{
  "opencollection": "1.0.0",
  "info": {
    "name": "Hugging Face Dataset Viewer Audio Natural Language Processing API",
    "version": "1.0.0"
  },
  "items": [
    {
      "info": {
        "name": "Natural Language Processing",
        "type": "folder"
      },
      "items": [
        {
          "info": {
            "name": "Run Inference on a Model",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id",
            "params": [
              {
                "name": "model_id",
                "value": "gpt2",
                "type": "path",
                "description": "The model ID on the Hugging Face Hub (e.g., gpt2, bert-base-uncased)"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Run inference on any model hosted on the Hugging Face Hub. The request and response format depends on the model's pipeline task."
        },
        {
          "info": {
            "name": "Run Inference by Task Pipeline",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/pipeline/:task",
            "params": [
              {
                "name": "task",
                "value": "text-generation",
                "type": "path",
                "description": "The pipeline task type (e.g., text-generation, text-classification, summarization)"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Run inference using a recommended model for a specific task pipeline. Hugging Face selects the best available model for the given task."
        },
        {
          "info": {
            "name": "Text Generation Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/text-generation",
            "params": [
              {
                "name": "model_id",
                "value": "gpt2",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Generate text using a language model. Supports parameters like temperature, top_p, max_new_tokens, and repetition_penalty."
        },
        {
          "info": {
            "name": "Text Classification Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/text-classification",
            "params": [
              {
                "name": "model_id",
                "value": "distilbert-base-uncased-finetuned-sst-2-english",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Classify text into predefined categories using a text classification model such as sentiment analysis."
        },
        {
          "info": {
            "name": "Question Answering Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/question-answering",
            "params": [
              {
                "name": "model_id",
                "value": "deepset/roberta-base-squad2",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Extract answers from a given context using a question answering model."
        },
        {
          "info": {
            "name": "Text Summarization Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/summarization",
            "params": [
              {
                "name": "model_id",
                "value": "facebook/bart-large-cnn",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Summarize a long text into a shorter version."
        },
        {
          "info": {
            "name": "Translation Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/translation",
            "params": [
              {
                "name": "model_id",
                "value": "Helsinki-NLP/opus-mt-en-fr",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Translate text from one language to another."
        },
        {
          "info": {
            "name": "Fill Mask Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/fill-mask",
            "params": [
              {
                "name": "model_id",
                "value": "bert-base-uncased",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Fill in a masked token in a sentence using a masked language model."
        },
        {
          "info": {
            "name": "Feature Extraction Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/feature-extraction",
            "params": [
              {
                "name": "model_id",
                "value": "sentence-transformers/all-MiniLM-L6-v2",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Extract dense vector representations (embeddings) from text input."
        },
        {
          "info": {
            "name": "Zero-shot Classification Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/zero-shot-classification",
            "params": [
              {
                "name": "model_id",
                "value": "facebook/bart-large-mnli",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Classify text into categories that the model has not been explicitly trained on using natural language inference."
        },
        {
          "info": {
            "name": "Sentence Similarity Inference",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://datasets-server.huggingface.co/models/:model_id/sentence-similarity",
            "params": [
              {
                "name": "model_id",
                "value": "sentence-transformers/all-MiniLM-L6-v2",
                "type": "path"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Compute similarity scores between a source sentence and target sentences."
        }
      ]
    }
  ],
  "bundled": true
}