This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
<!---
Copyright 2022 The Microsoft Inc. and The HuggingFace Inc. Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Run Table Tasks with TAPEX

TAPEX is a table pre-training approach for table-related tasks. By learning a neural SQL executor over a synthetic corpus based on generative language models (e.g., BART), it achieves state-of-the-art performance on several table-based question answering benchmarks and table-based fact verification benchmark. More details can be found in the original paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/pdf/2107.07653.pdf).

> If you are also familiar with [fairseq](https://github.com/pytorch/fairseq), you may also find [the official implementation](https://github.com/microsoft/Table-Pretraining) useful, which leverages the framework.

## Table Question Answering Tasks

### What is Table Question Answering

![Example](https://table-pretraining.github.io/assets/tableqa_task.png)

The task of Table Question Answering (TableQA) is to empower machines to answer users' questions over a given table. The resulting answer(s) can be a region in the table, or a number calculated by applying aggregation operators to a specific region.

### What Questions Can be Answered

Benefiting from the powerfulness of generative models, TAPEX can deal with almost all kinds of questions over tables (if there is training data). Below are some typical question and their answers taken from [WikiTableQuestion](https://nlp.stanford.edu/blog/wikitablequestions-a-complex-real-world-question-understanding-dataset).

| Question | Answer |
| :---: | :---: |
| What is the years won for each team? | 2004, 2008, 2012 |
| How long did Taiki Tsuchiya last? | 4:27 |
| What is the total amount of matches drawn? | 1 |
| Besides Tiger Woods, what other player won between 2007 and 2009? | Camilo Villegas |
| What was the last Baekje Temple? | Uija |
| What is the difference between White voters and Black voters in 1948? | 0 |
| What is the average number of sailors for each country during the worlds qualification tournament? | 2 |


### How to Fine-tune TAPEX on TableQA

We provide a fine-tuning script of tapex for TableQA on the WikiSQL benchmark: [WikiSQL](https://github.com/salesforce/WikiSQL).
This script is customized for tapex models, and can be easily adapted to other benchmarks such as WikiTableQuestion
(only some tweaks in the function `preprocess_tableqa_function`).

#### TAPEX-Base on WikiSQL

Here is how to run the script on the WikiSQL with `tapex-base`:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 16GB and 1 GPU card. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly.

```bash
export EXP_NAME=wikisql_tapex_base

python run_wikisql_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-base \
  --overwrite_output_dir \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 8 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000
```

#### TAPEX-Large on WikiSQL

Here is how to run the script on the WikiSQL with `tapex-large`:
> The default hyper-parameter may allow you to reproduce our reported tapex-large results within the memory budget of 16GB and 1 GPU card with fp16. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. If you do not install apex or other mixed-precision-training libs, you could disable the `predict_with_generate` option to save GPU memory and manually evaluate the model once the fine-tuning finished. Or just pick up the last checkpoint, which usually performs good enough on the dataset.

```bash
export EXP_NAME=wikisql_tapex_large

python run_wikisql_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-large \
  --overwrite_output_dir \
  --per_device_train_batch_size 1 \
  --gradient_accumulation_steps 32 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000 \
  --fp16
```

#### TAPEX-Base on WikiTableQuestions

Here is how to run the script on the WikiTableQuestions with `tapex-base`:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 16GB and 1 GPU card. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly.

```bash
export EXP_NAME=wikitablequestions_tapex_base

python run_wikitablequestions_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-base \
  --overwrite_output_dir \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 8 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000
```

#### TAPEX-Large on WikiTableQuestions

Here is how to run the script on the WikiTableQuestions with `tapex-large`:
> The default hyper-parameter may allow you to reproduce our reported tapex-large results within the memory budget of 16GB and 1 GPU card with fp16. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. If you do not install apex or other mixed-precision-training libs, you could reduce the `per_device_train_batch_size` and `per_device_eval_batch_size` and have another try. Or you could disable the `predict_with_generate` option to save GPU memory and manually evaluate the model once the fine-tuning finished. Or just pick up the last checkpoint, which usually performs good enough on the dataset.

```bash
export EXP_NAME=wikitablequestions_tapex_large

python run_wikitablequestions_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-large \
  --overwrite_output_dir \
  --per_device_train_batch_size 2 \
  --gradient_accumulation_steps 12 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000 \
  --fp16
```

