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: what happened at holy week'
  sentences:
  - 'passage: He replied, “Go into the city to a certain man and tell him, ‘The Teacher
    says: My appointed time is near. I am going to celebrate the Passover with my
    disciples at your house.’”'
  - "passage: Many nations will come and say,\n“Come, let us go up to the mountain\
    \ of the Lord,\n    to the temple of the God of Jacob.\nHe will teach us his ways,\n\
    \    so that we may walk in his paths.”\nThe law will go out from Zion,\n    the\
    \ word of the Lord from Jerusalem."
  - 'passage: But seek first his kingdom and his righteousness, and all these things
    will be given to you as well.'
- source_sentence: 'query: what is Cheek'
  sentences:
  - 'passage: But I tell you, do not resist an evil person. If anyone slaps you on
    the right cheek, turn to them the other cheek also.'
  - "passage: “I am the Lord; that is my name!\n    I will not yield my glory to another\n\
    \    or my praise to idols."
  - 'passage: Ham'
- source_sentence: 'query: what happened at prophecies of isaiah'
  sentences:
  - 'passage: The Israelites who were present in Jerusalem celebrated the Festival
    of Unleavened Bread for seven days with great rejoicing, while the Levites and
    priests praised the Lord every day with resounding instruments dedicated to the
    Lord.'
  - "passage: The blacksmith takes a tool\n    and works with it in the coals;\nhe\
    \ shapes an idol with hammers,\n    he forges it with the might of his arm.\n\
    He gets hungry and loses his strength;\n    he drinks no water and grows faint."
  - 'passage: “Take a census of the whole Israelite community by their clans and families,
    listing every man by name, one by one.'
- source_sentence: 'query: who was God'
  sentences:
  - 'passage: he will not listen to you. Then I will lay my hand on Egypt and with
    mighty acts of judgment I will bring out my divisions, my people the Israelites.'
  - 'passage: I looked, and there before me was a white horse! Its rider held a bow,
    and he was given a crown, and he rode out as a conqueror bent on conquest.'
  - 'passage: The Lord your God will put all these curses on your enemies who hate
    and persecute you.'
- source_sentence: 'query: Moses'
  sentences:
  - 'passage: King Ahaz cut off the side panels and removed the basins from the movable
    stands. He removed the Sea from the bronze bulls that supported it and set it
    on a stone base.'
  - 'passage: “Gad will have one portion; it will border the territory of Zebulun
    from east to west.'
  - 'passage: Now Joshua son of Nun was filled with the spirit of wisdom because Moses
    had laid his hands on him. So the Israelites listened to him and did what the
    Lord had commanded Moses.'
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: Moses',
    'passage: Now Joshua son of Nun was filled with the spirit of wisdom because Moses had laid his hands on him. So the Israelites listened to him and did what the Lord had commanded Moses.',
    'passage: King Ahaz cut off the side panels and removed the basins from the movable stands. He removed the Sea from the bronze bulls that supported it and set it on a stone base.',
]
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, 0.6713, 0.4499],
#         [0.6713, 1.0000, 0.4162],
#         [0.4499, 0.4162, 1.0000]])
```

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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: 26.15 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 35.44 tokens</li><li>max: 93 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: story of prophecies of isaiah</code>                                                | <code>passage: The oxen and donkeys that work the soil will eat fodder and mash, spread out with fork and shovel.</code>        | <code>1.0</code> |
  | <code>query: Abraham and Lot</code>                                                              | <code>passage: Now Lot, who was moving about with Abram, also had flocks and herds and tents.</code>                            | <code>1.0</code> |
  | <code>query: Why were the blind and lame not allowed to “enter the house” (2 Samuel 5:8)?</code> | <code>passage: And Mephibosheth lived in Jerusalem, because he always ate at the king’s table; he was lame in both feet.</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`: 75
- `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`: 75
- `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>

### 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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