This repository provides REST microservices for Explosion AI's interactive demos and visualisers. All requests and responses are JSON-encoded as text/string, so all requests require the header Content-Type: text/string.
A simple Falcon app for exposing a spaCy dependency parser and spaCy named entity recognition model as a REST microservice, formatted for the displaCy.js and displaCy ENT visualiser. For more info on the rendering on the front-end that consumes the data produced by this service, see this blog post.
The service exposes two endpoints that accept POST requests, and two endpoints that accept GET requests to describe the available models and schemas.
Example request:
{
"text": "They ate the pizza with anchovies",
"model":"en",
"collapse_punctuation": 0,
"collapse_phrases": 1
}| Name | Type | Description |
|---|---|---|
text |
string | text to be parsed |
model |
string | identifier string for a model installed on the server |
collapse_punctuation |
boolean | Merge punctuation onto the preceding token? |
collapse_phrases |
boolean | Merge noun chunks and named entities into single tokens? |
Example response:
{
"arcs": [
{ "dir": "left", "start": 0, "end": 1, "label": "nsubj" },
{ "dir": "right", "start": 1, "end": 2, "label": "dobj" },
{ "dir": "right", "start": 1, "end": 3, "label": "prep" },
{ "dir": "right", "start": 3, "end": 4, "label": "pobj" },
{ "dir": "left", "start": 2, "end": 3, "label": "prep" }
],
"words": [
{ "tag": "PRP", "text": "They" },
{ "tag": "VBD", "text": "ate" },
{ "tag": "NN", "text": "the pizza" },
{ "tag": "IN", "text": "with" },
{ "tag": "NNS", "text": "anchovies" }
]
}| Name | Type | Description |
|---|---|---|
arcs |
array | data to generate the arrows |
dir |
string | direction of arrow ("left" or "right") |
start |
integer | offset of word the arrow starts on |
end |
integer | offset of word the arrow ends on |
label |
string | dependency label |
words |
array | data to generate the words |
tag |
string | part-of-speech tag |
text |
string | token |
Example request:
{
"text": "When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously.",
"model": "en"
}| Name | Type | Description |
|---|---|---|
text |
string | text to be parsed |
model |
string | identifier string for a model installed on the server |
Example response:
[
{ "end": 20, "start": 5, "type": "PERSON" },
{ "end": 67, "start": 61, "type": "ORG" },
{ "end": 75, "start": 71, "type": "DATE" }
]| Name | Type | Description |
|---|---|---|
end |
integer | character offset the entity ends after |
start |
integer | character offset the entity starts on |
type |
string | entity type |
List the names of models installed on the server.
Example request:
GET /models
Example response:
["en", "de"]Example request:
GET /en/schema
| Name | Type | Description |
|---|---|---|
model |
string | identifier string for a model installed on the server |
Example response:
{
"dep_types": ["ROOT", "nsubj"],
"ent_types": ["PERSON", "LOC", "ORG"],
"pos_types": ["NN", "VBZ", "SP"]
}A simple Falcon app for exposing a sense2vec model as a REST microservice, as used in the sense2vec demo
The service exposes a single endpoint over GET.
Example query:
GET /natural_language_processing%7CNOUN
Example response:
[
{
"score": 0.1,
"key": "computational_linguistics|NOUN",
"text": "computational linguistics",
"count": 20,
"head": "linguistics"
}
]| Name | Type | Description |
|---|---|---|
score |
float | similarity to query |
key |
string | identifier string |
text |
string | human-readable token |
count |
integer | absolute frequency in training corpus |
head |
string | head word in text |
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