bangla-sentence-embedding

This is a sentence-transformers model finetuned from l3cube-pune/bengali-sentence-similarity-sbert on the Divyanshu/indicxnli 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 Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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 = [
    'বেকি একজন ছাত্র।',
    'আমাদের কি উড়ছে তা দেখা উচিত ছিল।',
    'সে কখনো ঐ কুকুরের মত হতে চায়নি.',
]
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]

Training Details

Training Dataset

Divyanshu/indicxnli

  • Dataset: Divyanshu/indicxnli at 7092c27
  • Size: 392,702 training samples
  • Columns: premise, hypothesis, and label
  • Approximate statistics based on the first 1000 samples:
    premise hypothesis label
    type string string int
    details
    • min: 4 tokens
    • mean: 25.14 tokens
    • max: 134 tokens
    • min: 4 tokens
    • mean: 13.87 tokens
    • max: 62 tokens
    • 0: ~34.30%
    • 1: ~28.20%
    • 2: ~37.50%
  • Samples:
    premise hypothesis label
    ধারণাগতভাবে ক্রিম স্কিমিং-এর দুটি মৌলিক মাত্রা রয়েছে-পণ্য এবং ভূগোল। পণ্য এবং ভূগোল ক্রিম স্কিমিং কাজ করে। 1
    যদি তারা মূল দলটিকে স্মরণ করার সিদ্ধান্ত নেয় তাহলে ব্রেভস ট্রিপল এ থেকে একজন লোককে ফিরিয়ে আনার সিদ্ধান্ত নেয় তখন একজন লোক তাকে প্রতিস্থাপন করতে যায় এবং একজন লোক তাকে প্রতিস্থাপন করতে যায় মানুষ যদি মনে রাখে, তাহলে আপনি নিচের স্তরটি হারিয়ে ফেলবেন। 0
    আমাদের একজন আপনার নির্দেশ পালন করবে। আমার টিমের একজন সদস্য তোমার আদেশ নির্ভুলভাবে পালন করবে। 0
  • Loss: SoftmaxLoss

