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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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widget: |
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- text: "George Washington went to Washington" |
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--- |
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## English NER in Flair (large model) |
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This is the large 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **94,36** (corrected 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 document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/). |
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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-large") |
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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 (1.0)] |
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Span [5]: "Washington" [− Labels: LOC (1.0)] |
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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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import torch |
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# 1. get the corpus |
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from flair.datasets import CONLL_03 |
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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 fine-tuneable transformer embeddings WITH document context |
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from flair.embeddings import TransformerWordEmbeddings |
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embeddings = TransformerWordEmbeddings( |
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model='xlm-roberta-large', |
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layers="-1", |
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subtoken_pooling="first", |
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fine_tune=True, |
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use_context=True, |
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) |
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# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection) |
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from flair.models import SequenceTagger |
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tagger = SequenceTagger( |
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hidden_size=256, |
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embeddings=embeddings, |
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tag_dictionary=tag_dictionary, |
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tag_type='ner', |
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use_crf=False, |
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use_rnn=False, |
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reproject_embeddings=False, |
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) |
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# 6. initialize trainer with AdamW optimizer |
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from flair.trainers import ModelTrainer |
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trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW) |
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# 7. run training with XLM parameters (20 epochs, small LR) |
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from torch.optim.lr_scheduler import OneCycleLR |
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trainer.train('resources/taggers/ner-english-large', |
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learning_rate=5.0e-6, |
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mini_batch_size=4, |
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mini_batch_chunk_size=1, |
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max_epochs=20, |
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scheduler=OneCycleLR, |
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embeddings_storage_mode='none', |
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weight_decay=0., |
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) |
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) |
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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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@misc{schweter2020flert, |
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title={FLERT: Document-Level Features for Named Entity Recognition}, |
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author={Stefan Schweter and Alan Akbik}, |
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year={2020}, |
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eprint={2011.06993}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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--- |
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### Issues? |
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The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/). |
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