Source code for transformers.pipelines.table_question_answering

import collections

import numpy as np

from ..file_utils import add_end_docstrings, is_torch_available, requires_backends
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Pipeline, PipelineException

if is_torch_available():
    import torch


class TableQuestionAnsweringArgumentHandler(ArgumentHandler):
    Handles arguments for the TableQuestionAnsweringPipeline

    def __call__(self, table=None, query=None, **kwargs):
        # Returns tqa_pipeline_inputs of shape:
        # [
        #   {"table": pd.DataFrame, "query": List[str]},
        #   ...,
        #   {"table": pd.DataFrame, "query" : List[str]}
        # ]
        requires_backends(self, "pandas")
        import pandas as pd

        if table is None:
            raise ValueError("Keyword argument `table` cannot be None.")
        elif query is None:
            if isinstance(table, dict) and table.get("query") is not None and table.get("table") is not None:
                tqa_pipeline_inputs = [table]
            elif isinstance(table, list) and len(table) > 0:
                if not all(isinstance(d, dict) for d in table):
                    raise ValueError(
                        f"Keyword argument `table` should be a list of dict, but is {(type(d) for d in table)}"

                if table[0].get("query") is not None and table[0].get("table") is not None:
                    tqa_pipeline_inputs = table
                    raise ValueError(
                        f"If keyword argument `table` is a list of dictionaries, each dictionary should have a `table` "
                        f"and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."
                raise ValueError(
                    f"Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
                    f"is {type(table)})"
            tqa_pipeline_inputs = [{"table": table, "query": query}]

        for tqa_pipeline_input in tqa_pipeline_inputs:
            if not isinstance(tqa_pipeline_input["table"], pd.DataFrame):
                if tqa_pipeline_input["table"] is None:
                    raise ValueError("Table cannot be None.")

                tqa_pipeline_input["table"] = pd.DataFrame(tqa_pipeline_input["table"])

