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nreimers 2021-06-21 11:42:20 +02:00
parent ced69de5b6
commit b2b5013007
1 changed files with 4 additions and 4 deletions

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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:
```python
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.')])
#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
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base')
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")
@ -53,7 +53,7 @@ with torch.no_grad():
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')