amorfati/custom-hindi-emb-model-contrastive-large

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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
• —Maximum Sequence Length: 128 tokens
• —Output Dimensionality: 384 tokens
• —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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("amorfati/custom-hindi-emb-model-contrastive-large")
# Run inference
sentences = [
    "  'सर पर लाल टोपी रूसी...' 70 साल पुराने दोस्त से मुलाकात, मोदी-पुतिन की बातों से क्या है उम्मीदें, चीन\xa0की\xa0बढ़ी\xa0धड़कन! ",
    '5 साल बाद एक बार फिर रूस जा सकता हैं पीएम मोदी, पुतिन के करीबी ने किया खुलासा',
    'T20 WC 2024 Semi Final Scenario: टीम इंडिया का बदला पूरा, लेकिन रोहित एंड कंपनी ने कर दी बड़ी मिस्टेक, ऑस्ट्रेलिया के पास मौका',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 13,500 training samples
• —Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | <ul><li>min: 12 tokens</li><li>mean: 31.76 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 31.33 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
• —Samples: | premise | hypothesis | label | |:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:---------------| | <code> Live: ओएसिस स्कूल के प्रिंसिपल और वाइस प्रिंसिपल गिरफ्तार, नीट पेपर लीक मामले में सीबीआई का एक्शन </code> | <code> शादी में क्या रखा है! विवाह के बंधन में बंधने से अब क्यों कतराने लगी हैं लड़कियां? ये हैं 5 कारण </code> | <code>0</code> | | <code> SSC Exam Preparation: एसएससी की तैयारी करने के लिए IIT कानपुर ने लॉन्च किया 'SATHEE SSC' प्लेटफॉर्म </code> | <code> Brain Health: बच्चों के दिमाग को नुकसान पहुंचा रहा शोर, लेटेस्ट स्टडी का चौंकाने वाला दावा </code> | <code>0</code> | | <code> IND vs SL: वनडे में संगाकारा के महारिकॉर्ड पर बड़ा खतरा, विराट कोहली इसे ध्वस्त कर रच देंगे इतिहास </code> | <code> Chandrashekhar: 'कहने आए हैं, सुनना पड़ेगा सबको', जानिए कौन है ये निर्दलीय उम्मीदवार जो संसद में गरज रहा? </code> | <code>0</code> |
• —Loss: <code>ContrastiveLoss</code> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 1,500 evaluation samples
• —Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | <ul><li>min: 15 tokens</li><li>mean: 31.8 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 31.86 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
• —Samples: | premise | hypothesis | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code> T20 WC 2024 Semi Final Scenario: टीम इंडिया का बदला पूरा, लेकिन रोहित एंड कंपनी ने कर दी बड़ी मिस्टेक, ऑस्ट्रेलिया के पास मौका </code> | <code>T20 WC 2024 Semi Final Scenario: टीम इंडिया का बदला पूरा, लेकिन रोहित एंड कंपनी ने कर दी बड़ी मिस्टेक, ऑस्ट्रेलिया के पास मौका</code> | <code>1</code> | | <code> Food Poisoning: जान्हवी कपूर को हुआ फूड पॉइजनिंग, 5 घरेलू उपायों से जल्द पाएं राहत </code> | <code>Food Poisoning: जान्हवी कपूर को हुआ फूड पॉइजनिंग, 5 घरेलू उपायों से जल्द पाएं राहत</code> | <code>1</code> | | <code> चाय बेचने वाले के बेटे ने बिना कोचिंग पहली बार में क्रैक किया UPSC, बने IAS ऑफिसर </code> | <code>चाय बेचने वाले के बेटे ने बिना कोचिंग पहली बार में क्रैक किया UPSC, बने IAS ऑफिसर</code> | <code>1</code> |
• —Loss: <code>ContrastiveLoss</code> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 16
• —learning_rate: 2e-05
• —num_train_epochs: 10
• —warmup_ratio: 0.1

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: 16
• —per_device_eval_batch_size: 16
• —per_gpu_train_batch_size: None
• —per_gpu_eval_batch_size: None
• —gradient_accumulation_steps: 1
• —eval_accumulation_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: 0.1
• —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: {'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}
• —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: 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
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

Framework Versions

• —Python: 3.10.12
• —Sentence Transformers: 3.0.1
• —Transformers: 4.42.4
• —PyTorch: 2.3.1+cu121
• —Accelerate: 0.32.1
• —Datasets: 2.20.0
• —Tokenizers: 0.19.1

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

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