This is a recently introduced model so the API hasn’t been tested extensively. There may be some bugs or slight breaking changes to fix them in the future.


The TAPAS model was proposed in TAPAS: Weakly Supervised Table Parsing via Pre-training by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It’s a BERT-based model specifically designed (and pre-trained) for answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or summing) among selected cells. TAPAS has been fine-tuned on several datasets: SQA (Sequential Question Answering by Microsoft), WTQ (Wiki Table Questions by Stanford University) and WikiSQL (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while having comparable performance to SOTA on WikiSQL, with a much simpler architecture.

The abstract from the paper is the following:

Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT’s architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.

In addition, the authors have further pre-trained TAPAS to recognize table entailment, by creating a balanced dataset of millions of automatically created training examples which are learned in an intermediate step prior to fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on TabFact, a large-scale dataset with 16k Wikipedia tables for table entailment (a binary classification task). For more details, see their follow-up paper: Understanding tables with intermediate pre-training by Julian Martin Eisenschlos, Syrine Krichene and Thomas Müller.

This model was contributed by nielsr. The original code can be found here.


  • TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows you to encode longer sequences without running out of embeddings. This is reflected in the reset_position_index_per_cell parameter of TapasConfig, which is set to True by default. The default versions of the models available in the model hub all use relative position embeddings. You can still use the ones with absolute position embeddings by passing in an additional argument revision="no_reset" when calling the .from_pretrained() method. Note that it’s usually advised to pad the inputs on the right rather than the left.

  • TAPAS is based on BERT, so TAPAS-base for example corresponds to a BERT-base architecture. Of course, TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of the various sized models are shown on the original Github repository.

  • TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a conversational set-up. This means that you can ask follow-up questions such as “what is his age?” related to the previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that case, you have to feed every table-question pair one by one to the model, such that the prev_labels token type ids can be overwritten by the predicted labels of the model to the previous question. See “Usage” section for more info.

  • TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language modeling (CLM) objective are better in that regard.

Usage: fine-tuning

Here we explain how you can fine-tune TapasForQuestionAnswering on your own dataset.

STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment

Basically, there are 3 different ways in which one can fine-tune TapasForQuestionAnswering, corresponding to the different datasets on which Tapas was fine-tuned:

  1. SQA: if you’re interested in asking follow-up questions related to a table, in a conversational set-up. For example if you first ask “what’s the name of the first actor?” then you can ask a follow-up question such as “how old is he?”. Here, questions do not involve any aggregation (all questions are cell selection questions).

  2. WTQ: if you’re not interested in asking questions in a conversational set-up, but rather just asking questions related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or averaging cell values. You can then for example ask “what’s the total number of goals Cristiano Ronaldo made in his career?”. This case is also called weak supervision, since the model itself must learn the appropriate aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.

  3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation operator during training. This is also called strong supervision. Here, learning the appropriate aggregation operator is much easier.

To summarize:


Example dataset




Conversational, only cell selection questions

Weak supervision for aggregation


Questions might involve aggregation, and the model must learn this given only the answer as supervision

Strong supervision for aggregation


Questions might involve aggregation, and the model must learn this given the gold aggregation operator

Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be done as follows (be sure to have installed the torch-scatter dependency for your environment):

>>> from transformers import TapasConfig, TapasForQuestionAnswering

>>> # for example, the base sized model with default SQA configuration
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')

>>> # or, the base sized model with WTQ configuration
>>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)

>>> # or, the base sized model with WikiSQL configuration
>>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)

Of course, you don’t necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing TapasConfig, and then create a TapasForQuestionAnswering based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here’s an example:

>>> from transformers import TapasConfig, TapasForQuestionAnswering

>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
>>> # initializing the pre-trained base sized model with our custom classification heads
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)

What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See here for more info.

For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see here.

STEP 2: Prepare your data in the SQA format

Second, no matter what you picked above, you should prepare your dataset in the SQA format. This format is a TSV/CSV file with the following columns:

  • id: optional, id of the table-question pair, for bookkeeping purposes.

  • annotator: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.

  • position: integer indicating if the question is the first, second, third,… related to the table. Only required in case of conversational setup (SQA). You don’t need this column in case you’re going for WTQ/WikiSQL-supervised.

