README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1000000
  - loss:MSELoss
base_model: distilbert/distilbert-base-multilingual-cased
widget:
  - source_sentence: "Agreement on Pledge and Right of Disposal of Cash Account based on agreement template dated 4 June 2015 1. This agreement (the â\x80\x9CPledged Cash Account Agreementâ\x80\x9D or â\x80\x9CAgreementâ\x80\x9D) has been entered into on this. [insert the day] day of [month] [year] by and between."
    sentences:
      - >-
        Perjanjian Gadai dan Hak Pelepasan Rekening Kas berdasarkan template
        perjanjian tanggal 4 Juni 2015 1. Perjanjian ini (¢Â€ÂœPerjanjian
        Gadai Rekening Kas†atau ¢ €ÂœPerjanjian†)
        telah ditandatangani mengenai hal ini. [masukkan hari] hari [bulan]
        [tahun] oleh dan di antara.
      - verizon.net yahoo
      - >-
        Teori sosial-kognitif adalah perspektif teoretis di mana belajar dengan
        mengamati orang lain adalah fokus studi. Teori sosial-kognitif
        didasarkan pada beberapa asumsi dasar. Salah satunya adalah bahwa orang
        dapat belajar dengan mengamati orang lain. Pembelajar dapat memperoleh
        perilaku dan pengetahuan baru hanya dengan mengamati model. Teori
        sosial-kognitif berfokus pada pembelajaran yang terjadi dalam konteks
        sosial. Dalam konteks sosial ini, peserta didik mengamati perilaku dan
        keterampilan baru dari seorang model. Mereka mungkin menginternalisasi
        dan mendemonstrasikan keterampilan tersebut atau terkadang tidak.
  - source_sentence: >-
      a poem of six six-line stanzas and a three-line envoy, originally without
      rhyme, in which each stanza repeats the end words of the lines of the
      first stanza, but in different order, the envoy using the six words again,
      three in the middle of the lines and three at the end. 1  Also called
      sextain.
    sentences:
      - >-
        puisi enam bait enam baris dan utusan tiga baris, awalnya tanpa sajak,
        di mana setiap bait mengulangi kata-kata akhir dari baris bait pertama,
        tetapi dalam urutan yang berbeda, utusan menggunakan enam kata lagi,
        tiga di tengah baris dan tiga di akhir. 1 Disebut juga sextain.
      - >-
        Lipase lambung adalah enzim pencernaan lain yang dibuat oleh sel utama.
        Ini membantu memecah lemak rantai pendek dan menengah. Amilase juga
        ditemukan dalam cairan lambung, tetapi tidak dibuat oleh lambung. Enzim
        ini berasal dari air liur dan berjalan bersama dengan bolus ke dalam
        lambung.
      - >-
        Masuk dengan ID Apple Anda dan atur iCloud Drive. Masukkan ID Apple dan
        kata sandi Anda. Untuk melewati langkah ini, atau jika Anda tidak
        memiliki ID Apple, ketuk Tidak memiliki ID Apple atau lupa. Jika Anda
        menggunakan lebih dari satu ID, ketuk Gunakan ID Apple yang berbeda
        untuk iCloud dan iTunes.masuk dengan ID Apple Anda dan atur iCloud
        Drive. Masukkan ID Apple dan kata sandi Anda. Untuk melewati langkah
        ini, atau jika Anda tidak memiliki ID Apple, ketuk Tidak memiliki ID
        Apple atau lupa. Jika Anda menggunakan lebih dari satu ID, ketuk Gunakan
        ID Apple yang berbeda untuk iCloud dan iTunes.
  - source_sentence: "Ways to Calculate GSAâ\x80\x99s. Industrial Funding Fee (IFF) Divide the original unit price by one (1) minus the IFF rate (e.g., 1 â\x80\x93 0.0075 = 0.9925) to establish the contract price. For example, if the original price of $198.50 is divided by .9925 , the selling price will be $200."
    sentences:
      - >-
        Kompetisi Pelatih Pertunjukan Dr. Oz. Dr. Oz sedang mencari pelatih
        untuk bergabung dalam acara ini untuk membantu menginspirasi jutaan
        orang lain di rumah agar bugar dan sehat! LIHAT SELENGKAPNYA
      - >-
        perbedaan antara tulang rusuk sejati, tulang rusuk palsu, dan tulang
        rusuk melayang
      - >-
        Cara Menghitung GSA¢Âââ. Biaya Pendanaan Industri (IFF) Bagi harga
        satuan asli dengan satu (1) dikurangi tarif IFF (misalnya, 1
        ¢Â€Â“ 0,0075 = 0,9925) untuk menetapkan harga kontrak. Misalnya,
        jika harga asli $198,50 dibagi ,9925 , harga jualnya adalah $200.
  - source_sentence: how does one get a scrapped vehicle registered again
    sentences:
      - bagaimana cara mendapatkan kendaraan bekas yang didaftarkan kembali?
      - definisi bisnis yang bertanggung jawab
      - apakah cwu mendapatkan perguruan tinggi?
  - source_sentence: "1 Concrete is typically measured by cubic yards (3â\x80\x99x3â\x80\x99x3â\x80\x99). 2  An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 3  Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot. An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 2  Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot."
    sentences:
      - >-
        1 Beton biasanya diukur dengan meter kubik
        (3âÂâââx3Ãâ¢Ã‚ââx3âÂââ„¢). 2 Biaya rata-rata untuk satu yard
        kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang
        dibutuhkan dan harga setempat. 3 Biaya tenaga kerja untuk menuangkan dan
        membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi.
        Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125,
        tergantung pada berapa banyak yang dibutuhkan dan harga lokal. 2 Biaya
        tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50
        hingga $7,00 per kaki persegi.
      - seberapa terlambat alat utara terbuka?
      - >-
        Parrot Tattoos - Polly ingin cracker.. Ungkapan ini identik dengan
        'parrot', terutama yang duduk di bahu bajak laut, seperti yang dibuat
        terkenal dalam cerita klasik Robert Louis Stevenson, Treasure Island
        (1883).lint', yang mungkin tidak adalah burung beo pertama yang
        diasosiasikan dengan nama 'Polly', tapi dia pasti mempopulerkannya. Dan
        Polly tentu memastikan burung beo itu akan menjadi simbol ikonik dari
        tradisi bajak laut. Sebagai pendamping legendaris bagi manusia, burung
        beo menyarankan semacam wali.
datasets:
  - carles-undergrad-thesis/en-id-parallel-sentences
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - negative_mse
  - src2trg_accuracy
  - trg2src_accuracy
  - mean_accuracy
model-index:
  - name: SentenceTransformer based on distilbert/distilbert-base-multilingual-cased
    results:
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: default
          type: default
        metrics:
          - type: negative_mse
            value: -3.55539433658123
            name: Negative Mse
      - task:
          type: translation
          name: Translation
        dataset:
          name: default
          type: default
        metrics:
          - type: src2trg_accuracy
            value: 0.9894
            name: Src2Trg Accuracy
          - type: trg2src_accuracy
            value: 0.9861
            name: Trg2Src Accuracy
          - type: mean_accuracy
            value: 0.9877499999999999
            name: Mean Accuracy

