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plt5-large-msmarco
clarin-knext/plt5-large-msmarco
plt5-large-msmarco is a machine learning model from clarin-knext. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
You can use transformer library and load model for conditional generation and expect those tokens or use monoT5 implementation from BEIR.
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At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- Sep 12, 2023
- Updated
- Dec 20, 2023
- SHA
- 4cadd3d4
Model card
From the Hugging Face model README
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)
- Library
- transformers
- Type
- t5
- Created
- Sep 12, 2023
- Updated
- Dec 20, 2023