  • question: string

  • table_file: string, name of a csv file containing the tabular data

  • answer_coordinates: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is part of the answer)

  • answer_text: list of one or more strings (each string being a cell value that is part of the answer)

  • aggregation_label: index of the aggregation operator. Only required in case of strong supervision for aggregation (the WikiSQL-supervised case)

  • float_answer: the float answer to the question, if there is one (np.nan if there isn’t). Only required in case of weak supervision for aggregation (such as WTQ and WikiSQL)

The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the SQA format. The author explains this here. Interestingly, these conversion scripts are not perfect (the answer_coordinates and float_answer fields are populated based on the answer_text), meaning that WTQ and WikiSQL results could actually be improved.

STEP 3: Convert your data into PyTorch tensors using TapasTokenizer

Third, given that you’ve prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use TapasTokenizer to convert table-question pairs into input_ids, attention_mask, token_type_ids and so on. Again, based on which of the three cases you picked above, TapasForQuestionAnswering requires different inputs to be fine-tuned:


Required inputs


input_ids, attention_mask, token_type_ids, labels

Weak supervision for aggregation

input_ids, attention_mask, token_type_ids, labels, numeric_values, numeric_values_scale, float_answer

Strong supervision for aggregation

input ids, attention mask, token type ids, labels, aggregation_labels

TapasTokenizer creates the labels, numeric_values and numeric_values_scale based on the answer_coordinates and answer_text columns of the TSV file. The float_answer and aggregation_labels are already in the TSV file of step 2. Here’s an example:

>>> from transformers import TapasTokenizer
>>> import pandas as pd

>>> model_name = 'google/tapas-base'
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)

>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
>>> inputs
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}

Note that TapasTokenizer expects the data of the table to be text-only. You can use .astype(str) on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single training example. It is advised to create a PyTorch dataset and a corresponding dataloader:

>>> import torch
>>> import pandas as pd

>>> tsv_path = "your_path_to_the_tsv_file"
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"

>>> class TableDataset(torch.utils.data.Dataset):
...     def __init__(self, data, tokenizer):
...         self.data = data
...         self.tokenizer = tokenizer
...     def __getitem__(self, idx):
...         item = data.iloc[idx]
...         table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
...         encoding = self.tokenizer(table=table,
...                                   queries=item.question,
...                                   answer_coordinates=item.answer_coordinates,
...                                   answer_text=item.answer_text,
...                                   truncation=True,
...                                   padding="max_length",
...                                   return_tensors="pt"
...         )
...         # remove the batch dimension which the tokenizer adds by default
...         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
...         # add the float_answer which is also required (weak supervision for aggregation case)
...         encoding["float_answer"] = torch.tensor(item.float_answer)
...         return encoding
...     def __len__(self):
...        return len(self.data)

>>> data = pd.read_csv(tsv_path, sep='\t')
>>> train_dataset = TableDataset(data, tokenizer)
>>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)

Note that here, we encode each table-question pair independently. This is fine as long as your dataset is not conversational. In case your dataset involves conversational questions (such as in SQA), then you should first group together the queries, answer_coordinates and answer_text per table (in the order of their position index) and batch encode each table with its questions. This will make sure that the prev_labels token types (see docs of TapasTokenizer) are set correctly. See this notebook for more info.

STEP 4: Train (fine-tune) TapasForQuestionAnswering

You can then fine-tune TapasForQuestionAnswering using native PyTorch as follows (shown here for the weak supervision for aggregation case):

>>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW

>>> # this is the default WTQ configuration
>>> config = TapasConfig(
...            num_aggregation_labels = 4,
...            use_answer_as_supervision = True,
...            answer_loss_cutoff = 0.664694,
...            cell_selection_preference = 0.207951,
...            huber_loss_delta = 0.121194,
...            init_cell_selection_weights_to_zero = True,
...            select_one_column = True,
...            allow_empty_column_selection = False,
...            temperature = 0.0352513,
... )
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)

>>> optimizer = AdamW(model.parameters(), lr=5e-5)

