Neural Model for Classification¶
In this repository one can find code for training and using classification models which are implemented as a number of different neural networks (for example, shallow-and-wide Convolutional Neural Network [1]). The model can be used for binary, multi-class or multi-label classification.
We also provide with pre-trained models for classification on DSTC 2 dataset, SNIPS dataset, “AG News” dataset, “Detecting Insults in Social Commentary”, Twitter sentiment in Russian dataset.
DSTC 2 dataset (http://camdial.org/~mh521/dstc/) does not initially contain information about intents,
therefore, Dstc2IntentsDatasetIterator
(deeppavlov/dataset_iterators/dstc2_intents_interator.py
) instance
extracts artificial intents for each user reply using information from acts and slots.
Below we give several examples of intent construction:
System: “Hello, welcome to the Cambridge restaurant system. You can ask for restaurants by area, price range or food type. How may I help you?”
User: “cheap restaurant”
In the original dataset this user reply has characteristics
"goals": {"pricerange": "cheap"},
"db_result": null,
"dialog-acts": [{"slots": [["pricerange", "cheap"]], "act": "inform"}]}
This message contains only one intent: inform_pricerange
.
User: “thank you good bye”,
In the original dataset this user reply has characteristics
"goals": {"food": "dontcare", "pricerange": "cheap", "area": "south"},
"db_result": null,
"dialog-acts": [{"slots": [], "act": "thankyou"}, {"slots": [], "act": "bye"}]}
This message contains two intents (thankyou, bye)
. Train, valid and
test division is the same as on web-site.
SNIPS dataset (https://github.com/snipsco/nlu-benchmark/tree/master/2017-06-custom-intent-engines) contains intent classification task for 7 intents (approximately 2.4 samples per intent):
- GetWeather
- BookRestaurant
- PlayMusic
- AddToPlaylist
- RateBook
- SearchScreeningEvent
- SearchCreativeWork
Initially, classification model on SNIPS dataset was trained only as an
example of usage that is why we provide pre-trained model for SNIPS with
embeddings trained on DSTC-2 dataset that is not the best choice for
this task. Train set is divided to train and validation sets to
illustrate basic_classification_iterator
work.
Detecting Insults in Social Commentary dataset (https://www.kaggle.com/c/detecting-insults-in-social-commentary) contains binary classification task for detecting insults for participants of conversation. Train, valid and test division is the same as for the Kaggle challenge.
AG News dataset (https://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html) contains topic classification task for 5 classes (range from 0 to 4 points scale). Test set is initial one from a web-site, valid is a Stratified division 1/5 from the train set from web-site with 42 seed, and the train set is the rest.
Twitter mokoron dataset (http://study.mokoron.com/) contains sentiment classification of Russian tweets for positive and negative replies [5]. It was automatically labeled. Train, valid and test division is made by hands (Stratified division: 1/5 from all dataset for test set with 42 seed, then 1/5 from the rest for validation set with 42 seed). Two provided pre-trained models were trained on the same dataset but with and without preprocessing. The main difference between scores is caused by the fact that some symbols (deleted during preprocessing) were used for automatic labelling. Therefore, it can be considered that model trained on preprocessed data is based on semantics while model trained on unprocessed data is based on punctuation and syntax.
RuSentiment dataset (http://text-machine.cs.uml.edu/projects/rusentiment/) contains sentiment classification of social media posts for Russian language within 5 classes ‘positive’, ‘negative’, ‘neutral’, ‘speech’, ‘skip’.
Dataset | Model | Task | Lang | Metric | Valid | Test |
DSTC 2 | DSTC 2 on DSTC 2 embeddings | 28 intents | En | Accuracy | 0.8554 | 0.8658 |
DSTC 2 | DSTC 2 on Wiki embeddings | 28 intents | En | Accuracy | 0.9659 | 0.9659 |
SNIPS-2017 | SNIPS on DSTC 2 embeddings | 7 intents | En | F1 | 0.8821 | – |
SNIPS-2017 | SNIPS on Wiki embeddings | 7 intents | En | F1 | 0.9852 | – |
Insults | InsultsKaggle on Reddit embeddings | Insult detection | En | ROC-AUC | 0.9287 | 0.8602 |
AG News | AG News on Wiki embeddings | 5 topics | En | Accuracy | 0.8735 | 0.8859 |
Twitter mokoron | Twitter on RuWiki+Lenta embeddings without any preprocessing | Sentiment | Ru | Accuracy | 0.9968 | 0.9971 |
Twitter mokoron | Twitter on RuWiki+Lenta embeddings with preprocessing | Sentiment | Ru | Accuracy | 0.7944 | 0.7879 |
RuSentiment | RuSentiment on RuWiki+Lenta embeddings | Sentiment | Ru | F1 | 0.7843 | 0.6556 |
Download pre-trained model¶
DeepPavlov provides the following pre-trained models:
- intents_dstc2.json – DSTC 2 - intent model for English language with embeddings trained via fastText on DSTC 2 (800 Mb).
