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input_ids
sequencelengths 64
64
| token_type_ids
sequencelengths 64
64
| attention_mask
sequencelengths 64
64
|
|---|---|---|
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End of preview. Expand
in Data Studio
YAML Metadata
Warning:
The task_categories "masked-language-modeling" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other
YAML Metadata
Warning:
The task_categories "sentiment-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other
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-casedtokenizer from thetransformerslibrary. - 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-casedtokenizer with these settings:padding: longesttruncation: True
- The following columns from the original JSON data were removed:
Colloquial_TeluguStandard_TeluguEnglishSourceNotesTypeAnswerMeaningAuthor
[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-casedtokenizer 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]
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