sentence-transformers tensorboard safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:65698 loss:ContrastiveLoss arxiv:1908.10084 base_model:B0ketto/tmp_trainer base_model:finetune:B0ketto/tmp_trainer autotrain_compatible endpoints_compatible region:us
---
base_model: B0ketto/tmp_trainer
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:65698
- loss:ContrastiveLoss
widget:
- source_sentence: Enforcement of minor traffic offenses leads to the discovery of
    more serious crimes.
  sentences:
  - Western culture has created independent women who are strong on their own and
    do not need the protection or support of their husband. This reduces the subjugation
    of women.
  - Philando Castile, stopped for a broken tailight, was shot seven times and killed
    trying to comply with the officer's request for identification.
  - The children will have several older / more mature stepmothers.
- source_sentence: Women and men can always file for divorce.
  sentences:
  - A partner having multiple partners is taken care of enough. There is probably
    less need to find even more partners. This is also a matter of free time, when
    having multiple partners free time is probably rare.
  - The power relations in polygamous marriages should be even more favorable to female
    sponsored divorce as it is more likely that women can keep their children while
    at the same time the man becomes less dependent on one woman emotionally.
  - People close to the individual who commits suicide may feel that they could and
    should have done more to prevent it, thus leaving them with intense feelings of
    guilt.
- source_sentence: 'It''s okay that specific groups of people are not allowed to vote.
    For example: children aren''t usually allowed to vote, because they are considered
    too young - too inexperienced. The same kind of logic could be used to "filter
    out" people who have very little knowledge of the world or terrible analytical
    capabilities.'
  sentences:
  - Those who have a medically diagnosed incapacity for voting should not be allowed
    to vote, because they may be far more easily swayed to vote one way or another.
    However, this must be regulated to medically diagnosed conditions on a mental
    level.
  - Representation is foundational to the American DNA. "No taxation without representation"
    is one of our oldest grievance slogans. Removing the ability of any group to vote
    reinstates this 400-year old injustice.
  - Retailers would supposedly be able to sell the discarded bottles on, thereby making
    a profit after the initial investment into the necessary infrastructure.
- source_sentence: 'It''s okay that specific groups of people are not allowed to vote.
    For example: children aren''t usually allowed to vote, because they are considered
    too young - too inexperienced. The same kind of logic could be used to "filter
    out" people who have very little knowledge of the world or terrible analytical
    capabilities.'
  sentences:
  - Planned Parenthood is not only offering abortions but a host of other services,
    such as clinical breast examination.
  - Some budgetary problems for local law enforcement would be alleviated by removing
    proactive policing duties from the officer's mission.
  - The benefit is to keep those who you do not wish to vote, unable to pass the test.
    This can lead to education suppression, as an example. There are vast amounts
    of education imbalance which can be furthered to suppress votes from those who
    wish to change the system-- ergo, suppressing those who would wrest power from
    those who wish to maintain it through unfair means.
- source_sentence: For children, it is bad to grow up in a polygamous family.
  sentences:
  - Polygamous families tend to have more children.
  - The right of adults to marry should not be precluded by a person's distaste for
    their marital structure. The same argument is used against same-sex marriage,
    and it is invariably irrelevant.
  - This threatens the idea of true democracy.
---

# SentenceTransformer based on B0ketto/tmp_trainer

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [B0ketto/tmp_trainer](https://huggingface.co/B0ketto/tmp_trainer). It maps sentences & paragraphs to a 768-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:** [B0ketto/tmp_trainer](https://huggingface.co/B0ketto/tmp_trainer) <!-- at revision 3ac152b5b7c2227049ce77084d6de8c3b57acc4a -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'For children, it is bad to grow up in a polygamous family.',
    'Polygamous families tend to have more children.',
    'This threatens the idea of true democracy.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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: 65,698 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: 7 tokens</li><li>mean: 25.0 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 31.05 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>0: ~55.50%</li><li>1: ~44.50%</li></ul> |
* Samples:
  | sentence1                                                                                     | sentence2                                                                                                                                                          | label          |
  |:----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Public opinion favors euthanasia which suggests some support for a right to die.</code> | <code>Europeans generally support euthanasia. For example, more than 70% of citizens of Spain, Germany, France and Britain are in favor.</code>                    | <code>1</code> |
  | <code>Public opinion favors euthanasia which suggests some support for a right to die.</code> | <code>In the US, support for assisted suicide has risen to 69% acceptance rate in the last few decades.</code>                                                     | <code>1</code> |
  | <code>Public opinion favors euthanasia which suggests some support for a right to die.</code> | <code>The young and healthy that are asked in polls cannot imagine a situation of disability. This, so the criticism goes, blurs their image of euthanasia.</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Training Hyperparameters

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 3.0
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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 |
|:------:|:-----:|:-------------:|
| 0.0609 | 500   | 0.0256        |
| 0.1218 | 1000  | 0.0257        |
| 0.1826 | 1500  | 0.0263        |
| 0.2435 | 2000  | 0.0291        |
| 0.3044 | 2500  | 0.0276        |
| 0.3653 | 3000  | 0.0304        |
| 0.4262 | 3500  | 0.0297        |
| 0.4870 | 4000  | 0.0332        |
| 0.5479 | 4500  | 0.033         |
| 0.6088 | 5000  | 0.0328        |
| 0.6697 | 5500  | 0.0328        |
| 0.7305 | 6000  | 0.0331        |
| 0.7914 | 6500  | 0.0321        |
| 0.8523 | 7000  | 0.0326        |
| 0.9132 | 7500  | 0.0329        |
| 0.9741 | 8000  | 0.0318        |
| 1.0349 | 8500  | 0.0323        |
| 1.0958 | 9000  | 0.0321        |
| 1.1567 | 9500  | 0.0321        |
| 1.2176 | 10000 | 0.0322        |
| 1.2785 | 10500 | 0.0321        |
| 1.3393 | 11000 | 0.0317        |
| 1.4002 | 11500 | 0.0317        |
| 1.4611 | 12000 | 0.0315        |
| 1.5220 | 12500 | 0.0318        |
| 1.5829 | 13000 | 0.0319        |
| 1.6437 | 13500 | 0.0315        |
| 1.7046 | 14000 | 0.0313        |
| 1.7655 | 14500 | 0.0294        |
| 1.8264 | 15000 | 0.0292        |
| 1.8873 | 15500 | 0.0278        |
| 1.9481 | 16000 | 0.0286        |
| 2.0090 | 16500 | 0.0274        |
| 2.0699 | 17000 | 0.0273        |
| 2.1308 | 17500 | 0.027         |
| 2.1916 | 18000 | 0.0271        |
| 2.2525 | 18500 | 0.0265        |
| 2.3134 | 19000 | 0.0262        |
| 2.3743 | 19500 | 0.0254        |
| 2.4352 | 20000 | 0.0255        |
| 2.4960 | 20500 | 0.0256        |
| 2.5569 | 21000 | 0.0252        |
| 2.6178 | 21500 | 0.0246        |
| 2.6787 | 22000 | 0.0251        |
| 2.7396 | 22500 | 0.0238        |
| 2.8004 | 23000 | 0.025         |
| 2.8613 | 23500 | 0.0247        |
| 2.9222 | 24000 | 0.0252        |
| 2.9831 | 24500 | 0.0237        |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.1
- 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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