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cross-encoder-ru-msmarco-passage
PitKoro/cross-encoder-ru-msmarco-passage
cross-encoder-ru-msmarco-passage is a machine learning model from PitKoro. 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 sentence-transformers. The card lists the license as apache-2.0.
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At a glance
- Library
- sentence-transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 26, 2023
- Updated
- Jan 14, 2025
- SHA
- 985b05cf
Datasets
Model card
From the Hugging Face model README
Пример 1
from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
import torch
sigmoid_fn = torch.nn.Sigmoid()
# model = AutoModelForSequenceClassification.from_pretrained("/home/jovyan/pakorolev/ranker/deep_pavlov_mrr_0_8413")
# tokenizer = AutoTokenizer.from_pretrained("/home/jovyan/pakorolev/ranker/deep_pavlov_mrr_0_8413")
model = AutoModelForSequenceClassification.from_pretrained("PitKoro/cross-encoder-ru-msmarco-passage")
tokenizer = AutoTokenizer.from_pretrained("PitKoro/cross-encoder-ru-msmarco-passage")
text = [['привет', 'привет'],['привет', 'пока']]
tokenized = tokenizer(text, return_tensors='pt')
logits = model(**tokenized).logits
output = sigmoid_fn(logits.flatten())
print(output)
Пример 2
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder("PitKoro/cross-encoder-ru-msmarco-passage", max_length=512)
text = [['привет', 'привет'],['привет', 'пока']]
output = model.predict(text)
print(output)
- Library
- sentence-transformers
- Type
- bert
- License
- apache-2.0
- Languages
- ru
- Created
- Oct 26, 2023
- Updated
- Jan 14, 2025