README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:262023
- loss:MultipleNegativesRankingLoss
base_model: intfloat/e5-base-v2
widget:
- source_sentence: 'query: Heir meaning'
  sentences:
  - 'passage: This is what the Lord commands for Zelophehad’s daughters: They may
    marry anyone they please as long as they marry within their father’s tribal clan.'
  - 'passage: The second one married the widow, but he also died, leaving no child.
    It was the same with the third.'
  - 'passage: and because the Lord loved him, he sent word through Nathan the prophet
    to name him Jedidiah.'
- source_sentence: 'query: story of wilderness wanderings'
  sentences:
  - 'passage: So Moses said to Aaron, “Take a jar and put an omer of manna in it.
    Then place it before the Lord to be kept for the generations to come.”'
  - 'passage: Sheba and Dedan and the merchants of Tarshish and all her villages will
    say to you, “Have you come to plunder? Have you gathered your hordes to loot,
    to carry off silver and gold, to take away livestock and goods and to seize much
    plunder?”’'
  - 'passage: “It was because your hearts were hard that Moses wrote you this law,”
    Jesus replied.'
- source_sentence: 'query: Alexandria in the Bible'
  sentences:
  - 'passage: And if the Spirit of him who raised Jesus from the dead is living in
    you, he who raised Christ from the dead will also give life to your mortal bodies
    because of his Spirit who lives in you.'
  - 'passage: After three months we put out to sea in a ship that had wintered in
    the island—it was an Alexandrian ship with the figurehead of the twin gods Castor
    and Pollux.'
  - 'passage: They should collect all the food of these good years that are coming
    and store up the grain under the authority of Pharaoh, to be kept in the cities
    for food.'
- source_sentence: 'query: Dragon: Heb. tannim, plural of tan. The name of some unknown
    creature inhabiting desert places and ruins (Job 30:29; Ps. 44:19; Isa. 13:22;
    34:13; 43:20; Jer. 10:22; Micah 1:8; Mal. 1:3); probably, as translated in the
    Revised Version, the jackal (q.v.).'
  sentences:
  - "passage: “But as a mountain erodes and crumbles\n    and as a rock is moved from\
    \ its place,"
  - "passage: Speak to him and say: ‘This is what the Sovereign Lord says:\n“‘I am\
    \ against you, Pharaoh king of Egypt,\n    you great monster lying among your\
    \ streams.\nYou say, “The Nile belongs to me;\n    I made it for myself.”"
  - "passage: But you crushed us and made us a haunt for jackals;\n    you covered\
    \ us over with deep darkness."
- source_sentence: 'query: Jacob (Israel):  the name conferred on Jacob after the
    great prayer-struggle at Peniel (  Genesis 32:28  ), because "as a prince he had
    power with God and prevailed." (See  JACOB  .) This is the common name given to
    Jacob''s descendants. The whole people of the twelve tribes are called "Israelites,"
    the "children of Israel" (  Joshua 3:17  ;   7:25  ;   Judges 8:27  ;   Jeremiah
    3:21  ), and the "house of Israel" (  Exodus 16:31  ;   40:38  ).    This name
    Israel is sometimes used emphatically for the true Israel (  Psalms 73:1  :   Isaiah
    45:17  ;   49:3  ;   John 1:47  ;   Romans 9:6  ;   11:26  ).    After the death
    of Saul the ten tribes arrogated to themselves this name, as if they were the
    whole nation (  2 Samuel 2:9   2 Samuel 2:10   2 Samuel 2:17   2 Samuel 2:28  ;   2
    Samuel 3:10   2 Samuel 3:17  ;   19:40-43  ), and the kings of the ten tribes
    were called "kings of Israel," while the kings of the two tribes were called "kings
    of Judah."    After the Exile the name Israel was assumed as designating the entire
    nation.'
  sentences:
  - 'passage: Greet Ampliatus, my dear friend in the Lord.'
  - 'passage: Jeremiah had written on a scroll about all the disasters that would
    come upon Babylon—all that had been recorded concerning Babylon.'
  - 'passage: then I will reject the descendants of Jacob and David my servant and
    will not choose one of his sons to rule over the descendants of Abraham, Isaac
    and Jacob. For I will restore their fortunes and have compassion on them.’”'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on intfloat/e5-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/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](https://huggingface.co/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

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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: Jacob (Israel):  the name conferred on Jacob after the great prayer-struggle at Peniel (  Genesis 32:28  ), because "as a prince he had power with God and prevailed." (See  JACOB  .) This is the common name given to Jacob\'s descendants. The whole people of the twelve tribes are called "Israelites," the "children of Israel" (  Joshua 3:17  ;   7:25  ;   Judges 8:27  ;   Jeremiah 3:21  ), and the "house of Israel" (  Exodus 16:31  ;   40:38  ).    This name Israel is sometimes used emphatically for the true Israel (  Psalms 73:1  :   Isaiah 45:17  ;   49:3  ;   John 1:47  ;   Romans 9:6  ;   11:26  ).    After the death of Saul the ten tribes arrogated to themselves this name, as if they were the whole nation (  2 Samuel 2:9   2 Samuel 2:10   2 Samuel 2:17   2 Samuel 2:28  ;   2 Samuel 3:10   2 Samuel 3:17  ;   19:40-43  ), and the kings of the ten tribes were called "kings of Israel," while the kings of the two tribes were called "kings of Judah."    After the Exile the name Israel was assumed as designating the entire nation.',
    'passage: then I will reject the descendants of Jacob and David my servant and will not choose one of his sons to rule over the descendants of Abraham, Isaac and Jacob. For I will restore their fortunes and have compassion on them.’”',
    'passage: Greet Ampliatus, my dear friend in the Lord.',
]
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, 0.4831, 0.1291],
#         [0.4831, 1.0000, 0.2341],
#         [0.1291, 0.2341, 1.0000]])
```

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

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 262,023 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        | label                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | float                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 28.18 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 36.17 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                  | sentence_1                                                                                                                                                                      | label            |
  |:------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>query: Holy Week in the Bible</code>                  | <code>passage: The master of that servant will come on a day when he does not expect him and at an hour he is not aware of.</code>                                              | <code>1.0</code> |
  | <code>query: what happened at prophecies of jeremiah</code> | <code>passage: They go up the hill to Luhith,<br>    weeping bitterly as they go;<br>on the road down to Horonaim<br>    anguished cries over the destruction are heard.</code> | <code>1.0</code> |
  | <code>query: Holy Week</code>                               | <code>passage: How dreadful it will be in those days for pregnant women and nursing mothers!</code>                                                                             | <code>1.0</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) 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`: 500
- `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`: 500
- `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`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0611 | 500  | 1.9442        |


### 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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