https://github.com/deeppavlov/intent_classifier
https://github.com/deeppavlov/intent_classifier
intent-classification natural-language-processing natural-language-understanding neural-networks nlp-machine-learning
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/deeppavlov/intent_classifier
- Owner: deeppavlov
- License: apache-2.0
- Created: 2017-12-28T07:53:27.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-08-24T07:45:47.000Z (about 7 years ago)
- Last Synced: 2024-03-20T03:01:04.920Z (over 1 year ago)
- Topics: intent-classification, natural-language-processing, natural-language-understanding, neural-networks, nlp-machine-learning
- Language: Python
- Size: 2.61 MB
- Stars: 83
- Watchers: 8
- Forks: 31
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# This repo is not currently supported as intent classifier became a part of DeepPavlov open-source library.
Try it [here](http://docs.deeppavlov.ai/en/latest/components/classifiers.html).
# Neural Networks for Intent Classifier
In this repo one can find code for training and infering intent classification
that is presented as _shallow-and-wide Convolutional Neural Network_[1].
#### Currently considered fasttext version in this repo does not works on Windows correctly.
Also this repo contains pre-trained model for intent classification on [SNIPS dataset](https://github.com/snipsco/nlu-benchmark/tree/master/2017-06-custom-intent-engines)
SNIPS dataset considers the following intents: `AddToPlaylist`, `BookRestaurant`, `GetWeather`, `PlayMusic`, `RateBook`, `SearchCreativeWork`, `SearchScreeningEvent`.
Test results for other intent recognition services are from https://www.slideshare.net/KonstantinSavenkov/nlu-intent-detection-benchmark-by-intento-august-2017
### How to install
First of all, one have to download this repo:
```
git clone https://github.com/deepmipt/intent_classifier.git
cd intent_classifier
```
The next step is to install requirements:
```
pip install -r requirements.txt
```
### How to use pre-trained model (SNIPS)
Now one is able to infer pre-trained model:
```
./intent_classifier.py ./snips_pretrained/snips_config.json
```
The script loads pre-trained model, if necessary downloads pre-trained fastText embedding model [2],
and then it is ready to predict class and probability of given phrase to belong with this class.
Example:
```
./intent_classifier.py ./snips_pretrained/snips_config.json
>I want you to add 'I love you, baby' to my playlist
>(0.99986315, 'AddToPlaylist')
```
### How to train on your own data
The repo contains script `train.py` for training multilabel classifier.
Training data file should be presented in the following `data.csv` form:
| request |class_0|class_1|class_2|class_3| ...|
|------------- |:-----:|:-----:|:-----:|:-----:|:--:|
| text_0 | 1 | 0 | 0 |0 |... |
| text_1 | 0 | 0 | 1 |0 |... |
| text_2 | 0 | 1 | 0 |0 |... |
| text_3 | 1 | 0 | 0 |0 |... |
| ... | ... | ... | ... |... |... ||
Then one is ready to run `train.py` that includes reading data, tokenization, constructing data,
building dataset, initializing and training model with given parameters on dataset from `data.csv`:
```
./train.py config.json data.csv
```
The model will be trained using parameters from `config.json` file.
There is a description of several parameters:
- Directory named `model_path` should exist.
For example, if `config.json` contains `"model_path": "./cnn_model"`,
then configuration parameters for the trained model will be saved to `./cnn_model/cnn_model_opt.json`
and weights of the model will be saved to `./cnn_model/cnn_model.h5`.
- Parameter `model_from_saved` means whether to load pre-trained model
- Parameter `lear_metrics` is a string that can include either metrics from `keras.metrics`
or custom metrics from the file `metrics.py` (for example, `fmeasure`).
- Parameter `confident_threshold` is within the range `[0,1]`
and means the boundary whether sample belongs to the class.
- Parameter `fasttext_model` contains path to pre-trained binary skipgram fastText [2] model for English language.
If one prefers to use default model, it will be downloaded when one will train model.
- Parameter `text_size` means the number of words for padding of each tokenized text request.
- Parameter `model_name` contains name of the class method from `multiclass.ry` returning uncompiled Keras model.
One can use `cnn_model` that is shallow-and-wide CNN (`config.json` contains parameters for this model),
`dcnn_model` that is deep CNN model (be attentive to provide necessary parameters for the model),
also it is possible to write own model.
- All other parameters refer to learning and network configuration.
### How to infer
Infering can be done in two ways:
```
./infer.py config.json
```
or
```
./intent_classifier.py config.json
```
The first one runs `infer.py` file that contains reading parameters from `config.json` file, initializing tokenizer,
initializing and infering model. The second one is doing the same but reads samples from command line.
### References
[1] Kim Y. Convolutional neural networks for sentence classification //arXiv preprint arXiv:1408.5882. – 2014.
[2] P. Bojanowski*, E. Grave*, A. Joulin, T. Mikolov, Enriching Word Vectors with Subword Information.