02)	`10m51s`	`4m21s`
`Rules + Memo`	0.3860 (0.00)	0.1438 (0.00)	`2m51s`	`1m30s`
`Rules + Memo + TED`	0.9576 (0.00)	0.6644 (0.01)	`10m41s`	`2m26s`
`Rules + TED`	0.9576 (0.00)	0.6644 (0.01)	`10m26s`	`2m11s`

Dataset: financial-demo, Dataset repository branch: main (external repository), commit: bc73f669152147ae7f880ecba172c6e44f59e2c5
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `45s`, train: `2m31s`, total: `3m16s`	1.0000 (0.00)	0.8333 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m23s`, train: `3m48s`, total: `5m11s`	1.0000 (0.00)	0.8333 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `47s`, train: `3m20s`, total: `4m7s`	1.0000 (0.00)	0.8333 (0.00)	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m16s`, train: `4m4s`, total: `5m19s`	1.0000 (0.00)	0.8800 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `17s`, train: `1m42s`, total: `1m59s`	0.9643 (0.00)	0.8333 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `43s`, train: `3m7s`, total: `3m50s`	0.9643 (0.00)	0.8800 (0.00)	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.7218 (0.00)	0.5417 (0.00)	`14s`	`11s`
`Rules + AugMemo`	1.0000 (0.00)	1.0000 (0.00)	`14s`	`11s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m44s`	`30s`
`Rules + Memo`	0.9807 (0.00)	0.9167 (0.00)	`13s`	`11s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m45s`	`29s`
`Rules + TED`	0.9144 (0.00)	0.7083 (0.00)	`1m41s`	`37s`

Dataset: helpdesk-assistant, Dataset repository branch: main (external repository), commit: 8e74cea8f931705239613a989282bba8bc6af5c7
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `40s`, train: `2m3s`, total: `2m43s`	1.0000 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m9s`, train: `2m44s`, total: `3m53s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `42s`, train: `2m32s`, total: `3m14s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m9s`, train: `2m59s`, total: `4m7s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `13s`, train: `1m10s`, total: `1m23s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `41s`, train: `2m2s`, total: `2m43s`	1.0000 (0.00)	`no data`	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.5714 (0.00)	0.2500 (0.00)	`8s`	`8s`
`Rules + AugMemo`	1.0000 (0.00)	1.0000 (0.00)	`7s`	`9s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m45s`	`26s`
`Rules + Memo`	0.9796 (0.00)	0.9167 (0.00)	`7s`	`8s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m17s`	`27s`
`Rules + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m17s`	`26s`

Dataset: insurance-demo, Dataset repository branch: main (external repository), commit: 03ec1d9b9f2a41a877381ad24bfd91a3994692b7
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `49s`, train: `1m27s`, total: `2m15s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m5s`, train: `2m3s`, total: `3m8s`	1.0000 (0.00)	0.0000 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `39s`, train: `1m40s`, total: `2m19s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m10s`, train: `2m5s`, total: `3m14s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `14s`, train: `1m0s`, total: `1m14s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `48s`, train: `2m3s`, total: `2m51s`	1.0000 (0.00)	1.0000 (0.00)	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.5909 (0.00)	0.0000 (0.00)	`12s`	`9s`
`Rules + AugMemo`	1.0000 (0.00)	1.0000 (0.00)	`9s`	`9s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`2m35s`	`28s`
`Rules + Memo`	0.7600 (0.00)	0.5000 (0.00)	`8s`	`8s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`2m28s`	`28s`
`Rules + TED`	1.0000 (0.00)	1.0000 (0.00)	`2m31s`	`27s`

Dataset: retail-demo, Dataset repository branch: main (external repository), commit: 4c6a477775dbd538782278ca4c2fb651a63d3c29
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `45s`, train: `1m26s`, total: `2m10s`	0.8387 (0.00)	0.2857 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m13s`, train: `2m13s`, total: `3m26s`	0.8750 (0.00)	0.2857 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `48s`, train: `2m5s`, total: `2m52s`	0.9375 (0.00)	0.2857 (0.00)	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m16s`, train: `2m46s`, total: `4m1s`	0.8125 (0.00)	0.2857 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `20s`, train: `1m1s`, total: `1m21s`	1.0000 (0.00)	0.2857 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `47s`, train: `1m33s`, total: `2m19s`	0.9375 (0.00)	0.2857 (0.00)	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.9531 (0.00)	0.7778 (0.00)	`6s`	`9s`
`Rules + AugMemo`	0.9692 (0.00)	0.8889 (0.00)	`6s`	`9s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`57s`	`24s`
`Rules + Memo`	0.9692 (0.00)	0.8889 (0.00)	`6s`	`9s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`58s`	`25s`
`Rules + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m1s`	`25s`

github-actions · 2021-08-05T21:08:31Z

Commit: 7ff8874, The full report is available as an artifact.