SentenceTransformer based on distilbert/distilbert-base-multilingual-cased

This is a sentence-transformers model finetuned from distilbert/distilbert-base-multilingual-cased on the default dataset. It maps sentences & paragraphs to a 768-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: distilbert/distilbert-base-multilingual-cased
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • default

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})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    '1 Concrete is typically measured by cubic yards (3â\x80\x99x3â\x80\x99x3â\x80\x99). 2  An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 3  Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot. An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 2  Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot.',
    '1 Beton biasanya diukur dengan meter kubik (3âÂâââx3Ãâ¢Ã‚ââx3âÂââ„¢). 2 Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang dibutuhkan dan harga setempat. 3 Biaya tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi. Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang dibutuhkan dan harga lokal. 2 Biaya tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi.',
    "Parrot Tattoos - Polly ingin cracker.. Ungkapan ini identik dengan 'parrot', terutama yang duduk di bahu bajak laut, seperti yang dibuat terkenal dalam cerita klasik Robert Louis Stevenson, Treasure Island (1883).lint', yang mungkin tidak adalah burung beo pertama yang diasosiasikan dengan nama 'Polly', tapi dia pasti mempopulerkannya. Dan Polly tentu memastikan burung beo itu akan menjadi simbol ikonik dari tradisi bajak laut. Sebagai pendamping legendaris bagi manusia, burung beo menyarankan semacam wali.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Knowledge Distillation

• Dataset: default
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-3.5554

Translation

• Dataset: default
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.9894
trg2src_accuracy	0.9861
mean_accuracy	0.9877

Training Details

Training Dataset

default

• Dataset: default at c8bc0cb
• Size: 1,000,000 training samples
• Columns: english, indonesian, and label
• Approximate statistics based on the first 1000 samples:

  	english	indonesian	label
  type	string	string	list
  details	min: 4 tokens mean: 44.27 tokens max: 128 tokens	min: 5 tokens mean: 48.93 tokens max: 128 tokens	size: 768 elements

