sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:7960 loss:CoSENTLoss arxiv:1908.10084 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
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:7960
- loss:CoSENTLoss
widget:
- source_sentence: 'Okay, I got it. So just to give you the second price if ever for
    the Samsung Galaxy is ##. It comes with a ## this one. Five gigabyte of data or
    ## gigabyte it will only it will only give you a £39.05. That is for that is for
    the #### G but I do suggest that you go with the equipment before because that
    is only around £31.'
  sentences:
  - I can provide to you . Are you happy to go ahead with this?
  - Thank you for calling over to my name is how can I help you.
  - Thank you and could you please confirm to me what is your full name.
- source_sentence: His number well, so you're looking to travel abroad anytime soon.
  sentences:
  - I'm now going to read out some terms and conditions to complete the order.
  - Can you provide me with character number one of your security answer please?
  - So looking at your usage of your mobile data. I just wanna share with you that
    your usage for the past six months. It says here it's up to gigabytes of mobile
    data. Okay and in order for us to.
- source_sentence: Hello. Hi, thank you so much for patiently waiting. So, I'd look
    into our accessory so for the airbags the one that we have an ongoing promotion
    right now for the accessories is the airport second generation. So you can.
  sentences:
  - The same discounts you can have been added as an additional line and do into your
    account. It needs be entitled to % discount off of the costs.
  - Are you planning to get a new sim only plan or a new phone?
  - I'm now going to send you a one time code. The first message is a warning to not
    give the code to scammers pretending to work for O2. The second message is the
    code to continue with your request.
- source_sentence: Okay, so you can know just spend. Yeah, but anytime via web chat
    or customer Services. Okay.
  sentences:
  - So looking at your usage of your mobile data. I just wanna share with you that
    your usage for the past six months. It says here it's up to gigabytes of mobile
    data. Okay and in order for us to.
  - Checking your account I can see you are on the and you have been paying £ per
    month. Is that correct?
  - So looking at your usage of your mobile data. I just wanna share with you that
    your usage for the past six months. It says here it's up to gigabytes of mobile
    data. Okay and in order for us to.
- source_sentence: 'Oh, okay, so just the iPhone ## only.'
  sentences:
  - So I'm actually now checking here just for me to get this deal that you had seen.
  - I'm now going to send you a one time code. The first message is a warning to not
    give the code to scammers pretending to work for O2. The second message is the
    code to continue with your request.
  - Yes, that's correct for know. Our price is £ and then it won't go down to £ after
    you apply the discount.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts_dev
    metrics:
    - type: pearson_cosine
      value: 0.5906538719225906
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.2789361723892506
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.630943535003128
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.27814879203445947
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6348761842006896
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.2789361726048565
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5906538598201696
      name: Pearson Dot
    - type: spearman_dot
      value: 0.2789361717424329
      name: Spearman Dot
    - type: pearson_max
      value: 0.6348761842006896
      name: Pearson Max
    - type: spearman_max
      value: 0.2789361726048565
      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 9a3225965996d404b775526de6dbfe85d3368642 -->
- **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("enochlev/xlm-similarity-large")
# Run inference
sentences = [
    'Oh, okay, so just the iPhone ## only.',
    "Yes, that's correct for know. Our price is £ and then it won't go down to £ after you apply the discount.",
    "I'm now going to send you a one time code. The first message is a warning to not give the code to scammers pretending to work for O2. The second message is the code to continue with your request.",
]
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
* Dataset: `sts_dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.5907     |
| spearman_cosine    | 0.2789     |
| pearson_manhattan  | 0.6309     |
| spearman_manhattan | 0.2781     |
| pearson_euclidean  | 0.6349     |
| spearman_euclidean | 0.2789     |
| pearson_dot        | 0.5907     |
| spearman_dot       | 0.2789     |
| pearson_max        | 0.6349     |
| **spearman_max**   | **0.2789** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 7,960 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | text1                                                                             | text2                                                                              | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 20.94 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 28.35 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.22</li><li>max: 1.0</li></ul> |
* Samples:
  | text1                                                                      | text2                                                                                                                                                      | label            |
  |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>Hello, welcome to O2. My name is __ How can I help you today?</code> | <code>Thank you for calling over to my name is how can I help you.</code>                                                                                  | <code>1.0</code> |
  | <code>Hello, welcome to O2. My name is __ How can I help you today?</code> | <code>I was about to ask us to confirm the email address that we have on the account or on your file. So what I can you tell me your email address.</code> | <code>0.2</code> |
  | <code>Hello, welcome to O2. My name is __ How can I help you today?</code> | <code>Are you planning to get a new sim only plan or a new phone?</code>                                                                                   | <code>0.2</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 1,980 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | text1                                                                              | text2                                                                              | label                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | float                                                          |
  | details | <ul><li>min: 8 tokens</li><li>mean: 36.02 tokens</li><li>max: 241 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 28.35 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.22</li><li>max: 1.0</li></ul> |
* Samples:
  | text1                                                                                                                                                                                             | text2                                                                                                                                                        | label            |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>So for example, since this is for the 2nd line bro more. So if you have any family that you want to add on your account. Yeah, we do have a same offer plan. This offer promo today.</code> | <code>The same discounts you can have been added as an additional line and do into your account. It needs be entitled to % discount off of the costs.</code> | <code>1.0</code> |
  | <code>So for example, since this is for the 2nd line bro more. So if you have any family that you want to add on your account. Yeah, we do have a same offer plan. This offer promo today.</code> | <code>I was about to ask us to confirm the email address that we have on the account or on your file. So what I can you tell me your email address.</code>   | <code>0.2</code> |
  | <code>So for example, since this is for the 2nd line bro more. So if you have any family that you want to add on your account. Yeah, we do have a same offer plan. This offer promo today.</code> | <code>Are you planning to get a new sim only plan or a new phone?</code>                                                                                     | <code>0.2</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 50
- `per_device_eval_batch_size`: 50
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 50
- `per_device_eval_batch_size`: 50
- `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`: 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`: 1
- `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`: 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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Validation Loss | sts_dev_spearman_max |
|:-----:|:----:|:---------------:|:--------------------:|
| 1.0   | 160  | 0.1772          | 0.2789               |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.1

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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