Evaluation Dataset

Divyanshu/indicxnli

  • Dataset: Divyanshu/indicxnli at 7092c27
  • Size: 2,490 evaluation samples
  • Columns: premise, hypothesis, and label
  • Approximate statistics based on the first 1000 samples:
    premise hypothesis label
    type string string int
    details
    • min: 7 tokens
    • mean: 21.63 tokens
    • max: 54 tokens
    • min: 4 tokens
    • mean: 12.48 tokens
    • max: 41 tokens
    • 0: ~33.30%
    • 1: ~33.40%
    • 2: ~33.30%
  • Samples:
    premise hypothesis label
    আর সে বললো, মা, আমি ঘরে আছি। স্কুল বাস থেকে নামতেই সে তার মাকে ফোন করে। 1
    আর সে বললো, মা, আমি ঘরে আছি। একটা কথাও বলল না। 2
    আর সে বললো, মা, আমি ঘরে আছি। সে তার মাকে জানায়, সে বাড়ি ফিরে এসেছে। 0
  • Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 62
  • per_device_eval_batch_size: 62
  • num_train_epochs: 20
  • warmup_ratio: 0.1
  • auto_find_batch_size: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 62
  • per_device_eval_batch_size: 62
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_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: 20
  • 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
  • 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: True
  • 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
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss loss
4.9893 15801 1.1307 -
4.9896 15802 1.1195 -
4.9899 15803 1.1197 -
4.9902 15804 1.0637 -
4.9905 15805 1.0501 -
4.9908 15806 0.9898 -
4.9912 15807 0.9666 -
4.9915 15808 0.9361 -
4.9918 15809 0.8909 -
4.9921 15810 0.8325 -
4.9924 15811 0.8529 -
4.9927 15812 0.8309 -
4.9931 15813 0.7165 -
4.9934 15814 0.8147 -
4.9937 15815 0.8114 -
4.9940 15816 0.6725 -
4.9943 15817 0.792 -
4.9946 15818 0.7525 -
4.9949 15819 0.6984 -
4.9953 15820 0.6965 -
4.9956 15821 0.6336 -
4.9959 15822 0.7219 -
4.9962 15823 0.648 -
4.9965 15824 0.687 -
4.9968 15825 0.8192 -
4.9972 15826 0.7313 -
4.9975 15827 0.6728 -
4.9978 15828 0.6837 -
4.9981 15829 0.6866 -
4.9984 15830 0.721 -
4.9987 15831 0.6844 -
4.9991 15832 0.7566 -
4.9994 15833 0.6337 -
4.9997 15834 0.6692 -
5.0 15835 0.6492 -
5.0003 15836 0.7393 -
5.0006 15837 0.672 -
5.0009 15838 0.7248 -
5.0013 15839 0.651 -
5.0016 15840 0.6245 -
5.0019 15841 0.5954 -
5.0022 15842 0.6322 -
5.0025 15843 0.6095 -
5.0028 15844 0.733 -
5.0032 15845 0.6364 -
5.0035 15846 0.7936 -
5.0038 15847 0.6406 -
5.0041 15848 0.7641 -
5.0044 15849 0.655 -
5.0047 15850 0.6941 -
5.0051 15851 0.7649 -
5.0054 15852 0.6368 -
5.0057 15853 0.7314 -
5.0060 15854 0.7298 -
5.0063 15855 0.6252 -
5.0066 15856 0.6026 -
5.0069 15857 0.6533 -
5.0073 15858 0.6717 -
5.0076 15859 0.7218 -
5.0079 15860 0.6083 -
5.0082 15861 0.6594 -
5.0085 15862 0.6628 -
5.0088 15863 0.5706 -
5.0092 15864 0.5838 -
5.0095 15865 0.6893 -
5.0098 15866 0.5638 -
5.0101 15867 0.6148 -
5.0104 15868 0.6759 -
5.0107 15869 0.6355 -
5.0111 15870 0.725 -
5.0114 15871 0.7011 -
5.0117 15872 0.5544 -
5.0120 15873 0.7206 -
5.0123 15874 0.6733 -
5.0126 15875 0.7631 -
5.0129 15876 0.6761 -
5.0133 15877 0.5704 -
5.0136 15878 0.6621 -
5.0139 15879 0.6477 -
5.0142 15880 0.6552 -
5.0145 15881 0.6429 -
5.0148 15882 0.6515 -
5.0152 15883 0.6791 -
5.0155 15884 0.6335 -
5.0158 15885 0.6563 -
5.0161 15886 0.5879 -
5.0164 15887 0.638 -
5.0167 15888 0.6093 -
5.0171 15889 0.559 -
5.0174 15890 0.6309 -
