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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: Ezekiel Prophecies of Ezekiel'
sentences:
- 'passage: Then he went to the east gate. He climbed its steps and measured the
threshold of the gate; it was one rod deep.'
- 'passage: But if you do not obey the Lord, and if you rebel against his commands,
his hand will be against you, as it was against your ancestors.'
- 'passage: When you were dead in your sins and in the uncircumcision of your flesh,
God made you alive with Christ. He forgave us all our sins,'
- source_sentence: 'query: The event ''Prophecies of Nahum'' as recorded in Scripture,
involving Nahum.'
sentences:
- "passage: Nothing can heal you;\n your wound is fatal.\nAll who hear the news\
\ about you\n clap their hands at your fall,\nfor who has not felt\n your\
\ endless cruelty?"
- 'passage: When David was told of this, he gathered all Israel and crossed the
Jordan; he advanced against them and formed his battle lines opposite them. David
formed his lines to meet the Arameans in battle, and they fought against him.'
- 'passage: Then the king of Assyria sent his field commander with a large army
from Lachish to King Hezekiah at Jerusalem. When the commander stopped at the
aqueduct of the Upper Pool, on the road to the Launderer’s Field,'
- source_sentence: 'query: what happened to Job'
sentences:
- "passage: If I hold my head high, you stalk me like a lion\n and again display\
\ your awesome power against me."
- "passage: But Job has not marshaled his words against me,\n and I will not\
\ answer him with your arguments."
- "passage: I will pronounce my judgments on my people\n because of their wickedness\
\ in forsaking me,\nin burning incense to other gods\n and in worshiping what\
\ their hands have made."
- source_sentence: 'query: what happened at peter meets cornelius'
sentences:
- 'passage: From the descendants of Bani:
Maadai, Amram, Uel,'
- 'passage: until I come and take you to a land like your own—a land of grain and
new wine, a land of bread and vineyards.'
- 'passage: So get up and go downstairs. Do not hesitate to go with them, for I
have sent them.”'
- source_sentence: 'query: Ahaz'
sentences:
- 'passage: We boarded a ship from Adramyttium about to sail for ports along the
coast of the province of Asia, and we put out to sea. Aristarchus, a Macedonian
from Thessalonica, was with us.'
- 'passage: This is what the Lord says: “If those who do not deserve to drink the
cup must drink it, why should you go unpunished? You will not go unpunished, but
must drink it.'
- 'passage: Ahaz sent messengers to say to Tiglath-Pileser king of Assyria, “I am
your servant and vassal. Come up and save me out of the hand of the king of Aram
and of the king of Israel, who are attacking me.”'
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: Ahaz',
'passage: Ahaz sent messengers to say to Tiglath-Pileser king of Assyria, “I am your servant and vassal. Come up and save me out of the hand of the king of Aram and of the king of Israel, who are attacking me.”',
'passage: We boarded a ship from Adramyttium about to sail for ports along the coast of the province of Asia, and we put out to sea. Aristarchus, a Macedonian from Thessalonica, was with us.',
]
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, 0.5851, 0.2630],
# [0.5851, 1.0000, 0.3747],
# [0.2630, 0.3747, 1.0000]])
```
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You can finetune this model on your own dataset.
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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: 26.46 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 34.73 tokens</li><li>max: 82 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: Gilead</code> | <code>passage: Now Elijah the Tishbite, from Tishbe in Gilead, said to Ahab, “As the Lord, the God of Israel, lives, whom I serve, there will be neither dew nor rain in the next few years except at my word.”</code> | <code>1.0</code> |
| <code>query: Canaanites: The descendants of Canaan, the son of Ham. Migrating from their original home, they seem to have reached the Persian Gulf, and to have there sojourned for some time. They thence “spread to the west, across the mountain chain of Lebanon to the very edge of the Mediterranean Sea, occupying all the land which later became Palestine, also to the north-west as far as the mountain chain of Taurus.</code> | <code>passage: She makes linen garments and sells them,<br> and supplies the merchants with sashes.</code> | <code>1.0</code> |
| <code>query: who is God</code> | <code>passage: “‘Observe my Sabbaths and have reverence for my sanctuary. I am the Lord.</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`: 150
- `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`: 150
- `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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