metadata
dataset_info:
features:
- name: answer
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 788165403
num_examples: 118695
- name: test
num_bytes: 98388509
num_examples: 14835
- name: validation
num_bytes: 98339161
num_examples: 14838
download_size: 45704542
dataset_size: 984893073
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
Dataset used for training text to sql. I've pre-tokenized this for faster loading.
Here is the prompt formation for the tokenizer code:
def tokenize_function(example):
start_prompt = "Tables:\n"
middle_prompt = "\n\nQuestion:\n"
end_prompt = "\n\nAnswer:\n"
data_zip = zip(example['context'], example['question'])
prompt = [start_prompt + context + middle_prompt + question + end_prompt for context, question in data_zip]
example['input_ids'] = tokenizer(prompt, padding="max_length", truncation=True, return_tensors="pt").input_ids
example['labels'] = tokenizer(example['answer'], padding="max_length", truncation=True, return_tensors="pt").input_ids
# print(prompt[0])
# print()
return example