lv12/esci-nomic-embed-text-v1_5

SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5. 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: nomic-ai/nomic-embed-text-v1.5 <!-- at revision 91d2d6bfdddf0b0da840f901b533e99bae30d757 -->
• —Maximum Sequence Length: 8192 tokens
• —Output Dimensionality: 768 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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 = [
    'search_query: お布団バッグ',
    'search_query: 足なしソファー',
    'search_query: all color handbag',
]
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]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Triplet

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

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 100,000 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: 7 tokens</li><li>mean: 12.11 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 49.91 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 50.64 tokens</li><li>max: 152 tokens</li></ul> |
• —Samples: | anchor | positive | negative | |:------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | <code>searchquery: blー5c</code> | <code>searchdocument: [EnergyPower] TECSUN PL-368 電池2個セット SSB・同期検波・長波 [交換用バッテリーBL-5C付] デジタルDSPポケット短波ラジオ 超小型 長・中波用外付アンテナ 10キー ポータブルBCL受信機 FMステレオ/LW/MW/SW ワールドバンドレシーバー 850局プリセットメモリー シグナルメーター USB充電 スリープタイマー アラー, TECSUN, PL-368 電池＋セット [ブラック]</code> | <code>searchdocument: RADIWOWで作る SIHUADON R108 ポータブル BCL短波ラジオAM FM LW SW 航空無線 DSPレシーバー LCD 良好屋内および屋外アクティビティの両親への贈り物, RADIWOW, グレー</code> | | <code>searchquery: かわいいロングtシャツ</code> | <code>searchdocument: レディース ロンt 半袖 tシャツ オーバーサイズ コットン スリット 大きいサイズ 白 シャツ ビッグシルエット ワンピース シャツワンピ ロングｔシャツ おおきいサイズ 夏 ピンク カジュアル カップ付き カーディガン キラキラ キャミソール キャミ サテン シンプル シニア シフォン シースルー シ, Sleeping Sheep(スリーピング シープ), ホワイト</code> | <code>searchdocument: Perkisboby スポーツウェア レディース ヨガウェア 4点セット 上下セット 5点セットウェア フィットネス 2点セット ジャージ スポーツブラ パンツ パーカー 半袖 ハーフパンツ, Perkisboby, 2点セット-グレー</code> | | <code>searchquery: iphone xr otterbox symmetry case</code> | <code>searchdocument: Symmetry Clear Series Case for iPhone XR (ONLY) Symmetry Case for iPhone XR Symmetry Case - Clear, VTSOU, Clear</code> | <code>search_document: OtterBox Symmetry Series Case for Apple iPhone XS Max - Tonic Violet / Purple, OtterBox, Tonic Violet / Purple</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 1,000 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: 7 tokens</li><li>mean: 12.13 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 50.76 tokens</li><li>max: 173 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 54.25 tokens</li><li>max: 161 tokens</li></ul> |
• —Samples: | anchor | positive | negative | |:---------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | <code>searchquery: snack vending machine</code> | <code>searchdocument: Red All Metal Triple Compartment Commercial Vending Machine for 1 inch Gumballs, 1 inch Toy Capsules, Bouncy Balls, Candy, Nuts with Stand by American Gumball Company, American Gumball Company, CANDY RED</code> | <code>searchdocument: Vending Machine Halloween Costume - Funny Snack Food Adult Men & Women Outfits, Hauntlook, Multicolored</code> | | <code>searchquery: slim credit card holder without id window</code> | <code>searchdocument: Banuce Top Grain Leather Card Holder for Women Men Unisex ID Credit Card Case Slim Card Wallet Black, Banuce, 1 ID + 5 Card Slots: Black</code> | <code>searchdocument: Mens Wallet RFID Genuine Leather Bifold Wallets For Men, ID Window 16 Card Holders Gift Box, Swallowmall, Black Stripe</code> | | <code>searchquery: gucci belts for women</code> | <code>searchdocument: Gucci Women's Gg0027o 50Mm Optical Glasses, Gucci, Havana</code> | <code>search_document: Gucci G-Gucci Gold PVD Women's Watch(Model:YA125511), Gucci, PVD/Brown</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 4
• —per_device_eval_batch_size: 4
• —gradient_accumulation_steps: 2
• —learning_rate: 1e-06
• —lr_scheduler_type: cosine
• —warmup_ratio: 0.1
• —dataloader_drop_last: True
• —dataloader_num_workers: 4
• —dataloader_prefetch_factor: 2
• —load_best_model_at_end: True
• —batch_sampler: no_duplicates

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —prediction_loss_only: True
• —per_device_train_batch_size: 4
• —per_device_eval_batch_size: 4
• —per_gpu_train_batch_size: None
• —per_gpu_eval_batch_size: None
• —gradient_accumulation_steps: 2
• —eval_accumulation_steps: None
• —learning_rate: 1e-06
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 3
• —max_steps: -1
• —lr_scheduler_type: cosine
• —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
• —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: True
• —dataloader_num_workers: 4
• —dataloader_prefetch_factor: 2
• —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, 'useseedable_sampler': True}
• —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
• —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
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

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

</details>

Framework Versions

• —Python: 3.10.12
• —Sentence Transformers: 3.0.0
• —Transformers: 4.38.2
• —PyTorch: 2.1.2+cu121
• —Accelerate: 0.27.2
• —Datasets: 2.19.1
• —Tokenizers: 0.15.2

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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