File size: 23,092 Bytes
ffd4367 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:2633
- loss:CosineSimilarityLoss
base_model: intfloat/e5-base-v2
widget:
- source_sentence: Many therefore of his disciples, when they had heard this, said,
This is an hard saying; who can hear it?
sentences:
- If ye keep my commandments, ye shall abide in my love; even as I have kept my
Father's commandments, and abide in his love.
- When Jesus knew in himself that his disciples murmured at it, he said unto them,
Doth this offend you?
- He said, I am the voice of one crying in the wilderness, Make straight the way
of the Lord, as said the prophet Esaias.
- source_sentence: 'Jesus and Nicodemus | participants: jesus_905, nicodemus_2204'
sentences:
- 'And as Moses lifted up the serpent in the wilderness, even so must the Son of
man be lifted up:'
- Then when Mary was come where Jesus was, and saw him, she fell down at his feet,
saying unto him, Lord, if thou hadst been here, my brother had not died.
- They answered him, Jesus of Nazareth. Jesus saith unto them, I am he. And Judas
also, which betrayed him, stood with them.
- source_sentence: 'For he whom God hath sent speaketh the words of God: for God giveth
not the Spirit by measure unto him.'
sentences:
- Then said Jesus unto the twelve, Will ye also go away?
- The Father loveth the Son, and hath given all things into his hand.
- 'Why askest thou me? ask them which heard me, what I have said unto them: behold,
they know what I said.'
- source_sentence: 'Lazarus Raised form the Dead | participants: jesus_905, mary_1939,
lazarus_1812'
sentences:
- But he saith unto them, It is I; be not afraid.
- But some of them went their ways to the Pharisees, and told them what things Jesus
had done.
- Jesus answered and said unto them, Destroy this temple, and in three days I will
raise it up.
- source_sentence: '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, (a) The cosmological,
by which it is proved that there must be a First Cause of all things, for every
effect must have a cause. (b) The teleological, or the argument from design.
We see everywhere the operations of an intelligent Cause in nature. (c) The
moral argument, called also the anthropological argument, based on the moral consciousness
and the history of mankind, which exhibits a moral order and purpose which can
only be explained on the supposition of the existence of God. Conscience and human
history testify that "verily there is a God that judgeth in the earth." The
attributes of God are set forth in order by Moses in Exodus 34:6 Exodus 34:7 .
(see also Deuteronomy 6:4 ; 10:17 ; Numbers 16:22 ; Exodus 15:11 ; 33:19 ; Isaiah
44:6 ; Habakkuk 3:6 ; Psalms 102:26 ; Job 34:12 .) They are also systematically
classified in Revelation 5:12 and 7:12 . God''s attributes are spoken
of by some as absolute, i.e., such as belong to his essence as Jehovah, Jah, etc.;
and relative, i.e., such as are ascribed to him with relation to his creatures.
Others distinguish them into communicable, i.e., those which can be imparted in
degree to his creatures: goodness, holiness, wisdom, etc.; and incommunicable,
which cannot be so imparted: independence, immutability, immensity, and eternity.
They are by some also divided into natural attributes, eternity, immensity, etc.;
and moral, holiness, goodness, etc.'
sentences:
- As he spake these words, many believed on him.
- 'Jesus said unto them, If God were your Father, ye would love me: for I proceeded
forth and came from God; neither came I of myself, but he sent me.'
- 'Jesus answered them, I told you, and ye believed not: the works that I do in
my Father''s name, they bear witness of 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 = [
'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, (a) The cosmological, by which it is proved that there must be a First Cause of all things, for every effect must have a cause. (b) The teleological, or the argument from design. We see everywhere the operations of an intelligent Cause in nature. (c) The moral argument, called also the anthropological argument, based on the moral consciousness and the history of mankind, which exhibits a moral order and purpose which can only be explained on the supposition of the existence of God. Conscience and human history testify that "verily there is a God that judgeth in the earth." The attributes of God are set forth in order by Moses in Exodus 34:6 Exodus 34:7 . (see also Deuteronomy 6:4 ; 10:17 ; Numbers 16:22 ; Exodus 15:11 ; 33:19 ; Isaiah 44:6 ; Habakkuk 3:6 ; Psalms 102:26 ; Job 34:12 .) They are also systematically classified in Revelation 5:12 and 7:12 . God\'s attributes are spoken of by some as absolute, i.e., such as belong to his essence as Jehovah, Jah, etc.; and relative, i.e., such as are ascribed to him with relation to his creatures. Others distinguish them into communicable, i.e., those which can be imparted in degree to his creatures: goodness, holiness, wisdom, etc.; and incommunicable, which cannot be so imparted: independence, immutability, immensity, and eternity. They are by some also divided into natural attributes, eternity, immensity, etc.; and moral, holiness, goodness, etc.',
'Jesus said unto them, If God were your Father, ye would love me: for I proceeded forth and came from God; neither came I of myself, but he sent me.',
'As he spake these words, many believed on him.',
]
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, 0.7557, 0.7462],
# [0.7557, 1.0000, 0.7852],
# [0.7462, 0.7852, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,633 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: 3 tokens</li><li>mean: 81.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.06 tokens</li><li>max: 73 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>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, (a) T...</code> | <code>For as the Father hath life in himself; so hath he given to the Son to have life in himself;</code> | <code>1.0</code> |
| <code>Bread of Life Sermon \| participants: jesus_905, peter_2745</code> | <code>Jesus therefore answered and said unto them, Murmur not among yourselves.</code> | <code>1.0</code> |
| <code>Verily, verily, I say unto thee, When thou wast young, thou girdest thyself, and walkedst whither thou wouldest: but when thou shalt be old, thou shalt stretch forth thy hands, and another shall gird thee, and carry thee whither thou wouldest not.</code> | <code>This spake he, signifying by what death he should glorify God. And when he had spoken this, he saith unto him, Follow me.</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: 5
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 5
- `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: 3.13.11
- 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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |