---
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: Sin in the Bible'
sentences:
- 'passage: but each person is tempted when they are dragged away by their own evil
desire and enticed.'
- 'passage: If they want to inquire about something, they should ask their own husbands
at home; for it is disgraceful for a woman to speak in the church.'
- 'passage: The crowds that went ahead of him and those that followed shouted,
“Hosanna to the Son of David!”
“Blessed is he who comes in the name of the Lord!”
“Hosanna in the highest heaven!”'
- source_sentence: 'query: Naphtali in the Bible'
sentences:
- "passage: About Naphtali he said:\n“Naphtali is abounding with the favor of the\
\ Lord\n and is full of his blessing;\n he will inherit southward to the\
\ lake.”"
- "passage: You have enlarged the nation, Lord;\n you have enlarged the nation.\n\
You have gained glory for yourself;\n you have extended all the borders of\
\ the land."
- 'passage: For Herod himself had given orders to have John arrested, and he had
him bound and put in prison. He did this because of Herodias, his brother Philip’s
wife, whom he had married.'
- source_sentence: 'query: Ten Commandments Given in the Bible'
sentences:
- 'passage: As they were shouting and throwing off their cloaks and flinging dust
into the air,'
- 'passage: On the first day of the third month after the Israelites left Egypt—on
that very day—they came to the Desert of Sinai.'
- "passage: Blessed are the meek,\n for they will inherit the earth."
- source_sentence: 'query: But Nahash the Ammonite replied, “I will make a treaty
with you only on the condition that I gouge out the right eye of every one of
you and so bring disgrace on all Israel.”'
sentences:
- 'passage: A certain man in Maon, who had property there at Carmel, was very wealthy.
He had a thousand goats and three thousand sheep, which he was shearing in Carmel.'
- 'passage: Nor did Asher drive out those living in Akko or Sidon or Ahlab or Akzib
or Helbah or Aphek or Rehob.'
- 'passage: The elders of Jabesh said to him, “Give us seven days so we can send
messengers throughout Israel; if no one comes to rescue us, we will surrender
to you.”'
- source_sentence: 'query: who is Ephraim'
sentences:
- "passage: Those who guide this people mislead them,\n and those who are guided\
\ are led astray."
- "passage: No longer will they teach their neighbor,\n or say to one another,\
\ ‘Know the Lord,’\nbecause they will all know me,\n from the least of them\
\ to the greatest."
- '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.'
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)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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: 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]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 262,023 training samples
* Columns: sentence_0, sentence_1, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 5 tokens
- mean: 26.25 tokens
- max: 256 tokens
| - min: 10 tokens
- mean: 35.61 tokens
- max: 94 tokens
| - min: 1.0
- mean: 1.0
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| 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 'El , from a word meaning to be strong; (2) of 'Eloah_, plural _'Elohim . The singular form, Eloah , 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: The a priori argument, which is the testimony afforded by reason. The a posteriori argument, by which we proceed logically from the facts of experience to causes. These arguments are, ... | 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.” | 1.0 |
| query: Predestination meaning | 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. | 1.0 |
| query: Noah and God | passage: And Noah did all that the Lord commanded him. | 1.0 |
* Loss: [MultipleNegativesRankingLoss](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`: 200
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `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`: {'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`: {}