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import torch
from torch.utils.data import Dataset
import json
import os
import collections
class IntentDataset(Dataset):
def __init__(self, loc, tokenizer, mode, toy=False, max_length=180):
'''
You can fine-tune a model with your own data!! Feel free to create (or collect!) your own utterances
and give it a shot!
loc: relative directory where the data lies
tokenizer: huggingface tokenizer to preprocess utterances
mode: one of train, val, test (should match the respective *.json files)
toy: load a very small amount of data (for debugging purposes)
max_length:max length of tokenized input
'''
self.tokenizer = tokenizer
self.mode = mode
self.max_length=max_length
with open(os.path.join(loc, 'all_intents.json'), 'r') as all_intents_json:
self.all_intents = json.load(all_intents_json) # contains the written out names of intents. also implicitly
# defines how many intents your chatbot's neural intent detection will support
with open(os.path.join(loc, mode + '.json'), 'r') as json_data:
self.all_data = json.load(json_data)
if toy:
self.all_data = self.all_data[:10]
print(f"Loaded Intent detection dataset. {len(self.all_data)} examples. ({mode}). {'Toy example' if toy else ''}")
def __len__(self): # torch Datasets need a __len__ method and __getitem__, with len as the total amount of examples...
return len(self.all_data)
def __getitem__(self, index): #... and __getitem__ as a way to get an example given an index >= 0 and < __len__
data_item = self.all_data[index]
if len(data_item) == 3:
tokenized_input = self.tokenizer(data_item[0], data_item[1], return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
else:
tokenized_input = self.tokenizer(data_item[0], return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
output_item = {
'input_ids': tokenized_input['input_ids'].squeeze(0),
'attention_mask': tokenized_input['attention_mask'].squeeze(0),
'label': torch.tensor(self.all_intents.index(data_item[-1]))
}
if 'token_type_ids' in tokenized_input:
output_item['token_type_ids'] = tokenized_input['token_type_ids'].squeeze(0),
return output_item
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