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Ehsanl/
robbert-2023-dutch-base-old_syn_filt_2neg

Name: robbert-2023-dutch-base-old_syn_filt_2neg
Rating: 2.5 (15 reviews)

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RobBERT-2023-base-ft

RobBERT-2023-base-ft is a fine-tuned version of DTAI-KULeuven/robbert-2023-dutch-base. It demonstrates strong performance on MTEB-NL. If you’re looking for a state-of-the-art model of comparable size, you may also want to consider clips/e5-base-trm-nl.

Usage

Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.

python

import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = [
    'query: hoeveel eiwitten moet een vrouw eten',
    'query: top definieer',
    "passage: Als algemene richtlijn geldt dat de gemiddelde eiwitbehoefte voor vrouwen van 19 tot 70 jaar volgens de CDC 46 gram per dag bedraagt. Maar, zoals je in deze tabel kunt zien, moet je dit verhogen als je zwanger bent of traint voor een marathon. Bekijk de onderstaande tabel om te zien hoeveel eiwitten je dagelijks zou moeten eten.",
    "passage: Definitie van top voor leerlingen Engels. : 1 het hoogste punt van een berg : de top van een berg. : 2 het hoogste niveau. : 3 een bijeenkomst of reeks bijeenkomsten tussen de leiders van twee of meer regeringen."
]

tokenizer = AutoTokenizer.from_pretrained('clips/robbert-2023-base-ft')
model = AutoModel.from_pretrained('clips/robbert-2023-base-ft')

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

Below is an example for usage with sentence_transformers.

python

from sentence_transformers import SentenceTransformer

# Load the model from Hugging Face
model = SentenceTransformer("clips/robbert-2023-base-ft")

# Perform inference using encode_query/encode_document for retrieval,
# or encode_query for general purpose embeddings. Prompt prefixes
# are automatically added with these two methods.
queries = [
    'hoeveel eiwitten moet een vrouw eten',
    'top definieer',
]
documents = [
    'Als algemene richtlijn geldt dat de gemiddelde eiwitbehoefte voor vrouwen van 19 tot 70 jaar volgens de CDC 46 gram per dag bedraagt. Maar, zoals je in deze tabel kunt zien, moet je dit verhogen als je zwanger bent of traint voor een marathon. Bekijk de onderstaande tabel om te zien hoeveel eiwitten je dagelijks zou moeten eten.',
    'Definitie van top voor leerlingen Engels. : 1 het hoogste punt van een berg : de top van een berg. : 2 het hoogste niveau. : 3 een bijeenkomst of reeks bijeenkomsten tussen de leiders van twee of meer regeringen.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# (2, 768) (2, 768)

similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7730, 0.2940],
#         [0.3368, 0.7256]])

Benchmark Evaluation

Results on MTEB-NL (models introduced in our paper and the best model per size category are highlighted in bold):

Model	Prm	Cls	MLCls	PCls	Rrnk	Rtr	Clust	STS	AvgD	AvgT
Num. Datasets (→)		12	3	2	1	12	8	2	40
Supervised (small, <100M)
e5-small-v2-t2t	33M	53.7	38.5	74.5	85.9	45.0	24.1	74.3	46.9	56.6
e5-small-v2-t2t-nl	33M	55.3	40.9	74.9	86.0	49.9	28.0	74.1	49.8	58.4
e5-small-trm	41M	56.3	43.5	76.5	87.3	53.1	28.2	74.2	51.4	59.9
e5-small-trm-nl	41M	58.2	44.7	76.0	87.1	56.0	32.2	74.6	53.8	61.3
Supervised (base, <305M)
granite-embedding-107m-multilingual	107M	53.9	41.8	70.1	84.7	50.2	29.8	68.4	49.4	57.0
e5-base-v2-t2t	109M	54.4	40.3	73.3	85.6	46.2	25.5	73.2	47.8	56.9
e5-base-v2-t2t-nl	109M	53.9	41.5	72.5	84.0	46.4	26.9	69.3	47.8	56.3
multilingual-e5-small	118M	56.3	43.5	76.5	87.1	53.1	28.2	74.2	51.4	59.8
paraphrase-multilingual-MiniLM-L12-v2	118M	55.0	38.1	78.2	80.6	37.7	29.6	76.3	46.3	56.5
RobBERT-2023-base-ft	124M	58.1	44.6	72.7	84.7	51.6	32.9	68.5	52.0	59.0
e5-base-trm	124M	58.1	44.4	76.7	88.3	55.8	28.1	74.9	52.9	60.9
e5-base-trm-nl	124M	59.6	45.9	78.4	87.5	56.5	34.3	75.8	55.0	62.6
potion-multilingual-128M	128M	51.8	40.0	60.4	80.3	35.7	26.1	62.0	42.6	50.9
multilingual-e5-base	278M	58.2	44.4	76.7	88.4	55.8	27.7	74.9	52.8	60.9
granite-embedding-278m-multilingual	278M	54.6	41.8	71.0	85.6	52.4	30.3	68.9	50.5	58.0
paraphrase-multilingual-mpnet-base-v2	278M	58.1	40.5	81.9	82.3	41.4	30.8	79.3	49.2	59.2
Arctic-embed-m-v2.0	305M	54.4	42.6	66.6	86.2	51.8	26.5	64.9	49.1	56.1
gte-multilingual-base	305M	59.1	37.7	77.8	82.3	56.8	31.3	78.6	53.8	60.5
Supervised (large, >305M)
e5-large-v2-t2t	335M	55.7	41.4	75.7	86.6	49.9	25.5	74.0	49.5	58.4
e5-large-v2-t2t-nl	335M	57.3	42.4	76.9	86.9	50.8	27.7	74.1	51.7	59.4
RobBERT-2023-large-ft	355M	59.3	45.2	68.7	82.3	48.3	31.6	70.6	51.0	58.0
e5-large-trm	355M	60.2	45.4	80.3	90.3	59.0	28.7	78.8	55.1	63.3
e5-large-trm-nl	355M	62.2	48.0	81.4	87.2	58.2	35.6	78.2	57.0	64.4
multilingual-e5-large	560M	60.2	45.4	80.3	90.3	59.1	29.5	78.8	55.3	63.4
Arctic-embed-l-v2.0	568M	59.3	45.2	74.2	88.2	59.0	29.8	71.7	54.3	61.1
bge-m3	568M	60.7	44.2	78.3	88.7	60.0	29.2	78.1	55.4	63.1
jina-embeddings-v3	572M	61.7	38.9	76.8	78.5	59.1	38.9	84.8	57.0	62.7

Citation Information

If you find our paper, benchmark or models helpful, please consider cite as follows:

latex

@misc{banar2025mtebnle5nlembeddingbenchmark,
      title={MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch}, 
      author={Nikolay Banar and Ehsan Lotfi and Jens Van Nooten and Cristina Arhiliuc and Marija Kliocaite and Walter Daelemans},
      year={2025},
      eprint={2509.12340},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.12340}, 
}

Ehsanl/robbert-2023-dutch-base-old_syn_filt_2neg

RobBERT-2023-base-ft

Usage

Benchmark Evaluation

Citation Information

Ehsanl/
robbert-2023-dutch-base-old_syn_filt_2neg