This spaCy Contributor Agreement (**"SCA"**) is based on the

[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).

The SCA applies to any contribution that you make to any product or project

managed by us (the **"project"**), and sets out the intellectual property rights

you grant to us in the contributed materials. The term **"us"** shall mean

[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term

**"you"** shall mean the person or entity identified below.

If you agree to be bound by these terms, fill in the information requested

below and include the filled-in version with your first pull request, under the

folder [`.github/contributors/`](/.github/contributors/). The name of the file

should be your GitHub username, with the extension `.md`. For example, the user

example_user would create the file `.github/contributors/example_user.md`.

Read this agreement carefully before signing. These terms and conditions

constitute a binding legal agreement.

1. The term "contribution" or "contributed materials" means any source code,

object code, patch, tool, sample, graphic, specification, manual,

documentation, or any other material posted or submitted by you to the project.

2. With respect to any worldwide copyrights, or copyright applications and

registrations, in your contribution:

* you hereby assign to us joint ownership, and to the extent that such

assignment is or becomes invalid, ineffective or unenforceable, you hereby

grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,

royalty-free, unrestricted license to exercise all rights under those

copyrights. This includes, at our option, the right to sublicense these same

rights to third parties through multiple levels of sublicensees or other

licensing arrangements;

* you agree that each of us can do all things in relation to your

contribution as if each of us were the sole owners, and if one of us makes

a derivative work of your contribution, the one who makes the derivative

work (or has it made will be the sole owner of that derivative work;

* you agree that you will not assert any moral rights in your contribution

against us, our licensees or transferees;

* you agree that we may register a copyright in your contribution and

exercise all ownership rights associated with it; and

* you agree that neither of us has any duty to consult with, obtain the

consent of, pay or render an accounting to the other for any use or

distribution of your contribution.

3. With respect to any patents you own, or that you can license without payment

to any third party, you hereby grant to us a perpetual, irrevocable,

non-exclusive, worldwide, no-charge, royalty-free license to:

* make, have made, use, sell, offer to sell, import, and otherwise transfer

your contribution in whole or in part, alone or in combination with or

included in any product, work or materials arising out of the project to

which your contribution was submitted, and

* at our option, to sublicense these same rights to third parties through

multiple levels of sublicensees or other licensing arrangements.

4. Except as set out above, you keep all right, title, and interest in your

contribution. The rights that you grant to us under these terms are effective

on the date you first submitted a contribution to us, even if your submission

took place before the date you sign these terms.

5. You covenant, represent, warrant and agree that:

* Each contribution that you submit is and shall be an original work of

authorship and you can legally grant the rights set out in this SCA;

* to the best of your knowledge, each contribution will not violate any

third party's copyrights, trademarks, patents, or other intellectual

property rights; and

* each contribution shall be in compliance with U.S. export control laws and

other applicable export and import laws. You agree to notify us if you

become aware of any circumstance which would make any of the foregoing

representations inaccurate in any respect. We may publicly disclose your

participation in the project, including the fact that you have signed the SCA.

6. This SCA is governed by the laws of the State of California and applicable

U.S. Federal law. Any choice of law rules will not apply.

7. Please place an “x” on one of the applicable statement below. Please do NOT

mark both statements:

* [x] I am signing on behalf of myself as an individual and no other person

or entity, including my employer, has or will have rights with respect to my

contributions.

* [ ] I am signing on behalf of my employer or a legal entity and I have the

actual authority to contractually bind that entity.

| Field | Entry |

|------------------------------- | -------------------- |

| Name | Ulrich Wolffgang |

| Company name (if applicable) | |

| Title or role (if applicable) | |

| Date | 2017-11-05 |

| GitHub username | uwol |

| Website (optional) | https://uwol.github.io/ |

__version__ = '2.0.0a19'

"_SP": {"pos": "SPACE"}

for d_input, bp_input in zip(d_inputs, bp_inputs):

if isinstance(doc.tensor, numpy.ndarray) \

and not isinstance(tensors[i], numpy.ndarray):

doc.extend_tensor(tensors[i].get())

else:

doc.extend_tensor(tensors[i])

tensors = [doc.tensor for doc in docs]

return scores, tensors

# Add a padding vector to the d_tokvecs gradient, so that missing

# values don't affect the real gradient.

d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1]))

# Add a padding vector to the d_tokvecs gradient, so that missing

# values don't affect the real gradient.

d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1]))

# Padded -- see update()

bp_tokvecs(d_tokvecs[:-1], sgd=sgd)

if isinstance(doc.tensor, numpy.ndarray) \

and not isinstance(tensors[i], numpy.ndarray):

doc.extend_tensor(tensors[i].get())

else:

doc.extend_tensor(tensors[i])

from ...language import Language

from ...pipeline import DependencyParser

def test_beam_parse_en(EN):

def test_beam_parse():

nlp = Language()

nlp.add_pipe(DependencyParser(nlp.vocab), name='parser')

nlp.parser.add_label('nsubj')

nlp.begin_training()

doc = nlp.make_doc(u'Australia is a country')

nlp.parser(doc, beam_width=2)

self.data = xp.load(str(path))