• Samples:

  english	indonesian	label
  This sample job description shares how one smaller sized, growing, multi-site nonprofit organization configured the role of executive director.The executive director is responsible for general management as well as designing a national expansion plan. There also is a heavy emphasis on program evaluation.Feel free to use this sample job description in creating one for your organization.osition. Reporting to the Board of Directors, the Executive Director (ED) will have overall strategic and operational responsibility for XYZ Nonprofit's staff, programs, expansion, and execution of its mission. S/he will initially develop deep knowledge of field, core programs, operations, and business plans.	Uraian tugas contoh ini membagikan bagaimana satu organisasi nirlaba multi-situs berukuran lebih kecil, berkembang, mengonfigurasi peran direktur eksekutif. Direktur eksekutif bertanggung jawab atas manajemen umum serta merancang rencana ekspansi nasional. Ada juga penekanan berat pada evaluasi program. Jangan ragu untuk menggunakan contoh deskripsi pekerjaan ini dalam membuat satu untuk posisi organisasi Anda. Melaporkan kepada Dewan Direksi, Direktur Eksekutif (ED) akan memiliki tanggung jawab strategis dan operasional secara keseluruhan untuk staf, program, ekspansi, dan pelaksanaan misi XYZ Nirlaba. Dia awalnya akan mengembangkan pengetahuan yang mendalam tentang lapangan, program inti, operasi, dan rencana bisnis.	[-0.4337165653705597, -0.0650932714343071, -0.04308838024735451, -0.1756953001022339, 0.32854965329170227, ...]
  Industrial revolution occured last in Russia. In Germany, France and United States industrial revolution occured in early-to-mid 1800's. While in Russia creation of railroads, and foundation of factories happened by govermental initiatives towards the end of XIX century.n Germany, France and United States industrial revolution occured in early-to-mid 1800's.	Revolusi industri terakhir terjadi di Rusia. Di Jerman, Perancis dan Amerika Serikat terjadi revolusi industri pada awal hingga pertengahan 1800-an. Sedangkan di Rusia pembuatan rel kereta api, dan pendirian pabrik terjadi atas inisiatif pemerintah menjelang akhir abad XIX. Revolusi industri Jerman, Prancis dan Amerika Serikat terjadi pada awal hingga pertengahan 1800-an.	[-0.22887374460697174, -0.17583712935447693, 0.08270637691020966, -0.15496928989887238, -0.18010610342025757, ...]
  what causes hordeolum internum left lower eyelid	apa penyebab hordeolum internum kelopak mata kiri bawah	[-0.19872592389583588, 0.4119395911693573, 0.3756648004055023, -0.4884617030620575, 0.15375499427318573, ...]

• Loss: MSELoss

Evaluation Dataset

default

• Dataset: default at c8bc0cb
• Size: 1,000,000 evaluation samples
• Columns: english, indonesian, and label
• Approximate statistics based on the first 1000 samples:

  	english	indonesian	label
  type	string	string	list
  details	min: 5 tokens mean: 46.58 tokens max: 128 tokens	min: 5 tokens mean: 51.0 tokens max: 128 tokens	size: 768 elements

• Samples:

  english	indonesian	label
  do appraisers give adjustments for lot size	apakah penilai memberikan penyesuaian untuk ukuran lot?	[0.12256570905447006, 0.011573846451938152, -0.19426874816417694, -0.17596185207366943, 0.35024771094322205, ...]
  hotels in binghamton ny	hotel di binghamton ny	[0.14259624481201172, -0.048470016568899155, 0.1078888401389122, 0.06728225946426392, 0.6096671223640442, ...]
  guitarist kenny greenberg	gitaris kenny greenberg	[-0.6973275542259216, 0.27737292647361755, -0.09295299649238586, 0.24035970866680145, 0.154855415225029, ...]

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 5
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• 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: 2e-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: 5
• 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: True
• 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

