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167 Commits

Author SHA1 Message Date
Matthew Honnibal
3db0a32fd6 Fix dropout for history features 2017-10-05 22:21:30 -05:00
Matthew Honnibal
fc06b0a333 Fix training when hist_size==0 2017-10-05 21:52:28 -05:00
Matthew Honnibal
dcdfa071aa Disable LayerNorm hack 2017-10-04 20:06:52 -05:00
Matthew Honnibal
bfabc333be Merge remote-tracking branch 'origin/develop' into feature/parser-history-model 2017-10-04 20:00:36 -05:00
Matthew Honnibal
92066b04d6 Fix Embed and HistoryFeatures 2017-10-04 19:55:34 -05:00
Matthew Honnibal
bd8e84998a Add nO attribute to TextCategorizer model 2017-10-04 16:07:30 +02:00
Matthew Honnibal
f8a0614527 Improve textcat model slightly 2017-10-04 15:15:53 +02:00
Matthew Honnibal
39798b0172 Uncomment layernorm adjustment hack 2017-10-04 15:12:09 +02:00
Matthew Honnibal
774f5732bd Fix dimensionality of textcat when no vectors available 2017-10-04 14:55:15 +02:00
Matthew Honnibal
af75b74208 Unset LayerNorm backwards compat hack 2017-10-03 20:47:10 -05:00
Matthew Honnibal
246612cb53 Merge remote-tracking branch 'origin/develop' into feature/parser-history-model 2017-10-03 16:56:42 -05:00
Matthew Honnibal
5cbefcba17 Set backwards compatibility flag 2017-10-03 20:29:58 +02:00
Matthew Honnibal
5454b20cd7 Update thinc imports for 6.9 2017-10-03 20:07:17 +02:00
Matthew Honnibal
e514d6aa0a Import thinc modules more explicitly, to avoid cycles 2017-10-03 18:49:25 +02:00
Matthew Honnibal
b770f4e108 Fix embed class in history features 2017-10-03 13:26:55 +02:00
Matthew Honnibal
6aa6a5bc25 Add a layer type for history features 2017-10-03 12:43:09 +02:00
Matthew Honnibal
f6330d69e6 Default embed size to 7000 2017-09-28 08:07:41 -05:00
Matthew Honnibal
1a37a2c0a0 Update training defaults 2017-09-27 11:48:07 -05:00
Matthew Honnibal
e34e70673f Allow tagger models to be built with pre-defined tok2vec layer 2017-09-26 05:51:52 -05:00
Matthew Honnibal
63bd87508d Don't use iterated convolutions 2017-09-23 04:39:17 -05:00
Matthew Honnibal
4348c479fc Merge pre-trained vectors and noshare patches 2017-09-22 20:07:28 -05:00
Matthew Honnibal
4bd6a12b1f Fix Tok2Vec 2017-09-23 02:58:54 +02:00
Matthew Honnibal
980fb6e854 Refactor Tok2Vec 2017-09-22 09:38:36 -05:00
Matthew Honnibal
d9124f1aa3 Add link_vectors_to_models function 2017-09-22 09:38:22 -05:00
Matthew Honnibal
a186596307 Add 'reapply' combinator, for iterated CNN 2017-09-22 09:37:03 -05:00
Matthew Honnibal
40a4873b70 Fix serialization of model options 2017-09-21 13:07:26 -05:00
Matthew Honnibal
20193371f5 Don't share CNN, to reduce complexities 2017-09-21 14:59:48 +02:00
Matthew Honnibal
f5144f04be Add argument for CNN maxout pieces 2017-09-20 19:14:41 -05:00
Matthew Honnibal
78301b2d29 Avoid comparison to None in Tok2Vec 2017-09-20 00:19:34 +02:00
Matthew Honnibal
3fa76c17d1 Refactor Tok2Vec 2017-09-18 15:00:05 -05:00
Matthew Honnibal
7b3f391f80 Try dropping the Affine layer, conditionally 2017-09-18 11:35:59 -05:00
Matthew Honnibal
2148ae605b Dont use iterated convolutions 2017-09-17 17:36:04 -05:00
Matthew Honnibal
8f42f8d305 Remove unused 'preprocess' argument in Tok2Vec' 2017-09-17 12:30:16 -05:00
Matthew Honnibal
8f913a74ca Fix defaults and args to build_tagger_model 2017-09-17 05:46:36 -05:00
Matthew Honnibal
2a93404da6 Support optional pre-trained vectors in tensorizer model 2017-09-16 12:45:37 -05:00
Matthew Honnibal
24ff6b0ad9 Fix parsing and tok2vec models 2017-09-06 05:50:58 -05:00
Matthew Honnibal
16e25ce3b5 Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2017-09-04 09:26:53 -05:00
Matthew Honnibal
9f512e657a Fix drop_layer calculation 2017-09-04 09:26:38 -05:00
Matthew Honnibal
c0eaba8b28 Fix low-data textcat 2017-09-02 15:17:32 +02:00
Matthew Honnibal
a3b69bcb3d Add low_data mode in textcat 2017-09-02 14:56:30 +02:00
Matthew Honnibal
a824cf8f9a Adjust text classification model 2017-09-02 11:41:00 +02:00
Matthew Honnibal
ac040b99bb Add support for pre-trained vectors in text classifier 2017-09-01 16:39:55 +02:00
Matthew Honnibal
6d4e8e14ca Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2017-08-25 12:37:16 -05:00
Matthew Honnibal
4ce5531389 Use layer norm instead of batch norm 2017-08-25 12:37:10 -05:00
Matthew Honnibal
1c5c256e58 Fix fine_tune when optimizer is None 2017-08-23 10:51:33 +02:00
Matthew Honnibal
9c580ad28a Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2017-08-22 17:02:04 -05:00
Matthew Honnibal
a4633fff6f Restore use of batch norm in model 2017-08-22 17:01:58 -05:00
Matthew Honnibal
df2745eb08 Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2017-08-22 19:00:43 +02:00
Matthew Honnibal
18b64e79ec Fix fine tuning 2017-08-21 19:18:26 -05:00
Matthew Honnibal
a21d8f3f0b Add predict paths to _ml models 2017-08-21 23:23:45 +02:00