### How to Evaluate TAPEX Fine-tuned Models on TableQA

We provide fine-tuned model weights to reproduce our results. You can evaluate them using the following command:
> You can also replace `microsoft/tapex-base-finetuned-wikisql` with your local directory to evaluate your fine-tuned models. Notice that if the model has a larger size, you should reduce `per_device_eval_batch_size` to fit the memory requirement.

```bash
export EXP_NAME=wikisql_tapex_base_eval

python run_wikisql_with_tapex.py \
  --do_eval \
  --model_name_or_path microsoft/tapex-base-finetuned-wikisql \
  --output_dir $EXP_NAME \
  --per_device_eval_batch_size 4 \
  --predict_with_generate \
  --num_beams 5
```

## Table Fact Verification Tasks

### What is Table Fact Verification

![Example](https://table-pretraining.github.io/assets/tableft_task.png)

The task of Table Fact Verification (TableFV) is to empower machines to justify if a statement follows facts in a given table. The result is a binary classification belonging to `1` (entailed) or `0` (refused).

### How to Fine-tune TAPEX on TableFV

#### TAPEX-Base on TabFact

We provide a fine-tuning script of tapex for TableFV on the TabFact benchmark: [TabFact](https://github.com/wenhuchen/Table-Fact-Checking).

Here is how to run the script on the TabFact:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 16GB and 1 GPU card. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. Note that the `eval_accumulation_steps` is necessary, otherwise GPU memory leaks will occur during the evaluation.

```bash
export EXP_NAME=tabfact_tapex_base

python run_tabfact_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-base \
  --overwrite_output_dir \
  --per_device_train_batch_size 3 \
  --gradient_accumulation_steps 16 \
  --per_device_eval_batch_size 12 \
  --eval_accumulation_steps 6 \
  --warm_steps 1000 \
  --logging_steps 10 \
  --learning_rate 3e-5 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --evaluation_strategy steps \
  --weight_decay 1e-2 \
  --max_steps 30000 \
  --max_grad_norm 0.1
```

#### TAPEX-Large on TabFact

Here is how to run the script on the TabFact:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 24GB and 1 GPU card. Sorry we cannot reduce the memory consumption since the model input in TabFact usually contains nearly ~1000 tokens. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. Note that the `eval_accumulation_steps` is necessary, otherwise GPU memory leaks will occur during the evaluation.

```bash
export EXP_NAME=tabfact_tapex_large

python run_tabfact_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-large \
  --overwrite_output_dir \
  --per_device_train_batch_size 2 \
  --gradient_accumulation_steps 18 \
  --per_device_eval_batch_size 4 \
  --eval_accumulation_steps 12 \
  --warm_steps 1000 \
  --logging_steps 10 \
  --learning_rate 3e-5 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --evaluation_strategy steps \
  --weight_decay 1e-2 \
  --max_steps 30000 \
  --max_grad_norm 0.1
```

### How to Evaluate TAPEX Fine-tuned Models on TableFV

We provide fine-tuned model weights to reproduce our results. You can evaluate them using the following command:
> You can also replace `microsoft/tapex-base-finetuned-tabfact` with your local directory to evaluate your fine-tuned models. Notice that if the model has a larger size, you should reduce `per_device_eval_batch_size` to fit the memory requirement.

```bash
export EXP_NAME=tabfact_tapex_base_eval

python run_tabfact_with_tapex.py \
  --do_eval \
  --model_name_or_path microsoft/tapex-base-finetuned-tabfact \
  --output_dir $EXP_NAME \
  --per_device_eval_batch_size 12 \
  --eval_accumulation_steps 6
```

## Reproduced Results

We get the following results on the dev set of the benchmark with the previous commands:

| Task | Model Size | Metric | Result |
|:---:|:---:|:---:|:---:|
| WikiSQL (Weak) | Base | Denotation Accuracy | 88.1 |
| WikiSQL (Weak) | Large | Denotation Accuracy | 89.5 |
| WikiTableQuestion | Base | Denotation Accuracy | 47.1 |
| WikiTableQuestion | Large | Denotation Accuracy | 57.2 |
| TabFact | Base | Accuracy | 78.7 |
| TabFact | Large | Accuracy | 83.6 |

<!---
Copyright 2022 The Microsoft Inc. and The HuggingFace Inc. Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Run Table Tasks with TAPEX

TAPEX is a table pre-training approach for table-related tasks. By learning a neural SQL executor over a synthetic corpus based on generative language models (e.g., BART), it achieves state-of-the-art performance on several table-based question answering benchmarks and table-based fact verification benchmark. More details can be found in the original paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/pdf/2107.07653.pdf).

