Add new SentenceTransformer model

880d5ef verified 10 days ago

67.1 kB

	---
	language:
	- bn
	- cs
	- de
	- en
	- et
	- fi
	- fr
	- gu
	- ha
	- hi
	- is
	- ja
	- kk
	- km
	- lt
	- lv
	- pl
	- ps
	- ru
	- ta
	- tr
	- uk
	- xh
	- zh
	- zu
	- ne
	- ro
	- si
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1327190
	- loss:CoSENTLoss
	base_model: sentence-transformers/distiluse-base-multilingual-cased-v2
	widget:
	- source_sentence: यहाँका केही धार्मिक सम्पदाहरू यस प्रकार रहेका छन्।
	sentences:
	- A party works journalists from advertisements about a massive Himalayan post.
	- Some religious affiliations here remain.
	- In Spain, the strict opposition of Roman Catholic churches is found to have assumed
	a marriage similar to male beach wives.
	- source_sentence: '"We can use this discovery to target both the assembly and stability
	of the capsid, to either prevent the formation of the virus when it infects the
	host cell, or break it apart after it''s formed," Luque said. "This could facilitate
	the characterization and identification of antiviral targets for viruses sharing
	the same icosahedral layout."'
	sentences:
	- FC inter have today released Shefki Kuqi from the club's representative team coach
	duties.
	- '"Wir können diese Entdeckung nutzen, um sowohl die Montage als auch die Stabilität
	des Kapsids anzustreben, um entweder die Bildung des Virus zu verhindern, wenn
	es die Wirtszelle infiziert oder nach seiner Bildung auseinanderbricht", sagte
	Luque. "Dies könnte die Charakterisierung und Identifizierung von antiviralen
	Zielen für Viren erleichtern, die das gleiche ikosaedrische Layout teilen".'
	- Quellen sagen, Jones sei „wütend“, als das goldene Mädchen des Fernsehens bei
	einem angespannten Treffen am Dienstag im Hauptquartier seines Geschäftsimperiums
	in Marlow, Buckinghamshire, zugab, dass ihre neuen Deals - im Wert von bis zu
	1,5 Millionen Pfund - bedeuteten, dass sie nicht mehr genug Zeit hatte, sich ihrer
	Hausbekleidungs- und Zubehörmarke Truly zu widmen.
	- source_sentence: He possesses a pistol with silver bullets for protection from vampires
	and werewolves.
	sentences:
	- Er besitzt eine Pistole mit silbernen Kugeln zum Schutz vor Vampiren und Werwölfen.
	- Bibimbap umfasst Reis, Spinat, Rettich, Bohnensprossen.
	- BSAC profitierte auch von den großen, aber nicht unbeschränkten persönlichen Vermögen
	von Rhodos und Beit vor ihrem Tod.
	- source_sentence: To the west of the Badger Head Inlier is the Port Sorell Formation,
	a tectonic mélange of marine sediments and dolerite.
	sentences:
	- Er brennt einen Speer und brennt Flammen aus seinem Mund, wenn er wütend ist.
	- Westlich des Badger Head Inlier befindet sich die Port Sorell Formation, eine
	tektonische Mischung aus Sedimenten und Dolerit.
	- Public Lynching and Mob Violence under Modi Government
	- source_sentence: Garnizoana otomană se retrage în sudul Dunării, iar după 164 de
	ani cetatea intră din nou sub stăpânirea europenilor.
	sentences:
	- This is because, once again, we have taken into account the fact that we have
	adopted a large number of legislative proposals.
	- Helsinki University ranks 75th among universities for 2010.
	- Ottoman garnisoana is withdrawing into the south of the Danube and, after 164
	years, it is once again under the control of Europeans.
	datasets:
	- RicardoRei/wmt-da-human-evaluation
	- wmt/wmt20_mlqe_task1
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts eval
	type: sts-eval
	metrics:
	- type: pearson_cosine
	value: 0.42415369784945883
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.4175469519194782
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.0772713008408403
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.13050905562438264
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.16731845692612535
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.18366199919315862
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.3567214608388243
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.3656734148567112
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.41267092498678554
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.41036446071667193
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.5254563854630899
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.4785530551765603
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.31194241573567016
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.2814160300891252
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.4253603788235729
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.4166117661445095
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.022187134575214738
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.04647559130832398
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.15979577569463932
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.2074497419832692
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.3698928748443983
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.3757690724227716
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.44937864470538347
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.45866193737582717
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.4466389646053608
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.4158920394678395
	name: Spearman Cosine
	- type: pearson_cosine
	value: 0.33243289478987115
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.2806845193699054
	name: Spearman Cosine
	---

	# SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) on the [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation), [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) and [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) datasets. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) <!-- at revision dad0fa1ee4fa6e982d3adbce87c73c02e6aee838 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 512 dimensions
	- Similarity Function: Cosine Similarity
	- Training Datasets:
	- [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation)
	- [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
	- [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
	- [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
	- [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
	- [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
	- [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
	- Languages: bn, cs, de, en, et, fi, fr, gu, ha, hi, is, ja, kk, km, lt, lv, pl, ps, ru, ta, tr, uk, xh, zh, zu, ne, ro, si
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
	(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("RomainDarous/distiluse-base-multilingual-cased-v2-sts")
	# Run inference
	sentences = [
	'Garnizoana otomană se retrage în sudul Dunării, iar după 164 de ani cetatea intră din nou sub stăpânirea europenilor.',
	'Ottoman garnisoana is withdrawing into the south of the Danube and, after 164 years, it is once again under the control of Europeans.',
	'This is because, once again, we have taken into account the fact that we have adopted a large number of legislative proposals.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 512]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Semantic Similarity

	* Datasets: `sts-eval`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test` and `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| sts-eval \| sts-test \|
	\|:--------------------\|:-----------\|:-----------\|
	\| pearson_cosine \| 0.4242 \| 0.3324 \|
	\| spearman_cosine \| 0.4175 \| 0.2807 \|

	#### Semantic Similarity

	* Dataset: `sts-eval`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.0773 \|
	\| spearman_cosine \| 0.1305 \|

	#### Semantic Similarity

	* Dataset: `sts-eval`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.1673 \|
	\| spearman_cosine \| 0.1837 \|

	#### Semantic Similarity

	* Dataset: `sts-eval`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.3567 \|
	\| spearman_cosine \| 0.3657 \|

	#### Semantic Similarity

	* Dataset: `sts-eval`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.4127 \|
	\| spearman_cosine \| 0.4104 \|

	#### Semantic Similarity

	* Dataset: `sts-eval`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.5255 \|
	\| spearman_cosine \| 0.4786 \|

	#### Semantic Similarity

	* Dataset: `sts-eval`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.3119 \|
	\| spearman_cosine \| 0.2814 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Datasets

	#### wmt_da

	* Dataset: [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation) at [301de38](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation/tree/301de385bf05b0c00a8f4be74965e186164dd425)
	* Size: 1,285,190 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 37.09 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 37.12 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.7</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Z dat ÚZIS také vyplývá, že se zastavil úbytek zdravotních sester v nemocnicích.</code> \| <code>The data from the IHIS also shows that the decline of nurses in hospitals has stopped.</code> \| <code>0.47</code> \|
	\| <code>Я был самым гордым, самым пьяным девственником, которого кто-либо когда-либо видел.</code> \| <code>I was the proudest, most drunk virgin anyone had ever seen.</code> \| <code>0.99</code> \|
	\| <code>Das Trampolinspringen hat einen gewissen Außenseitercharme, teilweise weil es für das unaufgeklärte Ohr passender für eine Clownsschule als die für die Olympischen Spiele klingt.</code> \| <code>The trampoline jumping has some outsider charm, in part because it sounds more appropriate for the unenlightened ear for a clowns school than the one for the Olympics.</code> \| <code>0.81</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_en_de