>>> for epoch in range(2):  # loop over the dataset multiple times
...    for idx, batch in enumerate(train_dataloader):
...         # get the inputs;
...         input_ids = batch["input_ids"]
...         attention_mask = batch["attention_mask"]
...         token_type_ids = batch["token_type_ids"]
...         labels = batch["labels"]
...         numeric_values = batch["numeric_values"]
...         numeric_values_scale = batch["numeric_values_scale"]
...         float_answer = batch["float_answer"]

...         # zero the parameter gradients
...         optimizer.zero_grad()

...         # forward + backward + optimize
...         outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids,
...                        labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale,
...                        float_answer=float_answer)
...         loss = outputs.loss
...         loss.backward()
...         optimizer.step()

Usage: inference

Here we explain how you can use TapasForQuestionAnswering for inference (i.e. making predictions on new data). For inference, only input_ids, attention_mask and token_type_ids (which you can obtain using TapasTokenizer) have to be provided to the model to obtain the logits. Next, you can use the handy convert_logits_to_predictions method of TapasTokenizer to convert these into predicted coordinates and optional aggregation indices.

However, note that inference is different depending on whether or not the setup is conversational. In a non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here’s an example of that:

>>> from transformers import TapasTokenizer, TapasForQuestionAnswering
>>> import pandas as pd

>>> model_name = 'google/tapas-base-finetuned-wtq'
>>> model = TapasForQuestionAnswering.from_pretrained(model_name)
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)

>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt")
>>> outputs = model(**inputs)
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
...         inputs,
...         outputs.logits.detach(),
...         outputs.logits_aggregation.detach()
... )

>>> # let's print out the results:
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]

>>> answers = []
>>> for coordinates in predicted_answer_coordinates:
...   if len(coordinates) == 1:
...     # only a single cell:
...     answers.append(table.iat[coordinates[0]])
...   else:
...     # multiple cells
...     cell_values = []
...     for coordinate in coordinates:
...        cell_values.append(table.iat[coordinate])
...     answers.append(", ".join(cell_values))

>>> display(table)
>>> print("")
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
...   print(query)
...   if predicted_agg == "NONE":
...     print("Predicted answer: " + answer)
...   else:
...     print("Predicted answer: " + predicted_agg + " > " + answer)
What is the name of the first actor?
Predicted answer: Brad Pitt
How many movies has George Clooney played in?
Predicted answer: COUNT > 69
What is the total number of movies?
Predicted answer: SUM > 87, 53, 69

In case of a conversational set-up, then each table-question pair must be provided sequentially to the model, such that the prev_labels token types can be overwritten by the predicted labels of the previous table-question pair. Again, more info can be found in this notebook.

Tapas specific outputs

class transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput(loss: Optional[torch.FloatTensor] = None, logits: torch.FloatTensor = None, logits_aggregation: torch.FloatTensor = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]

Output type of TapasForQuestionAnswering.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels (and possibly answer, aggregation_labels, numeric_values and numeric_values_scale are provided)) – Total loss as the sum of the hierarchical cell selection log-likelihood loss and (optionally) the semi-supervised regression loss and (optionally) supervised loss for aggregations.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length)) – Prediction scores of the cell selection head, for every token.

  • logits_aggregation (torch.FloatTensor, optional, of shape (batch_size, num_aggregation_labels)) – Prediction scores of the aggregation head, for every aggregation operator.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.


class transformers.TapasConfig(vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1024, type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10], initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, positive_label_weight=10.0, num_aggregation_labels=0, aggregation_loss_weight=1.0, use_answer_as_supervision=None, answer_loss_importance=1.0, use_normalized_answer_loss=False, huber_loss_delta=None, temperature=1.0, aggregation_temperature=1.0, use_gumbel_for_cells=False, use_gumbel_for_aggregation=False, average_approximation_function='ratio', cell_selection_preference=None, answer_loss_cutoff=None, max_num_rows=64, max_num_columns=32, average_logits_per_cell=False, select_one_column=True, allow_empty_column_selection=False, init_cell_selection_weights_to_zero=False, reset_position_index_per_cell=True, disable_per_token_loss=False, aggregation_labels=None, no_aggregation_label_index=None, **kwargs)[source]

This is the configuration class to store the configuration of a TapasModel. It is used to instantiate a TAPAS model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the TAPAS tapas-base-finetuned-sqa architecture. Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Hyperparameters additional to BERT are taken from run_task_main.py and hparam_utils.py of the original implementation. Original implementation available at https://github.com/google-research/tapas/tree/master.