- intents_dstc2_big.json – DSTC 2 - intent model for English language with embeddings trained on Wiki (https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md, 8.5 Gb). This model achieves higher accuracy than the first one.
- intents_snips.json – SNIPS - intent model for English language.
- insults_kaggle.json – Insults analysis for English language.
- topic_ag_news.json – AG News topic analysis for English language.
- sentiment_twitter.json – Twitter Mokoron sentiment analysis for Russian language.
To download pre-trained models, vocabs, embeddings on the dataset of interest one should run the following command providing corresponding name of the config file (see above):
python deep.py download configs/classifiers/intents_dstc2.json
or provide flag -d
for commands like interact
, interactbot
,
etc. The flag -d
provides downloading all the required components.
Infer from pre-trained model¶
To use a pre-trained model for inference one should run the following command providing corresponding name of the config file (see above):
python deep.py interact configs/classifiers/intents_dstc2.json
or
python deep.py interactbot configs/classifiers/intents_dstc2.json -t <TELEGRAM_TOKEN>
For ‘interactbot’ mode one should specify a Telegram bot token in -t
parameter or in the TELEGRAM_TOKEN
environment variable.
Now user can enter a text string and get output of two elements: the first one is an array of classes names which the string belongs to, and the second one is a dictionary with probability distribution among all the considered classes (take into account that for multi-class classification then sum of probabilities is not equal to 1).
An example of interacting the model from intents_dstc2.json
:: hey! I want cheap restaurant
>> (array(['inform_pricerange'], dtype='<U17'), {'ack': 0.0040760376, 'affirm': 0.017633557, 'bye': 0.023906048, 'confirm_area': 0.0040424005, 'confirm_food': 0.012261569, 'confirm_pricerange': 0.007227284, 'deny_food': 0.003502861, 'deny_name': 0.003412795, 'hello': 0.0061915903, 'inform_area': 0.15999688, 'inform_food': 0.18303667, 'inform_name': 0.0042709936, 'inform_pricerange': 0.30197725, 'inform_this': 0.03864918, 'negate': 0.016452404, 'repeat': 0.003964727, 'reqalts': 0.026930325, 'reqmore': 0.0030793257, 'request_addr': 0.08075432, 'request_area': 0.018258458, 'request_food': 0.018060096, 'request_phone': 0.07433994, 'request_postcode': 0.012727374, 'request_pricerange': 0.024933394, 'request_signature': 0.0034591882, 'restart': 0.0038622846, 'thankyou': 0.036836267, 'unknown': 0.045310754})
and an example of interacting the model from intents_dstc2_big.json
::I want cheap chinese restaurant
>> (array(['inform_food', 'inform_pricerange'], dtype='<U18'), {'ack': 0.008203662, 'affirm': 0.010941843, 'bye': 0.0058273915, 'confirm_area': 0.011861361, 'confirm_food': 0.017537124, 'confirm_pricerange': 0.012897875, 'deny_food': 0.009804511, 'deny_name': 0.008331243, 'hello': 0.009887574, 'inform_area': 0.009167877, 'inform_food': 0.9627541, 'inform_name': 0.008696462, 'inform_pricerange': 0.98613375, 'inform_this': 0.009358878, 'negate': 0.011380567, 'repeat': 0.00850759, 'reqalts': 0.012249454, 'reqmore': 0.008230184, 'request_addr': 0.006192594, 'request_area': 0.009336099, 'request_food': 0.008417402, 'request_phone': 0.004564096, 'request_postcode': 0.006752021, 'request_pricerange': 0.010917218, 'request_signature': 0.008601435, 'restart': 0.00838949, 'thankyou': 0.0060319724, 'unknown': 0.010502234})
Train model¶
Available models¶
DeepPavlov contains a number of different model configurations for classification task. Below the list of available models is presented:
cnn_model
– Shallow-and-wide CNN with max pooling after convolution,dcnn_model
– Deep CNN with number of layers determined by the given number of kernel sizes and filters,cnn_model_max_and_aver_pool
– Shallow-and-wide CNN with max and average pooling concatenation after convolution,bilstm_model
– Bidirectional LSTM,bilstm_bilstm_model
– 2-layers bidirectional LSTM,bilstm_cnn_model
– Bidirectional LSTM followed by shallow-and-wide CNN,cnn_bilstm_model
– Shallow-and-wide CNN followed by bidirectional LSTM,bilstm_self_add_attention_model
– Bidirectional LSTM followed by self additive attention layer,bilstm_self_mult_attention_model
– Bidirectional LSTM followed by self multiplicative attention layer,bigru_model
– Bidirectional GRU model.
