Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.

Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf

Contact: konrad.wojtasik@pwr.edu.pl


How to use:

With sentence transformers:
```
from sentence_transformers import CrossEncoder
model_path = "clarin-knext/herbert-base-reranker-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-base-reranker-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)
```
{"language": ["pl"], "license": "cc-by-sa-4.0"}
transformers pytorch bert text-classification pl arxiv:2305.19840 license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #pl #arxiv-2305.19840 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

Part of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language.

Link to arxiv: URL

Contact: konrad.wojtasik@URL


How to use:

With sentence transformers:


With transformers:

TAGS
#transformers #pytorch #bert #text-classification #pl #arxiv-2305.19840 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #bert #text-classification #pl #arxiv-2305.19840 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
