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1from sentence_transformers import CrossEncoder
2
3model_name = "agentlans/snowflake-arctic-embed-s-nli"
4model = CrossEncoder(model_name)
5scores = model.predict(
6 [
7 ("A man is eating pizza", "A man eats something"),
8 (
9 "A black race car starts up in front of a crowd of people.",
10 "A man is driving down a lonely road.",
11 ),
12 ]
13)
14
15label_mapping = ["entailment", "neutral", "contradiction"]
16labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
17print(labels)
18# Output: ['entailment', 'contradiction']1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4model_name = "agentlans/snowflake-arctic-embed-s-nli"
5model = AutoModelForSequenceClassification.from_pretrained(model_name)
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7
8features = tokenizer(
9 [
10 "A man is eating pizza",
11 "A black race car starts up in front of a crowd of people.",
12 ],
13 ["A man eats something", "A man is driving down a lonely road."],
14 padding=True,
15 truncation=True,
16 return_tensors="pt",
17)
18
19model.eval()
20with torch.no_grad():
21 scores = model(**features).logits
22 label_mapping = ["entailment", "neutral", "contradiction"]
23 labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
24 print(labels)
25# Output: ['entailment', 'contradiction']