  • vocab_size (int, optional, defaults to 30522) – Vocabulary size of the TAPAS model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling TapasModel.

  • hidden_size (int, optional, defaults to 768) – Dimensionality of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 12) – Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 12) – Number of attention heads for each attention layer in the Transformer encoder.

  • intermediate_size (int, optional, defaults to 3072) – Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.

  • hidden_act (str or Callable, optional, defaults to "gelu") – The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.

  • hidden_dropout_prob (float, optional, defaults to 0.1) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_probs_dropout_prob (float, optional, defaults to 0.1) – The dropout ratio for the attention probabilities.

  • max_position_embeddings (int, optional, defaults to 1024) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

  • type_vocab_sizes (List[int], optional, defaults to [3, 256, 256, 2, 256, 256, 10]) – The vocabulary sizes of the token_type_ids passed when calling TapasModel.

  • initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • layer_norm_eps (float, optional, defaults to 1e-12) – The epsilon used by the layer normalization layers.

  • positive_label_weight (float, optional, defaults to 10.0) – Weight for positive labels.

  • num_aggregation_labels (int, optional, defaults to 0) – The number of aggregation operators to predict.

  • aggregation_loss_weight (float, optional, defaults to 1.0) – Importance weight for the aggregation loss.

  • use_answer_as_supervision (bool, optional) – Whether to use the answer as the only supervision for aggregation examples.

  • answer_loss_importance (float, optional, defaults to 1.0) – Importance weight for the regression loss.

  • use_normalized_answer_loss (bool, optional, defaults to False) – Whether to normalize the answer loss by the maximum of the predicted and expected value.

  • huber_loss_delta (float, optional) – Delta parameter used to calculate the regression loss.

  • temperature (float, optional, defaults to 1.0) – Value used to control (OR change) the skewness of cell logits probabilities.

  • aggregation_temperature (float, optional, defaults to 1.0) – Scales aggregation logits to control the skewness of probabilities.

  • use_gumbel_for_cells (bool, optional, defaults to False) – Whether to apply Gumbel-Softmax to cell selection.

  • use_gumbel_for_aggregation (bool, optional, defaults to False) – Whether to apply Gumbel-Softmax to aggregation selection.

  • average_approximation_function (string, optional, defaults to "ratio") – Method to calculate the expected average of cells in the weak supervision case. One of "ratio", "first_order" or "second_order".

  • cell_selection_preference (float, optional) – Preference for cell selection in ambiguous cases. Only applicable in case of weak supervision for aggregation (WTQ, WikiSQL). If the total mass of the aggregation probabilities (excluding the “NONE” operator) is higher than this hyperparameter, then aggregation is predicted for an example.

  • answer_loss_cutoff (float, optional) – Ignore examples with answer loss larger than cutoff.

  • max_num_rows (int, optional, defaults to 64) – Maximum number of rows.

  • max_num_columns (int, optional, defaults to 32) – Maximum number of columns.

  • average_logits_per_cell (bool, optional, defaults to False) – Whether to average logits per cell.

  • select_one_column (bool, optional, defaults to True) – Whether to constrain the model to only select cells from a single column.

  • allow_empty_column_selection (bool, optional, defaults to False) – Whether to allow not to select any column.

  • init_cell_selection_weights_to_zero (bool, optional, defaults to False) – Whether to initialize cell selection weights to 0 so that the initial probabilities are 50%.

  • reset_position_index_per_cell (bool, optional, defaults to True) – Whether to restart position indexes at every cell (i.e. use relative position embeddings).

  • disable_per_token_loss (bool, optional, defaults to False) – Whether to disable any (strong or weak) supervision on cells.

  • aggregation_labels (Dict[int, label], optional) – The aggregation labels used to aggregate the results. For example, the WTQ models have the following aggregation labels: {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}

  • no_aggregation_label_index (int, optional) – If the aggregation labels are defined and one of these labels represents “No aggregation”, this should be set to its index. For example, the WTQ models have the “NONE” aggregation label at index 0, so that value should be set to 0 for these models.