Matthew Honnibal
80acbc5f1f Fix fine-tune weight mixture 2017-08-21 14:15:29 -05:00
Matthew Honnibal
c10f63bf10 Initialize fine tuning to 0.5 2017-08-20 15:59:48 -05:00
Matthew Honnibal
8a59718fd6 Fix fine-tuning 2017-08-20 18:17:35 +02:00
Matthew Honnibal
bae59bf92f Remove BiLSTM import 2017-08-18 22:46:59 +02:00
Matthew Honnibal
fe90dfc390 Restore changes from nn-beam-parser to spacy/_ml 2017-08-18 22:38:28 +02:00
Matthew Honnibal
ce321b0322 Restore changes from nn-beam-parser to spacy/_ml 2017-08-18 22:24:46 +02:00
Matthew Honnibal
931509d96a Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2017-08-18 21:57:15 +02:00
Matthew Honnibal
263366729e Don't import BiLSTM 2017-08-18 21:56:31 +02:00
Matthew Honnibal
85794c1167 Restore state of _ml.py 2017-08-18 14:55:23 -05:00
Matthew Honnibal
426f84937f Resolve conflicts when merging new beam parsing stuff 2017-08-18 13:38:32 -05:00
Matthew Honnibal
5181e8bedb Fix merge conflict in _ml 2017-08-18 13:35:51 -05:00
Matthew Honnibal
4b1e7bd6d8 Improve tensorizer model 2017-08-16 18:25:20 -05:00
Matthew Honnibal
6259490347 Fix mixture weights in fine_tune 2017-08-14 17:55:18 -05:00
Matthew Honnibal
335fa8b05c Fix gradient in fine_tune 2017-08-14 14:55:47 -05:00
Matthew Honnibal
52c180ecf5 Revert "Merge branch 'develop' of https://github.com/explosion/spaCy into develop" 
This reverts commit ea8de11ad5, reversing
changes made to 08e443e083.
 2017-08-14 13:00:23 +02:00
Matthew Honnibal
ac6c25f762 Check SGD is not None in update 2017-08-14 12:09:18 +02:00
Matthew Honnibal
4ab0c8c8e9 Try different drop_layer structure in Tok2Vec 2017-08-12 08:56:57 -05:00
Matthew Honnibal
ebe0f7f641 Pass embed size correctly in tagger, and cache embeddings for efficiency 2017-08-12 05:45:20 -05:00
Matthew Honnibal
f93f2bed58 Revert use of layer normalization in Tok2Vec 2017-08-09 17:47:03 -05:00
Matthew Honnibal
ac2de6dced Switch to ReLu layers in Tok2Vec 2017-08-09 16:41:25 -05:00
Matthew Honnibal
88bf1cf87c Update parser for fine tuning 2017-08-08 15:34:17 -05:00
Matthew Honnibal
5d837c3776 Add mix weights on fine_tune 2017-08-07 06:32:59 -05:00
Matthew Honnibal
3ed203de25 Use LayerNorm and SELU in Tok2Vec 2017-08-06 18:33:18 +02:00
Matthew Honnibal
4a5cc89138 Fix tagger 'fine_tune', to keep private CNN weights 2017-08-06 14:15:48 +02:00
Matthew Honnibal
4cfb7a54e7 Fix tagger 2017-08-06 01:53:31 +02:00
Matthew Honnibal
e9ab800e15 Fix tagging model 2017-08-06 01:50:08 +02:00
Matthew Honnibal
468c138ab3 WIP: Add fine-tuning logic to tagger model, re #1182 2017-08-06 01:13:23 +02:00
Matthew Honnibal
523b0df2c9 Update text classification model 2017-07-25 18:57:59 +02:00
Matthew Honnibal
2df563ad24 Remove optimization for textcat that caused loading problem 2017-07-23 14:10:51 +02:00
Matthew Honnibal
ded0df5e2f Expose hyper-param as keyword arg 2017-07-22 20:14:37 +02:00
Matthew Honnibal
6ffec9dfea Update _ml, for textcat model 2017-07-22 20:03:40 +02:00
Matthew Honnibal
727481377e Add text-classifer thinc models 2017-07-20 00:17:17 +02:00
Matthew Honnibal
8a17b99b1c Use NORM attribute, not LOWER 2017-06-03 15:30:16 -05:00
Matthew Honnibal
b92a89f87b Make it easier to reference embedding tables 2017-05-29 17:53:29 -05:00
Matthew Honnibal
c91b121aeb Move serialization functions to util 2017-05-29 10:13:42 +02:00
Matthew Honnibal
1fa2bfb600 Add model_to_bytes and model_from_bytes helpers. Probably belong in thinc. 2017-05-29 09:27:04 +02:00
Matthew Honnibal
6dad4117ad Work on serialization for models 2017-05-29 01:37:57 +02:00
Matthew Honnibal
8de9829f09 Don't overwrite model in initialization, when loading 2017-05-27 15:50:40 -05:00
Matthew Honnibal
b27c587800 Fix pieces argument to PrecomputedMaxout 2017-05-25 06:46:59 -05:00
Matthew Honnibal
c998776c25 Make single array for features, to reduce GPU copies 2017-05-22 04:51:08 -05:00
Matthew Honnibal
8904814c0e Add missing import 2017-05-21 09:07:56 -05:00
Matthew Honnibal
3b7c108246 Pass tokvecs through as a list, instead of concatenated. Also fix padding 2017-05-20 13:23:32 -05:00
Matthew Honnibal
b272890a8c Try to move parser to simpler PrecomputedAffine class. Currently broken -- maybe the previous change 2017-05-20 06:40:10 -05:00
Matthew Honnibal
a438cef8c5 Fix significant bug in feature calculation -- off by 1 2017-05-18 06:21:32 -05:00
Matthew Honnibal
711ad5edc4 Cache features in doc2feats 2017-05-18 04:22:20 -05:00
Matthew Honnibal
5211645af3 Get data flowing through pipeline. Needs redesign 2017-05-16 11:21:59 +02:00
Matthew Honnibal
a9edb3aa1d Improve integration of NN parser, to support unified training API 2017-05-15 21:53:27 +02:00
Matthew Honnibal
827b5af697 Update draft of parser neural network model 
Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU.

