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

This is a sentence-transformers model finetuned from sentence-transformers/distiluse-base-multilingual-cased-v2 on the wmt_da, mlqe_en_de, mlqe_en_zh, mlqe_et_en, mlqe_ne_en, mlqe_ro_en and mlqe_si_en 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
Maximum Sequence Length: 128 tokens
Output Dimensionality: 512 dimensions
Similarity Function: Cosine Similarity
Training Datasets:
- wmt_da
- mlqe_en_de
- mlqe_en_zh
- mlqe_et_en
- mlqe_ne_en
- mlqe_ro_en
- mlqe_si_en
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

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("RomainDarous/pre_trained_original")
# 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]

Evaluation

Metrics

Semantic Similarity

Datasets: sts-eval, sts-test, sts-test, sts-test, sts-test, sts-test, sts-test and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-eval	sts-test
pearson_cosine	0.3072	0.3217
spearman_cosine	0.2885	0.3007

Semantic Similarity

Dataset: sts-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.1581
spearman_cosine	0.2044

Semantic Similarity

Dataset: sts-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.2689
spearman_cosine	0.2804

Semantic Similarity

Dataset: sts-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5131
spearman_cosine	0.4982

Semantic Similarity

Dataset: sts-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.3618
spearman_cosine	0.3825

Semantic Similarity

Dataset: sts-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6868
spearman_cosine	0.6186

Semantic Similarity

Dataset: sts-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.2509
spearman_cosine	0.2492

Training Details

Training Datasets

wmt_da

Dataset: wmt_da at 301de38
Size: 1,285,190 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 37.55 tokens
max: 128 tokens

min: 5 tokens
mean: 37.35 tokens
max: 128 tokens

min: 0.0
mean: 0.69
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 37.55 tokens max: 128 tokens	min: 5 tokens mean: 37.35 tokens max: 128 tokens	min: 0.0 mean: 0.69 max: 1.0

Samples:

sentence1	sentence2	score
`维吉尼亚州的阿灵顿县星期五也加入了诉讼。`	`Arlington County, Virginia, joined the suit Friday.`	`0.94`
`Так. Розмір на 10-11 років. Але наші розміри не співпадають. Все треба міряти`	`Yeah. Size for 10-11 years. But our dimensions don't match. Everything needs to be measured`	`0.92`
`这项研究报告发表在《当代生物学》杂志上。`	`The research report was published in the journal Contemporary Biology.`	`0.99`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_en_de

Dataset: mlqe_en_de at 0783ed2
Size: 7,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 11 tokens
mean: 23.78 tokens
max: 44 tokens

min: 11 tokens
mean: 26.51 tokens
max: 54 tokens

min: 0.06
mean: 0.86
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 11 tokens mean: 23.78 tokens max: 44 tokens	min: 11 tokens mean: 26.51 tokens max: 54 tokens	min: 0.06 mean: 0.86 max: 1.0

Samples:

sentence1	sentence2	score
`Early Muslim traders and merchants visited Bengal while traversing the Silk Road in the first millennium.`	`Frühe muslimische Händler und Kaufleute besuchten Bengalen, während sie im ersten Jahrtausend die Seidenstraße durchquerten.`	`0.9233333468437195`
`While Fran dissipated shortly after that, the tropical wave progressed into the northeastern Pacific Ocean.`	`Während Fran kurz danach zerstreute, entwickelte sich die tropische Welle in den nordöstlichen Pazifischen Ozean.`	`0.8899999856948853`
`Distressed securities include such events as restructurings, recapitalizations, and bankruptcies.`	`Zu den belasteten Wertpapieren gehören Restrukturierungen, Rekapitalisierungen und Insolvenzen.`	`0.9300000071525574`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_en_zh

Dataset: mlqe_en_zh at 0783ed2
Size: 7,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 9 tokens
mean: 24.09 tokens
max: 47 tokens

min: 12 tokens
mean: 29.93 tokens
max: 74 tokens

min: 0.01
mean: 0.68
max: 0.98

	sentence1	sentence2	score
type	string	string	float
details	min: 9 tokens mean: 24.09 tokens max: 47 tokens	min: 12 tokens mean: 29.93 tokens max: 74 tokens	min: 0.01 mean: 0.68 max: 0.98

