Sentence Similarity
sentence-transformers
Safetensors
Norwegian
Norwegian Nynorsk
Norwegian Bokmål
feature-extraction
dense
custom_code
Eval Results (legacy)
Instructions to use Fremtind/norsbert4-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Fremtind/norsbert4-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Fremtind/norsbert4-large", trust_remote_code=True) sentences = [ "En gruppe barn leker og har det gøy.", "Barn leker på gresset omgitt av sterke farger.", "Barna er sammen.", "Barna leser bøker." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
links
Browse files
README.md
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language:
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- 'no'
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- nn
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tags:
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- sentence-transformers
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- sentence-similarity
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To verify the utility of our models, we evaluated them on a selection of classification and clustering tasks for Norwegian from [MTEBv2](https://embeddings-benchmark.github.io/mteb/).
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The heatmap below shows the results of evaluating five sentence-transformers on ten different tasks;
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three of the sentence-transformer models we have fine-tuned
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and the other two are relatively popular (and comparable) sentence similarity models ([FFI/SimCSE-NB-BERT-large](https://huggingface.co/FFI/SimCSE-NB-BERT-large) and [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base)).
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| Rank | Model | Borda Points |
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| 1 | **Fremtind/norsbert4-large** | **44** |
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| 2 | FFI/SimCSE-NB-BERT-large | 40 |
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| 3 | Fremtind/norsbert4-base | 24 |
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| 4 | NbAiLab/nb-sbert-base | 15 |
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| 5 | Fremtind/mmBERT-base-norwegian | 7 |
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## Training Details
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language:
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- 'no'
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- nn
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- nb
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tags:
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- sentence-transformers
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- sentence-similarity
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To verify the utility of our models, we evaluated them on a selection of classification and clustering tasks for Norwegian from [MTEBv2](https://embeddings-benchmark.github.io/mteb/).
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The heatmap below shows the results of evaluating five sentence-transformers on ten different tasks;
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three of the sentence-transformer models we have fine-tuned
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([Fremtind/norsbert4-large](https://huggingface.co/Fremtind/norsbert4-large), [Fremtind/norsbert4-base](https://huggingface.co/Fremtind/norsbert4-base), [Fremtind/mmBERT-base-norwegian](https://huggingface.co/Fremtind/mmBERT-base-norwegian))
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and the other two are relatively popular (and comparable) sentence similarity models ([FFI/SimCSE-NB-BERT-large](https://huggingface.co/FFI/SimCSE-NB-BERT-large) and [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base)).
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| Rank | Model | Borda Points |
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|:----:|:--------------------------------|:-------------:|
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| 1 | **Fremtind/norsbert4-large** | **44** |
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| 2 | [FFI/SimCSE-NB-BERT-large](https://huggingface.co/FFI/SimCSE-NB-BERT-large) | 40 |
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| 3 | [Fremtind/norsbert4-base](https://huggingface.co/Murhaf/norsbert4-base) | 24 |
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| 4 | [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base) | 15 |
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| 5 | [Fremtind/mmBERT-base-norwegian](https://huggingface.co/Fremtind/mmBERT-base-norwegian) | 7 |
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## Training Details
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