File size: 22,874 Bytes
151cba4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7960
- loss:CoSENTLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: And your phone. Okay do you already have a phone in mind, what
you wanted to upgrade to.
sentences:
- I'm now going to read out some terms and conditions to complete the order.
- The same discounts you can have been added as an additional line and do into your
account. It needs be entitled to % discount off of the costs.
- Thank you and could you please confirm to me what is your full name.
- source_sentence: 'So glad you''re on the right plan. I will also check your average
monthly usage for the past few months. Your usage is only ## gig of mobile data
and then the highest one, it''s around ##. Gig of mobile details. So definitely
the ## gig of mobile data will if broken.'
sentences:
- Thank you for calling over to my name is how can I help you.
- So the phone that you currently have is that currently a Samsung?
- So on that's something that you can they get that the shop and it's at a renewal
for our insurance. So just in case like once you get back to the UK and you don't
want to have the insurance anymore. You can possibly remove that. That and the
full garbage insurance.
- source_sentence: Okay, well, I just want to share with you that I'm happy to advise
that you have an amazing offer on our secondary ninth. So there any family members
like to join or to under your name with a same billing address so they will be
getting a 20% desk.
sentences:
- Yes, that's correct for know. Our price is £ and then it won't go down to £ after
you apply the discount.
- Thank you for calling over to my name is how can I help you.
- Checking your account I can see you are on the and you have been paying £ per
month. Is that correct?
- source_sentence: 'I just read to process this I just like to open your account here
to see if we can get this eligible for your upgrade for the new iPhone ## so here.'
sentences:
- I now need to read some insurance disclosures related to the Ultimate Plan you
have chosen.
- Thank you and could you please confirm to me what is your full name.
- I can provide to you . Are you happy to go ahead with this?
- source_sentence: Okay, and can you provide me your full name please.
sentences:
- So on that's something that you can they get that the shop and it's at a renewal
for our insurance. So just in case like once you get back to the UK and you don't
want to have the insurance anymore. You can possibly remove that. That and the
full garbage insurance.
- You. Okay, so for this one, how do you how do you normally use your mobile data.
- You. Okay, so for this one, how do you how do you normally use your mobile data.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts_dev
metrics:
- type: pearson_cosine
value: 0.5177189921265649
name: Pearson Cosine
- type: spearman_cosine
value: 0.2603983787734805
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5608459921843345
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.2595766499932607
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5641188480826617
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.26039837957858836
name: Spearman Euclidean
- type: pearson_dot
value: 0.5177189925954635
name: Pearson Dot
- type: spearman_dot
value: 0.26040366240168195
name: Spearman Dot
- type: pearson_max
value: 0.5641188480826617
name: Pearson Max
- type: spearman_max
value: 0.26040366240168195
name: Spearman Max
- type: pearson_cosine
value: 0.4585915541798693
name: Pearson Cosine
- type: spearman_cosine
value: 0.24734582807664446
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5059296028724503
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.2466879170820096
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.506069567328991
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.24734582912817787
name: Spearman Euclidean
- type: pearson_dot
value: 0.4585915495841867
name: Pearson Dot
- type: spearman_dot
value: 0.24734582759867477
name: Spearman Dot
- type: pearson_max
value: 0.506069567328991
name: Pearson Max
- type: spearman_max
value: 0.24734582912817787
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **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/UKPLab/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}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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("enochlev/xlm-similarity")
# Run inference
sentences = [
'Okay, and can you provide me your full name please.',
'You. Okay, so for this one, how do you how do you normally use your mobile data.',
'You. Okay, so for this one, how do you how do you normally use your mobile data.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts_dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.5177 |
| spearman_cosine | 0.2604 |
| pearson_manhattan | 0.5608 |
| spearman_manhattan | 0.2596 |
| pearson_euclidean | 0.5641 |
| spearman_euclidean | 0.2604 |
| pearson_dot | 0.5177 |
| spearman_dot | 0.2604 |
| pearson_max | 0.5641 |
| **spearman_max** | **0.2604** |
#### Semantic Similarity
* Dataset: `sts_dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.4586 |
| spearman_cosine | 0.2473 |
| pearson_manhattan | 0.5059 |
| spearman_manhattan | 0.2467 |
| pearson_euclidean | 0.5061 |
| spearman_euclidean | 0.2473 |
| pearson_dot | 0.4586 |
| spearman_dot | 0.2473 |
| pearson_max | 0.5061 |
| **spearman_max** | **0.2473** |
<!--
## 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: 7,960 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 21.6 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 28.35 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.22</li><li>max: 1.0</li></ul> |
* Samples:
| text1 | text2 | label |
|:---------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Hello, welcome to O2. My name is __ How can I help you today?</code> | <code>Thank you for calling over to my name is how can I help you.</code> | <code>1.0</code> |
| <code>Hello, welcome to O2. My name is __ How can I help you today?</code> | <code>So, I'd look into our accessory so for the airbags the one that we have an ongoing promotion right now for the accessories is the airport second generation. So you can. And either by there's like a great if you want to or I can also make it as an instalment for you. If you want to.</code> | <code>0.2</code> |
| <code>Hello, welcome to O2. My name is __ How can I help you today?</code> | <code>So on that's something that you can they get that the shop and it's at a renewal for our insurance. So just in case like once you get back to the UK and you don't want to have the insurance anymore. You can possibly remove that. That and the full garbage insurance.</code> | <code>0.2</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,980 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 39.04 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 28.35 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.22</li><li>max: 1.0</li></ul> |
* Samples:
| text1 | text2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Right perfect. Thank you for passenger security cyber. Now let me go ahead. Then I look for your option to do an upgrade. So you had mentioned that you're wanting to get an upgrade. Can you tell me is it for a devise or a single plan.</code> | <code>Are you planning to get a new sim only plan or a new phone?</code> | <code>1.0</code> |
| <code>Right perfect. Thank you for passenger security cyber. Now let me go ahead. Then I look for your option to do an upgrade. So you had mentioned that you're wanting to get an upgrade. Can you tell me is it for a devise or a single plan.</code> | <code>So, I'd look into our accessory so for the airbags the one that we have an ongoing promotion right now for the accessories is the airport second generation. So you can. And either by there's like a great if you want to or I can also make it as an instalment for you. If you want to.</code> | <code>0.2</code> |
| <code>Right perfect. Thank you for passenger security cyber. Now let me go ahead. Then I look for your option to do an upgrade. So you had mentioned that you're wanting to get an upgrade. Can you tell me is it for a devise or a single plan.</code> | <code>So on that's something that you can they get that the shop and it's at a renewal for our insurance. So just in case like once you get back to the UK and you don't want to have the insurance anymore. You can possibly remove that. That and the full garbage insurance.</code> | <code>0.2</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `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`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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
- `use_ipex`: 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}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Validation Loss | sts_dev_spearman_max |
|:-----:|:----:|:---------------:|:--------------------:|
| 4.0 | 128 | 0.4041 | 0.2604 |
| 1.0 | 32 | 0.6357 | 0.2473 |
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.1
## 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",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## 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.*
--> |