sentence-transformers safetensors bert sentence-similarity feature-extraction dataset_size:10K<n<100K loss:CoSENTLoss arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
language: []
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CoSENTLoss
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.
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.*
-->