sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:505654 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 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:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:505654
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'module: stationery & printed material & services group: stationery
    & printed material & services supergroup: stationery & printed material & services
    example descriptions: munchkin crayons hween printedsheet mask 2 pk printed tape
    tour os silver butterfly relax with art m ab hardbacknotebook stickers p val youmeyou
    text heat w mandalorian a 5 nbook nediun bubble envelopes 6 pk whs pastel expan
    org p poll decoration 1 airtricity payasyoug'
  sentences:
  - 'retailer: groveify description: rainbow magicbooks'
  - 'retailer: crispcorner description: glazed k kreme'
  - 'retailer: vitalveg description: may held aop fl'
- source_sentence: 'module: flavoured drinks carbonated cola group: drinks flavoured
    rtd supergroup: beverages non alcoholic example descriptions: cola w xcoke zero
    15 oml pepsi 240 k coke zero 500 ml d lepsi max chry 600 coke cherry can 009500
    pepsi max 500 ml tuo diet coke cf kloke zero coke zero 250 ml diet coke nin 15
    cocac 3 a 250 ml coca cola 330 ml 10 px coke 125 lzero coke 250 mlreg pmpg 5 p'
  sentences:
  - 'retailer: vitalveg description: coke 240 k'
  - 'retailer: vitalveg description: tala silicone icing'
  - 'retailer: bountify description: pah antibac wood 10 l'
- source_sentence: 'module: skin conditioning moisturising group: skin conditioning
    moisturising supergroup: personal care example descriptions: ss crmy bdy oil dove
    dm spa sr f m 7 nivea creme 50 carmex lime stick talc powder bo dry skn gel garnier
    milk bld lpblm orgnl vit a serum nv cr gran oh olay bright eye crm bio oil 2 x
    200 ml nvfc srm q 10 prlbst sf aa nt crm 50 aveeno cream 500 ml'
  sentences:
  - 'retailer: wilko description: radiator m key'
  - 'retailer: nourify description: okf lprp tblpbl un'
  - 'retailer: crispcorner description: 065 each fredflo 60 biodegradable'
- source_sentence: 'module: cakes gateaux ambient group: cakes gateaux ambient supergroup:
    food ambient example descriptions: x 20 pkmcvitiesjaffacakes 1 srn ban lunchbx
    js angel slices x 6 spk mr kipling frosty fancies plantastic cherry choc fl hr
    kipling angel slices 10 pk brompton choc brownies jschocchunknuffin loaded drip
    cake hobnbchoc fjack oreo muffins x 2 mr kipling victoria slices 6 pack mk kip
    choc rdsugar m the best brownies odby 5 choc mini'
  sentences:
  - 'retailer: flavorful description: nr choc brownies'
  - 'retailer: producify description: dettol srfc wipe'
  - 'retailer: noshify description: garden wheels plate'
- source_sentence: 'module: bread ambient group: bread ambient supergroup: food ambient
    example descriptions: 1 war 3 toastie 400 g cc 90 varburtons bread tovis snelwrspmpkin
    800 g warbutons medium bread spk giant crumpets z hovis med wht 600 g sandwich
    thins 5 pk warb pk crumpets mission plain tortilla 25 cm warburtons 4 protein
    thin bagels hovis soft wet med hovis wholemefl pataks pappadums 6 pk warb so bth
    disc pappajuns'
  sentences:
  - 'retailer: greenly description: pomodoro sauce'
  - 'retailer: crispcorner description: kingsmill 5050 medius bread 800 g'
  - 'retailer: vitalveg description: ready to eat prun'
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: sentence transformers/all mpnet base v2
      type: sentence-transformers/all-mpnet-base-v2
    metrics:
    - type: cosine_accuracy@1
      value: 0.498812351543943
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6342042755344418
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7102137767220903
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7838479809976246
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.498812351543943
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.21140142517814728
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14204275534441804
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07838479809976245
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.498812351543943
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6342042755344418
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7102137767220903
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7838479809976246
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6324346540369431
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5850111224220487
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5910447073012788
      name: Cosine Map@100
---

# 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) on the csv 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **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("carnival13/all-mpnet-base-v2-modulepred")
# Run inference
sentences = [
    'module: bread ambient group: bread ambient supergroup: food ambient example descriptions: 1 war 3 toastie 400 g cc 90 varburtons bread tovis snelwrspmpkin 800 g warbutons medium bread spk giant crumpets z hovis med wht 600 g sandwich thins 5 pk warb pk crumpets mission plain tortilla 25 cm warburtons 4 protein thin bagels hovis soft wet med hovis wholemefl pataks pappadums 6 pk warb so bth disc pappajuns',
    'retailer: crispcorner description: kingsmill 5050 medius bread 800 g',
    'retailer: vitalveg description: ready to eat prun',
]
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

