Bheri/ithasa-mmbert

SentenceTransformer based on jhu-clsp/mmBERT-base

This is a sentence-transformers model finetuned from jhu-clsp/mmBERT-base. 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: jhu-clsp/mmBERT-base <!-- at revision c5955035435e2bf121cde7f3c8863ef52ff35d82 -->
• —Maximum Sequence Length: 8192 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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 = [
    '"""Starting position (Sthiti), Tadasana"""',
    'Sanskrit: """प्रारम्भिका स्थितिः, ताडासनम् ।"""\nEnglish: """Starting position (Sthiti), Tadasana"""',
    "Sanskrit: भोजनपचनानन्तरं यदा व्यक्ति: तस्य पार्श्वे भोजनार्थं गच्छति तदा तं भोजनपरिवेषणं कथ्यते ।\nEnglish: Once the food is cooked it is brought to the persons who will consume it which we call 'Serving'.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0003, 0.8767, 0.7860],
#         [0.8767, 1.0003, 0.9032],
#         [0.7860, 0.9032, 1.0003]])

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Direct Usage (Transformers)

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Out-of-Scope Use

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Evaluation

Metrics

Triplet

• —Dataset: triplet-dev
• —Evaluated with <code>TripletEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 256,886 training samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 12 tokens</li><li>mean: 31.26 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 102.95 tokens</li><li>max: 280 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 81.61 tokens</li><li>max: 772 tokens</li></ul> |
• —Samples: | anchor | positive | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>There was no Mughal tradition of primogeniture, the systematic passing of rule, upon an emperor's death, to his eldest son.</code> | <code>Sanskrit: चक्रवर्तिनः मृत्योः अनन्तरं तस्य शासनस्य व्यवस्थितरूपेण सङ्क्रमणस्य, मुघलपरम्परायाः ज्येष्ठपुत्राधिकारपद्धतिः नासीत्।<br>English: There was no Mughal tradition of primogeniture, the systematic passing of rule, upon an emperor's death, to his eldest son.</code> | <code>Sanskrit: F6 नोदनेन व्यवस्थां रन् कुर्मः ।<br>English: Let's now run the application by pressing F6 on your keyboard.</code> | | <code>The four sons of Shah Jahan all held governorships during their father's reign.</code> | <code>Sanskrit: शाह्-जहाँ-नामकस्य चत्वारः पुत्राः, सर्वे पितुः शासनकाले शासकपदम् अधारयन्।<br>English: The four sons of Shah Jahan all held governorships during their father's reign.</code> | <code>Sanskrit: यदा च पृथिवीं सर्वां यजमानोऽनुपर्यगाः। शम्याक्षेपेण विधिना तदाऽऽसीत् किं तु ते हृदि॥ न ते पश्यामि भृङ्गारं न च्छत्रं व्यजने न च। ब्रह्मदत्तां च ते मालां न पश्याम्यसुराधिप॥<br>English: I do not now see that golden jar of yours, nor that umbrella of yours, nor those fans. I behold not also, O king of the Asuras, that garland which was given given to you by Grandfather.'</code> | | <code>In this regard he discusses the correlation between social opportunities of education and health and how both of these complement economic and political freedoms as a healthy and well-educated person is better suited to make informed economic decisions and be involved in fruitful political demonstrations etc.</code> | <code>Sanskrit: अस्मिन् विषये सः शिक्षणस्य स्वास्थ्यस्य च सामाजिकावकाशानाम् अन्योन्य-सम्बन्धस्य, तथा च एतद्द्वयम् अपि आर्थिक-राजनैतिक-स्वातन्त्र्ययोः कथं पूरकं भवतः इति च चर्चां करोति, यतोहि स्वस्था सुशिक्षिता च व्यक्तिः ज्ञानपूर्वम् आर्थिकविषयान् निर्णेतुं तथा फलप्रदेषु राजनैतिकेषु प्रतिपादनादिषु संलग्नः भवितुं च अधिकारी भवति इति।<br>English: In this regard he discusses the correlation between social opportunities of education and health and how both of these complement economic and political freedoms as a healthy and well-educated person is better suited to make informed economic decisions and be involved in fruitful political demonstrations etc.</code> | <code>Sanskrit: पदं देवस्य मीढुषोऽनाधृष्टाभिरूतिभिः । भद्रा सूर्य इवोपदृक्<br>English: O agni! You are light, admirable, enemyless and patient. Seeing you is as beneficial as seeing the sun.</code> |
• —Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 1.0
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 2,000 evaluation samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 18.39 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 56.57 tokens</li><li>max: 171 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 56.98 tokens</li><li>max: 169 tokens</li></ul> |
• —Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>You two were eloquent speakers.</code> | <code>Sanskrit: युवां वाक्पटू आस्तम् ।<br>English: You two were eloquent speakers.</code> | <code>Sanskrit: Mozilla Firefox - परिचय: इत्यस्मिन् Spoken tutorial मध्ये स्वागतम् \|<br>English: Welcome to the Spoken tutorial on Introduction to Mozilla Firefox.</code> | | <code>"""And James the son of Zebedee, and John the brother of James; and he surnamed them Boanerges, which is, The sons of thunder:"""</code> | <code>Sanskrit: """याकूब् तस्य भ्राता योहन् च आन्द्रियः फिलिपो बर्थलमयः,"""<br>English: """And James the son of Zebedee, and John the brother of James; and he surnamed them Boanerges, which is, The sons of thunder:"""</code> | <code>Sanskrit: पुनः एक्सिक्यूट् कुर्मः । अप् एरो कीलकं नुत्वा Enter नुदन्तु ।<br>English: "Let's execute again. Press the up arrow key, press Enter."</code> | | <code>The heart of a healthy adult person beats 60-80 times per minute</code> | <code>Sanskrit: स्वस्थस्य कस्यचन प्रौढस्य प्रति मिनट्-काले षष्टिवारात अधिकाधिकतया अशीतिवारं कम्पते।<br>English: The heart of a healthy adult person beats 60-80 times per minute</code> | <code>Sanskrit: यत्र कुत्रापि नुदन्तु । पश्यन्तु यत् द्वेऽपि नोड्स् नीले अभवतामिति ।<br>English: Click on any segment. Notice both the nodes turn blue.</code> |
• —Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 1.0
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 2
• —per_device_eval_batch_size: 128
• —learning_rate: 2e-05
• —num_train_epochs: 10
• —warmup_ratio: 0.05
• —bf16: True
• —load_best_model_at_end: True

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: 2
• —per_device_eval_batch_size: 128
• —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: 10
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.05
• —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
• —bf16: True
• —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: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —project: huggingface
• —trackio_space_id: trackio
• —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
• —hub_revision: None
• —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
• —include_tokens_per_second: False
• —include_num_input_tokens_seen: no
• —neftune_noise_alpha: None
• —optim_target_modules: None
• —batch_eval_metrics: False
• —eval_on_start: False
• —use_liger_kernel: False
• —liger_kernel_config: None
• —eval_use_gather_object: False
• —average_tokens_across_devices: True
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

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