Model Descripton
Fine tunes a cross encoder on the Amazon ESCI dataset.
Usage
Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch import no_grad
model_name = "lv12/esci-ms-marco-MiniLM-L-12-v2"
queries = [
"adidas shoes",
"adidas shoes",
"girls sandals",
"backpacks",
"shoes",
"mustard sleeveless gown"
]
documents = [
'{"title": "Nike Air Max", "description": "The best shoes you can get, with air cushion", "brand": "Nike", "color": "black"}',
'{"title": "Adidas Ultraboost", "description": "The shoes that represent the world", "brand": "Adidas", "color": "white"}',
'{"title": "Womens sandals", "description": "Sandals: wide width 9", "brand": "Chacos", "color": "blue"}',
'{"title": "Girls surf backpack", "description": "The best backpack in town", "brand": "Roxy", "color": "pink"}',
'{"title": "Fresh watermelon", "description": "The best fruit in town, all you can eat", "brand": "Fruitsellers Inc.", "color": "green"}',
'{"title": "Floral yellow dress with frills and lace", "description": "Brighten up your summers with a gorgeous dress", "brand": "Dressmakers Inc.", "color": "bright yellow"}'
]
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer(
queries,
documents,
padding=True,
truncation=True,
return_tensors="pt",
)
model.eval()
with no_grad():
scores = model(**inputs).logits.cpu().detach().numpy()
print(scores)
Sentence Transformers
from sentence_transformers import CrossEncoder
model_name = "lv12/esci-ms-marco-MiniLM-L-12-v2"
queries = [
"adidas shoes",
"adidas shoes",
"girls sandals",
"backpacks",
"shoes",
"mustard sleeveless gown"
]
documents = [
'{"title": "Nike Air Max", "description": "The best shoes you can get, with air cushion", "brand": "Nike", "color": "black"}',
'{"title": "Adidas Ultraboost", "description": "The shoes that represent the world", "brand": "Adidas", "color": "white"}',
'{"title": "Womens sandals", "description": "Sandals: wide width 9", "brand": "Chacos", "color": "blue"}',
'{"title": "Girls surf backpack", "description": "The best backpack in town", "brand": "Roxy", "color": "pink"}',
'{"title": "Fresh watermelon", "description": "The best fruit in town, all you can eat", "brand": "Fruitsellers Inc.", "color": "green"}',
'{"title": "Floral yellow dress with frills and lace", "description": "Brighten up your summers with a gorgeous dress", "brand": "Dressmakers Inc.", "color": "bright yellow"}'
]
model = CrossEncoder(model_name, max_length=512)
scores = model.predict([(q, d) for q, d in zip(queries, documents)])
print(scores)
[ 1.057739 1.6751697 1.039221 1.5969192 -0.8867093 0.5035825 ]
Training
Trained using CrossEntropyLoss
using <query, document>
pairs with grade
as the label.
from sentence_transformers import InputExample
train_samples = [
InputExample(texts=["query 1", "document 1"], label=0.3),
InputExample(texts=["query 1", "document 2"], label=0.8),
InputExample(texts=["query 2", "document 2"], label=0.1),
]
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