cross-encoder
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nli-roberta-base
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nreimers 2021-06-21 11:42:20 +02:00
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@ -24,7 +24,7 @@ For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.s
Pre-trained models can be used like this: Pre-trained models can be used like this:
```python ```python
from sentence_transformers import CrossEncoder from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name') model = CrossEncoder('cross-encoder/nli-roberta-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels #Convert scores to labels
@ -38,8 +38,8 @@ You can use the model also directly with Transformers library (without SentenceT
from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name') model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base')
tokenizer = AutoTokenizer.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt") features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
@ -53,7 +53,7 @@ with torch.no_grad():
## Zero-Shot Classification ## Zero-Shot Classification
This model can also be used for zero-shot-classification: This model can also be used for zero-shot-classification:
``` ```python
from transformers import pipeline from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base') classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')