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  1. README.md +134 -0
  2. deploy_endpoint_dix.ipynb +168 -0
  3. handler.py +46 -0
  4. loss.tsv +151 -0
  5. pytorch_model.bin +3 -0
  6. test.tsv +0 -0
  7. training.log +0 -0
README.md ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - flair
4
+ - token-classification
5
+ - sequence-tagger-model
6
+ language: nl
7
+ datasets:
8
+ - conll2003
9
+ widget:
10
+ - text: "George Washington ging naar Washington."
11
+ ---
12
+
13
+ # Dutch NER in Flair (default model)
14
+
15
+ This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
16
+
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+ F1-Score: **92,58** (CoNLL-03)
18
+
19
+ Predicts 4 tags:
20
+
21
+ | **tag** | **meaning** |
22
+ |---------------------------------|-----------|
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+ | PER | person name |
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+ | LOC | location name |
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+ | ORG | organization name |
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+ | MISC | other name |
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+
28
+ Based on Transformer embeddings and LSTM-CRF.
29
+
30
+ ---
31
+ # Demo: How to use in Flair
32
+
33
+ Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
34
+
35
+ ```python
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+ from flair.data import Sentence
37
+ from flair.models import SequenceTagger
38
+
39
+ # load tagger
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+ tagger = SequenceTagger.load("flair/ner-dutch")
41
+
42
+ # make example sentence
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+ sentence = Sentence("George Washington ging naar Washington")
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+
45
+ # predict NER tags
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+ tagger.predict(sentence)
47
+
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+ # print sentence
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+ print(sentence)
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+
51
+ # print predicted NER spans
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+ print('The following NER tags are found:')
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+ # iterate over entities and print
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+ for entity in sentence.get_spans('ner'):
55
+ print(entity)
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+
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+ ```
58
+
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+ This yields the following output:
60
+ ```
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+ Span [1,2]: "George Washington" [− Labels: PER (0.997)]
62
+ Span [5]: "Washington" [− Labels: LOC (0.9996)]
63
+ ```
64
+
65
+ So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar Washington*".
66
+
67
+
68
+ ---
69
+
70
+ ### Training: Script to train this model
71
+
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+ The following Flair script was used to train this model:
73
+
74
+ ```python
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+ from flair.data import Corpus
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+ from flair.datasets import CONLL_03_DUTCH
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+ from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
78
+
79
+
80
+ # 1. get the corpus
81
+ corpus: Corpus = CONLL_03_DUTCH()
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+
83
+ # 2. what tag do we want to predict?
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+ tag_type = 'ner'
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+
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+ # 3. make the tag dictionary from the corpus
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+ tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
88
+
89
+ # 4. initialize embeddings
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+ embeddings = TransformerWordEmbeddings('wietsedv/bert-base-dutch-cased')
91
+
92
+ # 5. initialize sequence tagger
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+ tagger: SequenceTagger = SequenceTagger(hidden_size=256,
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+ embeddings=embeddings,
95
+ tag_dictionary=tag_dictionary,
96
+ tag_type=tag_type)
97
+
98
+ # 6. initialize trainer
99
+ trainer: ModelTrainer = ModelTrainer(tagger, corpus)
100
+
101
+ # 7. run training
102
+ trainer.train('resources/taggers/ner-dutch',
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+ train_with_dev=True,
104
+ max_epochs=150)
105
+ ```
106
+
107
+
108
+ ---
109
+
110
+ ### Cite
111
+
112
+ Please cite the following paper when using this model.
113
+
114
+ ```
115
+ @inproceedings{akbik-etal-2019-flair,
116
+ title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
117
+ author = "Akbik, Alan and
118
+ Bergmann, Tanja and
119
+ Blythe, Duncan and
120
+ Rasul, Kashif and
121
+ Schweter, Stefan and
122
+ Vollgraf, Roland",
123
+ booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
124
+ year = "2019",
125
+ url = "https://www.aclweb.org/anthology/N19-4010",
126
+ pages = "54--59",
127
+ }
128
+ ```
129
+
130
+ ---
131
+
132
+ ### Issues?