Samples:

sentence1	sentence2	score
`In the late 1980s, the hotel's reputation declined, and it functioned partly as a "backpackers hangout."`	`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。`	`0.40666666626930237`
`From 1870 to 1915, 36 million Europeans migrated away from Europe.`	`从 1870 年到 1915 年， 3, 600 万欧洲人从欧洲移民。`	`0.8333333730697632`
`In some photos, the footpads did press into the regolith, especially when they moved sideways at touchdown.`	`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。`	`0.33000001311302185`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_et_en

Dataset: mlqe_et_en at 0783ed2
Size: 7,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 14 tokens
mean: 31.88 tokens
max: 63 tokens

min: 11 tokens
mean: 24.57 tokens
max: 56 tokens

min: 0.03
mean: 0.67
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 14 tokens mean: 31.88 tokens max: 63 tokens	min: 11 tokens mean: 24.57 tokens max: 56 tokens	min: 0.03 mean: 0.67 max: 1.0

Samples:

sentence1	sentence2	score
`Gruusias vahistati president Mihhail Saakašvili pressibüroo nõunik Simon Kiladze, keda süüdistati spioneerimises.`	`In Georgia, an adviser to the press office of President Mikhail Saakashvili, Simon Kiladze, was arrested and accused of spying.`	`0.9466666579246521`
`Nii teadmissotsioloogia pooldajad tavaliselt Kuhni tõlgendavadki, arendades tema vaated sõnaselgeks relativismiks.`	`This is how supporters of knowledge sociology usually interpret Kuhn by developing his views into an explicit relativism.`	`0.9366666674613953`
`18. jaanuaril 2003 haarasid mitmeid Canberra eeslinnu võsapõlengud, milles hukkus neli ja sai vigastada 435 inimest.`	`On 18 January 2003, several of the suburbs of Canberra were seized by debt fires which killed four people and injured 435 people.`	`0.8666666150093079`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_ne_en

Dataset: mlqe_ne_en at 0783ed2
Size: 7,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 17 tokens
mean: 40.67 tokens
max: 77 tokens

min: 9 tokens
mean: 24.66 tokens
max: 128 tokens

min: 0.01
mean: 0.39
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 17 tokens mean: 40.67 tokens max: 77 tokens	min: 9 tokens mean: 24.66 tokens max: 128 tokens	min: 0.01 mean: 0.39 max: 1.0

Samples:

sentence1	sentence2	score
`सामान्‍य बजट प्रायः फेब्रुअरीका अंतिम कार्य दिवसमा लाईन्छ।`	`A normal budget is usually awarded to the digital working day of February.`	`0.5600000023841858`
`कविताका यस्ता स्वरूपमा दुई, तिन वा चार पाउसम्मका मुक्तक, हाइकु, सायरी र लोकसूक्तिहरू पर्दछन् ।`	`The book consists of two, free of her or four paulets, haiku, Sairi, and locus in such forms.`	`0.23666666448116302`
`ब्रिट्नीले यस बारेमा प्रतिक्रिया ब्यक्ता गरदै भनिन,"कुन ठूलो कुरा हो र?`	`Britney did not respond to this, saying "which is a big thing and a big thing?`	`0.21666665375232697`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_ro_en

Dataset: mlqe_ro_en at 0783ed2
Size: 7,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 12 tokens
mean: 29.44 tokens
max: 60 tokens

min: 10 tokens
mean: 22.38 tokens
max: 65 tokens

min: 0.01
mean: 0.68
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 12 tokens mean: 29.44 tokens max: 60 tokens	min: 10 tokens mean: 22.38 tokens max: 65 tokens	min: 0.01 mean: 0.68 max: 1.0

