sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:10003 loss:TripletLoss dataset:mircoboettcher/banking77-triplets arxiv:1908.10084 arxiv:1703.07737 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mpnet-base
datasets:
- mircoboettcher/banking77-triplets
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10003
- loss:TripletLoss
widget:
- source_sentence: I withdrew cash but it still shows as pending?
  sentences:
  - I am not seeing recent cash withdrawal on my account.
  - I  have been charged more than I should for the presents I bought  when abroad,
    the problem seems to be the exchange rate.
  - When can I expect the transfer to arrive in my account?
- source_sentence: I question today's exchange rate for rubles into pounds. I was
    hoping for a better return.
  sentences:
  - I think the rate that was applied to my purchase using foreign currency is wrong.
    Help!
  - What are my payment options for topping off my account?
  - I need to know what these extra charges are on my statement?
- source_sentence: I want to reactivate my card, I thought I had lost it but I found
    it.
  sentences:
  - How long will it take to receive a virtual card?
  - Will you be adding more currencies in the future?
  - Can you tell me how to get my card on the app?
- source_sentence: Your foreign exchange rate is wrong.
  sentences:
  - Why is the exchange rate different from what I expected?
  - My Google pay top up isn't working.
  - If I find a card I lost do I need to dispose of it? Or can I re-active the card
    and continue to use it?
- source_sentence: How do the foreign exchange rates work?
  sentences:
  - How are your exchange rates calculated?
  - Hello, I see that I have been charged an extra $1 on my app.  Could you please
    provide me with a reason for this charge?
  - Explain to me why a transfer would be declined.
---

# SentenceTransformer based on microsoft/mpnet-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [banking77-triplets](https://huggingface.co/datasets/mircoboettcher/banking77-triplets) dataset. 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [banking77-triplets](https://huggingface.co/datasets/mircoboettcher/banking77-triplets)
<!-- - **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': 512, '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})
)
```

## 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("mircoboettcher/mpnet-base-finetune-triplet")
# Run inference
sentences = [
    'How do the foreign exchange rates work?',
    'How are your exchange rates calculated?',
    'Explain to me why a transfer would be declined.',
]
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

#### banking77-triplets

* Dataset: [banking77-triplets](https://huggingface.co/datasets/mircoboettcher/banking77-triplets) at [36f1a55](https://huggingface.co/datasets/mircoboettcher/banking77-triplets/tree/36f1a555e7afda3a2bbf1a30d2660723fc1ece4b)
* Size: 10,003 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 15.94 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.15 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.27 tokens</li><li>max: 79 tokens</li></ul> |
* Samples:
  | anchor                                                                    | positive                                                                    | negative                                                                                                       |
  |:--------------------------------------------------------------------------|:----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|
  | <code>I am still waiting on my card?</code>                               | <code>Is my new bank card on the way and is there a way to track it?</code> | <code>I got a notice from my app that I withdrew cash but I don't remember doing so. How do I fix this?</code> |
  | <code>What can I do if my card still hasn't arrived after 2 weeks?</code> | <code>I would like to track the card you sent to me.</code>                 | <code>Is ordering a new card from China available?</code>                                                      |
  | <code>I have been waiting over a week. Is the card still coming?</code>   | <code>Why has my new card still not come?</code>                            | <code>I think I was charged twice, please help!</code>                                                         |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 5
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 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`: 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`: 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 |
|:------:|:----:|:-------------:|
| 0.1597 | 100  | 4.6855        |
| 0.3195 | 200  | 4.3911        |
| 0.4792 | 300  | 4.2924        |
| 0.6390 | 400  | 4.2245        |
| 0.7987 | 500  | 4.1663        |
| 0.9585 | 600  | 4.1715        |


### Framework Versions
- Python: 3.10.12
- 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
```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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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