datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
errored_pythonThis is a subset of the python dataset provided but Ailurophile on Kaggle.
Important:Errors were introduced on purpose to try to test a sort of "specialized masking" in a realistic way.
Goal:The goal is to create a specialized agent, and add it to a chain with at least one other agent that generates code, and can hopefully "catch" any errors.
Inspiration:When working to generate datasets with other models, I found that even after multiple "passes" errors where still missed.
Out of curiosity… See the full description on the dataset page: https://huggingface.co/datasets/TacoPrime/errored_python.Referencing_Errors_Synthetic_EN
Synthetic Dataset for Automatic Error Correction in Referencing
This dataset includes 4,600 parallel sentences for Automatic Error Correction in referencing in German.It was synthetically created with gpt-4o-mini model according to the Institutional Guidelines of the Center for Translation Studies (CTS), University of Vienna.
Dataset Description
corrupted_sentence: the sentence containing the referencing error
clean_sentence: the correct version of the corrupted sentence… See the full description on the dataset page: https://huggingface.co/datasets/elizaveta-dev/Referencing_Errors_Synthetic_EN.Errors_Mod This dataset is the result of errors found in generated ecore files by different LLMs, mainly GTP4-Turbo and Llama3-70b-Instruct.
The errors have been classified into :
Wrong Type : This can occur if the generated type is non existant or used in a wrong way
Missing declaration : this can be due to either a missing declaration like xsi or nonexistant one
Start Token : this can mostly be due to start tag <?xml ..> <ecore ..> that are missing, happens when we can't read the file or error in… See the full description on the dataset page: https://huggingface.co/datasets/VeryMadSoul/Errors_Mod.granite-base-model-errors
Granite-1B Base Model Errors
Overview
This dataset contains 10 examples where the Granite-4.0-1B-Base language model produces incorrect or awkward outputs. Each row includes:
id: a unique identifier for each example
input: the prompt given to the model
expected_output: what the correct answer or completion should be
model_output: what the model actually produced
The dataset demonstrates common blind spots of a base causal language model, including factual errors, logic… See the full description on the dataset page: https://huggingface.co/datasets/thatgirltomiie/granite-base-model-errors.Referencing_Errors_Synthetic_DE
Synthetic Dataset for Automatic Error Correction in Referencing
This dataset includes 4,600 parallel sentences for Automatic Error Correction in referencing in German.It was synthetically created with gpt-4o-mini model according to the Institutional Guidelines of the Center for Translation Studies (CTS), University of Vienna.
Dataset Description
corrupted_sentence: the sentence containing the referencing error
clean_sentence: the correct version of the corrupted sentence… See the full description on the dataset page: https://huggingface.co/datasets/elizaveta-dev/Referencing_Errors_Synthetic_DE.Funny-Windows-Errors-Windows-Bibles