How to use:

With sentence transformers:
```
from sentence_transformers import CrossEncoder
model_path = "clarin-knext/herbert-large-msmarco"
model = CrossEncoder(model_path, max_length=512)

scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```

With transformers:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_path = "clarin-knext/herbert-large-msmarco"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

features = tokenizer(['Jakie miasto jest stolica Polski?', 'Stolicą Polski jest Warszawa.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print(scores)
```

{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

How to use:

With sentence transformers:


With transformers:


TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
