File size: 21,221 Bytes
696be8c |
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 |
---
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.
- For at least 2 years, or 1 year in children and adolescents, numerous periods
with hypomanic symptoms and depressive symptoms occur, neither meeting full criteria
for hypomanic or major depressive episodes.
- 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.
- For at least 2 years, or 1 year in children and adolescents, numerous periods
with hypomanic symptoms and depressive symptoms occur, neither meeting full criteria
for hypomanic or major depressive episodes.
- 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.
- For at least 2 years, or 1 year in children and adolescents, numerous periods
with hypomanic symptoms and depressive symptoms occur, neither meeting full criteria
for hypomanic or major depressive episodes.
- 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.
- The agoraphobic situations are actively avoided, require the presence of a companion,
or are endured with intense fear or anxiety.
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: 0.40571243927086686
name: Pearson Cosine
- type: spearman_cosine
value: 0.4157655660967662
name: Spearman Cosine
- type: pearson_manhattan
value: 0.4294377953337607
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.41636474785618866
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.4293067637823527
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.41576593946890283
name: Spearman Euclidean
- type: pearson_dot
value: 0.4057124337715868
name: Pearson Dot
- type: spearman_dot
value: 0.4157663124606592
name: Spearman Dot
- type: pearson_max
value: 0.4294377953337607
name: Pearson Max
- type: spearman_max
value: 0.41636474785618866
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-10")
# 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 | 0.4057 |
| **spearman_cosine** | **0.4158** |
| pearson_manhattan | 0.4294 |
| spearman_manhattan | 0.4164 |
| pearson_euclidean | 0.4293 |
| spearman_euclidean | 0.4158 |
| pearson_dot | 0.4057 |
| spearman_dot | 0.4158 |
| pearson_max | 0.4294 |
| spearman_max | 0.4164 |
<!--
## 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: 29 tokens</li><li>mean: 29.0 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 25.15 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>I often hear voices telling me things that are not real, even when I'm alone in my room.</code> | <code>1.0</code> |
| <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>I have strong beliefs that people are plotting against me and trying to harm me, which makes it hard for me to trust anyone.</code> | <code>1.0</code> |
| <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>Sometimes, I see things that others around me don't see, like strange figures or objects.</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: 18 tokens</li><li>mean: 31.8 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.59 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>People around me have noticed that my behavior is becoming more erratic and unpredictable.</code> | <code>1.0</code> |
| <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>There are times when I repeat certain actions or words without any clear purpose, almost like being stuck in a loop.</code> | <code>0.0</code> |
| <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>I feel detached from reality at times and have trouble distinguishing between what is real and what is not.</code> | <code>0.0</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`: 256
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 10
- `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`: 256
- `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`: 10
- `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 |
|:------:|:----:|:-------------:|:-------:|:------------------------:|
| 0.0377 | 10 | 11.8816 | - | - |
| 0.0755 | 20 | 12.0633 | - | - |
| 0.1132 | 30 | 11.2972 | - | - |
| 0.1509 | 40 | 11.4435 | - | - |
| 0.1887 | 50 | 10.9872 | - | - |
| 0.2264 | 60 | 10.3121 | - | - |
| 0.2642 | 70 | 10.0711 | - | - |
| 0.3019 | 80 | 9.6888 | - | - |
| 0.3396 | 90 | 9.2037 | - | - |
| 0.3774 | 100 | 8.6158 | - | - |
| 0.4151 | 110 | 8.4605 | - | - |
| 0.4528 | 120 | 8.202 | - | - |
| 0.4906 | 130 | 7.9642 | - | - |
| 0.5283 | 140 | 7.8384 | - | - |
| 0.5660 | 150 | 7.8803 | - | - |
| 0.6038 | 160 | 7.419 | - | - |
| 1.0 | 133 | 8.435 | 8.1138 | 0.3813 |
| 2.0 | 266 | 7.7886 | 8.2494 | 0.4003 |
| 3.0 | 399 | 7.164 | 8.7060 | 0.4048 |
| 4.0 | 532 | 6.5921 | 9.5854 | 0.3882 |
| 5.0 | 665 | 6.2349 | 10.5716 | 0.4042 |
| 6.0 | 798 | 5.7831 | 10.9500 | 0.4147 |
| 7.0 | 931 | 5.4894 | 11.6387 | 0.4120 |
| 8.0 | 1064 | 5.2348 | 12.2129 | 0.4113 |
| 9.0 | 1197 | 5.0118 | 12.4632 | 0.4099 |
| 10.0 | 1330 | 4.8566 | 12.7203 | 0.4158 |
### 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.*
--> |