README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L12-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: How does ZBo do it
  sentences:
  - That s how you do it RYU
  - Calum you need to follow me ok
  - fricken calum follow me im upset
- source_sentence: Judi was a crazy mf
  sentences:
  - ZBo is a baaad man
  - quel surprise it s the Canucks
  - nope Id buy Candice s and I will
- source_sentence: ZBo is a baaad man
  sentences:
  - Jeff Green is a BAAAAAAAAADDDDD man
  - Wow RIP Chris from Kriss Kross
  - Vick 32 and shady is 24
- source_sentence: OH GOD SING IT VEDO
  sentences:
  - Wow wow wow Vedo just killed it
  - It s over on his facebook page
  - Why do I get amber alerts tho
- source_sentence: ZBo is in top form
  sentences:
  - Miley Cyrus is over the top
  - Hiller flashing the leather eh
  - Im tryin to get to Chicago May 10th
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: semeval 15 dev
      type: semeval-15-dev
    metrics:
    - type: pearson_cosine
      value: 0.6231334838158124
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5854181889364861
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6182213570910924
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.583565039468049
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6202960321095145
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5854180844045054
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6231334928761973
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5854180353346093
      name: Spearman Dot
    - type: pearson_max
      value: 0.6231334928761973
      name: Pearson Max
    - type: spearman_max
      value: 0.5854181889364861
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision a05860a77cef7b37e0048a7864658139bc18a854 -->
- **Maximum Sequence Length:** 128 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': 128, '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("marrodion/minilm-l12-v2-simple")
# Run inference
sentences = [
    'ZBo is in top form',
    'Miley Cyrus is over the top',
    'Hiller flashing the leather eh',
]
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: `semeval-15-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6231     |
| **spearman_cosine** | **0.5854** |
| pearson_manhattan   | 0.6182     |
| spearman_manhattan  | 0.5836     |
| pearson_euclidean   | 0.6203     |
| spearman_euclidean  | 0.5854     |
| pearson_dot         | 0.6231     |
| spearman_dot        | 0.5854     |
| pearson_max         | 0.6231     |
| spearman_max        | 0.5854     |

<!--
## 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: 13,063 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: 7 tokens</li><li>mean: 11.16 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.31 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                             | sentence2                                                          | score            |
  |:------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
  | <code>EJ Manuel the 1st QB to go in this draft</code> | <code>But my bro from the 757 EJ Manuel is the 1st QB gone</code>  | <code>1.0</code> |
  | <code>EJ Manuel the 1st QB to go in this draft</code> | <code>Can believe EJ Manuel went as the 1st QB in the draft</code> | <code>1.0</code> |
  | <code>EJ Manuel the 1st QB to go in this draft</code> | <code>EJ MANUEL IS THE 1ST QB what</code>                          | <code>0.6</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 4,727 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: 7 tokens</li><li>mean: 10.04 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.22 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                      | sentence2                                                         | score            |
  |:---------------------------------------------------------------|:------------------------------------------------------------------|:-----------------|
  | <code>A Walk to Remember is the definition of true love</code> | <code>A Walk to Remember is on and Im in town and Im upset</code> | <code>0.2</code> |
  | <code>A Walk to Remember is the definition of true love</code> | <code>A Walk to Remember is the cutest thing</code>               | <code>0.6</code> |
  | <code>A Walk to Remember is the definition of true love</code> | <code>A walk to remember is on ABC family youre welcome</code>    | <code>0.2</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True

#### 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`: 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
- `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`: 0.1
- `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`: True
- `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      | semeval-15-dev_spearman_cosine |
|:----------:|:--------:|:-------------:|:---------:|:------------------------------:|
| 0.1837     | 300      | 0.0814        | 0.0718    | 0.5815                         |
| 0.3674     | 600      | 0.0567        | 0.0758    | 0.5458                         |
| 0.5511     | 900      | 0.0566        | 0.0759    | 0.5712                         |
| 0.7348     | 1200     | 0.0499        | 0.0748    | 0.5751                         |
| 0.9186     | 1500     | 0.0477        | 0.0771    | 0.5606                         |
| 1.1023     | 1800     | 0.0391        | 0.0762    | 0.5605                         |
| 1.2860     | 2100     | 0.0304        | 0.0738    | 0.5792                         |
| 1.4697     | 2400     | 0.0293        | 0.0741    | 0.5757                         |
| **1.6534** | **2700** | **0.0317**    | **0.072** | **0.5967**                     |
| 1.8371     | 3000     | 0.029         | 0.0764    | 0.5640                         |
| 2.0208     | 3300     | 0.0278        | 0.0757    | 0.5674                         |
| 2.2045     | 3600     | 0.0186        | 0.0750    | 0.5723                         |
| 2.3882     | 3900     | 0.0169        | 0.0719    | 0.5864                         |
| 2.5720     | 4200     | 0.0177        | 0.0718    | 0.5905                         |
| 2.7557     | 4500     | 0.0178        | 0.0719    | 0.5888                         |
| 2.9394     | 4800     | 0.0165        | 0.0725    | 0.5854                         |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0
- 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",
}
```

<!--
## 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.*
-->