---
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:13063
- loss:CosineSimilarityLoss
widget:
- source_sentence: I cant wait to leave Chicago
  sentences:
  - This is the shit Chicago needs to be recognized for not Keef
  - is candice singing again tonight
  - half time Chelsea were losing 10
- source_sentence: Andre miller best lobbing pg in the game
  sentences:
  - Am I the only one who dont get Amber alert
  - Backstrom hurt in warmup Harding could start
  - Andre miller is even slower in person
- source_sentence: Bayless couldve dunked that from the free throw
  sentences:
  - but what great finger roll by Bayless
  - Wow Bayless has to make EspnSCTop with that end of 3rd
  - i mean calum u didnt follow
- source_sentence: Backstrom Hurt in warmups Harding gets the start
  sentences:
  - Should I go to Nashville or Chicago for my 17th birthday
  - I hate Chelsea possibly more than most
  - Of course Backstrom would get injured during warmups
- source_sentence: Calum I love you plz follow me
  sentences:
  - CALUM PLEASE BE MY FIRST CELEBRITY TO FOLLOW ME
  - Walking around downtown Chicago in a dress and listening to the new Iggy Pop
  - I think Candice has what it takes to win American Idol AND Angie too
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.6949485250178733
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6626359968437283
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.688092975176289
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6630998028133662
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6880277270034267
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6626358741747785
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.694948520847878
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6626359082695851
      name: Spearman Dot
    - type: pearson_max
      value: 0.6949485250178733
      name: Pearson Max
    - type: spearman_max
      value: 0.6630998028133662
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 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': 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("mspy/twitter-paraphrase-embeddings")
# Run inference
sentences = [
    'Calum I love you plz follow me',
    'CALUM PLEASE BE MY FIRST CELEBRITY TO FOLLOW ME',
    'Walking around downtown Chicago in a dress and listening to the new Iggy Pop',
]
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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## Evaluation

### Metrics

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6949     |
| **spearman_cosine** | **0.6626** |
| pearson_manhattan   | 0.6881     |
| spearman_manhattan  | 0.6631     |
| pearson_euclidean   | 0.688      |
| spearman_euclidean  | 0.6626     |
| pearson_dot         | 0.6949     |
| spearman_dot        | 0.6626     |
| pearson_max         | 0.6949     |
| spearman_max        | 0.6631     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 13,063 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                                                                            | 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                                                          | label            |
  |:------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
  | <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>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | 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                                                         | label            |
  |:---------------------------------------------------------------|:------------------------------------------------------------------|:-----------------|
  | <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
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: 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`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-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`: 4
- `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`: True
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------:|
| 0.1225 | 100  | -             | 0.0729 | 0.6058          |
| 0.2449 | 200  | -             | 0.0646 | 0.6340          |
| 0.3674 | 300  | -             | 0.0627 | 0.6397          |
| 0.4899 | 400  | -             | 0.0621 | 0.6472          |
| 0.6124 | 500  | 0.0627        | 0.0626 | 0.6496          |
| 0.7348 | 600  | -             | 0.0621 | 0.6446          |
| 0.8573 | 700  | -             | 0.0593 | 0.6695          |
| 0.9798 | 800  | -             | 0.0636 | 0.6440          |
| 1.1023 | 900  | -             | 0.0618 | 0.6525          |
| 1.2247 | 1000 | 0.0383        | 0.0604 | 0.6639          |
| 1.3472 | 1100 | -             | 0.0608 | 0.6590          |
| 1.4697 | 1200 | -             | 0.0620 | 0.6504          |
| 1.5922 | 1300 | -             | 0.0617 | 0.6467          |
| 1.7146 | 1400 | -             | 0.0615 | 0.6574          |
| 1.8371 | 1500 | 0.0293        | 0.0622 | 0.6536          |
| 1.9596 | 1600 | -             | 0.0609 | 0.6599          |
| 2.0821 | 1700 | -             | 0.0605 | 0.6658          |
| 2.2045 | 1800 | -             | 0.0615 | 0.6588          |
| 2.3270 | 1900 | -             | 0.0615 | 0.6575          |
| 2.4495 | 2000 | 0.0215        | 0.0614 | 0.6598          |
| 2.5720 | 2100 | -             | 0.0603 | 0.6681          |
| 2.6944 | 2200 | -             | 0.0606 | 0.6669          |
| 2.8169 | 2300 | -             | 0.0605 | 0.6642          |
| 2.9394 | 2400 | -             | 0.0606 | 0.6630          |
| 3.0618 | 2500 | 0.018         | 0.0611 | 0.6616          |
| 3.1843 | 2600 | -             | 0.0611 | 0.6619          |
| 3.3068 | 2700 | -             | 0.0611 | 0.6608          |
| 3.4293 | 2800 | -             | 0.0608 | 0.6632          |
| 3.5517 | 2900 | -             | 0.0608 | 0.6623          |
| 3.6742 | 3000 | 0.014         | 0.0615 | 0.6596          |
| 3.7967 | 3100 | -             | 0.0612 | 0.6616          |
| 3.9192 | 3200 | -             | 0.0610 | 0.6626          |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.43.3
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.20.0
- 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",
}
```

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