# Understanding outputs for new texcat model **URL:** \ **Category:** Uncategorized\ **Tags:** usage, textcat, spacy, training\ **Created:** [September 27, 2021, 10:17pm UTC](https://support.prodi.gy/t/understanding-outputs-for-new-texcat-model/4730 "2021-09-27T22:17:47Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![VeronicaCPerez](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/veronicacperez/32/2153_2.png) [@VeronicaCPerez](https://support.prodi.gy/u/VeronicaCPerez)\ **Post date:** [September 27, 2021, 10:17pm UTC](https://support.prodi.gy/t/understanding-outputs-for-new-texcat-model/4730/1 "2021-09-27T22:17:47Z")
Hi, I recently updated to the newest version of prodigy and I'm very confused with how the results of the models are presented. From a previous entry I know that: - "E" refers to the number of epochs that you've trained, with one epoch representing one pass over all the data. But I don't understand what # is then. I haven't been able to find documentation on how to read the results. Is score the F-score? How do I get the usual Accuracy, Precision, Recall or F1? If I could be directed to documentation on this new format It'd be very helpful. ![Screen Shot 2021-09-27 at 6.17.25 PM](https://us1.discourse-cdn.com/flex020/uploads/prodigy/original/2X/d/dee807fcbec1c4b131b6fe71f58c54afdb23c2d6.jpeg) Thanks. ---
**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\ **Post date:** [September 28, 2021, 10:08am UTC](https://support.prodi.gy/t/understanding-outputs-for-new-texcat-model/4730/2 "2021-09-28T10:08:07Z")
Hi! The `E` refers to the epochs and `#` is the number of steps (iterations). Also see here: [Unclear what the column 'E' is outputting in the console output during training · Discussion #7731 · explosion/spaCy · GitHub](https://github.com/explosion/spaCy/discussions/7731) The score is the combined weighted score and you can read more about it here: [https://spacy.io/usage/training#metrics](https://spacy.io/usage/training#metrics) If you're only training one component, this is typically identical to the main score reported by the component (e.g. F-score). If you're training multiple components, you can use the score weights to define what to prioritise (e.g. best text classifier + NER recall combination, and so on). You can also use the `[training.score_weights]` block to define the scores shown in the table. The scores set by the individual components are available in the code via the `@Language.factory` decorator: [https://spacy.io/api/textcategorizer](https://spacy.io/api/textcategorizer) ---
**Author:** ![VeronicaCPerez](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/veronicacperez/32/2153_2.png) [@VeronicaCPerez](https://support.prodi.gy/u/VeronicaCPerez)\ **Post date:** [October 18, 2021, 6:48pm UTC](https://support.prodi.gy/t/understanding-outputs-for-new-texcat-model/4730/3 "2021-10-18T18:48:36Z")
Hi @ines, In the last answer you told me the score metric was equal to the F-1 when training for one component. However, as you can see in the attached image the score is actually equal to the ROC AUC instead of the F-1. I'm still confused on how to read the output because in the spacy documentation you only explain what P, R and F mean. However there's no explanation there about the "#", "LOSS TEXTCAT" and "CAT\_SCORE" Is there any documentation on how to read the output. Thanks, Veronica ![image](https://us1.discourse-cdn.com/flex020/uploads/prodigy/original/2X/3/3cd480e3814aa0eb21d114a6a3a6ad70b18eb2c1.jpeg) ---
**Author:** ![adriane](https://avatars.discourse-cdn.com/v4/letter/a/46a35a/32.png) [@adriane](https://support.prodi.gy/u/adriane)\ **Post date:** [October 19, 2021, 7:23am UTC](https://support.prodi.gy/t/understanding-outputs-for-new-texcat-model/4730/4 "2021-10-19T07:23:59Z")
Hi, Ines mentioned F1 as a example, but it's not necessarily the default score for all components+settings. The default textcat scoring, which is a bit complicated due to all the possible model configurations, is explained here: > **[Scorer · spaCy API Documentation](https://spacy.io/api/scorer/)** > > Compute evaluation scores Based on the config settings, the scorer tries to pick the most useful score to show in the "CATS\_SCORE" column in the training output. All the possible scores are included in `meta.json` in the saved model and this should include a text description of which score was selected for `cats_score` under `cats_score_desc`.