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| license: apache-2.0 |
| pretty_name: RONSTEINReadme |
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| # Dataset Card for Dataset Name |
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| <!-- Provide a quick readdatasets summary of the dataset. --> |
| readdataset |
| This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). |
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| ## Dataset Details |
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| ### Dataset Description |
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| <!-- Provide a longer summary of what this dataset is. --> |
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| - **Curated by:** [More information Needed] |
| - **Funded by [optional]:** [More Information Needed] |
| - **Shared by [optional]:** [More Information Needed] |
| - **Language(s) (NLP):** [More Information Needed] |
| - **License:** [More Information Needed] |
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| ### Dataset Sources [optional] |
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| <!-- Provide the basic links for the dataset. --> |
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| - **Repository:** [More Information Needed] |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** [More Information Needed] |
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| ## Uses |
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| <!-- Address questions around how the dataset is intended to be used. --> |
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| ### Direct Use |
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| <!-- This section describes suitable use cases for the dataset. --> |
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| [More Information Needed] |
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| ### Out-of-Scope Use |
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| <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> |
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| [More Information Needed] |
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| ## Dataset Structure |
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| <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
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| [More Information Needed] |
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| ## Dataset Creation |
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| ### Curation Rationale |
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| <!-- Motivation for the creation of this dataset. --> |
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| [More Information Needed] |
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| ### Source Data |
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| <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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| #### Data Collection and Processing |
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| <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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| [More Information Needed] |
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| #### Who are the source data producers? |
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| <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
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| [More Information Needed] |
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| ### Annotations [optional] |
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| <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
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| #### Annotation process |
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| <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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| [More Information Needed] |
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| #### Who are the annotators? |
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| <!-- This section describes the people or systems who created the annotations. --> |
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| [More Information Needed] |
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| #### Personal and Sensitive Information |
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| <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> |
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| [More Information Needed] |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| [More Information Needed] |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| [More Information Needed] |
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| **APA:** |
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| [More Information Needed] |
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| ## Glossary [optional] |
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| <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> |
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| [More Information Needed] |
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| ## More Information [optional] |
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| [More Information Needed] |
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| ## Dataset Card Authors [optional] |
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| [More Information Needed] |
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| ## Dataset Card Contact |
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| [More Information Needeprompt to make my bot read my data sets in hugging face |
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| **Prompt to Make a Bot Read and Process Datasets in Hugging Face** |
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| "Develop a bot that can seamlessly read, process, and utilize datasets hosted on Hugging Face for training and inference tasks. The bot should be capable of handling various dataset formats (e.g., prompt-completion, preference datasets) and perform the following functions: |
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| ### **Core Functionalities** |
| 1. **Dataset Loading**: |
| - Use `datasets.load_dataset()` to load datasets from the Hugging Face Hub by their short names (e.g., `"huggingface-tools/default-prompts"` or custom datasets like `"MohamedRashad/ChatGPT-prompts"`)[4][6]. |
| - Automatically detect the dataset format (e.g., prompt-only, prompt-completion, preference). |
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| 2. **Processing Datasets**: |
| - Handle different dataset transformations based on their type: |
| - **Prompt-Completion Datasets**: Concatenate `prompt` and `completion` columns into a single `text` column for training language models[1]. |
| - **Preference Datasets**: Extract the `prompt` and retain only the `"chosen"` column for training or evaluation tasks[1]. |
| - **Implicit Prompt Datasets**: Convert implicit prompts into explicit ones using concatenation methods[1]. |
| - Provide options to rename, reorder, or remove columns as needed for downstream tasks[8]. |
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| 3. **Dataset Analysis**: |
| - Summarize the dataset structure (e.g., number of rows, columns, and data types). |
| - Provide sample rows for quick inspection. |
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| 4. **Integration with Models**: |
| - Preprocess datasets into tokenized formats compatible with Hugging Face Transformers models. |
| - Enable soft-prompting methods for fine-tuning causal language models (e.g., GPT-based models) using datasets[2]. |
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| 5. **Custom Dataset Operations**: |
| - Allow users to upload custom datasets in JSON, CSV, or other supported formats. |
| - Provide preprocessing options like splitting datasets into training, validation, and test sets[8]. |
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| ### **Advanced Features** |
| 1. **Dynamic Prompt Engineering**: |
| - Automatically generate prompts or modify existing ones for specific tasks like classification or summarization. |
| - Support prompt chaining methods to enhance dataset utility. |
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| 2. **Interactive Dataset Exploration**: |
| - Enable users to interactively explore datasets via a dashboard or command-line interface. |
| - Allow filtering by specific criteria (e.g., keywords in prompts or completions). |
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| 3. **Error Handling**: |
| - Implement robust error detection for missing columns (e.g., `prompt`, `chosen`) or unsupported formats. |
| - Suggest fixes or transformations for incompatible datasets. |
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| ### **Implementation Example** |
| Here’s an example workflow for the bot: |
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| ```python |
| from datasets import load_dataset |
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| # Load dataset |
| dataset = load_dataset("huggingface-tools/default-prompts") |
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| # Process dataset: Convert prompt-completion format to text column |
| def concat_prompt_completion(example): |
| return {"text": example["prompt"] + example["completion"]} |
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| processed_dataset = dataset.map(concat_prompt_completion, remove_columns=["prompt", "completion"]) |
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| # Tokenize for model training |
| from transformers import AutoTokenizer |
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| tokenizer = AutoTokenizer.from_pretrained("gpt-neo-125M") |
| tokenized_dataset = processed_dataset.map(lambda x: tokenizer(x["text"], truncation=True), batched=True) |
| ``` |
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| ### **User-Friendly Features** |
| - Provide clear documentation and examples on how to load and process datasets. |
| - Include pre-configured pipelines for common tasks like summarization, classification, or preference modeling. |
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| This bot will simplify working with Hugging Face datasets while ensuring compatibility with various machine learning workflows." |
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| Citations: |
| [1] Dataset formats and types - Hugging Face https://huggingface.co/docs/trl/main/en/dataset_formats |
| [2] Prompt-based methods - Hugging Face https://huggingface.co/docs/peft/en/task_guides/prompt_based_methods |
| [3] huggingface-tools/default-prompts · Datasets at Hugging Face https://huggingface.co/datasets/huggingface-tools/default-prompts |
| [4] Loading a Dataset — datasets 1.8.0 documentation - Hugging Face https://huggingface.co/docs/datasets/v1.8.0/loading_datasets.html |
| [5] MohamedRashad/ChatGPT-prompts · Datasets at Hugging Face https://huggingface.co/datasets/MohamedRashad/ChatGPT-prompts |
| [6] Datasets - Hugging Face https://huggingface.co/docs/datasets/en/index |
| [7] Dataset formats and types - Hugging Face https://huggingface.co/docs/trl/en/dataset_formats |
| [8] Process - Hugging Face https://huggingface.co/docs/datasets/en/process |
| d] |
