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Upload pipeline.yaml with huggingface_hub

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  1. pipeline.yaml +62 -0
pipeline.yaml ADDED
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+ distilabel:
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+ version: 1.0.0.b0
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+ pipeline:
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+ name: DEITA
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+ description: ''
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+ steps:
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+ - step:
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+ name: load_data
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+ input_mappings: {}
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+ output_mappings: {}
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+ batch_size: 100
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+ repo_id: distilabel-internal-testing/deita-after-embeddings
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+ split: train
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+ config: null
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+ runtime_parameters_info:
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+ - name: repo_id
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+ optional: false
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+ description: The Hugging Face Hub repository ID of the dataset to load.
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+ - name: split
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+ optional: true
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+ description: The split of the dataset to load. Defaults to 'train'.
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+ - name: config
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+ optional: true
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+ description: The configuration of the dataset to load. This is optional and
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+ only needed if the dataset has multiple configurations.
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+ type_info:
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+ module: distilabel.steps.generators.huggingface
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+ name: LoadHubDataset
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+ name: load_data
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+ - step:
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+ name: deita_filtering
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+ input_mappings: {}
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+ output_mappings: {}
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+ input_batch_size: 50
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+ data_budget: 500
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+ diversity_threshold: 0.04
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+ normalize_embeddings: false
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+ runtime_parameters_info:
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+ - name: data_budget
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+ optional: false
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+ description: The desired size of the dataset after filtering.
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+ - name: diversity_threshold
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+ optional: true
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+ description: If a row has a cosine distance with respect to it's nearest neighbor
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+ greater than this value, it will be included in the filtered dataset.
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+ - name: normalize_embeddings
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+ optional: true
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+ description: Whether to normalize the embeddings before computing the cosine
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+ distance.
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+ type_info:
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+ module: distilabel.steps.deita
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+ name: DeitaFiltering
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+ name: deita_filtering
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+ connections:
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+ - from: load_data
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+ to:
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+ - deita_filtering
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+ - from: deita_filtering
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+ to: []
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+ type_info:
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+ module: distilabel.pipeline.local
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+ name: Pipeline