Dataset: Carbon Bot, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `3m4s`, train: `14m53s`, total: `17m57s`	0.7942 (0.00)	0.7529 (0.00)	0.5316 (-0.01)
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `3m7s`, train: `12m36s`, total: `15m42s`	0.8000 (0.00)	0.7766 (0.00)	0.5298 (-0.01)
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `2m31s`, train: `16m34s`, total: `19m4s`	0.7981 (0.02)	0.7529 (0.00)	0.5762 (0.04)
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `2m44s`, train: `13m18s`, total: `16m2s`	0.7806 (0.00)	0.7935 (0.00)	0.5430 (0.00)
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `37s`, train: `8m55s`, total: `9m32s`	0.7398 (-0.00)	0.7529 (0.00)	0.5099 (0.02)
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m48s`, train: `16m58s`, total: `18m47s`	0.7398 (0.00)	0.6819 (0.00)	0.5232 (0.01)

Dataset: Private 2, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `3m8s`, train: `37m55s`, total: `41m2s`	0.8745 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `3m19s`, train: `45m55s`, total: `49m13s`	0.8830 (0.00)	`no data`	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `31s`, train: `24m24s`, total: `24m55s`	0.7253 (0.00)	`no data`	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `45s`, train: `52m3s`, total: `52m47s`	0.7811 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `26s`, train: `22m13s`, total: `22m39s`	0.8584 (0.02)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `32s`, train: `37m37s`, total: `38m9s`	0.8541 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `37s`, train: `33m48s`, total: `34m24s`	0.8616 (0.01)	`no data`	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `42s`, train: `45m6s`, total: `45m48s`	0.8498 (-0.00)	`no data`	`no data`

Dataset: Private 3, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `1m37s`, train: `2m54s`, total: `4m31s`	0.9177 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `2m13s`, train: `4m19s`, total: `6m32s`	0.8436 (0.00)	`no data`	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `29s`, train: `1m14s`, total: `1m42s`	0.6173 (0.00)	`no data`	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `46s`, train: `2m33s`, total: `3m18s`	0.6214 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `37s`, train: `2m9s`, total: `2m46s`	0.8683 (0.00)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `29s`, train: `1m39s`, total: `2m8s`	0.8642 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `30s`, train: `1m59s`, total: `2m29s`	0.8477 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `42s`, train: `2m21s`, total: `3m3s`	0.8601 (0.00)	`no data`	`no data`

github-actions · 2021-08-06T09:59:11Z

Hey @koernerfelicia! 👋 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 · 2021-08-06T09:59:14Z

/modeltest

include:
 - dataset: ["all"]
   config: ["all"]

github-actions · 2021-08-06T09:59:16Z

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 · 2021-08-07T11:50:33Z

Commit: 7ff8874, The full report is available as an artifact.

Dataset: Carbon Bot, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `2m2s`, train: `9m59s`, total: `12m0s`	0.7942 (0.00)	0.7529 (0.00)	0.5316 (-0.01)
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `2m51s`, train: `12m11s`, total: `15m2s`	0.8000 (0.00)	0.7766 (0.00)	0.5298 (-0.01)
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `2m18s`, train: `17m28s`, total: `19m46s`	0.7981 (0.02)	0.7529 (0.00)	0.5762 (0.04)
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `2m57s`, train: `15m22s`, total: `18m18s`	0.7806 (0.00)	0.7935 (0.00)	0.5430 (0.00)
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `31s`, train: `8m54s`, total: `9m25s`	0.7398 (-0.00)	0.7529 (0.00)	0.5099 (0.02)
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m14s`, train: `9m46s`, total: `11m0s`	0.7398 (0.00)	0.6819 (0.00)	0.5430 (0.03)

Dataset: Hermit, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `4m55s`, train: `41m7s`, total: `46m1s`	0.8987 (0.00)	0.7504 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `3m52s`, train: `56m47s`, total: `1h0m39s`	0.8690 (0.00)	0.7504 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `55s`, train: `43m49s`, total: `44m44s`	0.8374 (0.01)	0.7504 (0.00)	`no data`

