Named Entity Recognition
In short
- The Named Entity Recognition (NER) module is a Weaviate module for token classification.
- The module depends on a NER Transformers model that should be running with Weaviate. There are pre-built models available, but you can also attach another HuggingFace Transformer or custom NER model.
- The module adds a
tokens {}
filter to the GraphQL_additional {}
field. - The module returns data objects as usual, with recognized tokens in the GraphQL
_additional { tokens {} }
field.
Introduction
Named Entity Recognition (NER) module is a Weaviate module to extract entities from your existing Weaviate (text) objects on the fly. Entity Extraction happens at query time. Note that for maximum performance, transformer-based models should run with GPUs. CPUs can be used, but the throughput will be lower.
There are currently three different NER modules available (taken from Hugging Face): dbmdz-bert-large-cased-finetuned-conll03-english
, dslim-bert-base-NER
, davlan-bert-base-multilingual-cased-ner-hrl
.
How to enable (module configuration)
Docker Compose
The NER module can be added as a service to the Docker Compose file. You must have a text vectorizer like text2vec-contextionary
or text2vec-transformers
running. An example Docker Compose file for using the ner-transformers
module (dbmdz-bert-large-cased-finetuned-conll03-english
) in combination with the text2vec-contextionary
:
---
services:
weaviate:
command:
- --host
- 0.0.0.0
- --port
- '8080'
- --scheme
- http
image: cr.weaviate.io/semitechnologies/weaviate:1.27.4
ports:
- 8080:8080
- 50051:50051
restart: on-failure:0
environment:
CONTEXTIONARY_URL: contextionary:9999
NER_INFERENCE_API: "http://ner-transformers:8080"
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'text2vec-contextionary'
ENABLE_MODULES: 'text2vec-contextionary,ner-transformers'
CLUSTER_HOSTNAME: 'node1'
contextionary:
environment:
OCCURRENCE_WEIGHT_LINEAR_FACTOR: 0.75
EXTENSIONS_STORAGE_MODE: weaviate
EXTENSIONS_STORAGE_ORIGIN: http://weaviate:8080
NEIGHBOR_OCCURRENCE_IGNORE_PERCENTILE: 5
ENABLE_COMPOUND_SPLITTING: 'false'
image: cr.weaviate.io/semitechnologies/contextionary:en0.16.0-v1.0.2
ports:
- 9999:9999
ner-transformers:
image: cr.weaviate.io/semitechnologies/ner-transformers:dbmdz-bert-large-cased-finetuned-conll03-english
...
Variable explanations:
NER_INFERENCE_API
: where the qna module is running
How to use (GraphQL)
To make use of the modules capabilities, simply extend your query with the following new _additional
property:
GraphQL Token
This module adds a search filter to the GraphQL _additional
field in queries: token{}
. This new filter takes the following arguments:
Field | Data Type | Required | Example value | Description |
---|---|---|---|---|
properties | list of strings | yes | ["summary"] | The properties of the queries Class which contains text (text or string Datatype). You must provide at least one property |
certainty | float | no | 0.75 | Desired minimal certainty or confidence that the recognized token must have. The higher the value, the stricter the token classification. If no certainty is set, all tokens that are found by the model will be returned. |
limit | int | no | 1 | The maximum amount of tokens returned per data object in total. |
Example query
- GraphQL
- Python
- JS/TS Client v2
- Go
- Java
- Curl
{
Get {
Article(
limit: 1
) {
title
_additional{
tokens(
properties: ["title"],
limit: 10,
certainty: 0.7
) {
certainty
endPosition
entity
property
startPosition
word
}
}
}
}
}
import weaviate
client = weaviate.Client("http://localhost:8080")
result = (
client.query
.get("Article", ["title", "_additional {tokens ( properties: [\"title\"], limit: 1, certainty: 0.7) {entity property word certainty startPosition endPosition }}"])
.do()
)
print(result)
import weaviate from 'weaviate-ts-client';
const client = weaviate.client({
scheme: 'http',
host: 'localhost:8080',
});
const response = await client.graphql
.get()
.withClassName('Article')
.withFields('title _additional { tokens (properties: [\'title\'], limit: 1, certainty: 0.7) {entity property word certainty startPosition endPosition} }')
.do();
console.log(response);
package main
import (
"context"
"fmt"
"github.com/weaviate/weaviate-go-client/v4/weaviate"
"github.com/weaviate/weaviate-go-client/v4/weaviate/graphql"
)
func main() {
cfg := weaviate.Config{
Host: "localhost:8080",
Scheme: "http",
}
client, err := weaviate.NewClient(cfg)
if err != nil {
panic(err)
}
className := "Article"
fields := []graphql.Field{
{Name: "title"},
{Name: "_additional", Fields: []graphql.Field{
{Name: "tokens(properties: [\"title\"], limit: 1, certainty: 0.7)", Fields: []graphql.Field{
{Name: "entity"},
{Name: "property"},
{Name: "word"},
{Name: "certainty"},
{Name: "startPosition"},
{Name: "endPosition"},
}},
}},
}
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(fields...).
