Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: input_ids
-    sequence: int32
-  - name: token_type_ids
-    sequence: int8
-  - name: attention_mask
-    sequence: int8
-  splits:
-  - name: train
-    num_bytes: 120384
-    num_examples: 304
-  download_size: 10680
-  dataset_size: 120384
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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]