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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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This is a very small model I use for testing my [ner eval dashboard](https://github.com/helpmefindaname/ner-eval-dashboard) |
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F1-Score: **48,73** (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 huggingface minimal testing embeddings |
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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("helpmefindaname/mini-sequence-tagger-conll03") |
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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 command was used to train this model: |
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where `examples\ner\run_ner.py` refers to [this script](https://github.com/flairNLP/flair/blob/master/examples/ner/run_ner.py) |
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``` |
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python examples\ner\run_ner.py --model_name_or_path hf-internal-testing/tiny-random-bert --dataset_name CONLL_03 --learning_rate 0.002 --mini_batch_chunk_size 1024 --batch_size 64 --num_epochs 100 |
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``` |
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--- |
