SliceX AI Medley API (1.0)

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Binary Text Classification

Inference with 2 possible classes (e.g. POSITIVE and NEGATIVE).

Customer Review Sentiment (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Toxicity Detection (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Multi-Class Text Classification

Inference with multiple possible classes. Scores sum up to 1.

News Classification (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Sentiment Analysis (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Financial Customer Support (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Financial Sentiment Analysis (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Conversational Language Understanding (En) (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Conversational Language Understanding (Jp) (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Multi-Label Text Classification

Inference with multiple possible classes. Scores don't sum up to 1.

Emotion Detection (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Sequence Labeling

Named Entity Recognition. Finds the tokens (e.g. person, location, etc.) in the query.

Named Entity Recognition (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Question Answering

Looks to answer questions based on text passages.

Research Papers Search (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Custom Trained Model

User Model Inference

path Parameters

model_id

required

string (Model Id)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries

Array of strings (The list of strings to run the inference on.) <= 20 items

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
]
}

Response samples

200
422

Content type

application/json

{"data": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"metadata": {"model_inference_time_ms": 7.8
}
}

Medley

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

query	string (The string to run the inference on.)
models	Array of strings (List of models to run.)

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"query": "string",
"models": ["sentiment-classification"
]
}

Response samples

200
422

Content type

application/json

{"data": {"property1": {"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
},
"property2": {"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
},
"metadata": {"model_inference_time_ms": 7.8
}
}

Medley (Batch)

header Parameters

x-api-key

required

string (x-api-key)

Request Body schema: application/json

queries	Array of strings (The list of strings to run the inference on.) <= 50 items
models	Array of strings (List of models to run.)

Responses

Request samples

Payload
cURL
Python3 (requests)
JavaScript (axios)

Content type

application/json

{"queries": ["string"
],
"models": ["sentiment-classification"
]
}

Response samples

200
422

Content type

application/json

{"data": {"property1": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
],
"property2": [{"labels": ["POSITIVE",
"NEGATIVE"
],
"scores": [0.95,
0.05
],
"tokens": ["New York"
],
"token_start_positions": [2
]
}
]
},
"metadata": {"model_inference_time_ms": 7.8
}
}