RomainDarous/preTrained_meanPooling_mistranslationModel

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/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]

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	0.4242	0.3324
spearman_cosine	0.4175	0.2807

Semantic Similarity

• Dataset: sts-eval
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.0773
spearman_cosine	0.1305

Semantic Similarity

• Dataset: sts-eval
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.1673
spearman_cosine	0.1837

Semantic Similarity

• Dataset: sts-eval
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.3567
spearman_cosine	0.3657

Semantic Similarity

• Dataset: sts-eval
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.4127
spearman_cosine	0.4104

Semantic Similarity

• Dataset: sts-eval
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5255
spearman_cosine	0.4786

Semantic Similarity

• Dataset: sts-eval
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.3119
spearman_cosine	0.2814

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: 4 tokens mean: 37.09 tokens max: 128 tokens	min: 4 tokens mean: 37.12 tokens max: 128 tokens	min: 0.0 mean: 0.7 max: 1.0

• Samples:

  sentence1	sentence2	score
  Z dat ÚZIS také vyplývá, že se zastavil úbytek zdravotních sester v nemocnicích.	The data from the IHIS also shows that the decline of nurses in hospitals has stopped.	0.47
  Я был самым гордым, самым пьяным девственником, которого кто-либо когда-либо видел.	I was the proudest, most drunk virgin anyone had ever seen.	0.99
  Das Trampolinspringen hat einen gewissen Außenseitercharme, teilweise weil es für das unaufgeklärte Ohr passender für eine Clownsschule als die für die Olympischen Spiele klingt.	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.	0.81

• 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

• 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

• 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

• 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

• 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

• 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

• 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: 4 tokens mean: 36.52 tokens max: 128 tokens	min: 4 tokens mean: 36.59 tokens max: 128 tokens	min: 0.0 mean: 0.7 max: 1.0

• Samples:

  sentence1	sentence2	score
  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.	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.	0.95
  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	In the first half of the year, telecommunication network fraud crimes rose by 70%. The highest inspection summary characteristics-Zhongxin.com	0.72
  Als zentrale Herausforderungen für den Bundesnachrichtendienst (BND) nannte Merkel den Kampf gegen die Verbreitung von Falschmeldungen im Internet und die Abwehr von Cyberattacken.	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).	0.87

• 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

• 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

• 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

• 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

• 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

• 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

• 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: 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

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

@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},
}