🪐 spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the GoEmotions dataset

Feature	Description
Name	`en_textcat_goemotions`
Version	`0.0.1`
spaCy	`>=3.1.1,<3.2.0`
Default Pipeline	`transformer`, `textcat_multilabel`
Components	`transformer`, `textcat_multilabel`
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	GoEmotions dataset
License	`MIT`
Author	Explosion

The dataset that this model is trained on has known flaws described here as well as label errors resulting from annotator disagreement. Anyone using this model should be aware of these limitations of the dataset.

Label Scheme

View label scheme (28 labels for 1 components)

Component	Labels
`textcat_multilabel`	`admiration`, `amusement`, `anger`, `annoyance`, `approval`, `caring`, `confusion`, `curiosity`, `desire`, `disappointment`, `disapproval`, `disgust`, `embarrassment`, `excitement`, `fear`, `gratitude`, `grief`, `joy`, `love`, `nervousness`, `optimism`, `pride`, `realization`, `relief`, `remorse`, `sadness`, `surprise`, `neutral`

Accuracy

Type	Score
`CATS_SCORE`	90.22
`CATS_MICRO_P`	66.67
`CATS_MICRO_R`	47.81
`CATS_MICRO_F`	55.68
`CATS_MACRO_P`	55.00
`CATS_MACRO_R`	41.93
`CATS_MACRO_F`	46.29
`CATS_MACRO_AUC`	90.22
`CATS_MACRO_AUC_PER_TYPE`	0.00
`TRANSFORMER_LOSS`	83.51
`TEXTCAT_MULTILABEL_LOSS`	4549.84

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