Create README.md

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers.js
+- onnx
+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
+---
+
+# robbert-2022-dutch-sentence-transformers - Onnx
+- Model creator: [Netherlands Forensic Institute](https://huggingface.co/NetherlandsForensicInstitute)
+- Original model: [robbert-2022-dutch-sentence-transformers](https://huggingface.co/NetherlandsForensicInstitute/robbert-2022-dutch-sentence-transformers)
+
+# Description
+This Onnx model is a converted version of robbert-2022-dutch-sentence-transformers using the transformers.js script found [here](https://github.com/xenova/transformers.js?tab=readme-ov-file#convert-your-models-to-onnx).
+
+# Original model card: robbert-2022-dutch-sentence-transformers
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+This model is based on [KU Leuven's RobBERT model](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base). 
+It has been finetuned on the [Paraphrase dataset](https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/datasets/paraphrases/), 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](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+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](https://www.SBERT.net), 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.
+
+```python
+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})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->