Do(context.Background())
if err != nil {
panic(err)
}
fmt.Printf("%v", result)
}
package io.weaviate;
import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.graphql.model.GraphQLResponse;
import io.weaviate.client.v1.graphql.query.fields.Field;
public class App {
public static void main(String[] args) {
Config config = new Config("http", "localhost:8080");
WeaviateClient client = new WeaviateClient(config);
Field title = Field.builder().name("title").build();
Field _additional = Field.builder()
.name("_additional")
.fields(new Field[]{
Field.builder()
.name("tokens (properties: [\"title\"], limit: 1, certainty: 0.7)")
.fields(new Field[]{
Field.builder().name("entity").build(),
Field.builder().name("property").build(),
Field.builder().name("word").build(),
Field.builder().name("certainty").build(),
Field.builder().name("startPosition").build(),
Field.builder().name("endPosition").build()
}).build()
}).build();
Result<GraphQLResponse> result = client.graphQL().get()
.withClassName("Article")
.withFields(title, _additional)
.run();
if (result.hasErrors()) {
System.out.println(result.getError());
return;
}
System.out.println(result.getResult());
}
}
echo '{
"query": "{
Get {
Article(
limit: 1
) {
title
_additional {
tokens(
properties: [\"title\"],
limit: 10,
certainty: 0.7
) {
certainty
endPosition
entity
property
startPosition
word
}
}
}
}
}"
}' | curl \
-X POST \
-H 'Content-Type: application/json' \
-d @- \
http://localhost:8080/v1/graphql
GraphQL response
The answer is contained in a new GraphQL _additional
property called tokens
, which returns a list of tokens. It contains the following fields:
entity
(string
): The Entity group (classified token)word
(string
): The word that is recognized as entityproperty
(string
): The property in which the token is foundcertainty
(float
): 0.0-1.0 of how certain the model is that the token is correctly classifiedstartPosition
(int
): The position of the first character of the word in the property valueendPosition
(int
): The position of the last character of the word in the property value
Example response
{
"data": {
"Get": {
"Article": [
{
"_additional": {
"tokens": [
{
"property": "title",
"entity": "PER",
"certainty": 0.9894614815711975,
"word": "Sarah",
"startPosition": 11,
"endPosition": 16
},
{
"property": "title",
"entity": "LOC",
"certainty": 0.7529033422470093,
"word": "London",
"startPosition": 31,
"endPosition": 37
}
]
},
"title": "My name is Sarah and I live in London"
}
]
}
},
"errors": null
}
Use another NER Transformer module from HuggingFace
You can build a Docker image which supports any model from the Hugging Face model hub with a two-line Dockerfile. In the following example, we are going to build a custom image for the Davlan/bert-base-multilingual-cased-ner-hrl
model.
Step 1: Create a Dockerfile
Create a new Dockerfile
. We will name it my-model.Dockerfile
. Add the following lines to it:
FROM semitechnologies/ner-transformers:custom
RUN chmod +x ./download.py
RUN MODEL_NAME=Davlan/bert-base-multilingual-cased-ner-hrl ./download.py
Step 2: Build and tag your Dockerfile.
We will tag our Dockerfile as davlan-bert-base-multilingual-cased-ner-hrl
:
docker build -f my-model.Dockerfile -t davlan-bert-base-multilingual-cased-ner-hrl .
Step 3: That's it!
You can now push your image to your favorite registry or reference it locally in your Weaviate docker-compose.yml
using the Docker tag davlan-bert-base-multilingual-cased-ner-hrl
.
How it works (under the hood)
The code for the application in this repo works well with models that take in a text input like My name is Sarah and I live in London
and return information in JSON format like this:
[
{
"entity_group": "PER",
"score": 0.9985478520393372,
"word": "Sarah",
"start": 11,
"end": 16
},
{
"entity_group": "LOC",
"score": 0.999621570110321,
"word": "London",
"start": 31,
"end": 37
}
]
The Weaviate NER Module then takes this output and processes this to GraphQL output.
Model license(s)
The ner-transformers
module is compatible with various models, each with their own license. For detailed information, see the license of the model you are using in the Hugging Face Model Hub.
It is your responsibility to evaluate whether the terms of its license(s), if any, are appropriate for your intended use.
Questions and feedback
If you have any questions or feedback, let us know in the user forum.