133
+
134
+ The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
deploy_endpoint_dix.ipynb ADDED
@@ -0,0 +1,168 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "id": "initial_id",
7
+ "metadata": {
8
+ "collapsed": true,
9
+ "ExecuteTime": {
10
+ "end_time": "2023-10-05T07:20:29.202015200Z",
11
+ "start_time": "2023-10-05T07:20:29.190080700Z"
12
+ }
13
+ },
14
+ "outputs": [
15
+ {
16
+ "name": "stdout",
17
+ "output_type": "stream",
18
+ "text": [
19
+ "True\n",
20
+ "0\n",
21
+ "<torch.cuda.device object at 0x0000028DAB1DB580>\n",
22
+ "1\n",
23
+ "NVIDIA GeForce RTX 3090\n"
24
+ ]
25
+ }
26
+ ],
27
+ "source": [
28
+ "import torch\n",
29
+ "\n",
30
+ "print(torch.cuda.is_available()) # Returns a bool indicating if CUDA is currently available.\n",
31
+ "print(torch.cuda.current_device()) # Returns the index of a currently selected device.\n",
32
+ "print(torch.cuda.device(0)) # Context-manager that changes the selected device.\n",
33
+ "print(torch.cuda.device_count()) # Returns the number of GPUs available.\n",
34
+ "print(torch.cuda.get_device_name(0)) # Gets the name of a device."
35
+ ]
36
+ },
37
+ {
38
+ "cell_type": "code",
39
+ "execution_count": 5,
40
+ "outputs": [
41
+ {
42
+ "name": "stdout",
43
+ "output_type": "stream",
44
+ "text": [
45
+ "2023-10-05 10:37:05,350 SequenceTagger predicts: Dictionary with 20 tags: <unk>, O, S-ORG, S-MISC, B-PER, E-PER, S-PER, S-LOC, B-MISC, E-MISC, B-ORG, E-ORG, I-ORG, I-PER, B-LOC, I-LOC, E-LOC, I-MISC, <START>, <STOP>\n",
46
+ "non_holiday_pred [{'entity_group': 'PER', 'word': 'George Washington', 'start': 0, 'end': 17, 'score': 0.9970293045043945}, {'entity_group': 'LOC', 'word': 'Washington', 'start': 28, 'end': 38, 'score': 0.9996309280395508}]\n"
47
+ ]
48
+ }
49
+ ],
50
+ "source": [
51
+ "from handler import EndpointHandler\n",
52
+ "\n",
53
+ "# init handler\n",
54
+ "my_handler = EndpointHandler(path=\".\")\n",
55
+ "\n",
56
+ "# prepare sample payload\n",
57
+ "non_holiday_payload = {\"inputs\": \"George Washington ging naar Washington\"}\n",
58
+ "\n",
59
+ "\n",
60
+ "# test the handler\n",
61
+ "non_holiday_pred=my_handler(non_holiday_payload)\n",
62
+ "\n",
63
+ "\n",
64
+ "# show results\n",
65
+ "print(\"non_holiday_pred\", non_holiday_pred)\n",
66
+ "\n"
67
+ ],
68
+ "metadata": {
69
+ "collapsed": false,
70
+ "ExecuteTime": {
71
+ "end_time": "2023-10-05T07:37:05.789680900Z",
72
+ "start_time": "2023-10-05T07:37:03.091564500Z"
73
+ }
74
+ },
75
+ "id": "a12c4a4792afc707"
76
+ },
77
+ {
78
+ "cell_type": "code",
79
+ "execution_count": 3,
80
+ "outputs": [],
81
+ "source": [
82
+ "from typing import Any, Dict, List\n",
83
+ "import os\n",
84
+ "from flair.data import Sentence\n",
85
+ "from flair.models import SequenceTagger"
86
+ ],
87
+ "metadata": {
88
+ "collapsed": false,
89
+ "ExecuteTime": {
90
+ "end_time": "2023-10-05T07:36:53.389033800Z",
91
+ "start_time": "2023-10-05T07:36:53.382053200Z"
92
+ }
93
+ },
94
+ "id": "f411919d7d047065"
95
+ },
96
+ {
97
+ "cell_type": "code",
98
+ "execution_count": 4,
99
+ "outputs": [
100
+ {
101
+ "name": "stdout",
102
+ "output_type": "stream",
103
+ "text": [
104
+ "2023-10-05 10:36:58,072 SequenceTagger predicts: Dictionary with 20 tags: <unk>, O, S-ORG, S-MISC, B-PER, E-PER, S-PER, S-LOC, B-MISC, E-MISC, B-ORG, E-ORG, I-ORG, I-PER, B-LOC, I-LOC, E-LOC, I-MISC, <START>, <STOP>\n"
105
+ ]
106
+ }
107
+ ],
108
+ "source": [
109
+ "tagger = SequenceTagger.load('pytorch_model.bin')"
110
+ ],
111
+ "metadata": {
112
+ "collapsed": false,
113
