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distilabel:
  version: 1.5.3
pipeline:
  name: generate
  description: null
  steps:
  - step:
      name: load_data_from_hub_0
      resources:
        replicas: 1
        cpus: null
        gpus: null
        memory: null
        resources: null
      input_mappings: {}
      output_mappings:
        Job Role Description: anchor
      use_cache: false
      batch_size: 50
      repo_id: dnth/ssf-dataset
      split: train
      config: null
      revision: null
      streaming: false
      num_examples: 10
      storage_options: null
      runtime_parameters_info:
      - name: resources
        runtime_parameters_info:
        - name: replicas
          optional: true
          description: The number of replicas for the step.
        - name: cpus
          optional: true
          description: The number of CPUs assigned to each step replica.
        - name: gpus
          optional: true
          description: The number of GPUs assigned to each step replica.
        - name: memory
          optional: true
          description: The memory in bytes required for each step replica.
        - name: resources
          optional: true
          description: A dictionary containing names of custom resources and the number
            of those resources required for each step replica.
      - name: batch_size
        optional: true
        description: The number of rows that will contain the batches generated by
          the step.
      - name: repo_id
        optional: false
        description: The Hugging Face Hub repository ID of the dataset to load.
      - name: split
        optional: true
        description: The split of the dataset to load. Defaults to 'train'.
      - name: config
        optional: true
        description: The configuration of the dataset to load. This is optional and
          only needed if the dataset has multiple configurations.
      - name: revision
        optional: true
        description: The revision of the dataset to load. Defaults to the latest revision.
      - name: streaming
        optional: true
        description: Whether to load the dataset in streaming mode or not. Defaults
          to False.
      - name: num_examples
        optional: true
        description: The number of examples to load from the dataset. By default will
          load all examples.
      type_info:
        module: distilabel.steps.generators.huggingface
        name: LoadDataFromHub
    name: load_data_from_hub_0
  - step:
      name: hard_triplets
      resources:
        replicas: 1
        cpus: null
        gpus: null
        memory: null
        resources: null
      input_mappings: {}
      output_mappings: {}
      use_cache: true
      input_batch_size: 100
      llm:
        generation_kwargs:
          temperature: 0.6
          max_new_tokens: 512
        use_offline_batch_generation: false
        offline_batch_generation_block_until_done: null
        jobs_ids: null
        model: gpt-4.1-mini
        base_url: https://api.openai.com/v1
        default_headers: null
        max_retries: 6
        timeout: 120
        structured_output: null
        type_info:
          module: distilabel.models.llms.openai
          name: OpenAILLM
      group_generations: false
      add_raw_output: true
      add_raw_input: true
      num_generations: 1
      use_default_structured_output: false
      triplet: true
      action: paraphrase
      hard_negative: true
      context: "\n\n## Task Overview\nYou are tasked with generating realistic job\
        \ descriptions based on Singapore SkillsFuture Framework job descriptions.\
        \ Your goal is to create both positive and a few negative examples for training\
        \ a retrieval model.\n\n## Input Format\nYou will receive a job description\
        \ from the Singapore SkillsFuture Framework containing:\n- Job title (e.g.,\
        \ Audit Associate/Audit Assistant Associate)\n- Role responsibilities and\
        \ duties\n- Work environment and supervision structure\n- Required skills\
        \ and attributes\n- Professional conduct expectations\n\nThe text is a job\
        \ description from the Singapore SkillsFuture Framework. Your task is to generate\
        \ a realistic positive variation and a few hard negative variation.\n\n\n\
        Hard negative is a job description that is similar in some ways but is ultimately\
        \ incorrect for the given job. It must not be a simple paraphrase of a different,\
        \ unrelated job.\n\nChoose from these strategies for hard negatives:\n1. **Different\
        \ seniority level:** (e.g., Senior vs. Junior)\n2. **Different function:**\
        \ (e.g., Business Valuation vs. Risk Management)\n3. **Different technology\
        \ stack:** (e.g., Python/AWS vs. Scala/Azure for a Data Engineer role)\n4.\
        \ **Different job title, same responsibilities:** (e.g., \"Web Developer\"\
        \ vs. \"Frontend Engineer\")\n5. **Different industry jargon:** (e.g., \"\
        Project Manager\" in Tech vs. \"Project Manager\" in Construction)\n6. **Broad\
        \ vs. specialized role:** (e.g., a \"Data Scientist\" role for a \"Machine\
        \ Learning Engineer\" positive)\n\nExample:\nJob Role: \"Data Analyst\"\n\
        Positive: \"As a Data Analyst, you will interpret data and analyze results\
        \ using statistical techniques. You will work to develop and implement data\
        \ collection systems and other strategies that optimize statistical efficiency\
        \ and quality.\"\n\n\nNegative: \"As a Senior Data Analyst, you will lead\
        \ a team of junior analysts, manage end-to-end data projects, and present\
        \ insights to key stakeholders to drive business strategy.\"\n\n         \
        \ \"As a Financial Analyst, you will analyze financial data to provide insights\
        \ into business performance and support strategic decision-making.\"\n\n \
        \         \"As a Healthcare Data Analyst, you will manage electronic health\
        \ records and assist in clinical research data analysis.\"\n\n\nMake sure\
        \ to mix a few strategies in one negative query. you could also use different\