	* Dataset: [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 7,000 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 11 tokens</li><li>mean: 23.78 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 26.51 tokens</li><li>max: 54 tokens</li></ul> \| <ul><li>min: 0.06</li><li>mean: 0.86</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Early Muslim traders and merchants visited Bengal while traversing the Silk Road in the first millennium.</code> \| <code>Frühe muslimische Händler und Kaufleute besuchten Bengalen, während sie im ersten Jahrtausend die Seidenstraße durchquerten.</code> \| <code>0.9233333468437195</code> \|
	\| <code>While Fran dissipated shortly after that, the tropical wave progressed into the northeastern Pacific Ocean.</code> \| <code>Während Fran kurz danach zerstreute, entwickelte sich die tropische Welle in den nordöstlichen Pazifischen Ozean.</code> \| <code>0.8899999856948853</code> \|
	\| <code>Distressed securities include such events as restructurings, recapitalizations, and bankruptcies.</code> \| <code>Zu den belasteten Wertpapieren gehören Restrukturierungen, Rekapitalisierungen und Insolvenzen.</code> \| <code>0.9300000071525574</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_en_zh

	* Dataset: [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 7,000 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 24.09 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 29.93 tokens</li><li>max: 74 tokens</li></ul> \| <ul><li>min: 0.01</li><li>mean: 0.68</li><li>max: 0.98</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------\|:---------------------------------\|
	\| <code>In the late 1980s, the hotel's reputation declined, and it functioned partly as a "backpackers hangout."</code> \| <code>在 20 世纪 80 年代末，这家旅馆的声誉下降了，部分地起到了 "背包吊销" 的作用。</code> \| <code>0.40666666626930237</code> \|
	\| <code>From 1870 to 1915, 36 million Europeans migrated away from Europe.</code> \| <code>从 1870 年到 1915 年， 3, 600 万欧洲人从欧洲移民。</code> \| <code>0.8333333730697632</code> \|
	\| <code>In some photos, the footpads did press into the regolith, especially when they moved sideways at touchdown.</code> \| <code>在一些照片中，脚垫确实挤进了后台，尤其是当他们在触地时侧面移动时。</code> \| <code>0.33000001311302185</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_et_en

	* Dataset: [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 7,000 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 14 tokens</li><li>mean: 31.88 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 24.57 tokens</li><li>max: 56 tokens</li></ul> \| <ul><li>min: 0.03</li><li>mean: 0.67</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Gruusias vahistati president Mihhail Saakašvili pressibüroo nõunik Simon Kiladze, keda süüdistati spioneerimises.</code> \| <code>In Georgia, an adviser to the press office of President Mikhail Saakashvili, Simon Kiladze, was arrested and accused of spying.</code> \| <code>0.9466666579246521</code> \|
	\| <code>Nii teadmissotsioloogia pooldajad tavaliselt Kuhni tõlgendavadki, arendades tema vaated sõnaselgeks relativismiks.</code> \| <code>This is how supporters of knowledge sociology usually interpret Kuhn by developing his views into an explicit relativism.</code> \| <code>0.9366666674613953</code> \|
	\| <code>18. jaanuaril 2003 haarasid mitmeid Canberra eeslinnu võsapõlengud, milles hukkus neli ja sai vigastada 435 inimest.</code> \| <code>On 18 January 2003, several of the suburbs of Canberra were seized by debt fires which killed four people and injured 435 people.</code> \| <code>0.8666666150093079</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_ne_en

	* Dataset: [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 7,000 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 17 tokens</li><li>mean: 40.67 tokens</li><li>max: 77 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 24.66 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.01</li><li>mean: 0.39</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------\|:---------------------------------\|
	\| <code>सामान्‍य बजट प्रायः फेब्रुअरीका अंतिम कार्य दिवसमा लाईन्छ।</code> \| <code>A normal budget is usually awarded to the digital working day of February.</code> \| <code>0.5600000023841858</code> \|
	\| <code>कविताका यस्ता स्वरूपमा दुई, तिन वा चार पाउसम्मका मुक्तक, हाइकु, सायरी र लोकसूक्तिहरू पर्दछन् ।</code> \| <code>The book consists of two, free of her or four paulets, haiku, Sairi, and locus in such forms.</code> \| <code>0.23666666448116302</code> \|
	\| <code>ब्रिट्नीले यस बारेमा प्रतिक्रिया ब्यक्ता गरदै भनिन,"कुन ठूलो कुरा हो र?</code> \| <code>Britney did not respond to this, saying "which is a big thing and a big thing?</code> \| <code>0.21666665375232697</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_ro_en

