Description
This model is trained with glove_100d embeddings to detect restaurant-related terminology.
Predicted Entities
Location, Cuisine, Amenity, Restaurant_Name, Dish, Rating, Hours, Price
Live Demo Open in Colab Download Copy S3 URI
How to use
...
embeddings = WordEmbeddingsModel.pretrained("glove_100d") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nerdl = NerDLModel.pretrained("nerdl_restaurant_100d")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings, nerdl, ner_converter])
text = """Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day."""
data = spark.createDataFrame([[text]]).toDF("text")
result = nlp_pipeline.fit(data).transform(data)
...
val embeddings = WordEmbeddingsModel.pretrained("glove_100d")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val nerdl = NerDLModel.pretrained("nerdl_restaurant_100d")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings, nerdl, ner_converter))
val data = Seq("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.ner.restaurant").predict("""Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.""")
Results
+---------------------------+---------------+
|chunk |ner_label |
+---------------------------+---------------+
|favourite |Rating |
|pasta bar |Dish |
|most reasonably |Price |
|lunch |Hours |
|in town! |Location |
|Sha Tin – Pici’s |Restaurant_Name|
|burrata |Dish |
|arugula salad |Dish |
|freshly tossed tuna tartare|Dish |
|reliable |Price |
|handmade pasta |Dish |
|pappardelle |Dish |
|effortless |Amenity |
|Italian |Cuisine |
|tidy one-pot |Amenity |
|espresso |Dish |
+---------------------------+---------------+
Model Information
| Model Name: | nerdl_restaurant_100d |
| Type: | ner |
| Compatibility: | Spark NLP 3.3.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.2 MB |
Data Source
https://groups.csail.mit.edu/sls/downloads/restaurant/
Benchmarking
label precision recall f1-score support
B-Amenity 0.77 0.75 0.76 545
B-Cuisine 0.86 0.88 0.87 524
B-Dish 0.84 0.80 0.82 303
B-Hours 0.67 0.72 0.69 197
B-Location 0.89 0.89 0.89 807
B-Price 0.86 0.87 0.86 169
B-Rating 0.87 0.79 0.83 221
B-Restaurant_Name 0.91 0.94 0.92 388
I-Amenity 0.80 0.75 0.77 561
I-Cuisine 0.71 0.71 0.71 135
I-Dish 0.66 0.77 0.71 104
I-Hours 0.87 0.84 0.86 306
I-Location 0.92 0.87 0.89 834
I-Price 0.64 0.81 0.71 52
I-Rating 0.80 0.85 0.82 118
I-Restaurant_Name 0.82 0.89 0.85 359
O 0.95 0.96 0.96 8634
accuracy - - 0.91 14257
macro-avg 0.81 0.83 0.82 14257
weighted-avg 0.91 0.91 0.91 14257