French model
Browse files- README.md +139 -0
- loss.tsv +151 -0
- pytorch_model.bin +3 -0
- test.tsv +0 -0
- training.log +0 -0
README.md
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---
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tags:
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- flair
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- token-classification
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- sequence-tagger-model
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language: en
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datasets:
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- conll2003
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inference: false
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---
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## English NER in Flair (default model)
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This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
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F1-Score: **92,98** (CoNLL-03)
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Predicts 4 tags:
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| **tag** | **meaning** |
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|---------------------------------|-----------|
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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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Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
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---
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### Demo: How to use in Flair
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Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
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```python
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from flair.data import Sentence
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from flair.models import SequenceTagger
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# load tagger
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tagger = SequenceTagger.load("flair/ner-english")
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# make example sentence
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sentence = Sentence("George Washington went to Washington")
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# predict NER tags
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tagger.predict(sentence)
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# print sentence
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print(sentence)
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# 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'):
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print(entity)
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```
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This yields the following output:
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```
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Span [1,2]: "George Washington" [− Labels: PER (0.9968)]
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Span [5]: "Washington" [− Labels: LOC (0.9994)]
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```
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So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
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---
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### Training: Script to train this model
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The following Flair script was used to train this model:
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```python
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from flair.data import Corpus
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from flair.datasets import CONLL_03
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from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
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# 1. get the corpus
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corpus: Corpus = CONLL_03()
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# 2. what tag do we want to predict?
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tag_type = 'ner'
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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)
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# 4. initialize each embedding we use
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embedding_types = [
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# GloVe embeddings
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WordEmbeddings('glove'),
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# contextual string embeddings, forward
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FlairEmbeddings('news-forward'),
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# contextual string embeddings, backward
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FlairEmbeddings('news-backward'),
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]
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# embedding stack consists of Flair and GloVe embeddings
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embeddings = StackedEmbeddings(embeddings=embedding_types)
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# 5. initialize sequence tagger
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from flair.models import SequenceTagger
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tagger = SequenceTagger(hidden_size=256,
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embeddings=embeddings,
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tag_dictionary=tag_dictionary,
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tag_type=tag_type)
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# 6. initialize trainer
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from flair.trainers import ModelTrainer
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trainer = ModelTrainer(tagger, corpus)
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# 7. run training
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trainer.train('resources/taggers/ner-english',
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train_with_dev=True,
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max_epochs=150)
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```
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---
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### Cite
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Please cite the following paper when using this model.
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```
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@inproceedings{akbik2018coling,
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title={Contextual String Embeddings for Sequence Labeling},
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author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
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booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
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pages = {1638--1649},
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year = {2018}
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}
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```
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loss.tsv
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EPOCH TIMESTAMP BAD_EPOCHS LEARNING_RATE TRAIN_LOSS
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0 19:26:37 0 0.1000 2.318331193891905
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1 19:54:00 0 0.1000 1.4398467032979894
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2 20:21:10 0 0.1000 1.2915569943365872
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3 20:48:33 0 0.1000 1.2083583032411913
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4 21:15:44 0 0.1000 1.1410765122341853
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5 21:42:55 0 0.1000 1.0989998259531555
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6 22:10:08 0 0.1000 1.0612774609958613
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7 22:37:15 0 0.1000 1.0287262405038522
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8 23:04:18 0 0.1000 1.0085712932130342
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9 23:31:39 0 0.1000 0.989349162009775
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10 23:58:50 0 0.1000 0.9717200679324006
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11 00:25:49 0 0.1000 0.9578037910804312
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12 00:52:51 0 0.1000 0.9408924878925405
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13 01:19:55 0 0.1000 0.929271377663138
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14 01:47:12 0 0.1000 0.9172978740465897
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15 02:14:12 0 0.1000 0.9044446516581761
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16 02:41:22 0 0.1000 0.8984834992917635
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17 03:08:35 0 0.1000 0.8855764541094021
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18 03:35:46 0 0.1000 0.8802844247830811
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19 04:03:03 0 0.1000 0.8727591283138721
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20 04:30:16 0 0.1000 0.869586915186336
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21 04:57:34 0 0.1000 0.8578218414498273
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22 05:24:44 0 0.1000 0.8486296324159509
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23 05:51:50 0 0.1000 0.8477299566590978
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24 06:19:03 0 0.1000 0.843290219976697
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25 06:46:17 0 0.1000 0.8351735146776322
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26 07:13:28 0 0.1000 0.833952986104514
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28 08:07:37 0 0.1000 0.8232993299842521
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31 09:28:46 0 0.1000 0.8077815129071153
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33 10:23:19 0 0.1000 0.8029743133453272
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37 12:12:11 0 0.1000 0.7862848894810804
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42 14:28:34 0 0.1000 0.7746844343520621
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55 20:22:19 1 0.1000 0.7478391278975753
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57 21:16:48 0 0.1000 0.7435161036068714
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58 21:43:52 0 0.1000 0.7407694635452122
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59 22:11:05 0 0.1000 0.7395734377285486
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62 23:32:45 1 0.1000 0.7374787427204591
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63 00:00:00 2 0.1000 0.7380644889528393
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124 05:40:16 4 0.0500 0.5834616551636368
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125 06:07:30 0 0.0250 0.566932532944346
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126 06:34:47 0 0.0250 0.56178293470093
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136 11:09:52 0 0.0250 0.5414540123715196
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149 19:44:58 2 0.0250 0.5291525019593136
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pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:f0dc8f7d09d9bfbe1f89bbea91dab03a6ec5a0cd7d28189aa6589b52dfb94b09
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3 |
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size 1331932638
|
test.tsv
ADDED
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training.log
ADDED
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