Dataset: Private 1, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `3m31s`, train: `11m4s`, total: `14m34s`	0.9096 (0.00)	0.9612 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `3m29s`, train: `11m11s`, total: `14m39s`	0.9137 (0.00)	0.9709 (0.00)	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `27s`, train: `5m52s`, total: `6m19s`	0.8420 (0.00)	0.9574 (0.00)	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `1m21s`, train: `13m35s`, total: `14m56s`	0.8555 (0.00)	0.9402 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `21s`, train: `7m35s`, total: `7m56s`	0.9012 (0.00)	0.9612 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `49s`, train: `8m40s`, total: `9m28s`	0.8992 (-0.00)	0.9671 (-0.00)	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `29s`, train: `10m20s`, total: `10m49s`	0.8940 (0.00)	0.9574 (0.00)	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `1m26s`, train: `13m57s`, total: `15m22s`	0.9002 (0.00)	0.9672 (-0.00)	`no data`

Dataset: Private 2, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `4m57s`, train: `1h3m15s`, total: `1h8m11s`	0.8745 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `3m21s`, train: `49m5s`, total: `52m26s`	0.8830 (0.00)	`no data`	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `27s`, train: `23m6s`, total: `23m32s`	0.7253 (0.00)	`no data`	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `38s`, train: `42m46s`, total: `43m23s`	0.7811 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `35s`, train: `28m32s`, total: `29m6s`	0.8509 (0.01)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `34s`, train: `37m16s`, total: `37m50s`	0.8541 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `33s`, train: `33m5s`, total: `33m38s`	0.8616 (0.01)	`no data`	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `48s`, train: `50m42s`, total: `51m29s`	0.8498 (-0.00)	`no data`	`no data`

Dataset: Private 3, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `1m33s`, train: `2m51s`, total: `4m24s`	0.9177 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m34s`, train: `2m46s`, total: `4m19s`	0.8436 (0.00)	`no data`	`no data`
`Spacy + DIET(bow) + ResponseSelector(bow)` test: `30s`, train: `1m10s`, total: `1m40s`	0.6173 (0.00)	`no data`	`no data`
`Spacy + DIET(seq) + ResponseSelector(t2t)` test: `40s`, train: `2m12s`, total: `2m51s`	0.6214 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `37s`, train: `2m12s`, total: `2m48s`	0.8683 (0.00)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `30s`, train: `1m35s`, total: `2m5s`	0.8642 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(bow) + ResponseSelector(bow)` test: `34s`, train: `2m10s`, total: `2m43s`	0.8477 (0.00)	`no data`	`no data`
`Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)` test: `33s`, train: `1m48s`, total: `2m21s`	0.8601 (0.00)	`no data`	`no data`

Dataset: Sara, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `5m55s`, train: `14m21s`, total: `20m15s`	0.7126 (-0.00)	0.7895 (0.00)	0.7975 (0.01)
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `9m11s`, train: `24m15s`, total: `33m26s`	0.7087 (0.00)	0.7903 (0.00)	0.7736 (-0.00)
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `5m47s`, train: `26m22s`, total: `32m9s`	0.6923 (0.00)	0.7895 (0.00)	0.8031 (0.03)
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `7m8s`, train: `21m35s`, total: `28m43s`	0.6962 (-0.00)	0.7849 (-0.00)	0.7953 (-0.00)
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `1m27s`, train: `24m20s`, total: `25m47s`	0.6577 (-0.01)	0.7895 (0.00)	0.7758 (-0.01)
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `1m38s`, train: `15m6s`, total: `16m43s`	0.6731 (-0.00)	0.7778 (0.01)	0.7876 (-0.01)

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.1266 (0.00)	0.0000 (0.00)	`2m37s`	`1m10s`
`Rules + AugMemo`	0.9229 (0.00)	0.6644 (0.00)	`2m38s`	`2m52s`
`Rules + AugMemo + TED`	0.9733 (0.00)	0.7603 (0.01)	`9m49s`	`4m46s`
`Rules + Memo`	0.3860 (0.00)	0.1438 (0.00)	`2m40s`	`1m18s`
`Rules + Memo + TED`	0.9570 (-0.00)	0.6575 (-0.00)	`6m59s`	`1m53s`
`Rules + TED`	0.9570 (0.00)	0.6575 (-0.00)	`7m5s`	`1m47s`