Please, pay attention that each model has its own parameters that should be specified in config.
Configuration parameters¶
One can find examples of config files in deeppavlov/configs/classifiers
.
Detailed description of configuration file and specific parameters for all presented classification models can be found in reference.
Some clue parameters for intents_dstc2.json config file are presented in the table below.
Parameter | Description |
---|---|
dataset_reader | an object that reads datasets from files |
name | registered name of the dataset reader SetOfValues: “dstc2_reader”, “basic_classification_reader” |
data_path | directory where data files are stored |
dataset_iterator | an object that provides models with data in the standard form (each example is a tuple (x, y) where x and y could be numbers, booleans, lists or strings) |
name | registered name of the dataset SetOfValues: “dstc2_intents_iterator”, basic_classification_iterator” |
seed | seed for the batch generator |
fields_to_merge | list of fields to merge SetOfValues: list of fields, i.e [“train”, “valid”, “test”] |
merged_field | name of the field where the merged fields should be saved SetOfValues: field, i.e “train”, “valid”, “test” |
field_to_split | name of the field to split SetOfValues: field, i.e “train”, “valid”, “test” |
split_fields | list of fields where the splitted field should be saved SetOfValues: list of fields, i.e [“train”, “valid”, “test”] |
split_proportions | list of corresponding proportions for splitting SetOfValues: list of floats each of which is in the range [0., 1.] |
chainer | chainer is a structure that receives tuples (in, in_y) and produces out |
in | user-defined name of input (or list of names in case of multiple inputs) SetOfValues: list of names, i.e [“x”], [“x0”, “x1”] |
in_y | user-defined name of input targets (or list of names in case of multiple input targets) SetOfValues: list of names, i.e [“y”], [“y0”, “y1”] |
out | user-defined name of output (or list of names in case of multiple outputs) SetOfValues: list of names, i.e [“y_pred”], [“y_pred0”, “y_pred1”] |
pipe | list that contains the sequence of model components (including vocabs, preprocessors, postprocessors etc.) |
parameters of the vocabulary | |
id | name of the considered model for further references |
name | registered name of the vocab SetOfValues: “default_vocab” |
fit_on | whether to create the vocab over x and/or y fields of dataset SetOfValues: list of names defined in chainer.in or chainer.in_y |
level | character-level or token-level tokenization SetOfValues: “char”, “token” |
load_path | path to file from which the vocab with classes will be loaded |
save_path | path to file where vocab with classes will be saved |
parameters of the embedder | |
id | name of the considered model for further references |
name | registered name of the embedder SetOfValues: “fasttext”, “glove”, “dict_embed” |
load_path | path to file from which the vocab with classes will be loaded |
save_path | path to file where vocab with classes will be saved |
dim | dimension of the considered embedder |
parameters of the tokenizer | |
id | name of the considered model for further references |
name | registered name of the tokenizer SetOfValues: “nltk_tokenizer” |
tokenizer | tokenizer from nltk.tokenize to use SetOfValues: any method from nltk.tokenize |
parameters for building the main part of a model | |
in | training samples to the model SetOfValues: list of names from chainer.in, chainer.in_y or outputs of previous models |
in_y | target values for the training samples, compulsory for training SetOfValues: list of names from chainer.in, chainer.in_y or outputs of previous models |
out | user-defined name of the output (or list of names in case of multiple outputs) SetOfValues: list of names |
main | determines which part of the pipe to train |
name | registered name of model |
load_path | path to file from which model files will be loaded |
save_path | path to file where model files will be saved |
classes | list of class names. In this case they could be simply obtained from vocab classes_vocab.keys() method. To make reference one has to set value to “#classes_vocab.keys()” |
model_name | method of the class KerasClassificationModel that corresponds to the model SetOfValues: cnn_model , dcnn_model , cnn_model_max_and_aver_pool , bilstm_model , bilstm_bilstm_model , bilstm_cnn_model , cnn_bilstm_model , bilstm_self_add_attention_model , bilstm_self_mult_attention_model , bigru_model |
text_size | length of each sample in words |
confident_threshold | probability threshold for an instance belonging to a class SetOfValues: [0., 1.] |
lear_rate | learning rate for training |
lear_rate_decay | learning rate decay for training |
optimizer | optimizer for training SetOfValues: any method from keras.optimizers |
loss | loss for training SetOfValues: any method from keras.losses |
embedder | To make reference one has to set value to “#{id of embedder}”, e.g. “#my_embedder” |
tokenizer | To make reference one has to set value to “#{id of tokenizer}”, e.g. “#my_tokenizer” |
train | parameters for training |
epochs | number of epochs for training |
batch_size | batch size for training |
metrics | metrics to be used for training. The first one is the main which determines whther to stop training or not SetOfValues: “classification_accuracy”, “classification_f1”, “classification_roc_auc” |
metric_optimization | whther to minimize or maximize the main metric SetOfValues: “minimize”, “maximize” |
validation_patience | parameter of early stopping: for how many epochs the training can continue without improvement of metric value on the validation set |
val_every_n_epochs | frequency of validation during training (validate every n epochs) |
val_every_n_batches | frequency of validation during training (validate every n batches) |
show_examples | whether to print training information or not |
metadata | parameters for training |
labels | labels or tags to make reference to this model |
download | links for downloading all the components required for the considered model |
Train again on provided datasets¶
To train from pre-trained model, re-train a model or train it
with other parameters on one of the provided datasets,
one should set save_path
to a directory where the trained
model will be saved (pre-trained model will be loaded if load_path
is provided and files exist, otherwise it will be created from scratch).
