Description
This model annotates named entities in a text, that can be used to find features such as names of people, places, and organizations. The model does not read words directly but instead reads word embeddings, which represent words as points such that more semantically similar words are closer together.
This model uses the pre-trained glove_840B_300 embeddings model from WordEmbeddings annotator as an input, so be sure to use the same embeddings in the pipeline.
Predicted Entities
Brands-BRN, Designations (position or job title)-DES, Date and time-DTM, Locations-LOC, Measurements-MEA, Names-NAME, Numbers-NUM, Organizations-ORG, Persons-PER, Terminology-TRM, Titles-TTL.
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
word_segmenter = WordSegmenterModel.pretrained("wordseg_best", "th")\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("glove_840B_300", "xx")\
.setInputCols("document", "token") \
.setOutputCol("embeddings")
ner = NerDLModel.pretrained("ner_lst20_glove_840B_300d", "th") \
.setInputCols(["document", "token", "embeddings"]) \
.setOutputCol("ner")
...
pipeline = Pipeline(stages=[document_assembler, sentence_detector, word_segmenter, embeddings, ner, ner_converter])
example = spark.createDataFrame([['Mona Lisa เป็นภาพวาดสีน้ำมันในศตวรรษที่ 16 ที่สร้างโดย Leonardo จัดขึ้นที่พิพิธภัณฑ์ลูฟร์ในปารีส']], ["text"])
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val word_segmenter = WordSegmenterModel.pretrained("wordseg_best", "th")
.setInputCols(Array("sentence"))
.setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("glove_840B_300", "xx")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val ner = NerDLModel.pretrained("ner_lst20_glove_840B_300d", "th")
.setInputCols(Array("document", "token", "embeddings"))
.setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, word_segmenter, embeddings, ner, ner_converter))
val data = Seq("Mona Lisa เป็นภาพวาดสีน้ำมันในศตวรรษที่ 16 ที่สร้างโดย Leonardo จัดขึ้นที่พิพิธภัณฑ์ลูฟร์ในปารีส").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""Mona Lisa เป็นภาพวาดสีน้ำมันในศตวรรษที่ 16 ที่สร้างโดย Leonardo จัดขึ้นที่พิพิธภัณฑ์ลูฟร์ในปารีส"""]
ner_df = nlu.load('th.ner.lst20.glove_840B_300D').predict(text, output_level='token')
ner_df
Results
----------+-----+
|token |ner |
+----------+-----+
|Mona |B_PER|
|Lisa |E_PER|
|เป็น |O |
|ภาพวาด |O |
|สีน้ำมัน |O |
|ใน |O |
|ศตวรรษ |O |
|ที่ |O |
|16 |O |
|ที่ |O |
|สร้าง |O |
|โดย |O |
|Leonardo |B_PER|
|จัด |O |
|ขึ้น |O |
|ที่ |O |
|พิพิธภัณฑ์ |O |
|ลูฟร์ |O |
|ใน |O |
|ปารีส |O |
+----------+-----+
Model Information
| Model Name: | ner_lst20_glove_840B_300d |
| Type: | ner |
| Compatibility: | Spark NLP 2.7.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | th |
Data Source
The model was trained on the LST20 Corpus from National Electronics and Computer Technology Center (NECTEC) .
Benchmarking
| | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| B_BRN | 0.26 | 0.23 | 0.24 | 47 |
| B_DES | 0.92 | 0.89 | 0.91 | 1176 |
| B_DTM | 0.83 | 0.81 | 0.82 | 1329 |
| B_LOC | 0.75 | 0.70 | 0.72 | 2344 |
| B_MEA | 0.76 | 0.80 | 0.78 | 3155 |
| B_NUM | 0.71 | 0.59 | 0.64 | 1240 |
| B_ORG | 0.79 | 0.78 | 0.78 | 4248 |
| B_PER | 0.92 | 0.92 | 0.92 | 3269 |
| B_TRM | 0.87 | 0.77 | 0.81 | 128 |
| B_TTL | 0.97 | 0.98 | 0.98 | 1379 |
| E_BRN | 0.86 | 0.75 | 0.80 | 8 |
| E_DES | 0.94 | 0.82 | 0.88 | 198 |
| E_DTM | 0.80 | 0.79 | 0.80 | 1151 |
| E_LOC | 0.71 | 0.70 | 0.71 | 851 |
| E_MEA | 0.69 | 0.77 | 0.73 | 830 |
| E_NUM | 0.80 | 0.61 | 0.69 | 79 |
| E_ORG | 0.80 | 0.76 | 0.78 | 2090 |
| E_PER | 0.93 | 0.96 | 0.94 | 1586 |
| E_TRM | 0.33 | 0.17 | 0.22 | 12 |
| I_BRN | 0.75 | 0.60 | 0.67 | 5 |
| I_DES | 0.79 | 0.63 | 0.70 | 204 |
| I_DTM | 0.92 | 0.86 | 0.89 | 2969 |
| I_LOC | 0.47 | 0.46 | 0.47 | 462 |
| I_MEA | 0.64 | 0.74 | 0.69 | 935 |
| I_NUM | 0.87 | 0.71 | 0.78 | 115 |
| I_ORG | 0.81 | 0.75 | 0.78 | 3015 |
| I_PER | 0.93 | 0.95 | 0.94 | 1604 |
| I_TRM | 0.40 | 0.13 | 0.20 | 15 |
| I_TTL | 0.67 | 0.50 | 0.57 | 4 |
| accuracy | 0.95 | 207278 | | |
| macro avg | 0.76 | 0.71 | 0.73 | 207278 |
| weighted avg | 0.95 | 0.95 | 0.95 | 207278 |