        return tqa_pipeline_inputs

[docs]@add_end_docstrings(PIPELINE_INIT_ARGS) class TableQuestionAnsweringPipeline(Pipeline): """ Table Question Answering pipeline using a :obj:`ModelForTableQuestionAnswering`. This pipeline is only available in PyTorch. This tabular question answering pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task identifier: :obj:`"table-question-answering"`. The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task. See the up-to-date list of available models on ` <>`__. """ default_input_names = "table,query" def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, **kwargs): super().__init__(*args, **kwargs) self._args_parser = args_parser if self.framework == "tf": raise ValueError("The TableQuestionAnsweringPipeline is only available in PyTorch.") self.check_model_type(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING) self.aggregate = bool(getattr(self.model.config, "aggregation_labels")) and bool( getattr(self.model.config, "num_aggregation_labels") ) def batch_inference(self, **inputs): with torch.no_grad(): return self.model(**inputs) def sequential_inference(self, **inputs): """ Inference used for models that need to process sequences in a sequential fashion, like the SQA models which handle conversational query related to a table. """ with torch.no_grad(): all_logits = [] all_aggregations = [] prev_answers = None batch_size = inputs["input_ids"].shape[0] input_ids = inputs["input_ids"].to(self.device) attention_mask = inputs["attention_mask"].to(self.device) token_type_ids = inputs["token_type_ids"].to(self.device) token_type_ids_example = None for index in range(batch_size): # If sequences have already been processed, the token type IDs will be created according to the previous # answer. if prev_answers is not None: prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,) model_labels = np.zeros_like(prev_labels_example.cpu().numpy()) # shape (seq_len,) token_type_ids_example = token_type_ids[index] # shape (seq_len, 7) for i in range(model_labels.shape[0]): segment_id = token_type_ids_example[:, 0].tolist()[i] col_id = token_type_ids_example[:, 1].tolist()[i] - 1 row_id = token_type_ids_example[:, 2].tolist()[i] - 1 if row_id >= 0 and col_id >= 0 and segment_id == 1: model_labels[i] = int(prev_answers[(col_id, row_id)]) token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device) input_ids_example = input_ids[index] attention_mask_example = attention_mask[index] # shape (seq_len,) token_type_ids_example = token_type_ids[index] # shape (seq_len, 7) outputs = self.model( input_ids=input_ids_example.unsqueeze(0), attention_mask=attention_mask_example.unsqueeze(0), token_type_ids=token_type_ids_example.unsqueeze(0), ) logits = outputs.logits if self.aggregate: all_aggregations.append(outputs.logits_aggregation) all_logits.append(logits) dist_per_token = torch.distributions.Bernoulli(logits=logits) probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to( dist_per_token.probs.device ) coords_to_probs = collections.defaultdict(list) for i, p in enumerate(probabilities.squeeze().tolist()): segment_id = token_type_ids_example[:, 0].tolist()[i] col = token_type_ids_example[:, 1].tolist()[i] - 1 row = token_type_ids_example[:, 2].tolist()[i] - 1 if col >= 0 and row >= 0 and segment_id == 1: coords_to_probs[(col, row)].append(p) prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs} logits_batch =, 0) return (logits_batch,) if not self.aggregate else (logits_batch,, 0))
[docs] def __call__(self, *args, **kwargs): r""" Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below: - ``pipeline(table, query)`` - ``pipeline(table, [query])`` - ``pipeline(table=table, query=query)`` - ``pipeline(table=table, query=[query])`` - ``pipeline({"table": table, "query": query})`` - ``pipeline({"table": table, "query": [query]})`` - ``pipeline([{"table": table, "query": query}, {"table": table, "query": query}])`` The :obj:`table` argument should be a dict or a DataFrame built from that dict, containing the whole table: Example:: data = { "actors": ["brad pitt", "leonardo di caprio", "george clooney"], "age": ["56", "45", "59"], "number of movies": ["87", "53", "69"], "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"], } This dictionary can be passed in as such, or can be converted to a pandas DataFrame: Example:: import pandas as pd table = pd.DataFrame.from_dict(data) Args: table (:obj:`pd.DataFrame` or :obj:`Dict`): Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values. See above for an example of dictionary. query (:obj:`str` or :obj:`List[str]`): Query or list of queries that will be sent to the model alongside the table. sequential (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the inference to be done sequentially to extract relations within sequences, given their conversational nature. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`): Activates and controls padding. Accepts the following values: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.TapasTruncationStrategy`, `optional`, defaults to :obj:`False`): Activates and controls truncation. Accepts the following values: * :obj:`True` or :obj:`'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate row by row, removing rows from the table. * :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). Return: A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following keys: - **answer** (:obj:`str`) -- The answer of the query given the table. If there is an aggregator, the answer will be preceded by :obj:`AGGREGATOR >`. - **coordinates** (:obj:`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers. - **cells** (:obj:`List[str]`) -- List of strings made up of the answer cell values. - **aggregator** (:obj:`str`) -- If the model has an aggregator, this returns the aggregator. """ pipeline_inputs = self._args_parser(*args, **kwargs) results = super().__call__(pipeline_inputs, **kwargs) if len(results) == 1: return results[0] return results
def _sanitize_parameters(self, sequential=None, padding=None, truncation=None, **kwargs): preprocess_params = {} if padding is not None: preprocess_params["padding"] = padding if truncation is not None: preprocess_params["truncation"] = truncation forward_params = {} if sequential is not None: forward_params["sequential"] = sequential return preprocess_params, forward_params, {} def preprocess(self, pipeline_input, sequential=None, padding=True, truncation="drop_rows_to_fit"): table, query = pipeline_input["table"], pipeline_input["query"] if table.empty: raise ValueError("table is empty") if query is None or query == "": raise ValueError("query is empty") inputs = self.tokenizer(table, query, return_tensors=self.framework, truncation=truncation, padding=padding) inputs["table"] = table return inputs def _forward(self, model_inputs, sequential=False): table = model_inputs.pop("table") outputs = self.sequential_inference(**model_inputs) if sequential else self.batch_inference(**model_inputs) model_outputs = {"model_inputs": model_inputs, "table": table, "outputs": outputs} return model_outputs def postprocess(self, model_outputs): inputs = model_outputs["model_inputs"] table = model_outputs["table"] outputs = model_outputs["outputs"] if self.aggregate: logits, logits_agg = outputs[:2] predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits.detach(), logits_agg) answer_coordinates_batch, agg_predictions = predictions aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)} no_agg_label_index = self.model.config.no_aggregation_label_index aggregators_prefix = { i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index } else: logits = outputs[0] predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits.detach()) answer_coordinates_batch = predictions[0] aggregators = {} aggregators_prefix = {} answers = [] for index, coordinates in enumerate(answer_coordinates_batch): cells = [table.iat[coordinate] for coordinate in coordinates] aggregator = aggregators.get(index, "") aggregator_prefix = aggregators_prefix.get(index, "") answer = { "answer": aggregator_prefix + ", ".join(cells), "coordinates": coordinates, "cells": [table.iat[coordinate] for coordinate in coordinates], } if aggregator: answer["aggregator"] = aggregator answers.append(answer) if len(answer) == 0: raise PipelineException("Empty answer") return answers if len(answers) > 1 else answers[0]