| You can use transformer library and load model for conditional generation and expect those tokens or use monoT5 implementation from BEIR. | |
| prompt = `Query: {query} Document: {document} Relevant:` | |
| Model returns tokens if relevant or not: | |
| ``` token_false='▁fałsz', token_true='▁prawda'``` | |
| MonoT5 implementation is included in BEIR benchmark(https://github.com/beir-cellar/beir): | |
| ``` | |
| from beir.reranking.models import MonoT5 | |
| from beir.reranking import Rerank | |
| queries = YOUR_QUERIES | |
| corpus = YOUR_CORPUS | |
| queries = {query['id'] : query['text'] for query in queries} | |
| corpus = {doc['id']: {'title': doc['title'] , 'text': doc['text']} for doc in corpus} | |
| cross_encoder_model = MonoT5(model_path, use_amp=False, token_false='▁fałsz', token_true='▁prawda') | |
| reranker = Rerank(cross_encoder_model, batch_size=100) | |
| rerank_results = reranker.rerank(corpus, queries, results, top_k=100) | |
| ``` |