        \ strategies that is not mentioned above as long as it is a hard negative.\n\
        Makesure to have a few negatives in one query like in the example given and\
        \ make a new line for each new query.\nThe Job description of the negatives\
        \ should give the real job description to a real job title.\nThe query should\
        \ always include the job role and start with \"The <job role>\".\n"
      runtime_parameters_info:
      - name: resources
        runtime_parameters_info:
        - name: replicas
          optional: true
          description: The number of replicas for the step.
        - name: cpus
          optional: true
          description: The number of CPUs assigned to each step replica.
        - name: gpus
          optional: true
          description: The number of GPUs assigned to each step replica.
        - name: memory
          optional: true
          description: The memory in bytes required for each step replica.
        - name: resources
          optional: true
          description: A dictionary containing names of custom resources and the number
            of those resources required for each step replica.
      - name: input_batch_size
        optional: true
        description: The number of rows that will contain the batches processed by
          the step.
      - name: llm
        runtime_parameters_info:
        - name: generation_kwargs
          description: The kwargs to be propagated to either `generate` or `agenerate`
            methods within each `LLM`.
          keys:
          - name: max_new_tokens
            optional: true
          - name: logprobs
            optional: true
          - name: top_logprobs
            optional: true
          - name: echo
            optional: true
          - name: frequency_penalty
            optional: true
          - name: presence_penalty
            optional: true
          - name: temperature
            optional: true
          - name: top_p
            optional: true
          - name: stop
            optional: true
          - name: response_format
            optional: true
          - name: extra_body
            optional: true
        - name: use_offline_batch_generation
          optional: true
          description: Whether to use the `offline_batch_generate` method to generate
            the responses.
        - name: offline_batch_generation_block_until_done
          optional: true
          description: If provided, then polling will be done until the `ofline_batch_generate`
            method is able to retrieve the results. The value indicate the time to
            wait between each polling.
        - name: base_url
          optional: true
          description: The base URL to use for the OpenAI API requests.
        - name: api_key
          optional: true
          description: The API key to authenticate the requests to the OpenAI API.
        - name: default_headers
          optional: true
          description: The default headers to use for the OpenAI API requests.
        - name: max_retries
          optional: true
          description: The maximum number of times to retry the request to the API
            before failing.
        - name: timeout
          optional: true
          description: The maximum time in seconds to wait for a response from the
            API.
        - name: structured_output
          optional: true
          description: The structured output format to use across all the generations.
      - name: add_raw_output
        optional: true
        description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
          of the `distilabel_metadata` dictionary output column
      - name: add_raw_input
        optional: true
        description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`
          of the `distilabel_metadata` dictionary column
      - name: num_generations
        optional: true
        description: The number of generations to be produced per input.
      type_info:
        module: distilabel.steps.tasks.sentence_transformers
        name: GenerateSentencePair
    name: hard_triplets
  - step:
      name: easy_triplets
      resources:
        replicas: 1
        cpus: null
        gpus: null
        memory: null
        resources: null
      input_mappings: {}
      output_mappings: {}
      use_cache: true
      input_batch_size: 100
      llm:
        generation_kwargs:
          temperature: 0.6
          max_new_tokens: 512
        use_offline_batch_generation: false
        offline_batch_generation_block_until_done: null
        jobs_ids: null
        model: gpt-4.1-mini
        base_url: https://api.openai.com/v1
        default_headers: null
        max_retries: 6
        timeout: 120
        structured_output: null
        type_info:
          module: distilabel.models.llms.openai
          name: OpenAILLM
      group_generations: false
      add_raw_output: true
      add_raw_input: true
      num_generations: 1
      use_default_structured_output: false
      triplet: true
      action: paraphrase
      hard_negative: false
      context: "\n\n\n## Task Overview\nYou are tasked with generating realistic job\
        \ descriptions based on Singapore SkillsFuture Framework job descriptions.\
        \ Your goal is to create both positive and a few negative examples for training\
        \ a retrieval model.\n\n## Input Format\nYou will receive a job description\
        \ from the Singapore SkillsFuture Framework containing:\n- Job title (e.g.,\
        \ Audit Associate/Audit Assistant Associate)\n- Role responsibilities and\
        \ duties\n- Work environment and supervision structure\n- Required skills\
        \ and attributes\n- Professional conduct expectations\n\nThe text is a job\
        \ description from the Singapore SkillsFuture Framework. Your task is to generate\
        \ a realistic positive variation and a few easy negative variation.\n\nEasy\
        \ negative is a job description and title that is completely irrelevant to\
        \ the original job's industry, domain, or skills. It should be easy for the\
        \ model to distinguish from the positive document.\n\nChoose from these strategies\