>>> from transformers import TapasModel, TapasConfig
>>> # Initializing a default (SQA) Tapas configuration
>>> configuration = TapasConfig()
>>> # Initializing a model from the configuration
>>> model = TapasModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config


class transformers.TapasTokenizer(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', empty_token='[EMPTY]', tokenize_chinese_chars=True, strip_accents=None, cell_trim_length: int = - 1, max_column_id: int = None, max_row_id: int = None, strip_column_names: bool = False, update_answer_coordinates: bool = False, min_question_length=None, max_question_length=None, model_max_length: int = 512, additional_special_tokens: Optional[List[str]] = None, **kwargs)[source]

Construct a TAPAS tokenizer. Based on WordPiece. Flattens a table and one or more related sentences to be used by TAPAS models.

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. TapasTokenizer creates several token type ids to encode tabular structure. To be more precise, it adds 7 token type ids, in the following order: segment_ids, column_ids, row_ids, prev_labels, column_ranks, inv_column_ranks and numeric_relations:

  • segment_ids: indicate whether a token belongs to the question (0) or the table (1). 0 for special tokens and padding.

  • column_ids: indicate to which column of the table a token belongs (starting from 1). Is 0 for all question tokens, special tokens and padding.

  • row_ids: indicate to which row of the table a token belongs (starting from 1). Is 0 for all question tokens, special tokens and padding. Tokens of column headers are also 0.

  • prev_labels: indicate whether a token was (part of) an answer to the previous question (1) or not (0). Useful in a conversational setup (such as SQA).

  • column_ranks: indicate the rank of a table token relative to a column, if applicable. For example, if you have a column “number of movies” with values 87, 53 and 69, then the column ranks of these tokens are 3, 1 and 2 respectively. 0 for all question tokens, special tokens and padding.

  • inv_column_ranks: indicate the inverse rank of a table token relative to a column, if applicable. For example, if you have a column “number of movies” with values 87, 53 and 69, then the inverse column ranks of these tokens are 1, 3 and 2 respectively. 0 for all question tokens, special tokens and padding.

  • numeric_relations: indicate numeric relations between the question and the tokens of the table. 0 for all question tokens, special tokens and padding.

TapasTokenizer runs end-to-end tokenization on a table and associated sentences: punctuation splitting and wordpiece.

  • vocab_file (str) – File containing the vocabulary.

  • do_lower_case (bool, optional, defaults to True) – Whether or not to lowercase the input when tokenizing.

  • do_basic_tokenize (bool, optional, defaults to True) – Whether or not to do basic tokenization before WordPiece.

  • never_split (Iterable, optional) – Collection of tokens which will never be split during tokenization. Only has an effect when do_basic_tokenize=True

  • unk_token (str, optional, defaults to "[UNK]") – The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

  • sep_token (str, optional, defaults to "[SEP]") – The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

  • pad_token (str, optional, defaults to "[PAD]") – The token used for padding, for example when batching sequences of different lengths.

  • cls_token (str, optional, defaults to "[CLS]") – The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

  • mask_token (str, optional, defaults to "[MASK]") – The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.

  • empty_token (str, optional, defaults to "[EMPTY]") – The token used for empty cell values in a table. Empty cell values include “”, “n/a”, “nan” and “?”.

  • tokenize_chinese_chars (bool, optional, defaults to True) – Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this issue).

  • strip_accents – (bool, optional): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original BERT).

  • cell_trim_length (int, optional, defaults to -1) – If > 0: Trim cells so that the length is <= this value. Also disables further cell trimming, should thus be used with truncation set to True.

  • max_column_id (int, optional) – Max column id to extract.

  • max_row_id (int, optional) – Max row id to extract.

  • strip_column_names (bool, optional, defaults to False) – Whether to add empty strings instead of column names.

  • update_answer_coordinates (bool, optional, defaults to False) – Whether to recompute the answer coordinates from the answer text.

  • min_question_length (int, optional) – Minimum length of each question in terms of tokens (will be skipped otherwise).

  • max_question_length (int, optional) – Maximum length of each question in terms of tokens (will be skipped otherwise).