Dataset: financial-demo, Dataset repository branch: main (external repository), commit: bc73f669152147ae7f880ecba172c6e44f59e2c5
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `1m0s`, train: `2m36s`, total: `3m35s`	1.0000 (0.00)	0.8333 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m19s`, train: `3m31s`, total: `4m49s`	1.0000 (0.00)	0.8333 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `49s`, train: `2m28s`, total: `3m17s`	1.0000 (0.00)	0.8333 (0.00)	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m34s`, train: `3m38s`, total: `5m12s`	1.0000 (0.00)	0.8800 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `17s`, train: `1m4s`, total: `1m21s`	0.9643 (0.00)	0.8333 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `35s`, train: `1m27s`, total: `2m1s`	0.9643 (0.00)	0.8800 (0.00)	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.7218 (0.00)	0.5417 (0.00)	`15s`	`11s`
`Rules + AugMemo`	1.0000 (0.00)	1.0000 (0.00)	`15s`	`11s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m19s`	`33s`
`Rules + Memo`	0.9807 (0.00)	0.9167 (0.00)	`13s`	`10s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m20s`	`30s`
`Rules + TED`	0.9144 (0.00)	0.7083 (0.00)	`1m7s`	`27s`

Dataset: helpdesk-assistant, Dataset repository branch: main (external repository), commit: 8e74cea8f931705239613a989282bba8bc6af5c7
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `50s`, train: `1m58s`, total: `2m48s`	1.0000 (0.00)	`no data`	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m29s`, train: `3m36s`, total: `5m4s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `52s`, train: `2m18s`, total: `3m10s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m24s`, train: `2m38s`, total: `4m2s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `17s`, train: `1m2s`, total: `1m18s`	1.0000 (0.00)	`no data`	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `33s`, train: `1m11s`, total: `1m43s`	1.0000 (0.00)	`no data`	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.5714 (0.00)	0.2500 (0.00)	`6s`	`7s`
`Rules + AugMemo`	1.0000 (0.00)	1.0000 (0.00)	`7s`	`7s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`57s`	`25s`
`Rules + Memo`	0.9796 (0.00)	0.9167 (0.00)	`8s`	`10s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`50s`	`23s`
`Rules + TED`	1.0000 (0.00)	1.0000 (0.00)	`51s`	`22s`

Dataset: insurance-demo, Dataset repository branch: main (external repository), commit: 03ec1d9b9f2a41a877381ad24bfd91a3994692b7
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `58s`, train: `2m35s`, total: `3m33s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m12s`, train: `1m57s`, total: `3m9s`	1.0000 (0.00)	0.0000 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `54s`, train: `2m17s`, total: `3m12s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m30s`, train: `2m22s`, total: `3m51s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `13s`, train: `34s`, total: `47s`	1.0000 (0.00)	1.0000 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `41s`, train: `1m9s`, total: `1m49s`	1.0000 (0.00)	1.0000 (0.00)	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.5909 (0.00)	0.0000 (0.00)	`11s`	`14s`
`Rules + AugMemo`	1.0000 (0.00)	1.0000 (0.00)	`9s`	`9s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m28s`	`24s`
`Rules + Memo`	0.7600 (0.00)	0.5000 (0.00)	`9s`	`8s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m54s`	`29s`
`Rules + TED`	1.0000 (0.00)	1.0000 (0.00)	`1m27s`	`23s`

Dataset: retail-demo, Dataset repository branch: main (external repository), commit: 4c6a477775dbd538782278ca4c2fb651a63d3c29
Configuration repository branch: main

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
`BERT + DIET(bow) + ResponseSelector(bow)` test: `50s`, train: `1m37s`, total: `2m27s`	0.8387 (0.00)	0.2857 (0.00)	`no data`
`BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m34s`, train: `2m36s`, total: `4m9s`	0.8750 (0.00)	0.2857 (0.00)	`no data`
`Sparse + BERT + DIET(bow) + ResponseSelector(bow)` test: `50s`, train: `1m48s`, total: `2m37s`	0.9375 (0.00)	0.2857 (0.00)	`no data`
`Sparse + BERT + DIET(seq) + ResponseSelector(t2t)` test: `1m27s`, train: `2m19s`, total: `3m45s`	0.8125 (0.00)	0.2857 (0.00)	`no data`
`Sparse + DIET(bow) + ResponseSelector(bow)` test: `18s`, train: `48s`, total: `1m6s`	1.0000 (0.00)	0.2857 (0.00)	`no data`
`Sparse + DIET(seq) + ResponseSelector(t2t)` test: `42s`, train: `1m5s`, total: `1m47s`	0.9375 (0.00)	0.2857 (0.00)	`no data`

