| <!--- |
| Copyright 2021 The HuggingFace Team. All rights reserved. |
|
|
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
|
|
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| --> |
|
|
| # Token classification |
|
|
| Fine-tuning the library models for token classification task such as Named Entity Recognition (NER), Parts-of-speech |
| tagging (POS) or phrase extraction (CHUNKS). The main script `run_ner.py` leverages the [🤗 Datasets](https://github.com/huggingface/datasets) library. You can easily |
| customize it to your needs if you need extra processing on your datasets. |
|
|
| It will either run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own text files for |
| training and validation, you might just need to add some tweaks in the data preprocessing. |
|
|
| The following example fine-tunes BERT on CoNLL-2003: |
|
|
| ```bash |
| python run_ner.py \ |
| --model_name_or_path google-bert/bert-base-uncased \ |
| --dataset_name conll2003 \ |
| --output_dir /tmp/test-ner |
| ``` |
|
|
| To run on your own training and validation files, use the following command: |
|
|
| ```bash |
| python run_ner.py \ |
| --model_name_or_path google-bert/bert-base-uncased \ |
| --train_file path_to_train_file \ |
| --validation_file path_to_validation_file \ |
| --output_dir /tmp/test-ner |
| ``` |
|
|
| **Note:** This script only works with models that have a fast tokenizer (backed by the [🤗 Tokenizers](https://github.com/huggingface/tokenizers) library) as it |
| uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in |
| [this table](https://huggingface.co/transformers/index.html#supported-frameworks). |
|
|
