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	---
	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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