#### Information Retrieval
* Dataset: `sentence-transformers/all-mpnet-base-v2`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.4988    |
| cosine_accuracy@3   | 0.6342    |
| cosine_accuracy@5   | 0.7102    |
| cosine_accuracy@10  | 0.7838    |
| cosine_precision@1  | 0.4988    |
| cosine_precision@3  | 0.2114    |
| cosine_precision@5  | 0.142     |
| cosine_precision@10 | 0.0784    |
| cosine_recall@1     | 0.4988    |
| cosine_recall@3     | 0.6342    |
| cosine_recall@5     | 0.7102    |
| cosine_recall@10    | 0.7838    |
| cosine_ndcg@10      | 0.6324    |
| cosine_mrr@10       | 0.585     |
| **cosine_map@100**  | **0.591** |

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 505,654 training samples
* Columns: <code>query</code> and <code>full_doc</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | full_doc                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 10 tokens</li><li>mean: 14.8 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 83 tokens</li><li>mean: 115.71 tokens</li><li>max: 176 tokens</li></ul> |
* Samples:
  | query                                                         | full_doc                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>retailer: vitalveg description: twin xira</code>        | <code>module: chocolate single variety group: chocolate chocolate substitutes supergroup: biscuits & confectionery & snacks example descriptions: milky way twin 43 crml prtzlarum rai galaxy mnstr pipnut 34 g dark pb cup nest mnch foge p nestle smarties shar dark choc chun x 10 pk kinder bueno 1 dr oetker 72 da poppets choc offee pouch yorkie biscuit zpk haltesers truffles bog cadbury mini snowballs p terrys choc orange 3435 g galaxy fusion dark 704 100 g</code> |
  | <code>retailer: freshnosh description: mab pop sockt</code>   | <code>module: clothing & personal accessories group: clothing & personal accessories supergroup: clothing & personal accessories example descriptions: pk blue trad ging 40 d 3 pk opaque tight t 74 green cali jogger ss animal swing yb denim stripe pump aw 21 ff vest aw 21 girls 5 pk lounge toplo sku 1 pk fleecy tight knitted pom hat pk briefs timeless double pom pomkids hat cute face twosie sku coral jersey str pun faded petrol t 32 seamfree waist c</code>       |
  | <code>retailer: nourify description: bts prwn ckt swch</code> | <code>module: bread sandwiches filled rolls wraps group: bread fresh fixed weight supergroup: food perishable example descriptions: us chicken may hamche sw jo dbs allbtr pp st 4 js baconfree ran posh cheesy bea naturify cb swich sp eggcress f cpdfeggbacon js cheeseonion sv duck wrap reduced price takeout egg mayo sandwich 7 takeout cheeseonion s wich 2 ad leicester plough bts cheese pman 2 1 cp bacon chese s</code>                                               |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 4
- `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`: 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`: 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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | sentence-transformers/all-mpnet-base-v2_cosine_map@100 |
|:------:|:----:|:-------------:|:------------------------------------------------------:|
| 0.0016 | 100  | 1.6195        | 0.2567                                                 |
| 0.0032 | 200  | 1.47          | 0.3166                                                 |
| 0.0047 | 300  | 1.2703        | 0.3814                                                 |
| 0.0063 | 400  | 1.1335        | 0.4495                                                 |
| 0.0079 | 500  | 0.9942        | 0.4827                                                 |
| 0.0095 | 600  | 0.9004        | 0.5058                                                 |
| 0.0111 | 700  | 0.8838        | 0.5069                                                 |
| 0.0016 | 100  | 0.951         | 0.5197                                                 |
| 0.0032 | 200  | 0.9597        | 0.5323                                                 |
| 0.0047 | 300  | 0.9241        | 0.5406                                                 |
| 0.0063 | 400  | 0.8225        | 0.5484                                                 |
| 0.0079 | 500  | 0.7961        | 0.5568                                                 |
| 0.0095 | 600  | 0.7536        | 0.5621                                                 |
| 0.0111 | 700  | 0.7387        | 0.5623                                                 |
| 0.0127 | 800  | 0.7716        | 0.5746                                                 |
| 0.0142 | 900  | 0.7921        | 0.5651                                                 |
| 0.0158 | 1000 | 0.7744        | 0.5707                                                 |
| 0.0174 | 1100 | 0.8021        | 0.5770                                                 |
| 0.0190 | 1200 | 0.732         | 0.5756                                                 |
| 0.0206 | 1300 | 0.764         | 0.5798                                                 |
| 0.0221 | 1400 | 0.7726        | 0.5873                                                 |
| 0.0237 | 1500 | 0.6676        | 0.5921                                                 |
| 0.0253 | 1600 | 0.6851        | 0.5841                                                 |
| 0.0269 | 1700 | 0.7404        | 0.5964                                                 |
| 0.0285 | 1800 | 0.6798        | 0.5928                                                 |
| 0.0301 | 1900 | 0.6485        | 0.5753                                                 |
| 0.0316 | 2000 | 0.649         | 0.5839                                                 |
| 0.0332 | 2100 | 0.6739        | 0.5891                                                 |
| 0.0348 | 2200 | 0.6616        | 0.6045                                                 |
| 0.0364 | 2300 | 0.6287        | 0.5863                                                 |
| 0.0380 | 2400 | 0.6602        | 0.5898                                                 |
| 0.0396 | 2500 | 0.5667        | 0.5910                                                 |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu124
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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