        \ for easy negatives:\n1. Completely different industry (e.g., IT vs. Retail).\n\
        2. Unrelated skills and responsibilities.\n3. Completely different job function\
        \ (e.g., Software Engineering vs. Human Resources).\n\nExample:\nJob Role:\
        \ \"Data Analyst\"\n\nPositive: \"As a Data Analyst, you will interpret data\
        \ and analyze results using statistical techniques. You will work to develop\
        \ and implement data collection systems and other strategies that optimize\
        \ statistical efficiency and quality.\"\n\nEasy Negative : \"As a Retail Manager,\
        \ you will be responsible for overseeing daily store operations, managing\
        \ inventory, and training staff to provide excellent customer service.\"\n\
        \n                \"As a Human Resources Assistant, you will provide administrative\
        \ support to the HR department, assist with recruitment and onboarding processes,\
        \ and maintain employee records.\"\n\n                \"As a Professional\
        \ Chef, you will be responsible for managing kitchen staff, creating new menu\
        \ items, and ensuring all dishes are prepared to the highest quality standards.\"\
        \n\n\nMake sure, that there is a variety of easy negatives across the dataset,\
        \ using different strategies as outlined above.\nMakesure to have a few negatives\
        \ in one query like in the example given.\nThe query should always include\
        \ the job role and start with \"The <job role>\".\n"
      runtime_parameters_info:
      - name: resources
        runtime_parameters_info:
        - name: replicas
          optional: true
          description: The number of replicas for the step.
        - name: cpus
          optional: true
          description: The number of CPUs assigned to each step replica.
        - name: gpus
          optional: true
          description: The number of GPUs assigned to each step replica.
        - name: memory
          optional: true
          description: The memory in bytes required for each step replica.
        - name: resources
          optional: true
          description: A dictionary containing names of custom resources and the number
            of those resources required for each step replica.
      - name: input_batch_size
        optional: true
        description: The number of rows that will contain the batches processed by
          the step.
      - name: llm
        runtime_parameters_info:
        - name: generation_kwargs
          description: The kwargs to be propagated to either `generate` or `agenerate`
            methods within each `LLM`.
          keys:
          - name: max_new_tokens
            optional: true
          - name: logprobs
            optional: true
          - name: top_logprobs
            optional: true
          - name: echo
            optional: true
          - name: frequency_penalty
            optional: true
          - name: presence_penalty
            optional: true
          - name: temperature
            optional: true
          - name: top_p
            optional: true
          - name: stop
            optional: true
          - name: response_format
            optional: true
          - name: extra_body
            optional: true
        - name: use_offline_batch_generation
          optional: true
          description: Whether to use the `offline_batch_generate` method to generate
            the responses.
        - name: offline_batch_generation_block_until_done
          optional: true
          description: If provided, then polling will be done until the `ofline_batch_generate`
            method is able to retrieve the results. The value indicate the time to
            wait between each polling.
        - name: base_url
          optional: true
          description: The base URL to use for the OpenAI API requests.
        - name: api_key
          optional: true
          description: The API key to authenticate the requests to the OpenAI API.
        - name: default_headers
          optional: true
          description: The default headers to use for the OpenAI API requests.
        - name: max_retries
          optional: true
          description: The maximum number of times to retry the request to the API
            before failing.
        - name: timeout
          optional: true
          description: The maximum time in seconds to wait for a response from the
            API.
        - name: structured_output
          optional: true
          description: The structured output format to use across all the generations.
      - name: add_raw_output
        optional: true
        description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
          of the `distilabel_metadata` dictionary output column
      - name: add_raw_input
        optional: true
        description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`
          of the `distilabel_metadata` dictionary column
      - name: num_generations
        optional: true
        description: The number of generations to be produced per input.
      type_info:
        module: distilabel.steps.tasks.sentence_transformers
        name: GenerateSentencePair
    name: easy_triplets
  - step:
      name: easy_keywords
      resources:
        replicas: 1
        cpus: null
        gpus: null
        memory: null
        resources: null
      input_mappings: {}
      output_mappings: {}
      use_cache: true
      input_batch_size: 100
      llm:
        generation_kwargs:
          temperature: 0.6
          max_new_tokens: 512
        use_offline_batch_generation: false
        offline_batch_generation_block_until_done: null
        jobs_ids: null
        model: gpt-4.1-mini
        base_url: https://api.openai.com/v1
        default_headers: null
        max_retries: 6
        timeout: 120
        structured_output: null
        type_info:
          module: distilabel.models.llms.openai
          name: OpenAILLM
      group_generations: false
      add_raw_output: true
      add_raw_input: true
      num_generations: 1
      use_default_structured_output: false
      triplet: true
      action: query
      hard_negative: false
      context: '

