Robbert 2022 Dutch Sentence Transformers

🚀 robbert-2022-dutch-sentence-transformers

This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.

🚀 Quick Start

This model is based on KU Leuven's RobBERT model. It has been fine - tuned on the Paraphrase dataset, which was machine - translated to Dutch. The Paraphrase dataset consists of multiple datasets with pairs of similar texts, like duplicate questions on a forum. The translated data used to train this model has been released on our Huggingface page.

✨ Features

• Pipeline Tag: sentence - similarity
• Tags: sentence - transformers, feature - extraction, sentence - similarity, transformers
• 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
• Base Model: DTAI - KULeuven/robbert - 2022 - dutch - base

📦 Installation

Using this model becomes easy when you have sentence - transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

Using Sentence - Transformers

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)

Using HuggingFace Transformers

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)

🔧 Technical Details

Training

The model was trained with the following 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})
)

📄 License

This model is licensed under the apache - 2.0 license.

📚 Documentation

In order to cite this model, click on the three dots in the top right of this page and click "Cite this model".