---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8000
- loss:SoftmaxLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: I suspect their compliments are disingenuous
  sentences:
  - I feel skeptical about their ideas during planning sessions
  - I believe they intentionally withhold information from me
  - I notice the tension in rushed work hours.
- source_sentence: I decline their invitations to mutual events
  sentences:
  - I accept moments of uncertainty as part of the job.
  - I embrace constructive criticism for personal growth.
  - I accept change as an integral part of progress.
- source_sentence: I feel anger simmering when they speak up in meetings
  sentences:
  - I concentrate on tasks without getting sidetracked by emails.
  - I maintain focus by taking regular breaks.
  - I focus on one work task at a time.
- source_sentence: I stay conscious of my patterned responses to pressure.
  sentences:
  - I ignore background noise to maintain task concentration.
  - I concentrate fully on reading reports.
  - I accept that criticism is a growth opportunity.
- source_sentence: I accept my mistakes as part of my learning process.
  sentences:
  - I fully concentrate on client communications.
  - I suspect their compliments are disingenuous
  - I remain conscious of my work-life balance.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# 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-10epoch")
# Run inference
sentences = [
    'I accept my mistakes as part of my learning process.',
    'I fully concentrate on client communications.',
    'I remain conscious of my work-life balance.',
]
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.*
-->

<!--
## 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: 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>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### 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>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 9
- `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`: 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`: 9
- `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  | 0.9566        | 0.8119          |
| 0.8   | 200  | 0.7499        | 0.6819          |
| 1.2   | 300  | 0.6541        | 0.5908          |
| 1.6   | 400  | 0.5759        | 0.5258          |
| 2.0   | 500  | 0.5112        | 0.4811          |
| 2.4   | 600  | 0.4659        | 0.4377          |
| 2.8   | 700  | 0.44          | 0.4020          |
| 3.2   | 800  | 0.4112        | 0.3721          |
| 3.6   | 900  | 0.3751        | 0.3462          |
| 4.0   | 1000 | 0.3517        | 0.3233          |
| 4.4   | 1100 | 0.3232        | 0.3033          |
| 4.8   | 1200 | 0.3189        | 0.2871          |
| 5.2   | 1300 | 0.2961        | 0.2711          |
| 5.6   | 1400 | 0.2865        | 0.2597          |
| 6.0   | 1500 | 0.2715        | 0.2499          |
| 6.4   | 1600 | 0.2639        | 0.2403          |
| 6.8   | 1700 | 0.2528        | 0.2339          |
| 7.2   | 1800 | 0.2482        | 0.2277          |
| 7.6   | 1900 | 0.2406        | 0.2236          |
| 8.0   | 2000 | 0.2403        | 0.2207          |
| 8.4   | 2100 | 0.2382        | 0.2184          |
| 8.8   | 2200 | 0.2314        | 0.2166          |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

<!--
## 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.*
-->