---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
datasets:
- ms_marco
model-index:
  - name: E:\HuggingFaceDataDownloader\results\finetuned_models\2000\2000_finetune
    results:
      - task:
          type: Classification
        dataset:
          type: DDSC/angry-tweets
          name: MTEB AngryTweetsClassification
          config: default
          split: test
          revision: 20b0e6081892e78179356fada741b7afa381443d
        metrics:
          - type: accuracy
            value: 56.084049665711554
          - type: f1
            value: 55.198013156852625
      - task:
          type: BitextMining
        dataset:
          type: strombergnlp/bornholmsk_parallel
          name: MTEB BornholmBitextMining
          config: default
          split: test
          revision: 3bc5cfb4ec514264fe2db5615fac9016f7251552
        metrics:
          - type: accuracy
            value: 47
          - type: f1
            value: 37.97365079365079
          - type: precision
            value: 34.48333333333334
          - type: recall
            value: 47
      - task:
          type: Classification
        dataset:
          type: danish_political_comments
          name: MTEB DanishPoliticalCommentsClassification
          config: default
          split: train
          revision: edbb03726c04a0efab14fc8c3b8b79e4d420e5a1
        metrics:
          - type: accuracy
            value: 40.88398556758257
          - type: f1
            value: 37.604524785367076
      - task:
          type: Classification
        dataset:
          type: DDSC/lcc
          name: MTEB LccSentimentClassification
          config: default
          split: test
          revision: de7ba3406ee55ea2cc52a0a41408fa6aede6d3c6
        metrics:
          - type: accuracy
            value: 59.599999999999994
          - type: f1
            value: 59.0619246469949
      - task:
          type: Classification
        dataset:
          type: strombergnlp/nordic_langid
          name: MTEB NordicLangClassification
          config: default
          split: test
          revision: e254179d18ab0165fdb6dbef91178266222bee2a
        metrics:
          - type: accuracy
            value: 61.00333333333333
          - type: f1
            value: 60.45633325804296
      - task:
          type: Classification
        dataset:
          type: ScandEval/scala-da
          name: MTEB ScalaDaClassification
          config: default
          split: test
          revision: 1de08520a7b361e92ffa2a2201ebd41942c54675
        metrics:
          - type: accuracy
            value: 50.43457031250001
          - type: ap
            value: 50.22017546538257
          - type: f1
            value: 50.03426509926491
---

# e5-dansk-test

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

The model was trained by MS-MARCO english dataset machine translated into the danish language to test whether Machine translation high quality datasets to a foreign language produces good results

## 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 = ["Dette er en dansk sætning", "Dette er en også en dansk sætning"]

model = SentenceTransformer('Jechto/e5-dansk-test-0.1')
embeddings = model.encode(sentences)
print(embeddings)
```

## Training
The model was trained with the parameters:

**DataLoader**:

`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 10327 with parameters:
```
{'batch_size': 16}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 2000,
    "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adam.Adam'>",
    "optimizer_params": {
        "lr": 1e-05
    },
    "scheduler": "warmupconstant",
    "steps_per_epoch": null,
    "warmup_steps": 10000,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
  (2): Normalize()
)
```

## Citing & Authors

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