dhammanana/Tipitaka_MiniLM-L12

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 <!-- at revision e8f8c211226b894fcb81acc59f3b34ba3efd5f42 -->
• —Maximum Sequence Length: 128 tokens
• —Output Dimensionality: 384 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text <!-- - 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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 = [
    'anāpatti hi so rukkho, hoti ekakulassa ce.',
    'there is indeed no offense if that tree belongs to a single family.',
    'there are four kinds of purity: purity of instruction, purity of restraint, purity of seeking, and purity of reflection.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.8338, -0.1214],
#         [ 0.8338,  1.0000, -0.1454],
#         [-0.1214, -0.1454,  1.0000]])

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 844,384 training samples
• —Columns: <code>sentence0</code> and <code>sentence1</code>
• —Approximate statistics based on the first 100 samples: | | sentence0 | sentence1 | |:---------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | <ul><li>min: 11 tokens</li><li>mean: 40.61 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 38.81 tokens</li><li>max: 128 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>tasi alaṅkāre, bhūvādi.</code> | <code>tasi [is used] in the sense of adorning; it belongs to the bhūvādi group.</code> | | <code>evaṃ phussena yutto māso phusso, maghāya yutto māso māgho, phagguniyā yutto māso phagguno, cittāya yutto māso citto, visākhāya yutto māso vesākho, jeṭṭhāya yutto māso jeṭṭho, uttarāsāḷhāya yutto māso āsāḷho, āsāḷhī vā, savaṇena yutto māso sāvaṇo, sāvaṇī.</code> | <code>similarly, a month conjoined with phussa (pusya) is phusso; a month conjoined with maghā is māgho; a month conjoined with phaggunī (phalgunī) is phagguno; a month conjoined with cittā is citto; a month conjoined with visākhā is vesākho; a month conjoined with jeṭṭhā (jyesthā) is jeṭṭho; a month conjoined with uttarāsāḷhā (uttarāṣāḍhā) is āsāḷho or āsāḷhī; a month conjoined with savaṇa (śravaṇa) is sāvaṇo or sāvaṇī.</code> | | <code>īādimhi-akari, kari, saṅkhari, abhisaṅkhari, akubbi, kubbi, akrubbi, krubbi, akayiri, kayiri, akaruṃ, karuṃ, saṅkharuṃ, abhi, saṅkharuṃ, akariṃsu, kariṃsu, saṅkhariṃsu, abhisaṅkhariṃsu, akubbiṃsu, kubbiṃsu, akrubbiṃsu, krubbiṃsu, akayiriṃsu, kayiriṃsu, akayiruṃ, kayiruṃ.</code> | <code>in the past tense (ī-ādi): akari, kari, saṅkhari, abhisaṅkhari, akubbi, kubbi, akrubbi, krubbi, akayiri, kayiri, akaruṃ, karuṃ, saṅkharuṃ, abhisaṅkharuṃ, akariṃsu, kariṃsu, saṅkhariṃsu, abhisaṅkhariṃsu, akubbiṃsu, kubbiṃsu, akrubbiṃsu, krubbiṃsu, akayiriṃsu, kayiriṃsu, akayiruṃ, kayiruṃ.</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 64
• —num_train_epochs: 1
• —per_device_eval_batch_size: 64
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• —per_device_train_batch_size: 64
• —num_train_epochs: 1
• —max_steps: -1
• —learning_rate: 5e-05
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_steps: 0
• —optim: adamwtorchfused
• —optim_args: None
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —optim_target_modules: None
• —gradient_accumulation_steps: 1
• —average_tokens_across_devices: True
• —max_grad_norm: 1
• —label_smoothing_factor: 0.0
• —bf16: False
• —fp16: False
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —use_liger_kernel: False
• —liger_kernel_config: None
• —use_cache: False
• —neftune_noise_alpha: None
• —torch_empty_cache_steps: None
• —auto_find_batch_size: False
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —include_num_input_tokens_seen: no
• —log_level: passive
• —log_level_replica: warning
• —disable_tqdm: False
• —project: huggingface
• —trackio_space_id: None
• —trackio_bucket_id: None
• —trackio_static_space_id: None
• —per_device_eval_batch_size: 64
• —prediction_loss_only: True
• —eval_on_start: False
• —eval_do_concat_batches: True
• —eval_use_gather_object: False
• —eval_accumulation_steps: None
• —include_for_metrics: []
• —batch_eval_metrics: False
• —save_only_model: False
• —save_on_each_node: False
• —enable_jit_checkpoint: False
• —push_to_hub: False
• —hub_private_repo: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_always_push: False
• —hub_revision: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —restore_callback_states_from_checkpoint: False
• —full_determinism: False
• —seed: 42
• —data_seed: None
• —use_cpu: False
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —dataloader_prefetch_factor: None
• —remove_unused_columns: True
• —label_names: None
• —train_sampling_strategy: random
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —ddp_static_graph: None
• —ddp_backend: None
• —ddp_timeout: 1800
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —deepspeed: None
• —debug: []
• —skip_memory_metrics: True
• —do_predict: False
• —resume_from_checkpoint: None
• —warmup_ratio: None
• —local_rank: -1
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: round_robin
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Training Time

• —Training: 3.1 hours

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.5.1
• —Transformers: 5.9.0
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.13.0
• —Datasets: 4.8.5
• —Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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