tapas-wtq

google/tapas-base fine-tuned on WikiTableQuestions for table question answering with cell selection and aggregation (NONE / SUM / AVERAGE / COUNT).

Results

Denotation accuracy is the fraction of questions whose predicted answer matches the gold answer after normalisation; it is the metric used for model selection.

Usage

import pandas as pd
from transformers import TapasForQuestionAnswering, TapasTokenizer

repo = "thealper2/tapas-wtq"
tokenizer = TapasTokenizer.from_pretrained(repo)
model = TapasForQuestionAnswering.from_pretrained(repo)

table = pd.DataFrame(
    {"City": ["Paris", "Berlin", "Madrid"], "Population": ["2148000", "3645000", "3223000"]}
).astype(str)
inputs = tokenizer(table=table, queries=["Which city has the largest population?"],
                   return_tensors="pt")
outputs = model(**inputs)
coords, agg = tokenizer.convert_logits_to_predictions(
    inputs, outputs.logits.detach(), outputs.logits_aggregation.detach()
)
print([table.iat[c] for c in coords[0]], agg)

Training hyperparameters

• learning_rate: 5e-05
• num_train_epochs: 4.0
• per_device_train_batch_size: 16
• gradient_accumulation_steps: 2
• weight_decay: 0.01
• warmup_ratio: 0.1
• lr_scheduler_type: linear
• max_seq_length: 512
• seed: 42
• training time: 19.8 min

The full configuration is included as training_config.json, the per-step training history as history.json.