5.0177 15891 0.5407 -
5.0180 15892 0.6464 -
5.0183 15893 0.6533 -
5.0186 15894 0.7004 -
5.0189 15895 0.5978 -
5.0193 15896 0.7072 -
5.0196 15897 0.7139 -
5.0199 15898 0.6452 -
5.0202 15899 0.6243 -
5.0205 15900 0.6032 -
5.0208 15901 0.617 -
5.0212 15902 0.5598 -
5.0215 15903 0.66 -
5.0218 15904 0.4775 -
5.0221 15905 0.6889 -
5.0224 15906 0.6438 -
5.0227 15907 0.6328 -
5.0231 15908 0.6287 -
5.0234 15909 0.7021 -
5.0237 15910 0.7062 -
5.0240 15911 0.6234 -
5.0243 15912 0.6493 -
5.0246 15913 0.6411 -
5.0249 15914 0.6175 -
5.0253 15915 0.6512 -
5.0256 15916 0.6414 -
5.0259 15917 0.716 -
5.0262 15918 0.6538 -
5.0265 15919 0.6943 -
5.0268 15920 0.6523 -
5.0272 15921 0.6278 -
5.0275 15922 0.651 -
5.0278 15923 0.6955 -
5.0281 15924 0.5965 -
5.0284 15925 0.6407 -
5.0287 15926 0.6434 -
5.0290 15927 0.6348 -
5.0294 15928 0.6038 -
5.0297 15929 0.7033 -
5.0300 15930 0.6104 -
5.0303 15931 0.6948 -
5.0306 15932 0.637 -
5.0309 15933 0.6604 -
5.0313 15934 0.6332 -
5.0316 15935 0.543 -
5.0319 15936 0.7226 -
5.0322 15937 0.5595 -
5.0325 15938 0.6171 -
5.0328 15939 0.595 -
5.0332 15940 0.646 -
5.0335 15941 0.6471 -
5.0338 15942 0.5903 -
5.0341 15943 0.6046 -
5.0344 15944 0.5806 -
5.0347 15945 0.5519 -
5.0350 15946 0.5998 -
5.0354 15947 0.6804 -
5.0357 15948 0.626 -
5.0360 15949 0.7278 -
5.0363 15950 0.6408 -
5.0366 15951 0.6832 -
5.0369 15952 0.6287 -
5.0373 15953 0.6417 -
5.0376 15954 0.6428 -
5.0379 15955 0.6903 -
5.0382 15956 0.6934 -
5.0385 15957 0.6141 -
5.0388 15958 0.6076 -
5.0392 15959 0.6013 -
5.0395 15960 0.7086 -
5.0398 15961 0.6702 -
5.0401 15962 0.6012 -
5.0404 15963 0.7193 -
5.0407 15964 0.6263 -
5.0410 15965 0.6653 -
5.0414 15966 0.5499 -
5.0417 15967 0.8102 -
5.0420 15968 0.7314 -
5.0423 15969 0.6129 -
5.0426 15970 0.6067 -
5.0429 15971 0.6859 -
5.0433 15972 0.6324 -
5.0436 15973 0.6203 -
5.0439 15974 0.6649 -
5.0442 15975 0.7243 -
5.0445 15976 0.6262 -
5.0448 15977 0.5481 -
5.0452 15978 0.6239 -
5.0455 15979 0.6313 -
5.0458 15980 0.6643 -
5.0461 15981 0.6508 -
5.0464 15982 0.6362 -
5.0467 15983 0.6676 -
5.0470 15984 0.6766 -
5.0474 15985 0.4995 -
5.0477 15986 0.6816 -
5.0480 15987 0.5363 -
5.0483 15988 0.7083 -
5.0486 15989 0.6867 -
5.0489 15990 0.6444 -
5.0493 15991 0.6045 -
5.0496 15992 0.6234 -
5.0499 15993 0.6884 -
5.0502 15994 0.5989 -
5.0505 15995 0.6015 -
5.0508 15996 0.6535 -
5.0512 15997 0.6129 -
5.0515 15998 0.6189 -
5.0518 15999 0.6553 -
5.0521 16000 0.5703 0.8470
5.0524 16001 0.7306 -
5.0527 16002 0.5775 -
5.0530 16003 0.6288 -
5.0534 16004 0.6148 -
5.0537 16005 0.6581 -
5.0540 16006 0.6782 -
5.0543 16007 0.7281 -
5.0546 16008 0.5736 -
5.0549 16009 0.6948 -
5.0553 16010 0.6986 -
5.0556 16011 0.6532 -
5.0559 16012 0.652 -
5.0562 16013 0.5963 -
5.0565 16014 0.605 -
5.0568 16015 0.7218 -
5.0572 16016 0.6464 -
5.0575 16017 0.6925 -
5.0578 16018 0.5996 -
5.0581 16019 0.5935 -
5.0584 16020 0.6801 -
5.0587 16021 0.7 -
5.0590 16022 0.631 -
5.0594 16023 0.6427 -
5.0597 16024 0.667 -
5.0600 16025 0.6433 -
5.0603 16026 0.6324 -
5.0606 16027 0.5704 -
5.0609 16028 0.6366 -
5.0613 16029 0.7066 -
5.0616 16030 0.6905 -
5.0619 16031 0.5948 -
5.0622 16032 0.6684 -
5.0625 16033 0.5443 -
5.0628 16034 0.633 -
5.0632 16035 0.6913 -
5.0635 16036 0.5794 -
5.0638 16037 0.6053 -
5.0641 16038 0.6655 -
5.0644 16039 0.6029 -
5.0647 16040 0.6652 -
