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122 lines
4.6 KiB
Markdown
122 lines
4.6 KiB
Markdown
<!---
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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## Token classification
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Fine-tuning the library models for token classification task such as Named Entity Recognition (NER) or Parts-of-speech
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tagging (POS). The main scrip `run_ner.py` leverages the 🤗 Datasets library and the Trainer API. You can easily
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customize it to your needs if you need extra processing on your datasets.
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It will either run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own text files for
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training and validation.
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The following example fine-tunes BERT on CoNLL-2003:
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```bash
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python run_ner.py \
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--model_name_or_path bert-base-uncased \
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--dataset_name conll2003 \
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--output_dir /tmp/test-ner \
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--do_train \
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--do_eval
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```
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or just can just run the bash script `run.sh`.
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To run on your own training and validation files, use the following command:
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```bash
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python run_ner.py \
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--model_name_or_path bert-base-uncased \
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--train_file path_to_train_file \
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--validation_file path_to_validation_file \
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--output_dir /tmp/test-ner \
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--do_train \
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--do_eval
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```
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**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
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uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
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[this table](https://huggingface.co/transformers/index.html#bigtable), if it doesn't you can still use the old version
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of the script.
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## Old version of the script
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You can find the old version of the PyTorch script [here](https://github.com/huggingface/transformers/blob/master/examples/legacy/token-classification/run_ner.py).
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### TensorFlow version
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The following examples are covered in this section:
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* NER on the GermEval 2014 (German NER) dataset
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* Emerging and Rare Entities task: WNUT’17 (English NER) dataset
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Details and results for the fine-tuning provided by @stefan-it.
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### GermEval 2014 (German NER) dataset
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#### Data (Download and pre-processing steps)
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Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page.
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Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted:
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```bash
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curl -L 'https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P' \
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| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
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curl -L 'https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm' \
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| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
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curl -L 'https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH' \
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| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
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```
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The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`.
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One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s.
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The `preprocess.py` script located in the `scripts` folder a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).
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Let's define some variables that we need for further pre-processing steps and training the model:
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```bash
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export MAX_LENGTH=128
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export BERT_MODEL=bert-base-multilingual-cased
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```
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Run the pre-processing script on training, dev and test datasets:
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```bash
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python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
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python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
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python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
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```
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The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used:
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```bash
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cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
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```
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#### Prepare the run
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Additional environment variables must be set:
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```bash
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export OUTPUT_DIR=germeval-model
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export BATCH_SIZE=32
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export NUM_EPOCHS=3
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export SAVE_STEPS=750
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export SEED=1
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```
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