	* Dataset: [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 7,000 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 12 tokens</li><li>mean: 29.44 tokens</li><li>max: 60 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 22.38 tokens</li><li>max: 65 tokens</li></ul> \| <ul><li>min: 0.01</li><li>mean: 0.68</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------\|
	\| <code>Orașul va fi împărțit în patru districte, iar suburbiile în 10 mahalale.</code> \| <code>The city will be divided into four districts and suburbs into 10 mahalals.</code> \| <code>0.4699999988079071</code> \|
	\| <code>La scurt timp după aceasta, au devenit cunoscute debarcările germane de la Trondheim, Bergen și Stavanger, precum și luptele din Oslofjord.</code> \| <code>In the light of the above, the Authority concludes that the aid granted to ADIF is compatible with the internal market pursuant to Article 61 (3) (c) of the EEA Agreement.</code> \| <code>0.02666666731238365</code> \|
	\| <code>Până în vara 1791, în Clubul iacobinilor au dominat reprezentanții monarhismului liberal constituțional.</code> \| <code>Until the summer of 1791, representatives of liberal constitutional monarchism dominated in the Jacobins Club.</code> \| <code>0.8733333349227905</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_si_en

	* Dataset: [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 7,000 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 18.19 tokens</li><li>max: 38 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 22.31 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.01</li><li>mean: 0.51</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------\|
	\| <code>ඇපලෝ 4 සැටර්න් V බූස්ටරයේ ප්‍රථම පර්යේෂණ පියාසැරිය විය.</code> \| <code>The first research flight of the Apollo 4 Saturn V Booster.</code> \| <code>0.7966666221618652</code> \|
	\| <code>මෙහි අවපාතය සැලකීමේ දී, මෙහි 48%ක අවරෝහණය $ මිලියන 125කට අධික චිත්‍රපටයක් ලද තෙවන කුඩාම අවපාතය වේ.</code> \| <code>In conjunction with the depression here, 48 % of obesity here is the third smallest depression in over $ 125 million film.</code> \| <code>0.17666666209697723</code> \|
	\| <code>එසේම "බකමූණන් මගින් මෙම රාක්ෂසියගේ රාත්‍රී හැසිරීම සංකේතවත් වන බව" පවසයි.</code> \| <code>Also "the owl says that this monster's night behavior is symbolic".</code> \| <code>0.8799999952316284</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	### Evaluation Datasets

	#### wmt_da

	* Dataset: [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation) at [301de38](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation/tree/301de385bf05b0c00a8f4be74965e186164dd425)
	* Size: 1,285,190 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 36.52 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 36.59 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.7</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>The note adds that should the departure from the White House be delayed, a second aircrew would be needed for the return flight due to duty-hour restrictions.</code> \| <code>V poznámce se dodává, že pokud by se odlet z Bílého domu zpozdil, byla by pro zpáteční let kvůli omezení pracovní doby nutná druhá letecká posádka.</code> \| <code>0.95</code> \|
	\| <code>上半年电信网络诈骗犯罪上升七成最高检总结特点-中新网</code> \| <code>In the first half of the year, telecommunication network fraud crimes rose by 70%. The highest inspection summary characteristics-Zhongxin.com</code> \| <code>0.72</code> \|
	\| <code>Als zentrale Herausforderungen für den Bundesnachrichtendienst (BND) nannte Merkel den Kampf gegen die Verbreitung von Falschmeldungen im Internet und die Abwehr von Cyberattacken.</code> \| <code>Merkel a cité la lutte contre la propagation de fausses nouvelles en ligne et la défense contre les cyberattaques comme des défis majeurs pour le service fédéral de renseignement (BND).</code> \| <code>0.87</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_en_de

