s444417 b4ee3f02af remove git submodule

2023-06-05 21:23:33 +02:00

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Process dataset

!pip -q install simpletransformers

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import os                                                                   
import glob
import json
from simpletransformers.classification import (
    MultiLabelClassificationModel, MultiLabelClassificationArgs
)
import pandas as pd
from sklearn.model_selection import train_test_split

!git clone https://github.com/YiweiJiang2015/CookDial.git

Cloning into 'CookDial'...
remote: Enumerating objects: 596, done.[K
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remote: Total 596 (delta 507), reused 585 (delta 501), pack-reused 0[K
Receiving objects: 100% (596/596), 1.23 MiB | 23.30 MiB/s, done.
Resolving deltas: 100% (507/507), done.

# 1st pass - get all possible intents, create translation dict
all_intents = set()

for file in list(glob.glob('CookDial/data/dialog/*.json')):                                 
  with open(file, encoding='utf-8') as dial_file:
    dial_data = json.load(dial_file)
    for message in dial_data['messages']:    
      if message['bot'] == False:
        intents = json.loads(message['annotations'])['intent']
        for intent in [intent.strip() for intent in intents.split(';')]:
          if intent != '':
            all_intents.add(intent)

intent2int = dict(zip(sorted(list(all_intents)), range(len(all_intents))))
intent2int

{'affirm': 0,
 'confirm': 1,
 'goodbye': 2,
 'greeting': 3,
 'negate': 4,
 'other': 5,
 'req_amount': 6,
 'req_duration': 7,
 'req_ingredient': 8,
 'req_ingredient_list': 9,
 'req_ingredient_list_ends': 10,
 'req_ingredient_list_length': 11,
 'req_instruction': 12,
 'req_is_recipe_finished': 13,
 'req_is_recipe_ongoing': 14,
 'req_parallel_action': 15,
 'req_repeat': 16,
 'req_start': 17,
 'req_substitute': 18,
 'req_temperature': 19,
 'req_title': 20,
 'req_tool': 21,
 'req_use_all': 22,
 'thank': 23}

# 2nd pass - append utterance + intent multi-hot vectors to processed data
processed_data = []

for file in list(glob.glob('CookDial/data/dialog/*.json')):                                 
  with open(file, encoding='utf-8') as dial_file:
    dial_data = json.load(dial_file)   
    for message in dial_data['messages']:
      if message['bot'] == False:
        annotations = json.loads(message['annotations'])
        intents = [intent.strip() for intent in annotations['intent'].split(';')]
        intents.remove('')
        intents_multi_hot = [0] * len(all_intents)
        for intent in intents:
          intents_multi_hot[intent2int[intent]] = 1
        processed_data.append([message['utterance'], intents_multi_hot])

processed_data[:5]

[['Hi what are we making today?',
  [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]],
 ['Yes. What are the first two ingredients?',
  [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
 ['Ok. I have them. What are the next ingredients?',
  [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
 ['Ok. I have them as well. What are the next?',
  [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
 ['Ok. what kind of milk should I use?',
  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]

processed_data_df = pd.DataFrame(processed_data)
train_data, test_data = train_test_split(processed_data_df, test_size=0.1, random_state=42, shuffle=True)

Train NLU model

# Very simple model definition, optimal epoch number is something between 5 and 10
model_args = MultiLabelClassificationArgs(num_train_epochs=5, overwrite_output_dir=True)
model = MultiLabelClassificationModel(
    'roberta',
    'roberta-base',
    num_labels=len(all_intents),
    args=model_args,
)

# Very simple training (run with GPU runtime!)
model.train_model(train_data)

# Very simple evaluation
result, model_outputs, wrong_predictions = model.eval_model(
    test_data
)

# Evaluation returns LRAP score - "The obtained score is always strictly greater than 0 and the best value is 1"
# From: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html
print(result)

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Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForMultiLabelSequenceClassification: ['lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias']
- This IS expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of RobertaForMultiLabelSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

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/usr/local/lib/python3.8/dist-packages/simpletransformers/classification/classification_model.py:612: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.
  warnings.warn(

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/usr/local/lib/python3.8/dist-packages/simpletransformers/classification/classification_model.py:1454: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.
  warnings.warn(

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{'LRAP': 0.932345722168039, 'eval_loss': 0.044855168964392664}

Create and test NLU class

# Only this class and the trained model directory ("checkpoint" folders inside not needed) have to be copied into the chatbot project
class NLU:
  def __init__(self):
    self.int2intent = {0: 'affirm', 1: 'confirm', 2: 'goodbye', 3: 'greeting',
                    4: 'negate', 5: 'other', 6: 'req_amount', 7: 'req_duration',
                    8: 'req_ingredient', 9: 'req_ingredient_list', 10: 'req_ingredient_list_ends',
                    11: 'req_ingredient_list_length', 12: 'req_instruction',
                    13: 'req_is_recipe_finished', 14: 'req_is_recipe_ongoing',
                    15: 'req_parallel_action', 16: 'req_repeat', 17: 'req_start', 
                    18: 'req_substitute', 19: 'req_temperature', 20: 'req_title',
                    21: 'req_tool', 22: 'req_use_all', 23: 'thank'}

    # 2nd argument is the directory containing the trained model
    self.model = MultiLabelClassificationModel('roberta', 'outputs', num_labels=len(self.int2intent))
  
  def predict(self, utterance):
    predictions_vector, raw_outputs = self.model.predict([utterance])
    predictions_vector = predictions_vector[0]
    predicted_intents = []
    for i in range(len(predictions_vector)):
      if predictions_vector[i] == 1:
        predicted_intents.append(self.int2intent[i])
    return predicted_intents

nlu = NLU()

nlu.predict('Hi, what do we cook today? Recommend me something.')

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['greeting', 'req_title']

nlu.predict('Continue, please.')

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['req_instruction']

nlu.predict('The chicken is golden brown now. What do I need to do?')

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['confirm', 'req_instruction']

nlu.predict('What are the next ingredients?')

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['req_ingredient']

nlu.predict("I didn't understand. Can you repeat this step?")

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['req_repeat']

322 KiB Raw Blame History Unescape Escape

Process dataset

Train NLU model

Create and test NLU class

322 KiB

Raw Blame History