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| Original file line number | Diff line number | Diff line change |
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| ::::{tab-set} | ||
| <div class="start-page__intro" markdown="1"> | ||
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| :::{tab-item} Feedback datasets | ||
| # Welcome to | ||
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| ```python | ||
| # install datasets library with pip install datasets | ||
| import argilla as rg | ||
| from datasets import load_dataset | ||
| ## Argilla is a platform to build high-quality AI datasets | ||
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| If you need support join the [Argilla Slack community](https://join.slack.com/t/rubrixworkspace/shared_invite/zt-whigkyjn-a3IUJLD7gDbTZ0rKlvcJ5g) | ||
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| </div> | ||
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| <div class="start-page__content" markdown="1"> | ||
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| Get started by publishing your first dataset. | ||
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| # load an Argilla Feedback Dataset from the Hugging Face Hub | ||
| # look for other datasets at https://huggingface.co/datasets?other=argilla | ||
| dataset = rg.FeedbackDataset.from_huggingface("argilla/oasst_response_quality", split="train") | ||
| ### 1. Open an IDE, Jupyter or Collab | ||
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| # push the dataset to Argilla | ||
| dataset.push_to_argilla("oasst_response_quality") | ||
| If you're a Collab user, you can directly use our [introductory tutorial](https://colab.research.google.com/github/argilla-io/argilla/blob/develop/docs/_source/getting_started/quickstart_workflow_feedback.ipynb). | ||
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| ### 2. Install the SDK with pip | ||
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| To work with Argilla datasets, you need to use the Argilla SDK. You can install the SDK with pip as follows: | ||
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| ```sh | ||
| pip install argilla -U | ||
| ``` | ||
| ::: | ||
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| :::{tab-item} Other datasets | ||
| ### 3. Connect to your Argilla server | ||
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| Get your `ARGILLA_API_URL`: | ||
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| - If you are using Docker, it is the URL shown in your browser (by default `http://localhost:6900`) | ||
| - If you are using HF Spaces, it should be constructed as follows: `https://[your-owner-name]-[your_space_name].hf.space` | ||
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| Get your `ARGILLA_API_KEY` you find in ["My settings"](/user-settings) and copy the API key. | ||
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| Make sure to replace `ARGILLA_API_URL` and `ARGILLA_API_KEY` in the code below. If you are using a private HF Space, you need to specify your `HF_TOKEN` which can be found [here](https://huggingface.co/settings/tokens). | ||
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| ```python | ||
| # install datasets library with pip install datasets | ||
| import argilla as rg | ||
| from datasets import load_dataset | ||
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| # load dataset from the hub | ||
| dataset = load_dataset("argilla/gutenberg_spacy-ner", split="train") | ||
| rg.init( | ||
| api_url="ARGILLA_API_URL", | ||
| api_key="ARGILLA_API_KEY", | ||
| # extra_headers={"Authorization": f"Bearer {"HF_TOKEN"}"} | ||
| ) | ||
| ``` | ||
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| ### 4. Create your first dataset | ||
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| Specify a workspace where the dataset will be created. Check your workspaces in ["My settings"](/user_settings). To create a new workspace, check the [docs](https://docs.argilla.io/en/latest/getting_started/installation/configurations/workspace_management.html). | ||
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| Create a Dataset with two labels ("sadness" and "joy"). Don't forget to replace "<your-workspace>". Here, we are using a task template, check the docs to [create a fully custom dataset](https://docs.argilla.io/en/latest/practical_guides/create_update_dataset/create_dataset.html). | ||
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| ```python | ||
| dataset = rg.FeedbackDataset.for_text_classification( | ||
| labels=["sadness", "joy"], | ||
| multi_label=False, | ||
| use_markdown=True, | ||
| guidelines=None, | ||
| metadata_properties=None, | ||
| vectors_settings=None, | ||
| ) | ||
| dataset.push_to_argilla(name="my-first-dataset", workspace="<your-workspace>") | ||
| ``` | ||
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| ### 5. Add records | ||
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| # read in dataset, assuming its a dataset for token classification | ||
| dataset_rg = rg.read_datasets(dataset, task="TokenClassification") | ||
| Create a list with the records you want to add. Ensure that you match the fields with the ones specified in the previous step. | ||
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| # log the dataset | ||
| rg.log(dataset_rg, "gutenberg_spacy-ner") | ||
| You can also use `pandas` or `load_dataset` to [read an existing dataset and create records from it](https://docs.argilla.io/en/latest/practical_guides/create_update_dataset/records.html#add-records). | ||
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| ```python | ||
| records = [ | ||
| rg.FeedbackRecord( | ||
| fields={ | ||
| "text": "I am so happy today", | ||
| }, | ||
| ), | ||
| rg.FeedbackRecord( | ||
| fields={ | ||
| "text": "I feel sad today", | ||
| }, | ||
| ) | ||
| ] | ||
| dataset.add_records(records) | ||
| ``` | ||
| ::: | ||
| :::: | ||
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| </div> |
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