pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- example_title: Nederlands
source_sentence: Deze week ga ik naar de kapper
sentences:
- Ik ga binnenkort mijn haren laten knippen
- Morgen wil ik uitslapen
- Gisteren ging ik naar de bioscoop
datasets:
- NetherlandsForensicInstitute/AllNLI-translated-nl
- NetherlandsForensicInstitute/altlex-translated-nl
- NetherlandsForensicInstitute/coco-captions-translated-nl
- NetherlandsForensicInstitute/flickr30k-captions-translated-nl
- NetherlandsForensicInstitute/msmarco-translated-nl
- NetherlandsForensicInstitute/quora-duplicates-translated-nl
- NetherlandsForensicInstitute/sentence-compression-translated-nl
- NetherlandsForensicInstitute/simplewiki-translated-nl
- NetherlandsForensicInstitute/stackexchange-duplicate-questions-translated-nl
- NetherlandsForensicInstitute/wiki-atomic-edits-translated-nl
language:
- nl
license: apache-2.0
robbert-2022-dutch-sentence-transformers
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is based on KU Leuven's RobBERT model. It has been finetuned on the Paraphrase dataset, which we (machine-) translated to Dutch. The Paraphrase dataset consists of multiple datasets that consist of duo's of similar texts, for example duplicate questions on a forum. We have released the translated data that we used to train this model on our Huggingface page.
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 = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('NetherlandsForensicInstitute/robbert-2022-dutch-sentence-transformers')
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('NetherlandsForensicInstitute/robbert-2022-dutch-sentence-transformers')
model = AutoModel.from_pretrained('NetherlandsForensicInstitute/robbert-2022-dutch-sentence-transformers')
# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Training
The model was trained with the parameters:
DataLoader:
MultiDatasetDataLoader.MultiDatasetDataLoader of length 414262 with parameters:
{'batch_size': 1}
Loss:
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
Parameters of the fit()-Method:
{
"epochs": 1,
"evaluation_steps": 50000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 500,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)