Samples:

sentence1	sentence2	score
`Orașul va fi împărțit în patru districte, iar suburbiile în 10 mahalale.`	`The city will be divided into four districts and suburbs into 10 mahalals.`	`0.4699999988079071`
`La scurt timp după aceasta, au devenit cunoscute debarcările germane de la Trondheim, Bergen și Stavanger, precum și luptele din Oslofjord.`	`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.`	`0.02666666731238365`
`Până în vara 1791, în Clubul iacobinilor au dominat reprezentanții monarhismului liberal constituțional.`	`Until the summer of 1791, representatives of liberal constitutional monarchism dominated in the Jacobins Club.`	`0.8733333349227905`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_si_en

Dataset: mlqe_si_en at 0783ed2
Size: 7,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 18.19 tokens
max: 38 tokens

min: 9 tokens
mean: 22.31 tokens
max: 128 tokens

min: 0.01
mean: 0.51
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 18.19 tokens max: 38 tokens	min: 9 tokens mean: 22.31 tokens max: 128 tokens	min: 0.01 mean: 0.51 max: 1.0

Samples:

sentence1	sentence2	score
`ඇපලෝ 4 සැටර්න් V බූස්ටරයේ ප්‍රථම පර්යේෂණ පියාසැරිය විය.`	`The first research flight of the Apollo 4 Saturn V Booster.`	`0.7966666221618652`
`මෙහි අවපාතය සැලකීමේ දී, මෙහි 48%ක අවරෝහණය $ මිලියන 125කට අධික චිත්‍රපටයක් ලද තෙවන කුඩාම අවපාතය වේ.`	`In conjunction with the depression here, 48 % of obesity here is the third smallest depression in over $ 125 million film.`	`0.17666666209697723`
`එසේම "බකමූණන් මගින් මෙම රාක්ෂසියගේ රාත්‍රී හැසිරීම සංකේතවත් වන බව" පවසයි.`	`Also "the owl says that this monster's night behavior is symbolic".`	`0.8799999952316284`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Datasets

wmt_da

Dataset: wmt_da at 301de38
Size: 1,285,190 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 36.83 tokens
max: 128 tokens

min: 5 tokens
mean: 36.88 tokens
max: 128 tokens

min: 0.0
mean: 0.7
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 36.83 tokens max: 128 tokens	min: 5 tokens mean: 36.88 tokens max: 128 tokens	min: 0.0 mean: 0.7 max: 1.0

Samples:

sentence1	sentence2	score
`The decision, which was already pre-determined last Autumn through the allocation of resources for the current budget, is sweetened by funding opportunities.`	`Die Entscheidung, die bereits im vergangenen Herbst durch die Mittelzuweisung für den aktuellen Haushalt vorbestimmt wurde, wird durch Finanzierungsmöglichkeiten gesüßt.`	`0.95`
`由于在一次游行期间试图封锁高速公路，费雷尔被判两年半以上缓刑。`	`Ferrer was given a suspended sentence of more than two-and-a-half years for trying to block the highway during a march.`	`0.88`
`"На этот раз я не буду поливать вас шампанским, да и в тот раз это был кто-то другой", - пошутил Хэмилтон во время короткого разговора с главой государства, передает ТАСС.`	`"This time I won't water you champagne, and that time it was someone else," Hamilton joked during a short conversation with the head of state, TASS reports.`	`0.94`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_en_de

Dataset: mlqe_en_de at 0783ed2
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 11 tokens
mean: 24.11 tokens
max: 49 tokens

min: 11 tokens
mean: 26.66 tokens
max: 55 tokens

min: 0.03
mean: 0.81
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 11 tokens mean: 24.11 tokens max: 49 tokens	min: 11 tokens mean: 26.66 tokens max: 55 tokens	min: 0.03 mean: 0.81 max: 1.0

