---
library_name: transformers
tags:
- tapas
- table
- question
license: mit
language:
- en
base_model:
- google/tapas-base-finetuned-wtq
pipeline_tag: table-question-answering
---

This is an experimental model fine-tuned on various balance sheets collected from financial services. The fine-tuning process was designed to adapt the TAPAS model to handle large numeric values and complex financial data structures commonly found in balance sheets.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
# !pip install sugardata

from transformers import TapasTokenizer, TapasForQuestionAnswering
from sugardata.utility.tapas import generate_financial_balance_sheet, get_real_tapas_answer

# generate a financial balance sheet and ask a question
table = generate_financial_balance_sheet()

question = "What was the reported value of Total Debt in 2021?"

# load the model and tokenizer
model_name = "yeniguno/tapas-base-wtq-balance-sheet-tuned"
model = TapasForQuestionAnswering.from_pretrained(model_name)
tokenizer = TapasTokenizer.from_pretrained(model_name)

inputs = tokenizer(table=table, queries=[question], padding="max_length", return_tensors="pt")

# get the answer
answer = get_real_tapas_answer(table, model, tokenizer, inputs)

# 8873000.0

```

## Training Details

- Epoch [1/5]  Train Loss: 0.1514  Val Loss: 0.0107
- Epoch [2/5]  Train Loss: 0.0135  Val Loss: 0.0098
- Epoch [3/5]  Train Loss: 0.0116  Val Loss: 0.0081
- Epoch [4/5]  Train Loss: 0.0081  Val Loss: 0.0071
- Epoch [5/5]  Train Loss: 0.0049  Val Loss: 0.0043