+ "ExecuteTime": {
114
+ "end_time": "2023-10-05T07:36:59.846440300Z",
115
+ "start_time": "2023-10-05T07:36:54.140093700Z"
116
+ }
117
+ },
118
+ "id": "8f497b3807de2e1"
119
+ },
120
+ {
121
+ "cell_type": "code",
122
+ "execution_count": 12,
123
+ "outputs": [
124
+ {
125
+ "data": {
126
+ "text/plain": "'0.12.2'"
127
+ },
128
+ "execution_count": 12,
129
+ "metadata": {},
130
+ "output_type": "execute_result"
131
+ }
132
+ ],
133
+ "source": [
134
+ "import flair\n",
135
+ "flair.__version__"
136
+ ],
137
+ "metadata": {
138
+ "collapsed": false,
139
+ "ExecuteTime": {
140
+ "end_time": "2023-10-05T07:36:37.788428800Z",
141
+ "start_time": "2023-10-05T07:36:37.754490Z"
142
+ }
143
+ },
144
+ "id": "df243c485fd370b"
145
+ }
146
+ ],
147
+ "metadata": {
148
+ "kernelspec": {
149
+ "name": "torch",
150
+ "language": "python",
151
+ "display_name": "torch"
152
+ },
153
+ "language_info": {
154
+ "codemirror_mode": {
155
+ "name": "ipython",
156
+ "version": 2
157
+ },
158
+ "file_extension": ".py",
159
+ "mimetype": "text/x-python",
160
+ "name": "python",
161
+ "nbconvert_exporter": "python",
162
+ "pygments_lexer": "ipython2",
163
+ "version": "2.7.6"
164
+ }
165
+ },
166
+ "nbformat": 4,
167
+ "nbformat_minor": 5
168
+ }
handler.py ADDED
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1
+ from typing import Any, Dict, List
2
+ import os
3
+ from flair.data import Sentence
4
+ from flair.models import SequenceTagger
5
+
6
+ class EndpointHandler():
7
+ def __init__(
8
+ self,
9
+ path: str,
10
+ ):
11
+ self.tagger = SequenceTagger.load(os.path.join(path,"pytorch_model.bin"))
12
+
13
+ def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
14
+ """
15
+ Args:
16
+ inputs (:obj:`str`):
17
+ a string containing some text
18
+ Return:
19
+ A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
20
+ - "entity_group": A string representing what the entity is.
21
+ - "word": A substring of the original string that was detected as an entity.
22
+ - "start": the offset within `input` leading to `answer`. context[start:stop] == word
23
+ - "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
24
+ - "score": A score between 0 and 1 describing how confident the model is for this entity.
25
+ """
26
+ inputs = data.pop("inputs", data)
27
+ sentence: Sentence = Sentence(inputs)
28
+
29
+ # Also show scores for recognized NEs
30
+ self.tagger.predict(sentence, label_name="predicted")
31
+
32
+ entities = []
33
+ for span in sentence.get_spans("predicted"):
34
+ if len(span.tokens) == 0:
35
+ continue
36
+ current_entity = {
37
+ "entity_group": span.tag,
38
+ "word": span.text,
39
+ "start": span.tokens[0].start_position,
40
+ "end": span.tokens[-1].end_position,
41
+ "score": span.score,
42
+ }
43
+
44
+ entities.append(current_entity)
45
+
46
+ return entities
loss.tsv ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ EPOCH TIMESTAMP BAD_EPOCHS LEARNING_RATE TRAIN_LOSS
2
+ 1 14:01:30 0 0.1000 2.779733479175812
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+ 3 14:04:30 0 0.1000 1.0816994036874201
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+ 4 14:06:01 0 0.1000 0.9621152551255674
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+ 6 14:09:01 0 0.1000 0.7716772701495733
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+ 10 14:15:01 1 0.1000 0.6467076227960423
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test.tsv ADDED
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training.log ADDED
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