README.md

---
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]
```

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## 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     |

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## 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},
}
```

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