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Add TextCatReduce.v1 #13181
Add TextCatReduce.v1 #13181
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This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer.
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Nice!
One thing I wasn't certain of is what to do with the
TextCatCNN.v2
docs. We could move them to legacy, however, this would mean that the existing TextCatCNN does not work anymore and there may be existing pages and tutorials linking to that.
The docs can be moved to legacy, and the code can go into spacy-legacy, but all existing code should ideally just continue to work as-is. Why wouldn't/couldn't it?
https://spacy.io/api/architectures#TextCatCNN could just refer to the legacy docs and/or the new TextCatReduce
.
I just noticed that there is a word missing in my initial comment:
Currently, pages can link to https://spacy.io/api/architectures#TextCatCNN |
Right - yes it dawned on me what you meant after writing the comment. We could just have a short dummy section, forwarding to the correct place. |
I think you can add a redirect to |
Cool, works with anchors too 🙏 . |
Or at least I got that impression from the existing rule files. But it doesn't seem to actually work, e.g. the existing rule for does not seem to cause a redirection to ? |
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add TextCatReduce.v1 This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. * Doc fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence * Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy * Add back a test for TextCatCNN.v2 * Replace TextCatCNN in pipe configurations and templates * Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor * Add last reduction (`use_reduce_last`) * Remove non-working TextCatCNN Netlify redirect * Revert layer changes for the quickstart * Revert one more quickstart change * Remove unused import * Fix docstring * Fix setting name in error message --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Description
This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer.
This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements
TextCatCNN.v2
using the newTextCatReduce.v1
layer.One thing I wasn't certain of is what to do with the
TextCatCNN.v2
docs. We could move them to legacy, however, this would mean that the existing TextCatCNN does not work anymore and there may be existing pages and tutorials linking to that.Types of change
Feature
Checklist