> If you are also familiar with [fairseq](https://github.com/pytorch/fairseq), you may also find [the official implementation](https://github.com/microsoft/Table-Pretraining) useful, which leverages the framework.

## Table Question Answering Tasks

### What is Table Question Answering

![Example](https://table-pretraining.github.io/assets/tableqa_task.png)

The task of Table Question Answering (TableQA) is to empower machines to answer users' questions over a given table. The resulting answer(s) can be a region in the table, or a number calculated by applying aggregation operators to a specific region.

### What Questions Can be Answered

Benefiting from the powerfulness of generative models, TAPEX can deal with almost all kinds of questions over tables (if there is training data). Below are some typical question and their answers taken from [WikiTableQuestion](https://nlp.stanford.edu/blog/wikitablequestions-a-complex-real-world-question-understanding-dataset).

| Question | Answer |
| :---: | :---: |
| What is the years won for each team? | 2004, 2008, 2012 |
| How long did Taiki Tsuchiya last? | 4:27 |
| What is the total amount of matches drawn? | 1 |
| Besides Tiger Woods, what other player won between 2007 and 2009? | Camilo Villegas |
| What was the last Baekje Temple? | Uija |
| What is the difference between White voters and Black voters in 1948? | 0 |
| What is the average number of sailors for each country during the worlds qualification tournament? | 2 |


### How to Fine-tune TAPEX on TableQA

We provide a fine-tuning script of tapex for TableQA on the WikiSQL benchmark: [WikiSQL](https://github.com/salesforce/WikiSQL).
This script is customized for tapex models, and can be easily adapted to other benchmarks such as WikiTableQuestion
(only some tweaks in the function `preprocess_tableqa_function`).

#### TAPEX-Base on WikiSQL

Here is how to run the script on the WikiSQL with `tapex-base`:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 16GB and 1 GPU card. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly.

```bash
export EXP_NAME=wikisql_tapex_base

python run_wikisql_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-base \
  --overwrite_output_dir \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 8 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000
```

#### TAPEX-Large on WikiSQL

Here is how to run the script on the WikiSQL with `tapex-large`:
> The default hyper-parameter may allow you to reproduce our reported tapex-large results within the memory budget of 16GB and 1 GPU card with fp16. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. If you do not install apex or other mixed-precision-training libs, you could disable the `predict_with_generate` option to save GPU memory and manually evaluate the model once the fine-tuning finished. Or just pick up the last checkpoint, which usually performs good enough on the dataset.

```bash
export EXP_NAME=wikisql_tapex_large

python run_wikisql_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-large \
  --overwrite_output_dir \
  --per_device_train_batch_size 1 \
  --gradient_accumulation_steps 32 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000 \
  --fp16
```

#### TAPEX-Base on WikiTableQuestions

Here is how to run the script on the WikiTableQuestions with `tapex-base`:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 16GB and 1 GPU card. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly.

```bash
export EXP_NAME=wikitablequestions_tapex_base

python run_wikitablequestions_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-base \
  --overwrite_output_dir \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 8 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000
```

#### TAPEX-Large on WikiTableQuestions

Here is how to run the script on the WikiTableQuestions with `tapex-large`:
> The default hyper-parameter may allow you to reproduce our reported tapex-large results within the memory budget of 16GB and 1 GPU card with fp16. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. If you do not install apex or other mixed-precision-training libs, you could reduce the `per_device_train_batch_size` and `per_device_eval_batch_size` and have another try. Or you could disable the `predict_with_generate` option to save GPU memory and manually evaluate the model once the fine-tuning finished. Or just pick up the last checkpoint, which usually performs good enough on the dataset.

```bash
export EXP_NAME=wikitablequestions_tapex_large

python run_wikitablequestions_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-large \
  --overwrite_output_dir \
  --per_device_train_batch_size 2 \
  --gradient_accumulation_steps 12 \
  --per_device_eval_batch_size 4 \
  --learning_rate 3e-5 \
  --logging_steps 10 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --warmup_steps 1000 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --num_beams 5 \
  --weight_decay 1e-2 \
  --label_smoothing_factor 0.1 \
  --max_steps 20000 \
  --fp16
```

