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# spaCy contributor agreement
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 GmbH](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.
## Contributor 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:
* [ ] 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.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Guillaume Claret |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | June 19, 2019 |
| GitHub username | clarus |
| Website (optional) | guillaume.claret.me |

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# spaCy contributor agreement
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 GmbH](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.
## Contributor 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.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Alejandro Alcalde |
| Company name (if applicable) | |
| Title or role (if applicable) | Data Scientist |
| Date | 07/17/2019 |
| GitHub username | elbaulp |
| Website (optional) | [elbauldelprogramador.com](https://elbauldelprogramador.com/en/) |

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spacy/cli/pretrain.py
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@ -23,18 +23,19 @@ from .train import _load_pretrained_tok2vec
@plac.annotations(
texts_loc=("Path to jsonl file with texts to learn from", "positional", None, str),
vectors_model=("Name or path to vectors model to learn from"),
output_dir=("Directory to write models each epoch", "positional", None, str),
texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
"key 'tokens'", "positional", None, str),
vectors_model=("Name or path to spaCy model with vectors to learn from"),
output_dir=("Directory to write models to on each epoch", "positional", None, str),
width=("Width of CNN layers", "option", "cw", int),
depth=("Depth of CNN layers", "option", "cd", int),
embed_rows=("Embedding rows", "option", "er", int),
loss_func=("Loss to use for the objective. L2 or cosine", "option", "L", str),
embed_rows=("Number of embedding rows", "option", "er", int),
loss_func=("Loss function to use for the objective. Either 'L2' or 'cosine'", "option", "L", str),
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
dropout=("Dropout", "option", "d", float),
dropout=("Dropout rate", "option", "d", float),
batch_size=("Number of words per training batch", "option", "bs", int),
max_length=("Max words per example.", "option", "xw", int),
min_length=("Min words per example.", "option", "nw", int),
max_length=("Max words per example. Longer examples are discarded", "option", "xw", int),
min_length=("Min words per example. Shorter examples are discarded", "option", "nw", int),
seed=("Seed for random number generators", "option", "s", int),
n_iter=("Number of iterations to pretrain", "option", "i", int),
n_save_every=("Save model every X batches.", "option", "se", int),
@ -250,6 +251,8 @@ def get_vectors_loss(ops, docs, prediction, objective="L2"):
loss = (d_target ** 2).sum()
elif objective == "cosine":
loss, d_target = get_cossim_loss(prediction, target)
else:
raise ValueError(Errors.E139.format(loss_func=objective))
return loss, d_target

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spacy/errors.py
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@ -402,6 +402,7 @@ class Errors(object):
E139 = ("Knowledge base for component '{name}' not initialized. Did you forget to call set_kb()?")
E140 = ("The list of entities, prior probabilities and entity vectors should be of equal length.")
E141 = ("Entity vectors should be of length {required} instead of the provided {found}.")
E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
@add_codes

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website/docs/api/cli.md
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@ -285,18 +285,19 @@ improvement.
```bash
$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
[--depth] [--embed-rows] [--dropout] [--seed] [--n-iter] [--use-vectors]
[--depth] [--embed-rows] [--loss_func] [--dropout] [--seed] [--n-iter] [--use-vectors]
[--n-save_every]
```
| Argument | Type | Description |
| ----------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `tokens`. [See here](#pretrain-jsonl) for details. |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `"tokens"`. [See here](#pretrain-jsonl) for details. |
| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. |
| `output_dir` | positional | Directory to write models to on each epoch. |
| `--width`, `-cw` | option | Width of CNN layers. |
| `--depth`, `-cd` | option | Depth of CNN layers. |
| `--embed-rows`, `-er` | option | Number of embedding rows. |
| `--loss-func`, `-L` | option | Loss function to use for the objective. Either `"L2"` or `"cosine"`. |
| `--dropout`, `-d` | option | Dropout rate. |
| `--batch-size`, `-bs` | option | Number of words per training batch. |
| `--max-length`, `-xw` | option | Maximum words per example. Longer examples are discarded. |
@ -304,7 +305,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
| `--seed`, `-s` | option | Seed for random number generators. |
| `--n-iter`, `-i` | option | Number of iterations to pretrain. |
| `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. |
| `--n-save_every`, `-se` | option | Save model every X batches. |
| `--n-save-every`, `-se` | option | Save model every X batches. |
| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.|
| **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. |

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website/docs/api/span.md
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@ -8,7 +8,7 @@ A slice from a [`Doc`](/api/doc) object.
## Span.\_\_init\_\_ {#init tag="method"}
Create a Span object from the `slice doc[start : end]`.
Create a Span object from the slice `doc[start : end]`.
> #### Example
>

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website/docs/usage/training.md
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@ -630,8 +630,8 @@ should be somewhat larger, especially if your documents are long.
### Learning rate, regularization and gradient clipping {#tips-hyperparams}
By default spaCy uses the Adam solver, with default settings (learning rate
`0.001`, `beta1=0.9`, `beta2=0.999`). Some researchers have said they found
By default spaCy uses the Adam solver, with default settings (`learn_rate=0.001`,
`beta1=0.9`, `beta2=0.999`). Some researchers have said they found
these settings terrible on their problems – but they've always performed very
well in training spaCy's models, in combination with the rest of our recipe. You
can change these settings directly, by modifying the corresponding attributes on