All other parameters of the model as well as embedder, tokenizer and preprocessor
could be changed. Then training can be run in the following way:
python deep.py train "path_to_config"
Train on other datasets¶
Constructing intents from DSTC 2 makes Dstc2IntentsDatasetIterator
difficult to use.
Therefore, we also provide another dataset reader BasicClassificationDatasetReader
and dataset
BasicClassificationDatasetIterator
to work with .csv
and .json
files. These classes are described in
deeppavlov/dataset_readers/basic_classification_reader.py
and
deeppavlov/dataset_iterators/basic_classification_dataset_iterator.py
.
Data files should be in the following format:
x | y |
---|---|
text_0 | intent_0 |
text_1 | intent_0 |
text_2 | intent_1,intent_2 |
text_3 | intent_1,intent_0,intent_2 |
… | … |
To train model one should
- set
data_path
to the directory to whichtrain.csv
should be downloaded, - set
save_path
to the directory where the trained model should be saved, - set all other parameters of model as well as embedder, tokenizer and preprocessor to desired ones.
Then training process can be run in the same way:
python deep.py train "path_to_config"
The current version of https://github.com/deepmipt/DeepPavlov/blob/0.0.7/deeppavlov/configs/intents_snips.json <classifiers/intents_snips.json>` contains parameters for
intent recognition for SNIPS benchmark dataset [2] that was restored in
.csv
format and will be downloaded automatically.
Important: we do not provide any special embedding binary file for SNIPS dataset. In order to train the model one should provide own embedding binary file, because embedding file trained on DSTC-2 dataset is not the best choice for this task.
Comparison¶
As no one had published intent recognition for DSTC-2 data, the comparison of the presented model is given on SNIPS dataset. The evaluation of model scores was conducted in the same way as in [3] to compare with the results from the report of the authors of the dataset. The results were achieved with tuning of parameters and embeddings trained on Reddit dataset.
Model | AddToPlaylist | BookRestaurant | GetWheather | PlayMusic | RateBook | SearchCreativeWork | SearchScreeningEvent |
---|---|---|---|---|---|---|---|
api.ai | 0.9931 | 0.9949 | 0.9935 | 0.9811 | 0.9992 | 0.9659 | 0.9801 |
ibm.watson | 0.9931 | 0.9950 | 0.9950 | 0.9822 | 0.9996 | 0.9643 | 0.9750 |
microsoft.luis | 0.9943 | 0.9935 | 0.9925 | 0.9815 | 0.9988 | 0.9620 | 0.9749 |
wit.ai | 0.9877 | 0.9913 | 0.9921 | 0.9766 | 0.9977 | 0.9458 | 0.9673 |
snips.ai | 0.9873 | 0.9921 | 0.9939 | 0.9729 | 0.9985 | 0.9455 | 0.9613 |
recast.ai | 0.9894 | 0.9943 | 0.9910 | 0.9660 | 0.9981 | 0.9424 | 0.9539 |
amazon.lex | 0.9930 | 0.9862 | 0.9825 | 0.9709 | 0.9981 | 0.9427 | 0.9581 |
Shallow-and-wide CNN | 0.9956 | 0.9973 | 0.9968 | 0.9871 | 0.9998 | 0.9752 | 0.9854 |
How to improve the performance¶
- One can use FastText [4] to train embeddings that are better suited for considered datasets.
- All the parameters should be tuned on the validation set.
References¶
[1] Kim Y. Convolutional neural networks for sentence classification //arXiv preprint arXiv:1408.5882. – 2014.
[2] https://github.com/snipsco/nlu-benchmark
[3] https://www.slideshare.net/KonstantinSavenkov/nlu-intent-detection-benchmark-by-intento-august-2017
[4] P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, Enriching Word Vectors with Subword Information.
[5] Ю. В. Рубцова. Построение корпуса текстов для настройки тонового классификатора // Программные продукты и системы, 2015, №1(109), –С.72-78