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https://github.com/guillaumegenthial/sequence_tagging
Named Entity Recognition (LSTM + CRF) - Tensorflow
https://github.com/guillaumegenthial/sequence_tagging
bi-lstm characters-embeddings conditional-random-fields crf glove named-entity-recognition ner state-of-art tensorflow
Last synced: 2 days ago
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Named Entity Recognition (LSTM + CRF) - Tensorflow
- Host: GitHub
- URL: https://github.com/guillaumegenthial/sequence_tagging
- Owner: guillaumegenthial
- License: apache-2.0
- Created: 2017-03-29T15:53:29.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-10-16T09:18:22.000Z (over 4 years ago)
- Last Synced: 2025-02-01T17:28:32.428Z (9 days ago)
- Topics: bi-lstm, characters-embeddings, conditional-random-fields, crf, glove, named-entity-recognition, ner, state-of-art, tensorflow
- Language: Python
- Homepage: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html
- Size: 54.7 KB
- Stars: 1,946
- Watchers: 73
- Forks: 701
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Named Entity Recognition with Tensorflow
This repo implements a NER model using Tensorflow (LSTM + CRF + chars embeddings).
__A [better implementation is available here, using `tf.data` and `tf.estimator`, and achieves an F1 of 91.21](https://github.com/guillaumegenthial/tf_ner)__
State-of-the-art performance (F1 score between 90 and 91).
Check the [blog post](https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html)
## Task
Given a sentence, give a tag to each word. A classical application is Named Entity Recognition (NER). Here is an example
```
John lives in New York
B-PER O O B-LOC I-LOC
```
## Model
Similar to [Lample et al.](https://arxiv.org/abs/1603.01360) and [Ma and Hovy](https://arxiv.org/pdf/1603.01354.pdf).
- concatenate final states of a bi-lstm on character embeddings to get a character-based representation of each word
- concatenate this representation to a standard word vector representation (GloVe here)
- run a bi-lstm on each sentence to extract contextual representation of each word
- decode with a linear chain CRF
## Getting started
1. Download the GloVe vectors with
```
make glove
```
Alternatively, you can download them manually [here](https://nlp.stanford.edu/projects/glove/) and update the `glove_filename` entry in `config.py`. You can also choose not to load pretrained word vectors by changing the entry `use_pretrained` to `False` in `model/config.py`.
2. Build the training data, train and evaluate the model with
```
make run
```
## Details
Here is the breakdown of the commands executed in `make run`:
1. [DO NOT MISS THIS STEP] Build vocab from the data and extract trimmed glove vectors according to the config in `model/config.py`.
```
python build_data.py
```
2. Train the model with
```
python train.py
```
3. Evaluate and interact with the model with
```
python evaluate.py
```
Data iterators and utils are in `model/data_utils.py` and the model with training/test procedures is in `model/ner_model.py`
Training time on NVidia Tesla K80 is 110 seconds per epoch on CoNLL train set using characters embeddings and CRF.
## Training Data
The training data must be in the following format (identical to the CoNLL2003 dataset).
A default test file is provided to help you getting started.
```
John B-PER
lives O
in O
New B-LOC
York I-LOC
. O
This O
is O
another O
sentence
```
Once you have produced your data files, change the parameters in `config.py` like
```
# dataset
dev_filename = "data/coNLL/eng/eng.testa.iob"
test_filename = "data/coNLL/eng/eng.testb.iob"
train_filename = "data/coNLL/eng/eng.train.iob"
```
## License
This project is licensed under the terms of the apache 2.0 license (as Tensorflow and derivatives). If used for research, citation would be appreciated.