diff --git a/website/docs/api/top-level.md b/website/docs/api/top-level.md index b1a2d9532..b747007b0 100644 --- a/website/docs/api/top-level.md +++ b/website/docs/api/top-level.md @@ -377,6 +377,37 @@ using one of the built-in loggers listed here, you can also Writes the results of a training step to the console in a tabular format. + + +``` +$ python -m spacy train config.cfg +ℹ Using CPU +ℹ Loading config and nlp from: config.cfg +ℹ Pipeline: ['tok2vec', 'tagger'] +ℹ Start training +ℹ Training. Initial learn rate: 0.0 +E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE +--- ------ ------------ ----------- ------- ------ + 1 0 0.00 86.20 0.22 0.00 + 1 200 3.08 18968.78 34.00 0.34 + 1 400 31.81 22539.06 33.64 0.34 + 1 600 92.13 22794.91 43.80 0.44 + 1 800 183.62 21541.39 56.05 0.56 + 1 1000 352.49 25461.82 65.15 0.65 + 1 1200 422.87 23708.82 71.84 0.72 + 1 1400 601.92 24994.79 76.57 0.77 + 1 1600 662.57 22268.02 80.20 0.80 + 1 1800 1101.50 28413.77 82.56 0.83 + 1 2000 1253.43 28736.36 85.00 0.85 + 1 2200 1411.02 28237.53 87.42 0.87 + 1 2400 1605.35 28439.95 88.70 0.89 +``` + +Note that the cumulative loss keeps increasing within one epoch, but should +start decreasing across epochs. + + + #### spacy.WandbLogger.v1 {#WandbLogger tag="registered function"} > #### Installation