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README.md
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---
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dataset_info:
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features:
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- name: instruction
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dtype: string
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- name: context
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dtype: string
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- name: response
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dtype: string
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- name: category
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dtype: string
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splits:
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- name: train
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num_bytes: 3243541
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num_examples: 4000
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download_size: 2050955
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dataset_size: 3243541
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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dataset_info:
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features:
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- name: instruction
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dtype: string
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- name: context
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dtype: string
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- name: response
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dtype: string
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- name: category
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dtype: string
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splits:
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- name: train
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num_bytes: 3243541
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num_examples: 4000
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download_size: 2050955
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dataset_size: 3243541
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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license: apache-2.0
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task_categories:
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- table-question-answering
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- question-answering
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- text-generation
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language:
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- en
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size_categories:
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- 1K<n<10K
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---
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# Databricks-Dolly-4k
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The resulting dataset contains **4000 samples** of the [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.
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This split of an even smaller subset is provided for very fast experimentation and evaluation of models when computational resources are highly limited or for quick prototyping.
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## Dataset Structure
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The dataset is provided as a `DatasetDict` with the following splits:
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* **`train`**: Contains 4000 samples.
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Each split contains the following features, identical to the original dataset:
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* `id`: The unique identifier for each sample.
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* `instruction`: The instruction or prompt for the task.
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* `response`: The response to the given instruction.
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* `context`: Additional context or information related to the instruction.
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* `source`: The source of the sample.
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## Usage
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You can easily load this split dataset using the `datasets` library:
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```python
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from datasets import load_dataset
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databricks_dolly_4k = load_dataset("Vishva007/Databricks-Dolly-4k")
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print(databricks_dolly_4k)
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print(databricks_dolly_4k["train"][0])
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```
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## Example Usage
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Here’s an example of how you might use this dataset in a Python script:
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```python
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from datasets import load_dataset
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# Load the dataset
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databricks_dolly_4k = load_dataset("Vishva007/Databricks-Dolly-4k")
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# Print the first sample in the training set
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print(databricks_dolly_4k["train"][0])
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# Access specific fields from the first sample
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sample = databricks_dolly_4k["train"][0]
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print(f"ID: {sample['id']}")
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print(f"Instruction: {sample['instruction']}")
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print(f"Response: {sample['response']}")
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print(f"Context: {sample['context']}")
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print(f"Source: {sample['source']}")
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```
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## Dataset Info
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### Features
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- `id`: The unique identifier for each sample.
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- `instruction`: The instruction or prompt for the task.
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- `response`: The response to the given instruction.
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- `context`: Additional context or information related to the instruction.
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- `source`: The source of the sample.
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### Splits
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- **`train`**: Contains 4000 samples.
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### Metadata
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- **Download Size**: 1310809668 bytes
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- **Dataset Size**: 1323148760.0 bytes
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## License
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This dataset is derived from the [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset, which is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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For more details about the original dataset, please refer to the [official documentation](https://huggingface.co/datasets/databricks/databricks-dolly-15k).
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---
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