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Merge branch 'main' into test-disable-transformer-containers-in-docs-ci
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‎.github/workflows/api_inference_build_documentation.yml‎

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name: Build Inference API documentation
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on:
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push:
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paths:
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- "docs/api-inference/**"
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branches:
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- main
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jobs:
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build:
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uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
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with:
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commit_sha: ${{ github.sha }}
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package: hub-docs
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package_name: api-inference
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path_to_docs: hub-docs/docs/api-inference/
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additional_args: --not_python_module
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secrets:
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hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

‎.github/workflows/api_inference_build_pr_documentation.yml‎

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name: Build Inference API PR Documentation
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on:
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pull_request:
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paths:
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- "docs/api-inference/**"
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concurrency:
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group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
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cancel-in-progress: true
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jobs:
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build:
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uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
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with:
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commit_sha: ${{ github.event.pull_request.head.sha }}
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pr_number: ${{ github.event.number }}
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package: hub-docs
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package_name: api-inference
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path_to_docs: hub-docs/docs/api-inference/
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additional_args: --not_python_module

‎.github/workflows/api_inference_upload_pr_documentation.yml‎

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name: Upload Inference API PR Documentation
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on:
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workflow_run:
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workflows: ["Build Inference API PR Documentation"]
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types:
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- completed
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jobs:
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build:
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uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
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with:
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package_name: api-inference
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secrets:
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hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
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comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

‎.github/workflows/sagemaker_upload_pr_documentation.yml‎

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on:
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workflow_run:
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workflows: ["Build sagemaker PR DocumentationDocumentation"]
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workflows: ["Build sagemaker PR Documentation"]
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types:
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- completed
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‎.github/workflows/trufflehog.yml‎

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name: Secret Leaks
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on:
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push:
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permissions:
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contents: read
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jobs:
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trufflehog:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Secret Scanning
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uses: trufflesecurity/trufflehog@main

‎.gitignore‎

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node_modules/
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__pycache__/
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.vscode/
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.idea/
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‎datasetcard.md‎

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- {subtask_0} # Example: extractive-qa
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- {subtask_1} # Example: multi-class-image-classification
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paperswithcode_id: {paperswithcode_id} # Dataset id on PapersWithCode (from the URL). Example for SQuAD: squad
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configs: # Optional for datasets with multiple configurations like glue.
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- {config_0} # Example for glue: sst2
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- {config_1} # Example for glue: cola
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configs: # Optional. This can be used to pass additional parameters to the dataset loader, such as `data_files`, `data_dir`, and any builder-specific parameters
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- config_name: {config_name_0} # Name of the dataset subset, if applicable. Example: default
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data_files:
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- split: {split_name_0} # Example: train
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path: {file_path_0} # Example: data.csv
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- split: {split_name_1} # Example: test
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path: {file_path_1} # Example: holdout.csv
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- config_name: {config_name_1} # Name of the dataset subset. Example: processed
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data_files:
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- split: {split_name_3} # Example: train
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path: {file_path_3} # Example: data_processed.csv
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# Optional. This part can be used to store the feature types and size of the dataset to be used in python. This can be automatically generated using the datasets-cli.
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dataset_info:
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# dtype: string
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# - name: answer_start
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# dtype: int32
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config_name: {config_name} # Example for glue: sst2
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config_name: {config_name} # Name of the dataset subset. Example for glue: sst2
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splits:
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- name: {split_name_0} # Example: train
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num_bytes: {split_num_bytes_0} # Example for SQuAD: 79317110
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num_examples: {split_num_examples_0} # Example for SQuAD: 87599
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download_size: {dataset_download_size} # Example for SQuAD: 35142551
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dataset_size: {dataset_size} # Example for SQuAD: 89789763
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# It can also be a list of multiple configurations:
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# It can also be a list of multiple subsets (also called "configurations"):
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# ```yaml
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# dataset_info:
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# - config_name: {config0}
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# Optional. Add this if you want to encode a train and evaluation info in a structured way for AutoTrain or Evaluation on the Hub
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train-eval-index:
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- config: {config_name} # The dataset config name to use. Example for datasets without configs: default. Example for glue: sst2
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- config: {config_name} # The dataset subset name to use. Example for datasets without subsets: default. Example for glue: sst2
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task: {task_name} # The task category name (same as task_category). Example: question-answering
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task_id: {task_type} # The AutoTrain task id. Example: extractive_question_answering
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splits:

‎docs/TODOs.md‎

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## For API-Inference docs:
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From https://github.com/huggingface/hub-docs/pull/1413:
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* Use `<inference> for getting started
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* Add some screenshots: supported models
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* Add flow chart of how API works
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* Add table with all tasks
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* Add missing tasks: depth estimation and zero shot image classification
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* Some tasks have no warm models, should we remove them for now? E.g. https://huggingface.co/models?inference=warm&pipeline_tag=fill-mask&sort=trending BUT many are cold and working, so actually linking to both could make sense - internal issue https://github.com/huggingface-internal/moon-landing/issues/10966
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* See also this [google doc](https://docs.google.com/document/d/1xy5Ug4C_qGbqp4x3T3rj_VOyjQzQLlyce-L6I_hYi94/edit?usp=sharing)
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* Add CI to auto-generate the docs when handlebars template are updated

‎docs/api-inference/_redirects.yml‎

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quicktour: index
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detailed_parameters: parameters
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parallelism: getting_started
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usage: getting_started
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faq: index

‎docs/api-inference/_toctree.yml‎

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- sections:
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- local: index
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title: Serverless Inference API
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- local: getting-started
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title: Getting Started
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- local: supported-models
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title: Supported Models
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- local: rate-limits
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title: Rate Limits
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- local: security
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title: Security
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title: Getting Started
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- sections:
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- local: parameters
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title: Parameters
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- sections:
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- local: tasks/audio-classification
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title: Audio Classification
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- local: tasks/automatic-speech-recognition
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title: Automatic Speech Recognition
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- local: tasks/chat-completion
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title: Chat Completion
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- local: tasks/feature-extraction
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title: Feature Extraction
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- local: tasks/fill-mask
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title: Fill Mask
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- local: tasks/image-classification
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title: Image Classification
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- local: tasks/image-segmentation
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title: Image Segmentation
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- local: tasks/image-to-image
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title: Image to Image
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- local: tasks/image-text-to-text
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title: Image-Text to Text
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- local: tasks/object-detection
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title: Object Detection
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- local: tasks/question-answering
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title: Question Answering
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- local: tasks/summarization
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title: Summarization
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- local: tasks/table-question-answering
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title: Table Question Answering
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- local: tasks/text-classification
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title: Text Classification
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- local: tasks/text-generation
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title: Text Generation
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- local: tasks/text-to-image
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title: Text to Image
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- local: tasks/token-classification
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title: Token Classification
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- local: tasks/translation
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title: Translation
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- local: tasks/zero-shot-classification
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title: Zero Shot Classification
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title: Detailed Task Parameters
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title: API Reference

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