Dialog Policy Configuration	Action Level Micro Avg. F1	Conversation Level Accuracy	Run Time Train	Run Time Test
`Rules`	0.9531 (0.00)	0.7778 (0.00)	`7s`	`11s`
`Rules + AugMemo`	0.9692 (0.00)	0.8889 (0.00)	`6s`	`10s`
`Rules + AugMemo + TED`	1.0000 (0.00)	1.0000 (0.00)	`46s`	`27s`
`Rules + Memo`	0.9692 (0.00)	0.8889 (0.00)	`6s`	`10s`
`Rules + Memo + TED`	1.0000 (0.00)	1.0000 (0.00)	`48s`	`30s`
`Rules + TED`	1.0000 (0.00)	1.0000 (0.00)	`46s`	`28s`

stale · 2022-04-16T06:55:10Z

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.

Ghostvv and others added 27 commits January 14, 2021 11:55

update the code for tf 2.4

d384804

Merge branch 'main' into tf-2.4

1556a94

Merge main

c515ced

Modify TODOs to include TF 2.5 text

1971f6c

Merge branch 'main' into tf-2.4

3c7a35e

Updated poetry.lock file

b6fc20c

Fix formatting

1f52496

Try increasing timeout

94464eb

Increase timeouts further

3d0b1ba

More increased timeouts

20a6d59

Increase timeouts further

50f035e

Merge from main, resolve conflicts, simplify the changes in pyproject…

219dc51

….toml.

Merge branch 'main' of github.com:RasaHQ/rasa into tf-2.4

14268eb

Merge branch 'main' into tf-2.4

5491e39

Merge branch 'main' of github.com:RasaHQ/rasa into tf-2.4

455a203

Start another pass through TF code to update bits that have changed i…

052e5a8

…n TF2.4 and 2.5.

Merge from main after 2.8.0 release.

044e47d

Verify that sync_to_numpy_or_python_type is the appropriate replace…

54552e7

…ment for previously used `to_numpy_or_python_type`.

Revert a change that introduced an import from the standalone keras p…

6f42425

…ackage as opposed to tensorflow.python.keras. We don't want to use or rely on standalone keras.

Resolve a TODO item relating to our use of `@training.enable_multi_wo…

e51bf33

…rker` - the decorator was just copy-pasted from TF code and has now been removed.

Small additions to TmpKerasModel.fit based on the implementation of…

b58a3d1

… `fit` in TF 2.5.

Verify that base_layer.keras_api_gauge can be used in place of `tra…

4233837

…ining._keras_api_gauge`.

Merge branch 'main' of github.com:RasaHQ/rasa into tf-2.5

285bb66

Correct a TF import.

c74540c

Merge from main and fix conflicts.

aa2674f

Merge from main and fix poetry.lock conflicts.

2bc51ff

Generate new poetry lock file.

10cfcb0

samsucik mentioned this pull request Jul 23, 2021

Upgrade to TF 2.6 #7619

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dakshvar22 added runner:gpu status:model-regression-tests labels Jul 29, 2021

dakshvar22 added status:model-regression-tests and removed status:model-regression-tests labels Jul 29, 2021

koernerfelicia and others added 4 commits July 29, 2021 18:51

Update poetry.lock file

2fd15ab

Merge branch 'main' into tf-2.5

1f54467

Try to update poetry.lock again

1a2a30a

Try to update poetry.lock again

af5bb7b

github-actions bot deleted a comment from koernerfelicia Jul 29, 2021

github-actions bot removed status:model-regression-tests runner:gpu labels Jul 31, 2021

koernerfelicia added the status:model-regression-tests label Aug 5, 2021

RasaHQ deleted a comment from github-actions bot Aug 5, 2021

Merge branch 'main' into tf-2.5

1ef75d5

github-actions bot deleted a comment from koernerfelicia Aug 5, 2021

github-actions bot removed the status:model-regression-tests label Aug 5, 2021

koernerfelicia added the status:model-regression-tests label Aug 6, 2021

github-actions bot removed the status:model-regression-tests label Aug 7, 2021

Crude timing..

a7f6579

stale bot added the stale label Apr 16, 2022

m-vdb closed this Jun 2, 2022

m-vdb deleted the tf-2.5 branch June 2, 2022 07:20

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Update TensorFlow to 2.5 #9123

Update TensorFlow to 2.5 #9123

samsucik commented Jul 14, 2021

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stale bot commented Apr 16, 2022

Update TensorFlow to 2.5 #9123

Update TensorFlow to 2.5 #9123

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stale bot commented Apr 16, 2022