* Rename to spans_key for consistency
* Implement spans length in debug data
* Implement how span bounds and spans are obtained
In this commit, I implemented how span boundaries (the tokens) around a
given span and spans are obtained. I've put them in the compile_gold()
function so that it's accessible later on. I will do the actual
computation of the span and boundary distinctiveness in the main
function above.
* Compute for p_spans and p_bounds
* Add computation for SD and BD
* Fix mypy issues
* Add weighted average computation
* Fix compile_gold conditional logic
* Add test for frequency distribution computation
* Add tests for kl-divergence computation
* Fix weighted average computation
* Make tables more compact by rounding them
* Add more descriptive checks for spans
* Modularize span computation methods
In this commit, I added the _get_span_characteristics and
_print_span_characteristics functions so that they can be reusable
anywhere.
* Remove unnecessary arguments and make fxs more compact
* Update a few parameter arguments
* Add tests for print_span and get_span methods
* Update API to talk about span characteristics in brief
* Add better reporting of spans_length
* Add test for span length reporting
* Update formatting of span length report
Removed '' to indicate that it's not a string, then
sort the n-grams by their length, not by their frequency.
* Apply suggestions from code review
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Show all frequency distribution when -V
In this commit, I displayed the full frequency distribution of the
span lengths when --verbose is passed. To make things simpler, I
rewrote some of the formatter functions so that I can call them
whenever.
Another notable change is that instead of showing percentages as
Integers, I showed them as floats (max 2-decimal places). I did this
because it looks weird when it displays (0%).
* Update logic on how total is computed
The way the 90% thresholding is computed now is that we keep
adding the percentages until we reach >= 90%. I also updated the wording
and used the term "At least" to denote that >= 90% of your spans have
these distributions.
* Fix display when showing the threshold percentage
* Apply suggestions from code review
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add better phrasing for span information
* Update spacy/cli/debug_data.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add minor edits for whitespaces etc.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add initial design for diff command
For now, the diffing process looks like this:
- The default config is created based from some values in the user
config (e.g. which pipeline components were used, the lang, etc.)
- The user must supply manually if it was optimized for acc/efficiency
and if pretraining was involved.
* Make diff command structure similar to siblings
* Include gpu as a user option for CLI
* Make variables more explicit
* Fix type declaration for optimize enum
* Improve docstrings for diff CLI
* Add debug-diff to website API docs
* Switch position of configs so that user config is modded
* Add markdown flag for debug diff
This commit adds a --markdown (--md) flag that allows easier
copy-pasting to Github issues. Please note that this commit is dependent
on an unreleased version of wasabi (for the time being).
For posterity, the related PR is found here: https://github.com/ines/wasabi/pull/20
* Bump version of wasabi to 0.9.1
So that we can use the add_symbols parameter.
* Apply suggestions from code review
Co-authored-by: Ines Montani <ines@ines.io>
* Update docs based on code review suggestions
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Change command name from diff -> diff-config
* Clarify when options are relevant or not
* Rerun prettier on cli.md
Co-authored-by: Ines Montani <ines@ines.io>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add section for spacy.cli.train.train
* Add link from training page to train function
* Ensure path in train helper
* Update docs
Co-authored-by: Ines Montani <ines@ines.io>
* Add support for fasttext-bloom hash-only vectors
Overview:
* Extend `Vectors` to have two modes: `default` and `ngram`
* `default` is the default mode and equivalent to the current
`Vectors`
* `ngram` supports the hash-only ngram tables from `fasttext-bloom`
* Extend `spacy.StaticVectors.v2` to handle both modes with no changes
for `default` vectors
* Extend `spacy init vectors` to support ngram tables
The `ngram` mode **only** supports vector tables produced by this
fork of fastText, which adds an option to represent all vectors using
only the ngram buckets table and which uses the exact same ngram
generation algorithm and hash function (`MurmurHash3_x64_128`).
`fasttext-bloom` produces an additional `.hashvec` table, which can be
loaded by `spacy init vectors --fasttext-bloom-vectors`.
https://github.com/adrianeboyd/fastText/tree/feature/bloom
Implementation details:
* `Vectors` now includes the `StringStore` as `Vectors.strings` so that
the API can stay consistent for both `default` (which can look up from
`str` or `int`) and `ngram` (which requires `str` to calculate the
ngrams).
* In ngram mode `Vectors` uses a default `Vectors` object as a cache
since the ngram vectors lookups are relatively expensive.
* The default cache size is the same size as the provided ngram vector
table.
* Once the cache is full, no more entries are added. The user is
responsible for managing the cache in cases where the initial
documents are not representative of the texts.
* The cache can be resized by setting `Vectors.ngram_cache_size` or
cleared with `vectors._ngram_cache.clear()`.
* The API ends up a bit split between methods for `default` and for
`ngram`, so functions that only make sense for `default` or `ngram`
include warnings with custom messages suggesting alternatives where
possible.
* `Vocab.vectors` becomes a property so that the string stores can be
synced when assigning vectors to a vocab.
* `Vectors` serializes its own config settings as `vectors.cfg`.
* The `Vectors` serialization methods have added support for `exclude`
so that the `Vocab` can exclude the `Vectors` strings while serializing.
Removed:
* The `minn` and `maxn` options and related code from
`Vocab.get_vector`, which does not work in a meaningful way for default
vector tables.
* The unused `GlobalRegistry` in `Vectors`.
* Refactor to use reduce_mean
Refactor to use reduce_mean and remove the ngram vectors cache.
* Rename to floret
* Rename to floret in error messages
* Use --vectors-mode in CLI, vector init
* Fix vectors mode in init
* Remove unused var
* Minor API and docstrings adjustments
* Rename `--vectors-mode` to `--mode` in `init vectors` CLI
* Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support
both modes.
* Minor updates to Vectors docstrings.
* Update API docs for Vectors and init vectors CLI
* Update types for StaticVectors
* use language-matching to allow language code aliases
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* link to "IETF language tags" in docs
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* Make requirements consistent
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* change "two-letter language ID" to "IETF language tag" in language docs
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* use langcodes 3.2 and handle language-tag errors better
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* all unknown language codes are ImportErrors
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
Co-authored-by: Elia Robyn Speer <elia@explosion.ai>
Sofie Van Landeghem