        The text below is a job description from the Singapore SkillsFuture Framework.
        Your task is to generate two things:

        1. A list of key skills for the job (the positive).

        2. A list of unrelated skills (the easy negative).


        An **easy negative** is a set of keywords that is completely unrelated to
        the job description and its industry. It should be easy for the model to distinguish
        and serves to give the model a clear sense of what is definitively irrelevant.


        Example:

        Job Role: "Network Engineer"

        Positive Keywords: "Network administration, Cisco, firewall management, VPN"

        Easy Negative Keywords: "Retail inventory management, customer service, cash
        handling, visual merchandising"

        '
      runtime_parameters_info:
      - name: resources
        runtime_parameters_info:
        - name: replicas
          optional: true
          description: The number of replicas for the step.
        - name: cpus
          optional: true
          description: The number of CPUs assigned to each step replica.
        - name: gpus
          optional: true
          description: The number of GPUs assigned to each step replica.
        - name: memory
          optional: true
          description: The memory in bytes required for each step replica.
        - name: resources
          optional: true
          description: A dictionary containing names of custom resources and the number
            of those resources required for each step replica.
      - name: input_batch_size
        optional: true
        description: The number of rows that will contain the batches processed by
          the step.
      - name: llm
        runtime_parameters_info:
        - name: generation_kwargs
          description: The kwargs to be propagated to either `generate` or `agenerate`
            methods within each `LLM`.
          keys:
          - name: max_new_tokens
            optional: true
          - name: logprobs
            optional: true
          - name: top_logprobs
            optional: true
          - name: echo
            optional: true
          - name: frequency_penalty
            optional: true
          - name: presence_penalty
            optional: true
          - name: temperature
            optional: true
          - name: top_p
            optional: true
          - name: stop
            optional: true
          - name: response_format
            optional: true
          - name: extra_body
            optional: true
        - name: use_offline_batch_generation
          optional: true
          description: Whether to use the `offline_batch_generate` method to generate
            the responses.
        - name: offline_batch_generation_block_until_done
          optional: true
          description: If provided, then polling will be done until the `ofline_batch_generate`
            method is able to retrieve the results. The value indicate the time to
            wait between each polling.
        - name: base_url
          optional: true
          description: The base URL to use for the OpenAI API requests.
        - name: api_key
          optional: true
          description: The API key to authenticate the requests to the OpenAI API.
        - name: default_headers
          optional: true
          description: The default headers to use for the OpenAI API requests.
        - name: max_retries
          optional: true
          description: The maximum number of times to retry the request to the API
            before failing.
        - name: timeout
          optional: true
          description: The maximum time in seconds to wait for a response from the
            API.
        - name: structured_output
          optional: true
          description: The structured output format to use across all the generations.
      - name: add_raw_output
        optional: true
        description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
          of the `distilabel_metadata` dictionary output column
      - name: add_raw_input
        optional: true
        description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`
          of the `distilabel_metadata` dictionary column
      - name: num_generations
        optional: true
        description: The number of generations to be produced per input.
      type_info:
        module: distilabel.steps.tasks.sentence_transformers
        name: GenerateSentencePair
    name: easy_keywords
  - step:
      name: hard_keywords
      resources:
        replicas: 1
        cpus: null
        gpus: null
        memory: null
        resources: null
      input_mappings: {}
      output_mappings: {}
      use_cache: true
      input_batch_size: 100
      llm:
        generation_kwargs:
          temperature: 0.6
          max_new_tokens: 512
        use_offline_batch_generation: false
        offline_batch_generation_block_until_done: null
        jobs_ids: null
        model: gpt-4.1-mini
        base_url: https://api.openai.com/v1
        default_headers: null
        max_retries: 6
        timeout: 120
        structured_output: null
        type_info:
          module: distilabel.models.llms.openai
          name: OpenAILLM
      group_generations: false
      add_raw_output: true
      add_raw_input: true
      num_generations: 1
      use_default_structured_output: false
      triplet: true
      action: query
      hard_negative: true
      context: '

