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dpshade22/hf-e5-bible-200

sourceHugging Faceupdated 8mo agoView on Hugging Face
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Model Card

SentenceTransformer based on intfloat/e5-base-v2

This is a sentence-transformers model finetuned from intfloat/e5-base-v2. 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: intfloat/e5-base-v2 <!-- at revision f52bf8ec8c7124536f0efb74aca902b2995e5bcd -->
  • —Maximum Sequence Length: 256 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash
pip install -U sentence-transformers

Then you can load this model and run inference.

python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'query: who is Ephraim',
    'passage: But a man of God came to him and said, “Your Majesty, these troops from Israel must not march with you, for the Lord is not with Israel—not with any of the people of Ephraim.',
    'passage: Those who guide this people mislead them,\n    and those who are guided are led astray.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7104, 0.2667],
#         [0.7104, 1.0000, 0.3225],
#         [0.2667, 0.3225, 1.0000]])

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 262,023 training samples
  • —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
  • —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 26.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 35.61 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
  • —Samples: | sentence0 | sentence1 | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>query: God: (A.S. and Dutch God; Dan. Gud; Ger. Gott), the name of the Divine Being. It is the rendering (1) of the Hebrew <i> 'El</i> , from a word meaning to be strong; (2) of <i> 'Eloah, plural 'Elohim</i> . The singular form, <i> Eloah</i> , is used only in poetry. The plural form is more commonly used in all parts of the Bible, The Hebrew word Jehovah (q.v.), the only other word generally employed to denote the Supreme Being, is uniformly rendered in the Authorized Version by "LORD," printed in small capitals. The existence of God is taken for granted in the Bible. There is nowhere any argument to prove it. He who disbelieves this truth is spoken of as one devoid of understanding ( Psalms 14:1 ). The arguments generally adduced by theologians in proof of the being of God are: <li> The a priori argument, which is the testimony afforded by reason. <li> The a posteriori argument, by which we proceed logically from the facts of experience to causes. These arguments are, ...</code> | <code>passage: But the Lord forbid that I should lay a hand on the Lord’s anointed. Now get the spear and water jug that are near his head, and let’s go.”</code> | <code>1.0</code> | | <code>query: Predestination meaning</code> | <code>passage: From one man he made all the nations, that they should inhabit the whole earth; and he marked out their appointed times in history and the boundaries of their lands.</code> | <code>1.0</code> | | <code>query: Noah and God</code> | <code>passage: And Noah did all that the Lord commanded him.</code> | <code>1.0</code> |
  • —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:
json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —per_device_train_batch_size: 32
  • —per_device_eval_batch_size: 32
  • —num_train_epochs: 1
  • —max_steps: 200
  • —multi_dataset_batch_sampler: round_robin
All Hyperparameters

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

  • —overwrite_output_dir: False
  • —do_predict: False
  • —eval_strategy: no
  • —prediction_loss_only: True
  • —per_device_train_batch_size: 32
  • —per_device_eval_batch_size: 32
  • —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: 5e-05
  • —weight_decay: 0.0
  • —adam_beta1: 0.9
  • —adam_beta2: 0.999
  • —adam_epsilon: 1e-08
  • —max_grad_norm: 1
  • —num_train_epochs: 1
  • —max_steps: 200
  • —lr_scheduler_type: linear
  • —lr_scheduler_kwargs: None
  • —warmup_ratio: 0.0
  • —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: 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}
  • —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: round_robin
  • —router_mapping: {}
  • —learning_rate_mapping: {}

</details>

Framework Versions

  • —Python: 3.11.14
  • —Sentence Transformers: 5.2.0
  • —Transformers: 4.57.6
  • —PyTorch: 2.10.0+cpu
  • —Accelerate: 1.12.0
  • —Datasets: 4.5.0
  • —Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers
bibtex
@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
bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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