Samples:

sentence1	sentence2	score
`Resuming her patrols, Constitution managed to recapture the American sloop Neutrality on 27 March and, a few days later, the French ship Carteret.`	`Mit der Wiederaufnahme ihrer Patrouillen gelang es der Verfassung, am 27. März die amerikanische Schleuderneutralität und wenige Tage später das französische Schiff Carteret zurückzuerobern.`	`0.9033333659172058`
`Blaine's nomination alienated many Republicans who viewed Blaine as ambitious and immoral.`	`Blaines Nominierung entfremdete viele Republikaner, die Blaine als ehrgeizig und unmoralisch betrachteten.`	`0.9216666221618652`
`This initiated a brief correspondence between the two which quickly descended into political rancor.`	`Dies leitete eine kurze Korrespondenz zwischen den beiden ein, die schnell zu politischem Groll abstieg.`	`0.878333330154419`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_en_zh

Dataset: mlqe_en_zh at 0783ed2
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 9 tokens
mean: 23.75 tokens
max: 49 tokens

min: 11 tokens
mean: 29.56 tokens
max: 67 tokens

min: 0.26
mean: 0.65
max: 0.9

	sentence1	sentence2	score
type	string	string	float
details	min: 9 tokens mean: 23.75 tokens max: 49 tokens	min: 11 tokens mean: 29.56 tokens max: 67 tokens	min: 0.26 mean: 0.65 max: 0.9

Samples:

sentence1	sentence2	score
`Freeman briefly stayed with the king before returning to Accra via Whydah, Ahgwey and Little Popo.`	`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。`	`0.6683333516120911`
`Fantastic Fiction "Scratches in the Sky, Ben Peek, Agog!`	`奇特的虚构 "天空中的碎片，本佩克，阿戈！`	`0.71833336353302`
`For Hermann Keller, the running quavers and semiquavers "suffuse the setting with health and strength."`	`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" 。`	`0.7066666483879089`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_et_en

Dataset: mlqe_et_en at 0783ed2
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 12 tokens
mean: 32.4 tokens
max: 58 tokens

min: 10 tokens
mean: 24.87 tokens
max: 47 tokens

min: 0.03
mean: 0.6
max: 0.99

	sentence1	sentence2	score
type	string	string	float
details	min: 12 tokens mean: 32.4 tokens max: 58 tokens	min: 10 tokens mean: 24.87 tokens max: 47 tokens	min: 0.03 mean: 0.6 max: 0.99

Samples:

sentence1	sentence2	score
`Jackson pidas seal kõne, öeldes, et James Brown on tema suurim inspiratsioon.`	`Jackson gave a speech there saying that James Brown is his greatest inspiration.`	`0.9833333492279053`
`Kaanelugu rääkis loo kolme ungarlase üleelamistest Ungari revolutsiooni päevil.`	`The life of the Man spoke of a story of three Hungarians living in the days of the Hungarian Revolution.`	`0.28999999165534973`
`Teise maailmasõja ajal oli ta mitme Saksa juhatusele alluvate eesti väeosa ülem.`	`During World War II, he was the commander of several of the German leadership.`	`0.4516666829586029`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_ne_en

Dataset: mlqe_ne_en at 0783ed2
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 17 tokens
mean: 41.03 tokens
max: 85 tokens

min: 10 tokens
mean: 24.77 tokens
max: 128 tokens

min: 0.05
mean: 0.36
max: 0.92

	sentence1	sentence2	score
type	string	string	float
details	min: 17 tokens mean: 41.03 tokens max: 85 tokens	min: 10 tokens mean: 24.77 tokens max: 128 tokens	min: 0.05 mean: 0.36 max: 0.92

Samples:

sentence1	sentence2	score
`१८९२ तिर भवानीदत्त पाण्डेले 'मुद्रा राक्षस'को अनुवाद गरे।`	`Around 1892, Bhavani Pandit translated the 'money monster'.`	`0.8416666388511658`
`यस बच्चाको मुखले आमाको स्तन यस बच्चाको मुखले आमाको स्तन राम्ररी च्यापेको छ ।`	`The breasts of this child's mouth are taped well with the mother's mouth.`	`0.2150000035762787`
`बुवाको बन्दुक चोरेर हिँडेका बराललाई केआई सिंहले अब गोली ल्याउन लगाए ।...`	`Kei Singh, who stole the boy's closet, took the bullet to bring it now..`	`0.27000001072883606`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_ro_en