Click to expand

Epoch	Step	Training Loss	default loss	default_negative_mse	default_mean_accuracy
0.0065	100	0.1968	-	-	-
0.0129	200	0.1797	-	-	-
0.0194	300	0.1596	-	-	-
0.0259	400	0.1367	-	-	-
0.0323	500	0.1167	-	-	-
0.0388	600	0.103	-	-	-
0.0453	700	0.0954	-	-	-
0.0517	800	0.0909	-	-	-
0.0582	900	0.088	-	-	-
0.0646	1000	0.0861	-	-	-
0.0711	1100	0.0847	-	-	-
0.0776	1200	0.082	-	-	-
0.0840	1300	0.0818	-	-	-
0.0905	1400	0.0813	-	-	-
0.0970	1500	0.0804	-	-	-
0.1034	1600	0.0817	-	-	-
0.1099	1700	0.0799	-	-	-
0.1164	1800	0.0804	-	-	-
0.1228	1900	0.0802	-	-	-
0.1293	2000	0.0791	-	-	-
0.1358	2100	0.0789	-	-	-
0.1422	2200	0.0783	-	-	-
0.1487	2300	0.0783	-	-	-
0.1551	2400	0.077	-	-	-
0.1616	2500	0.0762	-	-	-
0.1681	2600	0.0762	-	-	-
0.1745	2700	0.0754	-	-	-
0.1810	2800	0.075	-	-	-
0.1875	2900	0.0735	-	-	-
0.1939	3000	0.0745	-	-	-
0.2004	3100	0.0739	-	-	-
0.2069	3200	0.0732	-	-	-
0.2133	3300	0.0724	-	-	-
0.2198	3400	0.0727	-	-	-
0.2263	3500	0.0726	-	-	-
0.2327	3600	0.071	-	-	-
0.2392	3700	0.0713	-	-	-
0.2457	3800	0.0708	-	-	-
0.2521	3900	0.0704	-	-	-
0.2586	4000	0.0703	-	-	-
0.2650	4100	0.0704	-	-	-
0.2715	4200	0.0695	-	-	-
0.2780	4300	0.068	-	-	-
0.2844	4400	0.0681	-	-	-
0.2909	4500	0.0683	-	-	-
0.2974	4600	0.0674	-	-	-
0.3038	4700	0.0683	-	-	-
0.3103	4800	0.0674	-	-	-
0.3168	4900	0.0674	-	-	-
0.3232	5000	0.0666	-	-	-
0.3297	5100	0.0677	-	-	-
0.3362	5200	0.066	-	-	-
0.3426	5300	0.0655	-	-	-
0.3491	5400	0.0658	-	-	-
0.3555	5500	0.0658	-	-	-
0.3620	5600	0.0646	-	-	-
0.3685	5700	0.0638	-	-	-
0.3749	5800	0.065	-	-	-
0.3814	5900	0.0648	-	-	-
0.3879	6000	0.0636	-	-	-
0.3943	6100	0.0637	-	-	-
0.4008	6200	0.0636	-	-	-
0.4073	6300	0.0633	-	-	-
0.4137	6400	0.0629	-	-	-
0.4202	6500	0.0638	-	-	-
0.4267	6600	0.0625	-	-	-
0.4331	6700	0.0615	-	-	-
0.4396	6800	0.062	-	-	-
0.4461	6900	0.062	-	-	-
0.4525	7000	0.0614	-	-	-
0.4590	7100	0.0622	-	-	-
0.4654	7200	0.061	-	-	-
0.4719	7300	0.06	-	-	-
0.4784	7400	0.0606	-	-	-
0.4848	7500	0.0606	-	-	-
0.4913	7600	0.0597	-	-	-
0.4978	7700	0.0598	-	-	-
0.5042	7800	0.0594	-	-	-
0.5107	7900	0.0596	-	-	-
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2.0	30938	-	0.0394	-4.1528	0.9835
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4.0	61876	-	0.0339	-3.6284	0.9876
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4.7967	74200	0.0348	-	-	-
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4.8096	74400	0.0346	-	-	-
4.8161	74500	0.0344	-	-	-
4.8225	74600	0.0342	-	-	-
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4.8355	74800	0.0346	-	-	-
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4.8613	75200	0.0343	-	-	-
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4.8743	75400	0.0344	-	-	-
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4.8872	75600	0.0342	-	-	-
4.8937	75700	0.0341	-	-	-
4.9001	75800	0.0345	-	-	-
4.9066	75900	0.0347	-	-	-
4.9131	76000	0.0341	-	-	-
4.9195	76100	0.0339	-	-	-
4.9260	76200	0.0343	-	-	-
4.9324	76300	0.0346	-	-	-
4.9389	76400	0.0344	-	-	-
4.9454	76500	0.0341	-	-	-
4.9518	76600	0.034	-	-	-
4.9583	76700	0.0342	-	-	-
4.9648	76800	0.0344	-	-	-
4.9712	76900	0.0344	-	-	-
4.9777	77000	0.0343	-	-	-
4.9842	77100	0.0341	-	-	-
4.9906	77200	0.0341	-	-	-
4.9971	77300	0.0342	-	-	-
5.0	77345	-	0.0331	-3.5554	0.9877

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.3.1
• Transformers: 4.46.3
• PyTorch: 2.4.0
• 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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}