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Ines Montani
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.github/contributors/bintay.md
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI GmbH](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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If you agree to be bound by these terms, fill in the information requested
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below and include the filled-in version with your first pull request, under the
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folder [`.github/contributors/`](/.github/contributors/). The name of the file
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should be your GitHub username, with the extension `.md`. For example, the user
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example_user would create the file `.github/contributors/example_user.md`.
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Read this agreement carefully before signing. These terms and conditions
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constitute a binding legal agreement.
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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object code, patch, tool, sample, graphic, specification, manual,
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documentation, or any other material posted or submitted by you to the project.
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2. With respect to any worldwide copyrights, or copyright applications and
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registrations, in your contribution:
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* you hereby assign to us joint ownership, and to the extent that such
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assignment is or becomes invalid, ineffective or unenforceable, you hereby
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grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
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royalty-free, unrestricted license to exercise all rights under those
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copyrights. This includes, at our option, the right to sublicense these same
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rights to third parties through multiple levels of sublicensees or other
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licensing arrangements;
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* you agree that each of us can do all things in relation to your
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contribution as if each of us were the sole owners, and if one of us makes
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a derivative work of your contribution, the one who makes the derivative
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work (or has it made will be the sole owner of that derivative work;
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* you agree that you will not assert any moral rights in your contribution
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against us, our licensees or transferees;
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* you agree that we may register a copyright in your contribution and
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exercise all ownership rights associated with it; and
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* you agree that neither of us has any duty to consult with, obtain the
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consent of, pay or render an accounting to the other for any use or
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distribution of your contribution.
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3. With respect to any patents you own, or that you can license without payment
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to any third party, you hereby grant to us a perpetual, irrevocable,
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non-exclusive, worldwide, no-charge, royalty-free license to:
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* make, have made, use, sell, offer to sell, import, and otherwise transfer
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your contribution in whole or in part, alone or in combination with or
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included in any product, work or materials arising out of the project to
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which your contribution was submitted, and
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* at our option, to sublicense these same rights to third parties through
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multiple levels of sublicensees or other licensing arrangements.
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4. Except as set out above, you keep all right, title, and interest in your
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contribution. The rights that you grant to us under these terms are effective
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on the date you first submitted a contribution to us, even if your submission
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took place before the date you sign these terms.
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5. You covenant, represent, warrant and agree that:
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* Each contribution that you submit is and shall be an original work of
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authorship and you can legally grant the rights set out in this SCA;
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* to the best of your knowledge, each contribution will not violate any
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third party's copyrights, trademarks, patents, or other intellectual
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property rights; and
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* each contribution shall be in compliance with U.S. export control laws and
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other applicable export and import laws. You agree to notify us if you
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become aware of any circumstance which would make any of the foregoing
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representations inaccurate in any respect. We may publicly disclose your
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participation in the project, including the fact that you have signed the SCA.
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6. This SCA is governed by the laws of the State of California and applicable
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U.S. Federal law. Any choice of law rules will not apply.
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7. Please place an “x” on one of the applicable statement below. Please do NOT
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mark both statements:
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* [x] I am signing on behalf of myself as an individual and no other person
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or entity, including my employer, has or will have rights with respect to my
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contributions.
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* [ ] I am signing on behalf of my employer or a legal entity and I have the
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actual authority to contractually bind that entity.
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Ben Taylor |
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| Company name (if applicable) | |
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| Title or role (if applicable) | |
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| Date | October 2, 2019 |
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| GitHub username | bintay |
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| Website (optional) | bentaylor.xyz |
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@ -14,7 +14,7 @@ It's commercial open-source software, released under the MIT license.
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💫 **Version 2.2 out now!**
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[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
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[>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
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[>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
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[>)](https://travis-ci.org/explosion/spaCy)
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[](https://github.com/explosion/spaCy/releases)
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[](https://pypi.org/project/spacy/)
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@ -22,7 +22,7 @@ It's commercial open-source software, released under the MIT license.
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[](https://github.com/explosion/wheelwright/releases)
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[](https://pypi.org/project/spacy/)
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[](https://anaconda.org/conda-forge/spacy)
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[](https://github.com/explosion/spacy-models)
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[](https://github.com/explosion/spacy-models/releases)
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[](https://github.com/ambv/black)
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[](https://twitter.com/spacy_io)
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@ -277,9 +277,9 @@ def test_vocab_prune_vectors():
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_ = vocab["dog"] # noqa: F841
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_ = vocab["kitten"] # noqa: F841
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data = numpy.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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data[2] = 1.1
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data[0] = [1.0, 1.2, 1.1]
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data[1] = [0.3, 1.3, 1.0]
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data[2] = [0.9, 1.22, 1.05]
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vocab.set_vector("cat", data[0])
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vocab.set_vector("dog", data[1])
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vocab.set_vector("kitten", data[2])
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@ -303,8 +303,8 @@ cdef class Vectors:
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self._unset.erase(self._unset.find(row))
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return row
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def most_similar(self, queries, *, batch_size=1024):
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"""For each of the given vectors, find the single entry most similar
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def most_similar(self, queries, *, batch_size=1024, n=1, sort=True):
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"""For each of the given vectors, find the n most similar entries
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to it, by cosine.
