Metadata-Version: 2.1
Name: jonze
Version: 0.0.20
Summary: Reusable Joint Slot and Intent Extraction implementation in Tensorflow2.0
Home-page: https://github.com/zeionara/jonze
Author: Zeio Nara
Author-email: zeionara@gmail.com
License: UNKNOWN
Description: # Jonze
        Joint Slot and Intent Extraction implementation in Tensorflow2.0
        
        Contains restructured code from the following repo:
        https://github.com/shubham8111/Joint-NLU
        
        Implementation of Bi-LSTM based NLU baseline and SlotGated-SLU  (Goo et al, 2018)(https://www.csie.ntu.edu.tw/~yvchen/doc/NAACL18_SlotGated.pdf) 
        Models are evaulated on Snips and ATIS datasets.
        
        Experiments did not reproduce improvements by SlotGated model over Basline model, on snips dataset.
        
        
        Preprocessing modules reused from following repo:
        https://github.com/MiuLab/SlotGated-SLU/
        
        ## Usage
        To install package:  
        `pip install jonze`  
        To train model:  
        `from jonze import train  
        train(dataset = "joint-nlu", datasets_root = "dataset", models_root = "model", layer_size=12)`  
        To test model:  
        `from jonze import test  
        test(dataset = "joint-nlu", datasets_root = "dataset", models_root = "model", layer_size=12, batch_size=46)`  
        ## Results
        
        ### Snips Dataset:
        
        
        | Model      | Slot F1 | Intent accuracy | Semantic Accuracy |
        |------------|---------|-----------------|-------------------|
        | Baseline   | 84.30   | 96.57           | 66.43             |
        | Slot Gated | 83.5    | 95.57           | 66.85             |
        
        ### Atis Dataset:
        
        | Model      | Slot F1 | Intent accuracy | Semantic Accuracy |
        |------------|---------|-----------------|-------------------|
        | Baseline   | 95.08   | 94.62           | 81.97             |
        | Slot Gated | 94.57   | 96.41           | 83.65             |
        
        P.S.  Sometimes Slot F1 might get stuck at zero during training, better weight intialization or training a few epochs only on slot loss can resolve the issue. 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
