pipeline_tag: sentence-similarity
language:
- pl
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- ipipan/polqa
- ipipan/maupqa
HerBERT-base Retrieval (v2)
HerBERT Retrieval model encodes the Polish sentences or paragraphs into a 768-dimensional dense vector space and can be used for tasks like document retrieval or semantic search.
It was initialized from the HerBERT-base model and fine-tuned on the PolQA and MAUPQA datasets for 40,000 steps with a batch size of 256.
The model was trained on question-passage pairs and works best on similar tasks. The training passages consisted of title and text concatenated with the special token </s>. Even if your passages don't have a title, it is still beneficial to prefix a passage text with the </s> token.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = [
"W jakim mieście urodził się Zbigniew Herbert?",
"Zbigniew Herbert</s>Zbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg.",
]
model = SentenceTransformer('ipipan/herbert-base-retrieval-v2')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = [
"W jakim mieście urodził się Zbigniew Herbert?",
"Zbigniew Herbert</s>Zbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg.",
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ipipan/herbert-base-retrieval-v2')
model = AutoModel.from_pretrained('ipipan/herbert-base-retrieval-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
Additional Information
Dataset Curators
The model was created by Piotr Rybak from the Institute of Computer Science, Polish Academy of Sciences.
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]