### How to Evaluate TAPEX Fine-tuned Models on TableQA

We provide fine-tuned model weights to reproduce our results. You can evaluate them using the following command:
> You can also replace `microsoft/tapex-base-finetuned-wikisql` with your local directory to evaluate your fine-tuned models. Notice that if the model has a larger size, you should reduce `per_device_eval_batch_size` to fit the memory requirement.

```bash
export EXP_NAME=wikisql_tapex_base_eval

python run_wikisql_with_tapex.py \
  --do_eval \
  --model_name_or_path microsoft/tapex-base-finetuned-wikisql \
  --output_dir $EXP_NAME \
  --per_device_eval_batch_size 4 \
  --predict_with_generate \
  --num_beams 5
```

## Table Fact Verification Tasks

### What is Table Fact Verification

![Example](https://table-pretraining.github.io/assets/tableft_task.png)

The task of Table Fact Verification (TableFV) is to empower machines to justify if a statement follows facts in a given table. The result is a binary classification belonging to `1` (entailed) or `0` (refused).

### How to Fine-tune TAPEX on TableFV

#### TAPEX-Base on TabFact

We provide a fine-tuning script of tapex for TableFV on the TabFact benchmark: [TabFact](https://github.com/wenhuchen/Table-Fact-Checking).

Here is how to run the script on the TabFact:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 16GB and 1 GPU card. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. Note that the `eval_accumulation_steps` is necessary, otherwise GPU memory leaks will occur during the evaluation.

```bash
export EXP_NAME=tabfact_tapex_base

python run_tabfact_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-base \
  --overwrite_output_dir \
  --per_device_train_batch_size 3 \
  --gradient_accumulation_steps 16 \
  --per_device_eval_batch_size 12 \
  --eval_accumulation_steps 6 \
  --warm_steps 1000 \
  --logging_steps 10 \
  --learning_rate 3e-5 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --evaluation_strategy steps \
  --weight_decay 1e-2 \
  --max_steps 30000 \
  --max_grad_norm 0.1
```

#### TAPEX-Large on TabFact

Here is how to run the script on the TabFact:
> The default hyper-parameter may allow you to reproduce our reported tapex-base results within the memory budget of 24GB and 1 GPU card. Sorry we cannot reduce the memory consumption since the model input in TabFact usually contains nearly ~1000 tokens. If you have more GPU cards, you could reduce `gradient_accumulation_steps` accordingly. Note that the `eval_accumulation_steps` is necessary, otherwise GPU memory leaks will occur during the evaluation.

```bash
export EXP_NAME=tabfact_tapex_large

python run_tabfact_with_tapex.py \
  --do_train \
  --do_eval \
  --output_dir $EXP_NAME \
  --model_name_or_path microsoft/tapex-large \
  --overwrite_output_dir \
  --per_device_train_batch_size 2 \
  --gradient_accumulation_steps 18 \
  --per_device_eval_batch_size 4 \
  --eval_accumulation_steps 12 \
  --warm_steps 1000 \
  --logging_steps 10 \
  --learning_rate 3e-5 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --evaluation_strategy steps \
  --weight_decay 1e-2 \
  --max_steps 30000 \
  --max_grad_norm 0.1
```

### How to Evaluate TAPEX Fine-tuned Models on TableFV

We provide fine-tuned model weights to reproduce our results. You can evaluate them using the following command:
> You can also replace `microsoft/tapex-base-finetuned-tabfact` with your local directory to evaluate your fine-tuned models. Notice that if the model has a larger size, you should reduce `per_device_eval_batch_size` to fit the memory requirement.

```bash
export EXP_NAME=tabfact_tapex_base_eval

python run_tabfact_with_tapex.py \
  --do_eval \
  --model_name_or_path microsoft/tapex-base-finetuned-tabfact \
  --output_dir $EXP_NAME \
  --per_device_eval_batch_size 12 \
  --eval_accumulation_steps 6
```

## Reproduced Results

We get the following results on the dev set of the benchmark with the previous commands:

| Task | Model Size | Metric | Result |
|:---:|:---:|:---:|:---:|
| WikiSQL (Weak) | Base | Denotation Accuracy | 88.1 |
| WikiSQL (Weak) | Large | Denotation Accuracy | 89.5 |
| WikiTableQuestion | Base | Denotation Accuracy | 47.1 |
| WikiTableQuestion | Large | Denotation Accuracy | 57.2 |
| TabFact | Base | Accuracy | 78.7 |
| TabFact | Large | Accuracy | 83.6 |