	* Dataset: [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 11 tokens</li><li>mean: 24.11 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 26.66 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 0.03</li><li>mean: 0.81</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Resuming her patrols, Constitution managed to recapture the American sloop Neutrality on 27 March and, a few days later, the French ship Carteret.</code> \| <code>Mit der Wiederaufnahme ihrer Patrouillen gelang es der Verfassung, am 27. März die amerikanische Schleuderneutralität und wenige Tage später das französische Schiff Carteret zurückzuerobern.</code> \| <code>0.9033333659172058</code> \|
	\| <code>Blaine's nomination alienated many Republicans who viewed Blaine as ambitious and immoral.</code> \| <code>Blaines Nominierung entfremdete viele Republikaner, die Blaine als ehrgeizig und unmoralisch betrachteten.</code> \| <code>0.9216666221618652</code> \|
	\| <code>This initiated a brief correspondence between the two which quickly descended into political rancor.</code> \| <code>Dies leitete eine kurze Korrespondenz zwischen den beiden ein, die schnell zu politischem Groll abstieg.</code> \| <code>0.878333330154419</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_en_zh

	* Dataset: [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 23.75 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 29.56 tokens</li><li>max: 67 tokens</li></ul> \| <ul><li>min: 0.26</li><li>mean: 0.65</li><li>max: 0.9</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:---------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------\|:--------------------------------\|
	\| <code>Freeman briefly stayed with the king before returning to Accra via Whydah, Ahgwey and Little Popo.</code> \| <code>弗里曼在经过惠达、阿格威和小波波回到阿克拉之前与国王一起住了一会儿。</code> \| <code>0.6683333516120911</code> \|
	\| <code>Fantastic Fiction "Scratches in the Sky, Ben Peek, Agog!</code> \| <code>奇特的虚构 "天空中的碎片，本佩克，阿戈！</code> \| <code>0.71833336353302</code> \|
	\| <code>For Hermann Keller, the running quavers and semiquavers "suffuse the setting with health and strength."</code> \| <code>对赫尔曼 · 凯勒来说，跑步的跳跃者和半跳跃者 "让环境充满健康和力量" 。</code> \| <code>0.7066666483879089</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_et_en

	* Dataset: [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 12 tokens</li><li>mean: 32.4 tokens</li><li>max: 58 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 24.87 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 0.03</li><li>mean: 0.6</li><li>max: 0.99</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------\|:---------------------------------\|
	\| <code>Jackson pidas seal kõne, öeldes, et James Brown on tema suurim inspiratsioon.</code> \| <code>Jackson gave a speech there saying that James Brown is his greatest inspiration.</code> \| <code>0.9833333492279053</code> \|
	\| <code>Kaanelugu rääkis loo kolme ungarlase üleelamistest Ungari revolutsiooni päevil.</code> \| <code>The life of the Man spoke of a story of three Hungarians living in the days of the Hungarian Revolution.</code> \| <code>0.28999999165534973</code> \|
	\| <code>Teise maailmasõja ajal oli ta mitme Saksa juhatusele alluvate eesti väeosa ülem.</code> \| <code>During World War II, he was the commander of several of the German leadership.</code> \| <code>0.4516666829586029</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_ne_en

	* Dataset: [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 17 tokens</li><li>mean: 41.03 tokens</li><li>max: 85 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 24.77 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.05</li><li>mean: 0.36</li><li>max: 0.92</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|:---------------------------------\|
	\| <code>१८९२ तिर भवानीदत्त पाण्डेले 'मुद्रा राक्षस'को अनुवाद गरे।</code> \| <code>Around 1892, Bhavani Pandit translated the 'money monster'.</code> \| <code>0.8416666388511658</code> \|
	\| <code>यस बच्चाको मुखले आमाको स्तन यस बच्चाको मुखले आमाको स्तन राम्ररी च्यापेको छ ।</code> \| <code>The breasts of this child's mouth are taped well with the mother's mouth.</code> \| <code>0.2150000035762787</code> \|
	\| <code>बुवाको बन्दुक चोरेर हिँडेका बराललाई केआई सिंहले अब गोली ल्याउन लगाए ।...</code> \| <code>Kei Singh, who stole the boy's closet, took the bullet to bring it now..</code> \| <code>0.27000001072883606</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_ro_en

