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* feat: Ontonotes NER added * chore: train part removed from config * fix: readme dataset_iterator fixed, json removed from striong * feat: raw version of test added * fix: test modes * fix: folder name in ontonotes config and download path now consistent * fix: skip tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,52 @@ | ||
| { | ||
| "dataset_reader": { | ||
| "name": "conll2003_reader", | ||
| "data_path": "ontonotes/" | ||
| }, | ||
| "dataset_iterator": { | ||
| "name": "basic_dataset_iterator" | ||
| }, | ||
| "chainer": { | ||
| "in": ["x"], | ||
| "pipe": [ | ||
| { | ||
| "id": "pos_vocab", | ||
| "name": "default_vocab", | ||
| "load_path": "ner_ontonotes_senna/pos.dict", | ||
| "save_path": "ner_ontonotes_senna/pos.dict" | ||
| }, | ||
| { | ||
| "id": "tag_vocab", | ||
| "name": "default_vocab", | ||
| "load_path": "ner_ontonotes_senna/tag.dict", | ||
| "save_path": "ner_ontonotes_senna/tag.dict" | ||
| }, | ||
| { | ||
| "id": "ner_vocab", | ||
| "name": "default_vocab", | ||
| "load_path": "ner_ontonotes_senna/ner.dict", | ||
| "save_path": "ner_ontonotes_senna/ner.dict" | ||
| }, | ||
| { | ||
| "id": "glove_emb", | ||
| "name": "glove", | ||
| "load_path": "embeddings/glove.6B.100d.txt", | ||
| "save_path": "embeddings/glove.6B.100d.txt" | ||
| }, | ||
| { | ||
| "in": ["x"], | ||
| "out": ["y_predicted"], | ||
| "name": "ner_ontonotes", | ||
| "main": true, | ||
| "save_path": "ner_ontonotes_senna/model.ckpt", | ||
| "load_path": "ner_ontonotes_senna/model.ckpt", | ||
| "ner_vocab": "#ner_vocab", | ||
| "tag_vocab": "#tag_vocab", | ||
| "pos_vocab": "#pos_vocab", | ||
| "embedder": "#glove_emb" | ||
| } | ||
| ], | ||
| "out": ["y_predicted"] | ||
| } | ||
| } | ||
|
|
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| @@ -0,0 +1,79 @@ | ||
| """ | ||
| Copyright 2017 Neural Networks and Deep Learning lab, MIPT | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
| import tensorflow as tf | ||
| from overrides import overrides | ||
| from copy import deepcopy | ||
| import inspect | ||
| import json | ||
|
|
||
| from deeppavlov.core.common.registry import register | ||
| from deeppavlov.core.data.utils import tokenize_reg | ||
| from deeppavlov.models.ner.network_ontonotes import NerNetwork | ||
| from deeppavlov.core.models.tf_model import TFModel | ||
| from deeppavlov.core.common.log import get_logger | ||
|
|
||
| log = get_logger(__name__) | ||
|
|
||
|
|
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| @register('ner_ontonotes') | ||
| class NER(TFModel): | ||
| def __init__(self, **kwargs): | ||
| self.opt = deepcopy(kwargs) | ||
| vocabs = self.opt.pop('vocabs') | ||
| self.opt.update(vocabs) | ||
|
|
||
| # Find all input parameters of the network init | ||
| network_parameter_names = list(inspect.signature(NerNetwork.__init__).parameters) | ||
| # Fill all provided parameters from opt | ||
| network_parameters = {par: self.opt[par] for par in network_parameter_names if par in self.opt} | ||
|
|
||
| self.sess = tf.Session() | ||
| network_parameters['sess'] = self.sess | ||
| self._network_parameters = network_parameters | ||
| self._net = NerNetwork(**network_parameters) | ||
|
|
||
| # Try to load the model (if there are some model files the model will be loaded from them) | ||
| super().__init__(**kwargs) | ||
| if self.load_path is not None: | ||
| self.load() | ||
|
|
||
| def load(self, *args, **kwargs): | ||
| super().load(*args, **kwargs) | ||
|
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||
| def save(self, *args, **kwargs): | ||
| super().save(*args, **kwargs) | ||
| self.save_params() | ||
|
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| def save_params(self): | ||
| params_to_save = {param: self.opt.get(param, None) for param in self.GRAPH_PARAMS} | ||
| for vocab in self.VOCABS: | ||
| params_to_save[vocab] = [self.opt[vocab][i] for i in range(len(self.opt[vocab]))] | ||
| path = str(self.save_path.with_suffix('.json').resolve()) | ||
| log.info('[saving parameters to {}]'.format(path)) | ||
| with open(path, 'w') as fp: | ||
| json.dump(params_to_save, fp, indent=4) | ||
|
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||
| def train_on_batch(self, batch_x, batch_y): | ||
| raise NotImplementedError | ||
|
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| @overrides | ||
| def __call__(self, batch, *args, **kwargs): | ||
| if isinstance(batch[0], str): | ||
| batch = [tokenize_reg(utterance) for utterance in batch] | ||
| return self._net.predict_on_batch(batch) | ||
|
|
||
| def shutdown(self): | ||
| self._net.shutdown() |
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