5.0650 16041 0.5791 -
5.0654 16042 0.6615 -
5.0657 16043 0.5848 -
5.0660 16044 0.7948 -
5.0663 16045 0.6083 -
5.0666 16046 0.6869 -
5.0669 16047 0.6096 -
5.0673 16048 0.6053 -
5.0676 16049 0.645 -
5.0679 16050 0.6222 -
5.0682 16051 0.6898 -
5.0685 16052 0.6763 -
5.0688 16053 0.6241 -
5.0692 16054 0.649 -
5.0695 16055 0.7125 -
5.0698 16056 0.7383 -
5.0701 16057 0.5785 -
5.0704 16058 0.6348 -
5.0707 16059 0.6502 -
5.0710 16060 0.6311 -
5.0714 16061 0.6021 -
5.0717 16062 0.6309 -
5.0720 16063 0.6327 -
5.0723 16064 0.6501 -
5.0726 16065 0.7432 -
5.0729 16066 0.6313 -
5.0733 16067 0.6907 -
5.0736 16068 0.6499 -
5.0739 16069 0.6088 -
5.0742 16070 0.5928 -
5.0745 16071 0.6297 -
5.0748 16072 0.6267 -
5.0751 16073 0.6445 -
5.0755 16074 0.6648 -
5.0758 16075 0.7127 -
5.0761 16076 0.5874 -
5.0764 16077 0.6705 -
5.0767 16078 0.5869 -
5.0770 16079 0.6451 -
5.0774 16080 0.6469 -
5.0777 16081 0.6145 -
5.0780 16082 0.6745 -
5.0783 16083 0.6095 -
5.0786 16084 0.7205 -
5.0789 16085 0.6105 -
5.0793 16086 0.6709 -
5.0796 16087 0.6147 -
5.0799 16088 0.5822 -
5.0802 16089 0.6797 -
5.0805 16090 0.6282 -
5.0808 16091 0.5979 -
5.0811 16092 0.5583 -
5.0815 16093 0.5996 -
5.0818 16094 0.5854 -
5.0821 16095 0.6655 -
5.0824 16096 0.607 -
5.0827 16097 0.5853 -
5.0830 16098 0.6498 -
5.0834 16099 0.5649 -
5.0837 16100 0.6692 -
5.0840 16101 0.6804 -
5.0843 16102 0.6456 -
5.0846 16103 0.639 -
5.0849 16104 0.6981 -
5.0853 16105 0.6171 -
5.0856 16106 0.6169 -
5.0859 16107 0.547 -
5.0862 16108 0.6042 -
5.0865 16109 0.5853 -
5.0868 16110 0.6896 -
5.0871 16111 0.6208 -
5.0875 16112 0.6655 -
5.0878 16113 0.7055 -
5.0881 16114 0.5907 -
5.0884 16115 0.6101 -
5.0887 16116 0.6142 -
5.0890 16117 0.6043 -
5.0894 16118 0.6111 -
5.0897 16119 0.6902 -
5.0900 16120 0.5878 -
5.0903 16121 0.748 -
5.0906 16122 0.674 -
5.0909 16123 0.617 -
5.0913 16124 0.6115 -
5.0916 16125 0.6074 -
5.0919 16126 0.5944 -
5.0922 16127 0.687 -
5.0925 16128 0.678 -
5.0928 16129 0.6855 -
5.0931 16130 0.563 -
5.0935 16131 0.5958 -
5.0938 16132 0.6301 -
5.0941 16133 0.6422 -
5.0944 16134 0.6035 -
5.0947 16135 0.7047 -
5.0950 16136 0.64 -
5.0954 16137 0.5904 -
5.0957 16138 0.6717 -
5.0960 16139 0.6491 -
5.0963 16140 0.6442 -
5.0966 16141 0.6869 -
5.0969 16142 0.5471 -
5.0973 16143 0.6323 -
5.0976 16144 0.6893 -
5.0979 16145 0.5499 -
5.0982 16146 0.6495 -
5.0985 16147 0.5625 -
5.0988 16148 0.637 -
5.0991 16149 0.5817 -
5.0995 16150 0.5998 -
5.0998 16151 0.6284 -
5.1001 16152 0.7216 -
5.1004 16153 0.6699 -
5.1007 16154 0.6162 -
5.1010 16155 0.6681 -
5.1014 16156 0.5902 -
5.1017 16157 0.739 -
5.1020 16158 0.6908 -
5.1023 16159 0.6468 -
5.1026 16160 0.5698 -
5.1029 16161 0.5949 -
5.1033 16162 0.6278 -
5.1036 16163 0.6094 -
5.1039 16164 0.5615 -
5.1042 16165 0.5648 -
5.1045 16166 0.5564 -
5.1048 16167 0.5946 -
5.1051 16168 0.5547 -
5.1055 16169 0.6752 -
5.1058 16170 0.606 -
5.1061 16171 0.7034 -
5.1064 16172 0.759 -
5.1067 16173 0.7052 -
5.1070 16174 0.5067 -
5.1074 16175 0.5927 -
5.1077 16176 0.5514 -
5.1080 16177 0.5999 -
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Framework Versions

  • Python: 3.9.18
  • Sentence Transformers: 3.0.0
  • Transformers: 4.41.0
  • PyTorch: 2.2.2+cu118
  • Accelerate: 0.30.1
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}
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