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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 90, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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# LiteCoder Experiment Reproducing package
- To run the pre-train objective use the following scripts:
- Reproduce LiteCoder with all objectives:
- Navigate the folder `Pre-training` containing the `LiteCoder.py` file
- Then, run `Python LiteCoder.py --train-tt --train-cs --train-pd`
- The pretrained model is released on [hugging face](https://huggingface.co/LiteCoder/LiteCoder_pretrained), therefore it automatically loads.
- To run the ablation studies:
- Ablation 1: `Python LiteCoder.py --train-tt`
- Ablation 2: `Python LiteCoder.py --train-tt --train-cs`
- Ablation 3: `Python LiteCoder.py --train-tt --train-cs --train-pd`
- To `Fine-tuning` LiteCoder on downstream tasks:
- Navigate to the `Fine-tuning` folder and then `Downstream task` folder:
- Code Clone Detection:
- Follow the instruction of `readme.md` file.
- Code Translation:
- Run `setup.sh` file.
- Navigate to the `scripts/finetune` and run `translate.sh` file.
- To extract the programming language features (i.e., `token type`, `code sememe`, and `code dependencies`)
- We used open source datasets to extract language features. we released the extracted datasets on the Hugging Face:
- `LT_Java` : [LiteCoder/LT_Java](https://huggingface.co/datasets/LiteCoder/LT_Java)
- `LT_Python` : [LiteCoder/LT_Python](https://huggingface.co/datasets/LiteCoder/LT_Python)
- `LT_Java_Dependency` : [LiteCoder/LT_Java_Dependency](https://huggingface.co/datasets/LiteCoder/LT_Java_Dependency)
- Navigate to the utils directory:
- Use either the `Java` or `Python` notebook file to run over your dataset.
- Run the cells, for which, you want to extract the features.
- Dependencies:
- Feature extraction dependencies:
```bash
- pip install ast-comments
- pip install ast
- pip install javalang
- pip install tree-sitter
- Model training dependencies:
``` bash
- pip install transformers
- pip install datasets
- pip install pytorch_lightning
- pip install torch
- Install the required packages:
``` bash
- pip install -r requirements.txt
absl-py==1.4.0
accelerate==0.20.3
aiohttp==3.8.4
aiosignal==1.3.1
antlr4-python3-runtime==4.9.3
anyio==3.7.1
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
array-record==0.4.0
arrow==1.2.3
asgiref==3.6.0
astor==0.8.1
astroid==2.6.6
asttokens==2.2.1
astunparse==1.6.3
async-lru==2.0.3
async-timeout==4.0.2
attrs==23.1.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.12.2
bitsandbytes==0.39.1
bitsandbytes-cuda117==0.26.0.post2
bleach==6.0.0
boltons @ file:///croot/boltons_1677628692245/work
brotlipy==0.7.0
cachetools==5.3.1
certifi @ file:///croot/certifi_1690232220950/work/certifi
cffi @ file:///croot/cffi_1670423208954/work
chardet==4.0.0
charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
chex==0.1.7
click==8.1.6
clu==0.0.9
cmake==3.27.0
cohesion==1.0.0
colorama==0.4.6
comm==0.1.3
conda @ file:///croot/conda_1690381753336/work
conda-content-trust @ file:///tmp/abs_5952f1c8-355c-4855-ad2e-538535021ba5h26t22e5/croots/recipe/conda-content-trust_1658126371814/work
conda-package-handling @ file:///croot/conda-package-handling_1672865015732/work
conda_package_streaming @ file:///croot/conda-package-streaming_1670508151586/work
contextlib2==21.6.0
End of preview.
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LiteCoder Experiment Reproducing package

  • To run the pre-train objective use the following scripts:

    • Reproduce LiteCoder with all objectives:

      • Navigate the folder Pre-training containing the LiteCoder.py file

      • Then, run Python LiteCoder.py --train-tt --train-cs --train-pd

        • The pretrained model is released on hugging face, therefore it automatically loads.

    • To run the ablation studies:

      • Ablation 1: Python LiteCoder.py --train-tt
      • Ablation 2: Python LiteCoder.py --train-tt --train-cs
      • Ablation 3: Python LiteCoder.py --train-tt --train-cs --train-pd

  • To Fine-tuning LiteCoder on downstream tasks:

    • Navigate to the Fine-tuning folder and then Downstream task folder:

      • Code Clone Detection:

        • Follow the instruction of readme.md file.

      • Code Translation:

        • Run setup.sh file.
        • Navigate to the scripts/finetune and run translate.sh file.

  • To extract the programming language features (i.e., token type, code sememe, and code dependencies)

    • We used open source datasets to extract language features. we released the extracted datasets on the Hugging Face:

    • Navigate to the utils directory:

      • Use either the Java or Python notebook file to run over your dataset.
      • Run the cells, for which, you want to extract the features.

  • Dependencies:

    • Feature extraction dependencies:

      - pip install ast-comments
- pip install ast
- pip install javalang
- pip install tree-sitter

    • Model training dependencies:

      - pip install transformers 
- pip install datasets
- pip install pytorch_lightning
- pip install torch

    • Or pip install -r requirements.txt

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