	* Dataset: [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 14 tokens</li><li>mean: 30.25 tokens</li><li>max: 59 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 22.7 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 0.01</li><li>mean: 0.68</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------\|
	\| <code>Cornwallis se afla înconjurat pe uscat de forțe armate net superioare și retragerea pe mare era îndoielnică din cauza flotei franceze.</code> \| <code>Cornwallis was surrounded by shore by higher armed forces and the sea withdrawal was doubtful due to the French fleet.</code> \| <code>0.8199999928474426</code> \|
	\| <code>thumbrightuprightDansatori [[cretani de muzică tradițională.</code> \| <code>Number of employees employed in the production of the like product in the Union.</code> \| <code>0.009999999776482582</code> \|
	\| <code>Potrivit documentelor vremii și tradiției orale, aceasta a fost cea mai grea perioadă din istoria orașului.</code> \| <code>According to the documents of the oral weather and tradition, this was the hardest period in the city's history.</code> \| <code>0.5383332967758179</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	#### mlqe_si_en

	* Dataset: [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 18.12 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 22.18 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.03</li><li>mean: 0.51</li><li>max: 0.99</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>එයට ශි්‍ර ලංකාවේ සාමය ඇති කිරිමටත් නැති කිරිමටත් පුළුවන්.</code> \| <code>It can also cause peace in Sri Lanka.</code> \| <code>0.3199999928474426</code> \|
	\| <code>ඔහු මනෝ විද්‍යාව, සමාජ විද්‍යාව, ඉතිහාසය හා සන්නිවේදනය යන විෂය ක්ෂේත්‍රයන් පිලිබදවද අධ්‍යයනයන් සිදු කිරීමට උත්සාහ කරන ලදි.</code> \| <code>He attempted to do subjects in psychology, sociology, history and communication.</code> \| <code>0.5366666913032532</code> \|
	\| <code>එහෙත් කිසිදු මිනිසෙක්‌ හෝ ගැහැනියෙක්‌ එලිමහනක නොවූහ.</code> \| <code>But no man or woman was eliminated.</code> \| <code>0.2783333361148834</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `num_train_epochs`: 2
	- `warmup_ratio`: 0.1

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 2
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| wmt da loss \| mlqe en de loss \| mlqe en zh loss \| mlqe et en loss \| mlqe ne en loss \| mlqe ro en loss \| mlqe si en loss \| sts-eval_spearman_cosine \| sts-test_spearman_cosine \|
	\|:-----:\|:-----:\|:-------------:\|:-----------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\|:------------------------:\|:------------------------:\|
	\| 0.4 \| 6690 \| 7.8421 \| 7.5547 \| 7.5619 \| 7.5555 \| 7.5327 \| 7.5354 \| 7.5109 \| 7.5564 \| 0.1989 \| - \|
	\| 0.8 \| 13380 \| 7.552 \| 7.5420 \| 7.5757 \| 7.5739 \| 7.5185 \| 7.5126 \| 7.4994 \| 7.5511 \| 0.2336 \| - \|
	\| 1.2 \| 20070 \| 7.5216 \| 7.5465 \| 7.6072 \| 7.5942 \| 7.5217 \| 7.5141 \| 7.4871 \| 7.5471 \| 0.2694 \| - \|
	\| 1.6 \| 26760 \| 7.5024 \| 7.5329 \| 7.6123 \| 7.5814 \| 7.5230 \| 7.5141 \| 7.4679 \| 7.5379 \| 0.2866 \| - \|
	\| 2.0 \| 33450 \| 7.495 \| 7.5252 \| 7.6106 \| 7.5756 \| 7.5201 \| 7.5128 \| 7.4725 \| 7.5417 \| 0.2814 \| 0.2807 \|


	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.3.1+cu121
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### CoSENTLoss
	```bibtex
	@online{kexuefm-8847,
	title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
	author={Su Jianlin},
	year={2022},
	month={Jan},
	url={https://kexue.fm/archives/8847},
	}
	```

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