__call__(table: pandas.core.frame.DataFrame, queries: Optional[Union[str, List[str], List[int], List[List[str]], List[List[int]]]] = None, answer_coordinates: Optional[Union[List[Tuple], List[List[Tuple]]]] = None, answer_text: Optional[Union[List[str], List[List[str]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = False, truncation: Union[bool, str, transformers.models.tapas.tokenization_tapas.TapasTruncationStrategy] = False, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, transformers.file_utils.TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs) → transformers.tokenization_utils_base.BatchEncoding[source]

Main method to tokenize and prepare for the model one or several sequence(s) related to a table.

  • table (pd.DataFrame) – Table containing tabular data. Note that all cell values must be text. Use .astype(str) on a Pandas dataframe to convert it to string.

  • queries (str or List[str]) – Question or batch of questions related to a table to be encoded. Note that in case of a batch, all questions must refer to the same table.

  • answer_coordinates (List[Tuple] or List[List[Tuple]], optional) – Answer coordinates of each table-question pair in the batch. In case only a single table-question pair is provided, then the answer_coordinates must be a single list of one or more tuples. Each tuple must be a (row_index, column_index) pair. The first data row (not the column header row) has index 0. The first column has index 0. In case a batch of table-question pairs is provided, then the answer_coordinates must be a list of lists of tuples (each list corresponding to a single table-question pair).

  • answer_text (List[str] or List[List[str]], optional) – Answer text of each table-question pair in the batch. In case only a single table-question pair is provided, then the answer_text must be a single list of one or more strings. Each string must be the answer text of a corresponding answer coordinate. In case a batch of table-question pairs is provided, then the answer_coordinates must be a list of lists of strings (each list corresponding to a single table-question pair).

  • add_special_tokens (bool, optional, defaults to True) – Whether or not to encode the sequences with the special tokens relative to their model.

  • padding (bool, str or PaddingStrategy, optional, defaults to False) –

    Activates and controls padding. Accepts the following values:

    • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).

    • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.

    • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).

  • truncation (bool, str or TapasTruncationStrategy, optional, defaults to False) –

    Activates and controls truncation. Accepts the following values:

    • True or 'drop_rows_to_fit': Truncate to a maximum length specified with the argument 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.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • max_length (int, optional) –

    Controls the maximum length to use by one of the truncation/padding parameters.

    If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • is_split_into_words (bool, optional, defaults to False) – Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.

  • pad_to_multiple_of (int, optional) – If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

  • return_tensors (str or TensorType, optional) –

    If set, will return tensors instead of list of python integers. Acceptable values are:

    • 'tf': Return TensorFlow tf.constant objects.

    • 'pt': Return PyTorch torch.Tensor objects.

    • 'np': Return Numpy np.ndarray objects.

convert_logits_to_predictions(data, logits, logits_agg=None, cell_classification_threshold=0.5)[source]

Converts logits of TapasForQuestionAnswering to actual predicted answer coordinates and optional aggregation indices.

The original implementation, on which this function is based, can be found here.

  • data (dict) – Dictionary mapping features to actual values. Should be created using TapasTokenizer.

  • logits (np.ndarray of shape (batch_size, sequence_length)) – Tensor containing the logits at the token level.

  • logits_agg (np.ndarray of shape (batch_size, num_aggregation_labels), optional) – Tensor containing the aggregation logits.

  • cell_classification_threshold (float, optional, defaults to 0.5) – Threshold to be used for cell selection. All table cells for which their probability is larger than this threshold will be selected.


  • predicted_answer_coordinates (List[List[[tuple]] of length batch_size): Predicted answer coordinates as a list of lists of tuples. Each element in the list contains the predicted answer coordinates of a single example in the batch, as a list of tuples. Each tuple is a cell, i.e. (row index, column index).

  • predicted_aggregation_indices (List[int]``of length ``batch_size, optional, returned when logits_aggregation is provided): Predicted aggregation operator indices of the aggregation head.

Return type

tuple comprising various elements depending on the inputs

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

  • save_directory (str) – The directory in which to save the vocabulary.

  • filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.


Paths to the files saved.

Return type



class transformers.TapasModel(*args, **kwargs)[source]


class transformers.TapasForMaskedLM(*args, **kwargs)[source]


class transformers.TapasForSequenceClassification(*args, **kwargs)[source]


class transformers.TapasForQuestionAnswering(*args, **kwargs)[source]