| language: | |
| - en | |
| 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 | |
| ``` | |