Outline of the model:

We first predict context-sensitive vectors for each word in the input:

(embed_lower | embed_prefix | embed_suffix | embed_shape)
>> Maxout(token_width)
>> convolution ** 4

This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features.
To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this
by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a
representation that's one affine transform from this informative lexical information. This is obviously good for the
parser (which backprops to the convolutions too).

The parser model makes a state vector by concatenating the vector representations for its context tokens. Current
results suggest few context tokens works well. Maybe this is a bug.

The current context tokens:

* S0, S1, S2: Top three words on the stack
* B0, B1: First two words of the buffer
* S0L1, S0L2: Leftmost and second leftmost children of S0
* S0R1, S0R2: Rightmost and second rightmost children of S0
* S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0

This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately,
there's a way to structure the computation to save some expense (and make it more GPU friendly).

The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks
with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications
for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden
weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN
-- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model
is so big.)

This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity.
The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved
to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier.
We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle
in CUDA to train.

Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to
be 0 cost. This is defined as:

(exp(score) / Z) - (exp(score) / gZ)

Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly,
but so far this isn't working well.

Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit
greatly from the pre-computation trick.
 2017-05-12 16:09:15 -05:00
Matthew Honnibal
bef89ef23d Mergery 2017-05-08 08:29:36 -05:00
Matthew Honnibal
56073a11ef Don't use tags when calculating token vectors 2017-05-08 07:52:24 -05:00