Use for topic analysis, sentiment analysis, custom text moderation, chatbot and email smart replies. Understand purchasing intent; understand audience sentiment; moderate user generated content; determine brand reputation; improve customer satisfaction and more.
You can call the Predict API with the "Text Embedding" model. Simply pass in a text input with a publicly accessible URL or by directly sending raw text, .csv or .tsv files.
Learn more about the Predict API.
API Guide
//Python
from clarifai_grpc.grpc.api import service_pb2, service_pb2_grpc, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
import json
channel = ClarifaiChannel.get_json_channel()
stub = service_pb2_grpc.V2Stub(channel)
response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
model_id="54286fce2de6cdfa11fedb3b498d2523",
# By default, the latest model version will be used, but you can set it explicitly.
# version_id=model_version_id,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(text=resources_pb2.Text(raw="Butchart Gardens contains over 900 varieties of plants."))),
resources_pb2.Input(data=resources_pb2.Data(text=resources_pb2.Text(url="https://samples.clarifai.com/negative_sentence_12.txt"))),
]
),
metadata=(('authorization', 'Key YOUR_API_KEY'),)
)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: "+ str(response.status.code))
print(response)
// JS
app.models.predict("d2b6d1aa64c9541784e412347f582bc0, "This is a text string").then(
function(response) {
// do something with response
},
function(err) {
// there was an error
}
);
// cURL
curl -X POST https://api.clarifai.com/v2/models/{d2b6d1aa64c9541784e412347f582bc0}/versions/{version
_id}/outputs \
-H 'Authorization: Key YOUR_API_KEY' \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"text": {
"raw": "This is a text string"
}
}
}
]
}'
The Predict API returns ‘vector’ and ‘num_dimensions’. The ‘vector’ is a numerical vector that represents the input text in a 768-dimensional space. The numerical values within the vectors are between 0 and 1, inclusive. The ‘num_dimensions’ for this model is set at 768.
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "d2b6d1aa64c9541784e412347f582bc0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2017-04-06T17:43:28.573076Z",
"model": {
"name": "general-v1.3",
"id": "bbb5f41425b8468d9b7a554ff10f8581",
"created_at": "2016-06-05T17:26:32.302967Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "embed",
"type_ext": "embed"
},
"model_version": {
"id": "bb7ac05c86be42d38b67bc473d333e07",
"created_at": "2016-07-13T00:58:55.915745Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "afb815ae2cc84149bce35df866ade49e",
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
},
"data": {
"embeddings": [
{
"vector": [
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],
"num_dimensions": 1024
}
]
}
}
]
}
The Text moderation Model supports text moderation in 54 languages:
Arabic, Bulgarian, Catalan - Valencian, Czech, Danish, German, Greek - Modern, English, Spanish - Castilian, Estonian, Persian, Finnish, French, French - Canadian, Galician, Gujarati, Hebrew, Hindi, Croatian, Hungarian, Armenian, Indonesian, Italian, Japanese, Georgian, Korean, Kurdish, Lithuanian, Latvian, Macedonian, Mongolian, Marathi, Malay, Burmese, Norwegian, Bokmål, Dutch, Flemish, Polish, Portuguese, Portuguese - Brazilian, Romanian, Moldavian, Moldovan, Russian, Slovak, Slovenian, Albanian, Serbian, Swedish, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Chinese - Mainland, Chinese - Taiwan
Gather valuable business insights from images, text and data using machine learning, natural language processing and computer vision.
Detect toxic, obscene, racist or threatening language, or train your own custom moderation models.
Analyze images and returns numerical vectors that represent each detected face in the image in a 1024-dimensional space computed by our General model.
Analyze images and return probability scores on the likelihood that the media contains the face(s) of over 10,000 recognized celebrities.
Analyze images and returns numerical vectors that represent each detected face in the image in a 1024-dimensional space.
Recognize textures and patterns in a two-dimensional image e.g., feathers, woodgrain, petrified wood, glacial ice and overarching descriptive concepts (veined, metallic).
Identify different levels of nudity in your visual data. Ideal for moderating and filtering offensive content from your platform.
Identify unwanted content such as gore, drugs, explicit nudity or suggestive nudity.
Explore our pre-built, ready-to-use image recognition models to suit your specific needs.
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