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Queries are by vector. Results are returned as a `(keys, best_rows,
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@ -314,15 +314,17 @@ cdef class Vectors:
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queries (ndarray): An array with one or more vectors.
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batch_size (int): The batch size to use.
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RETURNS (tuple): The most similar entry as a `(keys, best_rows, scores)`
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n (int): The number of entries to return for each query.
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sort (bool): Whether to sort the n entries returned by score.
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RETURNS (tuple): The most similar entries as a `(keys, best_rows, scores)`
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tuple.
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"""
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xp = get_array_module(self.data)
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vectors = self.data / xp.linalg.norm(self.data, axis=1, keepdims=True)
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best_rows = xp.zeros((queries.shape[0],), dtype='i')
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scores = xp.zeros((queries.shape[0],), dtype='f')
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best_rows = xp.zeros((queries.shape[0], n), dtype='i')
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scores = xp.zeros((queries.shape[0], n), dtype='f')
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# Work in batches, to avoid memory problems.
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for i in range(0, queries.shape[0], batch_size):
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batch = queries[i : i+batch_size]
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@ -331,12 +333,19 @@ cdef class Vectors:
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# vectors e.g. (10000, 300)
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# sims e.g. (1024, 10000)
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sims = xp.dot(batch, vectors.T)
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best_rows[i:i+batch_size] = sims.argmax(axis=1)
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scores[i:i+batch_size] = sims.max(axis=1)
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best_rows[i:i+batch_size] = xp.argpartition(sims, -n, axis=1)[:,-n:]
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scores[i:i+batch_size] = xp.partition(sims, -n, axis=1)[:,-n:]
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if sort:
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sorted_index = xp.arange(scores.shape[0])[:,None],xp.argsort(scores, axis=1)[:,::-1]
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scores[i:i+batch_size] = scores[sorted_index]
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best_rows[i:i+batch_size] = best_rows[sorted_index]
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xp = get_array_module(self.data)
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row2key = {row: key for key, row in self.key2row.items()}
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keys = xp.asarray(
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[row2key[row] for row in best_rows if row in row2key], dtype="uint64")
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[[row2key[row] for row in best_rows[i] if row in row2key]
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for i in range(len(queries)) ], dtype="uint64")
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return (keys, best_rows, scores)
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def from_glove(self, path):
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@ -324,10 +324,10 @@ cdef class Vocab:
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syn_keys, syn_rows, scores = self.vectors.most_similar(toss, batch_size=batch_size)
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remap = {}
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for i, key in enumerate(keys[nr_row:]):
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self.vectors.add(key, row=syn_rows[i])
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self.vectors.add(key, row=syn_rows[i][0])
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word = self.strings[key]
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synonym = self.strings[syn_keys[i]]
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score = scores[i]
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synonym = self.strings[syn_keys[i][0]]
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score = scores[i][0]
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remap[word] = (synonym, score)
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link_vectors_to_models(self)
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return remap
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@ -303,6 +303,29 @@ vectors, they will be counted individually.
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| ----------- | ---- | ------------------------------------ |
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| **RETURNS** | int | The number of all keys in the table. |
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## Vectors.most_similar {#most_similar tag="method"}
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For each of the given vectors, find the `n` most similar entries to it, by
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cosine. Queries are by vector. Results are returned as a
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`(keys, best_rows, scores)` tuple. If `queries` is large, the calculations are
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performed in chunks, to avoid consuming too much memory. You can set the
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`batch_size` to control the size/space trade-off during the calculations.
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> #### Example
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>
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> ```python
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> queries = numpy.asarray([numpy.random.uniform(-1, 1, (300,))])
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> most_similar = nlp.vectors.most_similar(queries, n=10)
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> ```
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| Name | Type | Description |
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| ------------ | --------- | ------------------------------------------------------------------ |
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| `queries` | `ndarray` | An array with one or more vectors. |
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| `batch_size` | int | The batch size to use. Default to `1024`. |
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| `n` | int | The number of entries to return for each query. Defaults to `1`. |
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| `sort` | bool | Whether to sort the entries returned by score. Defaults to `True`. |
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| **RETURNS** | tuple | The most similar entries as a `(keys, best_rows, scores)` tuple. |
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## Vectors.from_glove {#from_glove tag="method"}
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Load [GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory.
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