        The text below is a job description from the Singapore SkillsFuture Framework.
        Your task is to generate two things:

        1. A list of key skills for the job (the positive).

        2. A list of unrelated skills (the easy negative).


        An **easy negative** is a set of keywords that is completely unrelated to
        the job description and its industry. It should be easy for the model to distinguish
        and serves to give the model a clear sense of what is definitively irrelevant.


        Example:

        Job Role: "Network Engineer"

        Positive Keywords: "Network administration, Cisco, firewall management, VPN"

        Easy Negative Keywords: "Retail inventory management, customer service, cash
        handling, visual merchandising"

        '
      runtime_parameters_info:
      - name: resources
        runtime_parameters_info:
        - name: replicas
          optional: true
          description: The number of replicas for the step.
        - name: cpus
          optional: true
          description: The number of CPUs assigned to each step replica.
        - name: gpus
          optional: true
          description: The number of GPUs assigned to each step replica.
        - name: memory
          optional: true
          description: The memory in bytes required for each step replica.
        - name: resources
          optional: true
          description: A dictionary containing names of custom resources and the number
            of those resources required for each step replica.
      - name: input_batch_size
        optional: true
        description: The number of rows that will contain the batches processed by
          the step.
      - name: llm
        runtime_parameters_info:
        - name: generation_kwargs
          description: The kwargs to be propagated to either `generate` or `agenerate`
            methods within each `LLM`.
          keys:
          - name: max_new_tokens
            optional: true
          - name: logprobs
            optional: true
          - name: top_logprobs
            optional: true
          - name: echo
            optional: true
          - name: frequency_penalty
            optional: true
          - name: presence_penalty
            optional: true
          - name: temperature
            optional: true
          - name: top_p
            optional: true
          - name: stop
            optional: true
          - name: response_format
            optional: true
          - name: extra_body
            optional: true
        - name: use_offline_batch_generation
          optional: true
          description: Whether to use the `offline_batch_generate` method to generate
            the responses.
        - name: offline_batch_generation_block_until_done
          optional: true
          description: If provided, then polling will be done until the `ofline_batch_generate`
            method is able to retrieve the results. The value indicate the time to
            wait between each polling.
        - name: base_url
          optional: true
          description: The base URL to use for the OpenAI API requests.
        - name: api_key
          optional: true
          description: The API key to authenticate the requests to the OpenAI API.
        - name: default_headers
          optional: true
          description: The default headers to use for the OpenAI API requests.
        - name: max_retries
          optional: true
          description: The maximum number of times to retry the request to the API
            before failing.
        - name: timeout
          optional: true
          description: The maximum time in seconds to wait for a response from the
            API.
        - name: structured_output
          optional: true
          description: The structured output format to use across all the generations.
      - name: add_raw_output
        optional: true
        description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
          of the `distilabel_metadata` dictionary output column
      - name: add_raw_input
        optional: true
        description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`
          of the `distilabel_metadata` dictionary column
      - name: num_generations
        optional: true
        description: The number of generations to be produced per input.
      type_info:
        module: distilabel.steps.tasks.sentence_transformers
        name: GenerateSentencePair
    name: hard_keywords
  connections:
  - from: load_data_from_hub_0
    to:
    - hard_triplets
    - easy_triplets
    - easy_keywords
    - hard_keywords
  - from: hard_triplets
    to: []
  - from: easy_triplets
    to: []
  - from: easy_keywords
    to: []
  - from: hard_keywords
    to: []
  routing_batch_functions: []
  type_info:
    module: distilabel.pipeline.local
    name: Pipeline
requirements: []