Dataset: mlqe_ro_en at 0783ed2
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 14 tokens
mean: 30.25 tokens
max: 59 tokens

min: 6 tokens
mean: 22.7 tokens
max: 55 tokens

min: 0.01
mean: 0.68
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 14 tokens mean: 30.25 tokens max: 59 tokens	min: 6 tokens mean: 22.7 tokens max: 55 tokens	min: 0.01 mean: 0.68 max: 1.0

Samples:

sentence1	sentence2	score
`Cornwallis se afla înconjurat pe uscat de forțe armate net superioare și retragerea pe mare era îndoielnică din cauza flotei franceze.`	`Cornwallis was surrounded by shore by higher armed forces and the sea withdrawal was doubtful due to the French fleet.`	`0.8199999928474426`
`thumbrightuprightDansatori [[cretani de muzică tradițională.`	`Number of employees employed in the production of the like product in the Union.`	`0.009999999776482582`
`Potrivit documentelor vremii și tradiției orale, aceasta a fost cea mai grea perioadă din istoria orașului.`	`According to the documents of the oral weather and tradition, this was the hardest period in the city's history.`	`0.5383332967758179`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

mlqe_si_en

Dataset: mlqe_si_en at 0783ed2
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 18.12 tokens
max: 36 tokens

min: 7 tokens
mean: 22.18 tokens
max: 128 tokens

min: 0.03
mean: 0.51
max: 0.99

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 18.12 tokens max: 36 tokens	min: 7 tokens mean: 22.18 tokens max: 128 tokens	min: 0.03 mean: 0.51 max: 0.99

Samples:

sentence1	sentence2	score
`එයට ශි්‍ර ලංකාවේ සාමය ඇති කිරිමටත් නැති කිරිමටත් පුළුවන්.`	`It can also cause peace in Sri Lanka.`	`0.3199999928474426`
`ඔහු මනෝ විද්‍යාව, සමාජ විද්‍යාව, ඉතිහාසය හා සන්නිවේදනය යන විෂය ක්ෂේත්‍රයන් පිලිබදවද අධ්‍යයනයන් සිදු කිරීමට උත්සාහ කරන ලදි.`	`He attempted to do subjects in psychology, sociology, history and communication.`	`0.5366666913032532`
`එහෙත් කිසිදු මිනිසෙක්‌ හෝ ගැහැනියෙක්‌ එලිමහනක නොවූහ.`	`But no man or woman was eliminated.`	`0.2783333361148834`

Loss: CoSENTLoss with these parameters:

{
    "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

Click to expand

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: False
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

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	10.3577	7.5743	7.5567	7.5526	7.4877	7.5382	7.4229	7.5659	0.1284	-
0.8	13380	7.5676	7.5664	7.5526	7.5457	7.4842	7.5255	7.4289	7.5537	0.2135	-
1.2	20070	7.5611	7.5634	7.5513	7.5434	7.4853	7.5201	7.4287	7.5495	0.2375	-
1.6	26760	7.5594	7.5623	7.5513	7.5438	7.4833	7.5186	7.4222	7.5466	0.2509	-
2.0	33450	7.5563	7.5621	7.5508	7.5432	7.4819	7.5177	7.4204	7.5472	0.2492	0.3007

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.3.1
Transformers: 4.46.3
PyTorch: 2.5.1+cu124
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers

@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

@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},
}

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

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Training Details

Training Datasets

wmt_da

mlqe_en_de

mlqe_en_zh

mlqe_et_en

mlqe_ne_en

mlqe_ro_en

mlqe_si_en

Evaluation Datasets

wmt_da

mlqe_en_de

mlqe_en_zh

mlqe_et_en

mlqe_ne_en

mlqe_ro_en

mlqe_si_en

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

Model tree for RomainDarous/pre_trained_original

Datasets used to train RomainDarous/pre_trained_original

Evaluation results