### Clone Repository and Set Up Environment

Source: https://github.com/csebuetnlp/banglabert/blob/master/README.md

Clone the BanglaBERT repository and set up a Conda environment with specified PyTorch and CUDA versions. Activate the environment and run the setup script.

```bash
git clone https://github.com/csebuetnlp/banglabert
cd banglabert/
conda create python==3.7.9 pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch -p ./env
conda activate ./env # or source activate ./env (for older versions of anaconda)
bash setup.sh 
```

--------------------------------

### Finetune BanglaBERT for Question Answering

Source: https://github.com/csebuetnlp/banglabert/blob/master/question_answering/README.md

Use this command to finetune the BanglaBERT model on a single GPU. Ensure your dataset is in the specified directory and configure training parameters as needed.

```bash
$ python ./question_answering.py \
    --model_name_or_path "csebuetnlp/banglabert" \
    --dataset_dir "sample_inputs/" \
    --output_dir "outputs/" \
    --learning_rate=2e-5 \
    --warmup_ratio 0.1 \
    --gradient_accumulation_steps 2 \
    --weight_decay 0.1 \
    --lr_scheduler_type "linear"  \
    --per_device_train_batch_size=16 \
    --per_device_eval_batch_size=16 \
    --max_seq_length 512 \
    --logging_strategy "epoch" \
    --save_strategy "epoch" \
    --evaluation_strategy "epoch" \
    --num_train_epochs=3 \
    --do_train --do_eval
```

--------------------------------

### Finetune Token Classification Model

Source: https://github.com/csebuetnlp/banglabert/blob/master/token_classification/README.md

Finetunes a token classification model on a single GPU. Requires specifying model name, dataset directory, output directory, and various training hyperparameters.

```bash
python ./token_classification.py \
    --model_name_or_path "csebuetnlp/banglabert" \
    --dataset_dir "sample_inputs/" \
    --output_dir "outputs/" \
    --learning_rate=2e-5 \
    --warmup_ratio 0.1 \
    --gradient_accumulation_steps 2 \
    --weight_decay 0.1 \
    --lr_scheduler_type "linear"  \
    --per_device_train_batch_size=16 \
    --per_device_eval_batch_size=16 \
    --max_seq_length 512 \
    --logging_strategy "epoch" \
    --save_strategy "epoch" \
    --evaluation_strategy "epoch" \
    --num_train_epochs=3 \
    --do_train --do_eval
```

--------------------------------

### Evaluate Sequence Classification Model

Source: https://github.com/csebuetnlp/banglabert/blob/master/sequence_classification/README.md

Use this command to calculate metrics on the test set or perform inference on raw data. Replace `<path/to/trained/model>` with the actual path to your trained model.

```bash
python ./sequence_classification.py \
    --model_name_or_path <path/to/trained/model> \
    --dataset_dir "sample_inputs/single_sequence/jsonl" \
    --output_dir "outputs/" \
    --per_device_eval_batch_size=16 \
    --overwrite_output_dir \
    --do_predict
```

--------------------------------

### Evaluate BanglaBERT Question Answering Model

Source: https://github.com/csebuetnlp/banglabert/blob/master/question_answering/README.md

This command calculates metrics on the test set or performs inference on raw data. It requires the path to your trained model and dataset directory.

```bash
$ python ./question_answering.py \
    --model_name_or_path <path/to/trained/model> \
    --dataset_dir "sample_inputs/" \
    --output_dir "outputs/" \
    --per_device_eval_batch_size=16 \
    --overwrite_output_dir \
    --do_predict
```

--------------------------------

### Finetune Sequence Classification Model

Source: https://github.com/csebuetnlp/banglabert/blob/master/sequence_classification/README.md

Use this command to fine-tune a sequence classification model on a single GPU. Ensure your dataset directory contains train, validation, and test files.

```bash
python ./sequence_classification.py \
    --model_name_or_path "csebuetnlp/banglabert" \
    --dataset_dir "sample_inputs/single_sequence/jsonl" \
    --output_dir "outputs/" \
    --learning_rate=2e-5 \
    --warmup_ratio 0.1 \
    --gradient_accumulation_steps 2 \
    --weight_decay 0.1 \
    --lr_scheduler_type "linear"  \
    --per_device_train_batch_size=16 \
    --per_device_eval_batch_size=16 \
    --max_seq_length 512 \
    --logging_strategy "epoch" \
    --save_strategy "epoch" \
    --evaluation_strategy "epoch" \
    --num_train_epochs=3 \
    --do_train --do_eval
```

--------------------------------

### Evaluate Token Classification Model

Source: https://github.com/csebuetnlp/banglabert/blob/master/token_classification/README.md

Calculates metrics on the test set or performs inference on raw data. Requires specifying the path to the trained model, dataset directory, and output directory.

```bash
python ./token_classification.py \
    --model_name_or_path <path/to/trained/model> \
    --dataset_dir "sample_inputs/" \
    --output_dir "outputs/" \
    --per_device_eval_batch_size=16 \
    --overwrite_output_dir \
    --do_predict
```

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