readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  - endpoints-template
  language: en
  datasets:
  - ontonotes
  widget:
  - text: "On September 1st George won 1 dollar while watching Game of Thrones."
  ---
  
  # Fork of [flair/ner-english-ontonotes-large](https://huggingface.co/flair/ner-english-ontonotes-large)
  
  > This is fork of [flair/ner-english-ontonotes-large](https://huggingface.co/flair/ner-english-ontonotes-large) implementing a custom `handler.py` as an example for how to use `flair` models with [inference-endpoints](https://hf.co/inference-endpoints)
  
  ## English NER in Flair (Ontonotes large model)
  
  This is the large 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **90.93** (Ontonotes)
  
  Predicts 18 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | CARDINAL    | cardinal value | 
  | DATE         | date value | 
  | EVENT         | event name | 
  | FAC         | building name | 
  | GPE         | geo-political entity | 
  | LANGUAGE         | language name | 
  | LAW         | law name | 
  | LOC         | location name | 
  | MONEY         | money name | 
  | NORP         | affiliation | 
  | ORDINAL         | ordinal value | 
  | ORG         | organization name | 
  | PERCENT         | percent value | 
  | PERSON         | person name | 
  | PRODUCT         | product name | 
  | QUANTITY         | quantity value | 
  | TIME         | time value | 
  | WORK_OF_ART         | name of work of art | 
  
  Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
  
  ---
  
  ### Demo: How to use in Flair
  
  Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  
  # load tagger
  tagger = SequenceTagger.load("flair/ner-english-ontonotes-large")
  
  # make example sentence
  sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
  
  # predict NER tags
  tagger.predict(sentence)
  
  # print sentence
  print(sentence)
  
  # print predicted NER spans
  print('The following NER tags are found:')
  # iterate over entities and print
  for entity in sentence.get_spans('ner'):
      print(entity)
  
  ```
  
  This yields the following output:
  ```
  Span [2,3]: "September 1st"   [− Labels: DATE (1.0)]
  Span [4]: "George"   [− Labels: PERSON (1.0)]
  Span [6,7]: "1 dollar"   [− Labels: MONEY (1.0)]
  Span [10,11,12]: "Game of Thrones"   [− Labels: WORK_OF_ART (1.0)]
  ```
  
  So, the entities "*September 1st*" (labeled as a **date**), "*George*" (labeled as a **person**), "*1 dollar*" (labeled as a **money**) and "Game of Thrones" (labeled as a **work of art**) are found in the sentence "*On September 1st George Washington won 1 dollar while watching Game of Thrones*". 
  
  
  ---
  
  ### Training: Script to train this model
  
  The following Flair script was used to train this model: 
  
  ```python
  from flair.data import Corpus
  from flair.datasets import ColumnCorpus
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  # 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
  corpus: Corpus = ColumnCorpus(
                  "resources/tasks/onto-ner",
                  column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
                  tag_to_bioes="ner",
              )
  
  # 2. what tag do we want to predict?
  tag_type = 'ner'
  
  # 3. make the tag dictionary from the corpus
  tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
  
  # 4. initialize fine-tuneable transformer embeddings WITH document context
  from flair.embeddings import TransformerWordEmbeddings
  
  embeddings = TransformerWordEmbeddings(
      model='xlm-roberta-large',
      layers="-1",
      subtoken_pooling="first",
      fine_tune=True,
      use_context=True,
  )
  
  # 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
  from flair.models import SequenceTagger
  
  tagger = SequenceTagger(
      hidden_size=256,
      embeddings=embeddings,
      tag_dictionary=tag_dictionary,
      tag_type='ner',
      use_crf=False,
      use_rnn=False,
      reproject_embeddings=False,
  )
  
  # 6. initialize trainer with AdamW optimizer
  from flair.trainers import ModelTrainer
  
  trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
  
  # 7. run training with XLM parameters (20 epochs, small LR)
  from torch.optim.lr_scheduler import OneCycleLR
  
  trainer.train('resources/taggers/ner-english-ontonotes-large',
                learning_rate=5.0e-6,
                mini_batch_size=4,
                mini_batch_chunk_size=1,
                max_epochs=20,
                scheduler=OneCycleLR,
                embeddings_storage_mode='none',
                weight_decay=0.,
                )
  ```
  
  
  
  ---
  
  ### Cite
  
  Please cite the following paper when using this model.
  
  ```
  @misc{schweter2020flert,
      title={FLERT: Document-Level Features for Named Entity Recognition},
      author={Stefan Schweter and Alan Akbik},