sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:8000 loss:AMSoftmaxLossNLI arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8000
- loss:AMSoftmaxLossNLI
widget:
- source_sentence: I avoid any financial dealings with them
  sentences:
  - I observe my reaction to shifting deadlines.
  - I avoid volunteering for projects they're involved in
  - I concentrate solely on task execution.
- source_sentence: I stay aware of my feelings towards team diversity.
  sentences:
  - I expect them to criticize my performance unfairly
  - I return focus when distracted by phone notifications.
  - I remain aware of my body's signals during long work hours.
- source_sentence: I avoid volunteering for projects they're involved in
  sentences:
  - I place primary attention on resolving immediate conflicts.
  - I avoid collaborating with them on projects due to past experiences
  - I feel irritation whenever they share their opinions
- source_sentence: I notice my mood changes during stressful work situations.
  sentences:
  - I am aware of my energy fluctuations throughout the workday.
  - I notice how I feel after completing significant tasks.
  - I accept varied perspectives from my team graciously.
- source_sentence: I re-align my focus after a mental lapse.
  sentences:
  - I acknowledge my internal responses to tight deadlines.
  - I accept and appreciate differences in colleagues' working styles.
  - I accept and learn from performance reviews.
---

# 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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **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("zihoo/all-MiniLM-L6-v2-WMNLI-margin")
# Run inference
sentences = [
    'I re-align my focus after a mental lapse.',
    'I acknowledge my internal responses to tight deadlines.',
    'I accept and learn from performance reviews.',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 8,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.65 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 11.77 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>0: ~25.80%</li><li>1: ~36.80%</li><li>2: ~37.40%</li></ul> |
* Samples:
  | sentence1                                                       | sentence2                                                            | label          |
  |:----------------------------------------------------------------|:---------------------------------------------------------------------|:---------------|
  | <code>I focus on one work task at a time.</code>                | <code>I keep my attention on the task despite office chatter.</code> | <code>0</code> |
  | <code>I worry they might spread false rumors about me</code>    | <code>I return focus to my work when my mind drifts.</code>          | <code>2</code> |
  | <code>I stay aware of my posture when working at a desk.</code> | <code>I pay attention to non-verbal cues from others.</code>         | <code>0</code> |
* Loss: <code>__main__.AMSoftmaxLossNLI</code>

### Evaluation Dataset

#### Unnamed Dataset


* Size: 2,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                |
  | details | <ul><li>min: 8 tokens</li><li>mean: 11.68 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 11.79 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>0: ~24.40%</li><li>1: ~36.30%</li><li>2: ~39.30%</li></ul> |
* Samples:
  | sentence1                                                                   | sentence2                                                              | label          |
  |:----------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
  | <code>I stay conscious of my emotional responses to work challenges.</code> | <code>I pay close attention to verbal instructions.</code>             | <code>1</code> |
  | <code>I accept varied perspectives from my team graciously.</code>          | <code>I accept team dynamics as they naturally evolve.</code>          | <code>0</code> |
  | <code>I accept technology upgrades with an open heart.</code>               | <code>I am mindful of my facial expressions during discussions.</code> | <code>1</code> |
* Loss: <code>__main__.AMSoftmaxLossNLI</code>

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 3e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.01

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-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`: 0.01
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.4   | 100  | 1.5746        | 0.9785          |
| 0.8   | 200  | 0.9714        | 0.9328          |
| 1.2   | 300  | 0.9572        | 0.9329          |
| 1.6   | 400  | 0.9315        | 0.9430          |
| 2.0   | 500  | 0.9393        | 0.9355          |
| 2.4   | 600  | 0.9332        | 0.9217          |
| 2.8   | 700  | 0.9291        | 0.9185          |
| 3.2   | 800  | 0.9394        | 0.9456          |
| 3.6   | 900  | 0.9256        | 0.9254          |
| 4.0   | 1000 | 0.9301        | 0.9147          |
| 4.4   | 1100 | 0.9055        | 0.9189          |
| 4.8   | 1200 | 0.9382        | 0.9231          |
| 5.2   | 1300 | 0.9198        | 0.9188          |
| 5.6   | 1400 | 0.915         | 0.9280          |
| 6.0   | 1500 | 0.9239        | 0.9323          |
| 6.4   | 1600 | 0.9078        | 0.9276          |
| 6.8   | 1700 | 0.918         | 0.9223          |
| 7.2   | 1800 | 0.9133        | 0.9225          |
| 7.6   | 1900 | 0.9097        | 0.9268          |
| 8.0   | 2000 | 0.9202        | 0.9271          |
| 8.4   | 2100 | 0.9204        | 0.9315          |
| 8.8   | 2200 | 0.9019        | 0.9252          |
| 9.2   | 2300 | 0.9074        | 0.9236          |
| 9.6   | 2400 | 0.9094        | 0.9234          |
| 10.0  | 2500 | 0.9026        | 0.9235          |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

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