metadata

pretty_name: Telugu Colloquial Corpus (Tokenized)
dataset_summary: >-
  A tokenized version of the Telugu Colloquial Corpus, containing examples of
  informal Telugu language use.
language:
  - te
task_categories:
  - text-generation
  - masked-language-modeling
  - sentiment-classification
tags:
  - telugu
  - colloquial
  - nlp
  - tokenization
license: cc-by-sa-4.0
size_categories:
  - 1K<n<10K

Telugu Colloquial Corpus (Tokenized)

This dataset is a tokenized version of the Telugu Colloquial Corpus (TeCC). It contains examples of informal, everyday Telugu language, including slang, regional variations, and conversational patterns.

Dataset Details

Language: Telugu (te)
Tokenization: Tokenized using the bert-base-multilingual-cased tokenizer from the transformers library.
Source: [Describe where the original data came from – e.g., collected from online forums, friends, family, manually transcribed conversations. Be as specific as possible.]
Purpose: This dataset is intended for use in NLP tasks such as:
- Training language models for Telugu to generate text.
- Fine-tuning pre-trained models for Telugu to improve understanding of informal language.
- Developing chatbots and other conversational AI applications for Telugu.
- Research on Telugu colloquial language.

Data Fields

The dataset contains the following features:

input_ids: The input token IDs (integers representing tokens in the tokenizer's vocabulary).
attention_mask: The attention mask (1 = attend to, 0 = ignore padding).
token_type_ids: Identifies which sequence a token belongs to (all 0s in this dataset, as it's a single-sequence task).

Data Collection and Preprocessing

The original data was collected from [Describe sources and methods used to collect the data]. It was then preprocessed as follows:

Tokenized using the bert-base-multilingual-cased tokenizer with these settings:
- padding: longest
- truncation: True
The following columns from the original JSON data were removed:
- Colloquial_Telugu
- Standard_Telugu
- English
- Source
- Notes
- Type
- Answer
- Meaning
- Author

[Optional: Describe any other cleaning, normalization, or data augmentation steps you performed.]

Ethical Considerations

[Discuss any ethical considerations related to the data, such as privacy, bias, cultural sensitivity, or potential misuse. Be transparent about any potential limitations of the dataset.]

Limitations

The dataset is relatively small (304 examples), which may limit the performance of models trained on it.
The dataset may contain biases present in the original data sources.
The coverage of different Telugu dialects and social groups may be uneven.
The bert-base-multilingual-cased tokenizer may not be ideal for Telugu. A tokenizer trained specifically on Telugu text could improve results.

Citation

Please cite this dataset as follows:

#contact Ankitha Chowdary [email protected]