Sync docs/llm_main with master
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* Restore spacy.cli.project API * Fix typing errors, add simple import test
* Support Any comparisons for Token and Span * Preserve previous behavior for None
* update all for pipeline.init * add all in training.init * add all in kb.init * alphabetically
…ns_key` for SpanCat (#13093) * Add note on score_weight if using a non-default span_key for SpanCat. * Fix formatting. * Fix formatting. * Fix typo. * Use warning infobox. * Fix infobox formatting.
* add comment that pipeline is a custom one * add link to NEL tutorial * prettier * revert prettier reformat * revert prettier reformat (2) * fix typo Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
* fix typo * add examples to specify custom model for task-specific factory
* Fix displacy span stacking. * Format. Remove counter. * Remove test files. * Add unit test. Refactor to allow for unit test. * Fix off-by-one error in tests.
- Replace `np.trapz` with vendored `trapezoid` from scipy - Replace `np.float_` with `np.float64`
* Update Tokenizer.explain for special cases with whitespace Update `Tokenizer.explain` to skip special case matches if the exact text has not been matched due to intervening whitespace. Enable fuzzy `Tokenizer.explain` tests with additional whitespace normalization. * Add unit test for special cases with whitespace, xfail fuzzy tests again
Co-authored-by: Ridge Kimani <ridgekimani@gmail.com>
Build with `build` if available. Warn and fall back to previous `setup.py`-based builds if `build` build fails.
* Update the "Missing factory" error message This accounts for model installations that took place during the current Python session. * Add a note about Jupyter notebooks * Move error to `spacy.cli.download` Add extra message for Jupyter sessions * Add additional note for interactive sessions * Remove note about `spacy-transformers` from error message * `isort` * Improve checks for colab (also helps displacy) * Update warning messages * Improve flow for multiple checks --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* add language extensions for norwegian nynorsk and faroese * update docstring for nn/examples.py * use relative imports * add fo and nn tokenizers to pytest fixtures * add unittests for fo and nn and fix bug in nn * remove module docstring from fo/__init__.py * add comments about example sentences' origin * add license information to faroese data credit * format unittests using black * add __init__ files to test/lang/nn and tests/lang/fo * fix import order and use relative imports in fo/__nit__.py and nn/__init__.py * Make the tests a bit more compact * Add fo and nn to website languages * Add note about jul. * Add "jul." as exception --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update `TextCatBOW` to use the fixed `SparseLinear` layer A while ago, we fixed the `SparseLinear` layer to use all available parameters: explosion/thinc#754 This change updates `TextCatBOW` to `v3` which uses the new `SparseLinear_v2` layer. This results in a sizeable improvement on a text categorization task that was tested. While at it, this `spacy.TextCatBOW.v3` also adds the `length_exponent` option to make it possible to change the hidden size. Ideally, we'd just have an option called `length`. But the way that `TextCatBOW` uses hashes results in a non-uniform distribution of parameters when the length is not a power of two. * Replace TexCatBOW `length_exponent` parameter by `length` We now round up the length to the next power of two if it isn't a power of two. * Remove some tests for TextCatBOW.v2 * Fix missing import
* correct char_span output type - can be None * unify type of exclude parameter * black * further fixes to from_dict and to_dict * formatting
…y blog. (#13197) * Update README.md to include links for GPU processing, LLM, and spaCy's blog. * Create ojo4f3.md * corrected README to most current version with links to GPU processing, LLM's, and the spaCy blog. * Delete .github/contributors/ojo4f3.md * changed LLM icon Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Apply suggestions from code review --------- 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 spacy.TextCatParametricAttention.v1 This layer provides is a simplification of the ensemble classifier that only uses paramteric attention. We have found empirically that with a sufficient amount of training data, using the ensemble classifier with BoW does not provide significant improvement in classifier accuracy. However, plugging in a BoW classifier does reduce GPU training and inference performance substantially, since it uses a GPU-only kernel. * Fix merge fallout
# Conflicts: # website/docs/api/large-language-models.mdx
Sync `master` with `docs/llm_main`
Before this change, the workers of pipe call with n_process != 1 were
stopped by calling `terminate` on the processes. However, terminating a
process can leave queues, pipes, and other concurrent data structures in
an invalid state.
With this change, we stop using terminate and take the following approach
instead:
* When the all documents are processed, the parent process puts a
sentinel in the queue of each worker.
* The parent process then calls `join` on each worker process to
let them finish up gracefully.
* Worker processes break from the queue processing loop when the
sentinel is encountered, so that they exit.
We need special handling when one of the workers encounters an error and
the error handler is set to raise an exception. In this case, we cannot
rely on the sentinel to finish all workers -- the queue is a FIFO queue
and there may be other work queued up before the sentinel. We use the
following approach to handle error scenarios:
* The parent puts the end-of-work sentinel in the queue of each worker.
* The parent closes the reading-end of the channel of each worker.
* Then:
- If the worker was waiting for work, it will encounter the sentinel
and break from the processing loop.
- If the worker was processing a batch, it will attempt to write
results to the channel. This will fail because the channel was
closed by the parent and the worker will break from the processing
loop.
macOS now uses port 5000 for the AirPlay receiver functionality, so this test will always fail on a macOS desktop (unless AirPlay receiver functionality is disabled like in CI).
Fix typo in method name
* add line to ensure that apple is in fact in the vocab * add that the vocab may be empty
* attempt to clarify additional annotations on .spacy file * suggestion by Daniël * pipeline instead of pipe
* add custom code support to CLI speed benchmark * sort imports * better copying for warmup docs
The doc/token extension serialization tests add extensions that are not serializable with pickle. This didn't cause issues before due to the implicit run order of tests. However, test ordering has changed with pytest 8.0.0, leading to failed tests in test_language. Update the fixtures in the extension serialization tests to do proper teardown and remove the extensions.
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Description
Sync
docs/llm_mainwithmaster.Types of change
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