LSTM+CRF模型项目完整代码

# Named Entity Recognition with Tensorflow This repo implements a NER model using Tensorflow (LSTM + CRF + chars embeddings). 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.