idNet
This package supports a generalized architecture for language identification (LID) and dialect identification (DID) using a multi-layer perceptron built using Keras. DID also supports a Linear SVM classifier using scikit-learn.
To load a model:
from idNet import idNet_Enrich
lid = idNet_Enrich("Path to model file", s3_bucket)
did = idNet_Enrich("Path to model file", s3_bucket)
s3_bucket takes a str containing an optional s3 bucket to load the model from. The model filename must contain the necessary prefixes.
Once a LID model is loaded, it has the following properties:
| Method | Description |
|---|---|
| lid.n_features | Number of features in the model (i.e., hashing bins) |
| lid.n_classes | Number of languages in the model |
| lid.lang_mappings | Dictionary of {"iso_code": "language_name"} mappings for all ISO 639-3 codes |
| lid.langs | List of ISO 639-3 codes for languages present in the current model |
Once a DID model is loaded, it has the following properties:
| Method | Description |
|---|---|
| did.n_features | Number of features in the grammar used to learn the model |
| did.n_classes | Number of countries in the model |
| did.country_mappings | Dictionary of {"iso_code": "country_name"} mappings for all country codes used |
| did.countries | List of country codes for regional dialects (country-level) present in the current model |
Loaded models perform the following tasks:
| Method | Description |
|---|---|
| lid.predict(data) | Takes an array of strings or individual strings; returns array of predicted language codes |
| did.predict(data) | Takes an array of strings or individual strings; returns array of predicted country codes |
Note: Model filenames need to include ".DID"/".LID" and ".MLP"/".SVM" because this information is used to determine the model type!
Training New Models
To train new models, the training data needs to be prepared. This process is automated; see the Data_DID and Data_LID directories for directions and scripts.
from idNet import idNet_Train
id = idNet_train()
| Argument | Type | Description |
|---|---|---|
| type | (str) | Whether to work with language or dialect identification |
| input | (str) | Path to input folder |
| output | (str) | Path to output folder |
| s3 = False | (boolean) | If True, use boto3 to interact with s3 bucket |
| s3_bucket = "" | (str) | s3 bucket name as string |
| nickname = "Language" | (str) | The nickname for saving / loading models |
| divide_data = True | (boolean) | If True, crawl for dataset; if False, just load it |
| test_samples = 20 | (int) | The number of files for each class to use for testing |
| threshold = 100 | (int) | Number of files required before language/country is included in model |
| samples_per_epoch = 5 | (int) | Number of samples to use per training epoch |
| language = "" | (str) | For DID, specifies the language of the current model |
| lid_sample_size = 200 | (int) | For LID, the number of characters to allow per sample |
| did_sample_size = 1 | (int) | For DID, the number of 100 word samples to combine |
| preannotate_cxg = False | (boolean) | For DID, if True enrich and save all CxG vectors |
| preannotated_cxg = False | (boolean) | For DID, if True just load pre-enriched CxG vectors |
| cxg_workers = 1 | (int) | For DID, if pre-enriching dataset, number of workers to use |
| class_constraints = [] | (list of strs) | Option to constrain the number of classes |
| merge_dict = {} | (dict) | Original:New name keys |
id.train()
| Argument | Type | Description |
|---|---|---|
| model_type = "MLP" | (str) | MLP or SVM |
| lid_features = 524288 | (int) | Number of character n-gram features to allow, hashing only |
| lid_ngrams = (1,3) | (tuple of ints) | Range of n-grams to hash |
| did_grammar = ".Grammar.p" | (str) | Name of C2xG grammar to use for annotation |
| c2xg_workers = 1 | (int) | For DID, number of workers for c2xg enrichments |
| mlp_sizes = (300, 300, 300) | (tuple of ints) | Size and number of layers; e.g., 3 layers at 300 neurons each |
| cross_val = False | (boolean) | Whether to use cross-validation rather than a held-out test set |
| dropout = 0.25 | (float) | The amount of dropout to apply to each layer |
| activation = "relu" | (str) | The type of activation; just passes name to Keras |
| optimizer = "sgd" | (str) | The type of optimization algorithm; just passes name to Keras |