sentence-transformers safetensors bert sentence-similarity feature-extraction dataset_size:10K<n<100K loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 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:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:TripletLoss
widget:
- source_sentence: The agoraphobic situations almost always provoke fear or anxiety.
  sentences:
  - Attending crowded events or public gatherings fills me with anxiety because of
    the fear of a potential threat in the crowd.
  - The struggle to focus during the day is often due to feeling exhausted even after
    a full night's sleep.
  - It's not uncommon for me to engage in risky behaviors like reckless driving or
    reckless sexual encounters.
- source_sentence: Due to my insomnia, I have frequent headaches and muscle soreness.
  sentences:
  - My insomnia results in frequent headaches and muscle soreness for me.
  - My fear of heights prevents me from going on roller coasters or visiting scenic
    overlooks on mountains.
  - Focusing on tasks becomes challenging due to my constant worry about when the
    next panic attack will strike.
- source_sentence: Commuting to work, even when it's a short distance, feels draining.
  sentences:
  - Even a short distance can make driving or commuting to work feel draining.
  - My fear of heights prevents me from going on roller coasters or visiting scenic
    overlooks on mountains.
  - The impact on my ability to focus on tasks is due to my constant worry about when
    the next panic attack will strike.
- source_sentence: Frequent headaches and muscle soreness are a result of my insomnia.
  sentences:
  - My frequent headaches and muscle soreness are a direct result of my insomnia.
  - My fear of heights prevents me from going on roller coasters or visiting scenic
    overlooks on mountains.
  - Focusing on tasks becomes challenging due to my constant worry about when the
    next panic attack will strike.
- source_sentence: Experience frequent headaches and muscle soreness due to my insomnia.
  sentences:
  - I experience frequent headaches and muscle soreness because of my insomnia.
  - The struggle to focus during the day is often due to feeling exhausted even after
    a full night's sleep.
  - Focusing on tasks becomes challenging due to my constant worry about when the
    next panic attack will strike.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: FT triple
      type: FT-triple
    metrics:
    - type: cosine_accuracy
      value: 0.8093060785368478
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.19069392146315223
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8103819257665411
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8093060785368478
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8103819257665411
      name: Max Accuracy
---

# 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 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **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-triple-2")
# Run inference
sentences = [
    'Experience frequent headaches and muscle soreness due to my insomnia.',
    'I experience frequent headaches and muscle soreness because of my insomnia.',
    "The struggle to focus during the day is often due to feeling exhausted even after a full night's sleep.",
]
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

#### Triplet
* Dataset: `FT-triple`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.8093     |
| dot_accuracy       | 0.1907     |
| manhattan_accuracy | 0.8104     |
| euclidean_accuracy | 0.8093     |
| **max_accuracy**   | **0.8104** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 52,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                             |
  | details | <ul><li>min: 29 tokens</li><li>mean: 29.0 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 23.16 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 24.81 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                               | positive                                                                                                     | negative                                                                                                                                     |
  |:-------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>Even in the privacy of my room, I hear voices that tell me things that are not real frequently.</code> | <code>My lack of pleasure in things I once enjoyed has caused me to lose interest in hobbies or activities that used to bring me joy.</code> |
  | <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>It's common for me to hear things that are not real, even when I'm in my room by myself.</code>        | <code>Starting multiple projects simultaneously during these episodes makes me feel like I can accomplish everything at once.</code>         |
  | <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>Even in the privacy of my room, I hear voices that tell me things that are not real frequently.</code> | <code>Even after a full night's sleep, I struggle to get out of bed in the morning, feeling tired.</code>                                    |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 3,718 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 18 tokens</li><li>mean: 32.73 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 22.72 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 24.7 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                               | positive                                                                                                                        | negative                                                                                                                                               |
  |:-------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>Observers in my vicinity have noted the escalation of my erratic and unpredictable behavior.</code>                       | <code>It's a challenge for me to seek assistance in public places, even when I clearly need help.</code>                                               |
  | <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>There has been a growing awareness among those around me about my increasingly erratic and unpredictable behavior.</code> | <code>The difficulty of connecting with others on a deeper level stems from feeling like I've lost a part of myself due to the traumatic event.</code> |
  | <code>Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period</code> | <code>It has come to the attention of those around me that my behavior is becoming more erratic and unpredictable.</code>       | <code>My thoughts exhibited a chaotic and disconnected pattern in that manic episode.</code>                                                           |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 2
- `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`: 64
- `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`: 2
- `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-triple_max_accuracy |
|:------:|:----:|:-------------:|:------:|:----------------------:|
| 0.2015 | 82   | 4.5671        | -      | -                      |
| 0.4029 | 164  | 4.0669        | -      | -                      |
| 0.6044 | 246  | 3.9861        | -      | -                      |
| 0.8059 | 328  | 3.9519        | -      | -                      |
| 1.0    | 407  | -             | 4.0778 | 0.8244                 |
| 1.0074 | 410  | 3.9194        | -      | -                      |
| 1.2088 | 492  | 3.8925        | -      | -                      |
| 1.4103 | 574  | 3.8823        | -      | -                      |
| 1.6118 | 656  | 3.8871        | -      | -                      |
| 1.8133 | 738  | 3.8603        | -      | -                      |
| 2.0    | 814  | -             | 4.0806 | 0.8104                 |


### 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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