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Personalized Glencairn Glass for Whiskey Bourbon Scotch Lovers
The Quinton Personalized Glencairn Glass is the ultimate whiskey glass for tasting. It allows harsher alcohol notes to be softened and more subtle flavor notes to become more palatable. This 4.5" x 3" glass holds 6 ounces of your favorite whiskey, and can be personalized with two initials.
Given this info, generate a list of bullet points (key features) …
The Quinton Personalized Glencairn Glass is the ultimate whiskey glass for tasting. It allows harsher alcohol notes to be softened and more subtle flavor notes to become more palatable. This 4.5" x 3" glass holds 6 ounces of your favorite whiskey, and can be personalized with two initials.
Given this info, generate a list of bullet points (key features) about the product that you would want to show on an e-commerce website.
Bullet Points:
- Ultimate whiskey glass for tasting
- Softens harsher alcohol notes
- Allows more subtle flavor notes to become more palatable
- Measures 4.5" x 3"
- Holds up to 6 ounces of liquid
- Can be personalized with two initials
## Solution
I will use Named Entity Recognition(NER) model from Spacy library in Python which is trained on OntoNotes corpus.
The NER model has been trained on CoNLL dataset and recognizes entities like PERSON, ORGANIZATION etc.
To train our own custom NER model we need training data in below format:
```
TRAIN_DATA = [
("Uber blew through $1 million", {"entities": [(0, 4, "ORG")]}),
Beschrijving
The Quinton Personalized Glencairn Glass is the ultimate whiskey glass for tasting. It allows harsher alcohol notes to be softened and more subtle flavor notes to become more palatable. This 4.5" x 3" glass holds 6 ounces of your favorite whiskey, and can be personalized with two initials.
Given this info, generate a list of bullet points (key features) about the product that you would want to show on an e-commerce website.
Bullet Points:
- Ultimate whiskey glass for tasting
- Softens harsher alcohol notes
- Allows more subtle flavor notes to become more palatable
- Measures 4.5" x 3"
- Holds up to 6 ounces of liquid
- Can be personalized with two initials
## Solution
I will use Named Entity Recognition(NER) model from Spacy library in Python which is trained on OntoNotes corpus.
The NER model has been trained on CoNLL dataset and recognizes entities like PERSON, ORGANIZATION etc.
To train our own custom NER model we need training data in below format:
```
TRAIN_DATA = [
("Uber blew through $1 million", {"entities": [(0, 4, "ORG")]}),
Specificaties
| Brand | Personalized |
| Material | Glass |
| Theme | Glencairn |
| material composition | Glass |