File size: 21,761 Bytes
f50d6f2 |
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 |
---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CoSENTLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Driving or commuting to work feels draining, even if it's a short
distance.
sentences:
- Symptoms during a manic episode include decreased need for sleep, more talkative
than usual, flight of ideas, distractibility
- I feel like I have lost a part of myself since the traumatic event, and I struggle
to connect with others on a deeper level.
- Diagnosis requires at least one hypomanic episode and one major depressive episode.
- source_sentence: I felt like my thoughts were disconnected and chaotic during a
manic episode.
sentences:
- Diagnosis requires one or more manic episodes, which may be preceded or followed
by hypomanic or major depressive episodes.
- I feel like I have lost a part of myself since the traumatic event, and I struggle
to connect with others on a deeper level.
- Depressed mood for most of the day, for more days than not, as indicated by subjective
account or observation, for at least 2 years.
- source_sentence: My insomnia has caused me to experience frequent headaches and
muscle soreness.
sentences:
- Insomnia or hypersomnia nearly every day.
- I have difficulty standing in long lines at the grocery store or the bank due
to the fear of feeling trapped or overwhelmed.
- Diagnosis requires at least one hypomanic episode and one major depressive episode.
- source_sentence: The phobic object or situation almost always provokes immediate
fear or anxiety.
sentences:
- The agoraphobic situations almost always provoke fear or anxiety.
- I have difficulty standing in long lines at the grocery store or the bank due
to the fear of feeling trapped or overwhelmed.
- Exclusion of schizoaffective disorder and depressive or bipolar disorder with
psychotic features, based on the absence of concurrent depressive or manic episodes
during the active-phase symptoms, or these mood episodes being present for a minority
of the total duration of the active and residual phases.
- source_sentence: I engage in risky behaviors like reckless driving or reckless sexual
encounters.
sentences:
- Symptoms during a manic episode include inflated self-esteem or grandiosity,increased
goal-directed activity, or excessive involvement in risky activities.
- Marked decrease in functioning in areas like work, interpersonal relations, or
self-care since the onset of the disturbance.
- During the specified period, symptoms from Criterion A are present at least half
the time with no symptom-free interval lasting longer than 2 months.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: FT label
type: FT_label
metrics:
- type: pearson_cosine
value: 0.4627701543833943
name: Pearson Cosine
- type: spearman_cosine
value: 0.4076356119364853
name: Spearman Cosine
- type: pearson_manhattan
value: 0.48164714740150605
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.406731043246377
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.4840582172096936
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.407636256115058
name: Spearman Euclidean
- type: pearson_dot
value: 0.46277015122653486
name: Pearson Dot
- type: spearman_dot
value: 0.4076359510487126
name: Spearman Dot
- type: pearson_max
value: 0.4840582172096936
name: Pearson Max
- type: spearman_max
value: 0.407636256115058
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 e4ce9877abf3edfe10b0d82785e83bdcb973e22e -->
- **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("Hgkang00/FT-label-consent-20")
# Run inference
sentences = [
'I engage in risky behaviors like reckless driving or reckless sexual encounters.',
'Symptoms during a manic episode include inflated self-esteem or grandiosity,increased goal-directed activity, or excessive involvement in risky activities.',
'Marked decrease in functioning in areas like work, interpersonal relations, or self-care since the onset of the disturbance.',
]
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: `FT_label`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.4628 |
| **spearman_cosine** | **0.4076** |
| pearson_manhattan | 0.4816 |
| spearman_manhattan | 0.4067 |
| pearson_euclidean | 0.4841 |
| spearman_euclidean | 0.4076 |
| pearson_dot | 0.4628 |
| spearman_dot | 0.4076 |
| pearson_max | 0.4841 |
| spearman_max | 0.4076 |
<!--
## 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: 33,800 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 11 tokens</li><li>mean: 31.63 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 25.22 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: -0.87</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>Presence of one or more of the following intrusion symptoms associated with the traumatic event: recurrent distressing memories, dreams, flashbacks, psychological distress, or physiological reactions to cues of the traumatic event.</code> | <code>I avoid making phone calls, even to close friends or family, because I'm afraid of saying something wrong or sounding awkward.</code> | <code>0.0</code> |
| <code>The phobic object or situation almost always provokes immediate fear or anxiety.</code> | <code>I find it hard to stick to a consistent eating schedule, sometimes going days without feeling the need to eat at all.</code> | <code>-1.0</code> |
| <code>The fear or anxiety is out of proportion to the actual danger posed by the specific object or situation and to the sociocultural context.</code> | <code>I have difficulty going to places where I feel there are no immediate exits, such as cinemas or auditoriums, as the fear of being stuck or unable to escape escalates my anxiety.</code> | <code>-1.0</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: 4,225 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 11 tokens</li><li>mean: 31.24 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.86 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: -0.87</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>Excessive anxiety and worry occurring more days than not for at least 6 months, about a number of events or activities such as work or school performance.</code> | <code>Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.</code> | <code>-1.0</code> |
| <code>The individual fears acting in a way or showing anxiety symptoms that will be negatively evaluated, leading to humiliation, embarrassment, rejection, or offense to others.</code> | <code>I often find myself mindlessly snacking throughout the day due to changes in my appetite.</code> | <code>-1.0</code> |
| <code>Persistent avoidance of stimuli associated with the trauma, evidenced by avoiding distressing memories, thoughts, or feelings, or external reminders of the event.</code> | <code>Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.</code> | <code>-1.0</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`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 20
- `warmup_ratio`: 0.1
#### 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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 20
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | FT_label_spearman_cosine |
|:-----:|:----:|:-------------:|:-------:|:------------------------:|
| 1.0 | 265 | - | 6.9529 | 0.3450 |
| 2.0 | 530 | 7.5663 | 7.1002 | 0.4103 |
| 3.0 | 795 | - | 7.4786 | 0.4155 |
| 4.0 | 1060 | 5.5492 | 8.6710 | 0.4115 |
| 5.0 | 1325 | - | 10.3786 | 0.4056 |
| 6.0 | 1590 | 4.3991 | 10.4239 | 0.3987 |
| 7.0 | 1855 | - | 11.8681 | 0.4238 |
| 8.0 | 2120 | 3.5916 | 13.0752 | 0.4030 |
| 9.0 | 2385 | - | 12.8567 | 0.4240 |
| 10.0 | 2650 | 3.1139 | 12.4373 | 0.4270 |
| 11.0 | 2915 | - | 13.6725 | 0.4212 |
| 12.0 | 3180 | 2.6658 | 15.0521 | 0.4134 |
| 13.0 | 3445 | - | 15.4305 | 0.4114 |
| 14.0 | 3710 | 2.2024 | 15.5511 | 0.4060 |
| 15.0 | 3975 | - | 14.9427 | 0.4165 |
| 16.0 | 4240 | 1.8955 | 14.8399 | 0.4162 |
| 17.0 | 4505 | - | 15.0070 | 0.4170 |
| 18.0 | 4770 | 1.712 | 15.4417 | 0.4105 |
| 19.0 | 5035 | - | 15.6241 | 0.4086 |
| 20.0 | 5300 | 1.5088 | 15.6818 | 0.4076 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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.*
--> |