sentence-transformers safetensors sentence-similarity feature-extraction generated_from_trainer dataset_size:3820 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 model-index autotrain_compatible endpoints_compatible region:us
---
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:3820
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: samsung ms23h3125ak/ms23h3125ak
  sentences:
  - Canon EOS M50 + 15-45mm IS STM
  - Bosch KIV32X23GB Integrated
  - Indesit DIF04B1 Integrated
  - Samsung MS23H3125AK Black
  - Samsung RB29FWRNDBC Black
  - Hisense RQ560N4WC1
  - Samsung UE32M5520
  - Nikon CoolPix A10
  - Hotpoint RPD10457JKK
  - HP Intel Xeon X5670 2.93GHz Socket 1366 3200MHz bus Upgrade Tray
  - Indesit DFG15B1S Silver
  - Samsung WW10M86DQOO
  - Bosch SMV46MX00G Integrated
  - LG 49SK8100PLA
  - Nikon CoolPix W300
  - AMD Ryzen 3 1300X 3.5GHz  Box
  - LG OLED65B8PLA
  - Samsung Galaxy J5 SM-J530
  - LG 65UK6500PLA
  - Siemens WM14T391GB
  - Apple iPhone SE 32GB
- source_sentence: lg oled65c8pla
  sentences:
  - Beko LCSM1545W White
  - Bosch KAN90VI20G Stainless Steel
  - Canon PowerShot SX60 HS
  - Hotpoint WMAQF621P
  - Apple iPhone 7 Plus 32GB
  - Hotpoint FFU4DK Black
  - Fujifilm Finepix XP130
  - Bosch WAN24108GB
  - LG OLED65E8PLA
  - Intel Core i7-8700K 3.7GHz  Box
  - Fujifilm X-Pro2
  - LG OLED65C8PLA
  - Samsung UE55NU8000
  - LG 49LK5900PLA
  - Apple iPhone 8 64GB
  - Samsung UE65NU7100
  - AEG L6FBG942R
  - AMD Ryzen 7 1700 3GHz Box
  - Panasonic TX-49FX750B
  - Bosch WKD28351GB
  - Bosch GUD15A50GB Integrated
- source_sentence: 15.748 cm 6.2 2960 x 1440 samoled octa core 2.3ghz quad 1.7gh
  sentences:
  - Apple iPhone SE 32GB
  - Apple iPhone X 64GB
  - LG 55SK9500PLA
  - Sony Cyber-shot DSC-WX500
  - Samsung Galaxy A5 SM-A520F
  - Apple iPhone 8 Plus 64GB
  - Indesit IWDD7123
  - Bosch SMS67MW01G White
  - Bosch KGV33XW30G White
  - Samsung WW80K5413UW
  - AMD Ryzen 3 1300X 3.5GHz  Box
  - Bosch WAW28750GB
  - Samsung Galaxy S8+ 64GB
  - Bosch KGN39VW35G White
  - Intel Core i7-7700K 4.2GHz  Box
  - Hotpoint RZAAV22P White
  - Samsung UE49NU8000
  - HP AMD Opteron 6276 2.3GHz Upgrade Tray
  - Praktica Luxmedia Z250
  - Hotpoint HFC2B19SV White
  - Hisense RB385N4EW1 White
- source_sentence: boxed processor amd ryzen 3 1200 4 x 3.1 ghz quad
  sentences:
  - Bosch KGN36HI32 Stainless Steel
  - Bosch SMS24AW01G White
  - Hotpoint WDAL8640P
  - Doro 6050
  - Samsung QE55Q7FN
  - AMD Ryzen 3 1200 3.1GHz Box
  - Samsung UE55NU7500
  - Huawei Honor 10 128GB Dual SIM
  - Sony Xperia L1
  - Hotpoint FFU4DK Black
  - Hoover DXOC 68C3B
  - Sony Xperia XA1
  - Nikon D7200 + 18-105mm VR
  - HP Intel Xeon DP E5640 2.66GHz Socket 1366 1066MHz bus Upgrade Tray
  - Samsung UE49NU8000
  - Panasonic Lumix DMC-FT30
  - Hotpoint FDL 9640K UK
  - Apple iPhone 6S Plus 128GB
  - Nikon D5600 + AF-P 18-55mm VR
  - HP AMD Opteron 6238 2.6GHz Upgrade Tray
  - Apple iPhone SE 32GB
- source_sentence: lg 49uk6300plb/49uk6300plb
  sentences:
  - Bosch KIR24V20GB Integrated
  - Bosch WAWH8660GB
  - Intel Core i5-7600K 3.80GHz  Box
  - Sony Bravia KD-65AF8
  - Samsung RL4362FBASL Stainless Steel
  - Bosch SMI50C15GB Silver
  - Apple iPhone XS Max 256GB
  - Fujifilm X-T100 + XC 15-45/f3.5-5.6 OIS PZ
  - Bosch KGN36VW35G White
  - Samsung WW70K5410UW
  - Samsung Galaxy J6
  - LG 49UK6300PLB
  - Doro Secure 580
  - Sony Xperia XZ1 Compact
  - Bosch SMV50C10GB Integrated
  - Bosch KGN34VB35G Black
  - Panasonic NN-E27JWMBPQ White
  - Samsung WW10M86DQOA/EU
  - LG 55SK9500PLA
  - Samsung QE65Q8DN
  - Canon EOS 80D
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Product Category Retrieval Test
      type: Product-Category-Retrieval-Test
    metrics:
    - type: cosine_accuracy@1
      value: 0.8085774058577406
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9476987447698745
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9644351464435147
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9769874476987448
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8085774058577406
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3158995815899582
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19288702928870294
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09769874476987449
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8085774058577406
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9476987447698745
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9644351464435147
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9769874476987448
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9041917131034228
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.879607906621505
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8805000617705705
      name: Cosine Map@100
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 512-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 512 dimensions
- **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): 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()
  )
  (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): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```

## 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("llmvetter/embedding_finetune")
# Run inference
sentences = [
    'lg 49uk6300plb/49uk6300plb',
    'LG 49UK6300PLB',
    'Samsung Galaxy J6',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# 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

#### Information Retrieval

* Dataset: `Product-Category-Retrieval-Test`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8086     |
| cosine_accuracy@3   | 0.9477     |
| cosine_accuracy@5   | 0.9644     |
| cosine_accuracy@10  | 0.977      |
| cosine_precision@1  | 0.8086     |
| cosine_precision@3  | 0.3159     |
| cosine_precision@5  | 0.1929     |
| cosine_precision@10 | 0.0977     |
| cosine_recall@1     | 0.8086     |
| cosine_recall@3     | 0.9477     |
| cosine_recall@5     | 0.9644     |
| cosine_recall@10    | 0.977      |
| **cosine_ndcg@10**  | **0.9042** |
| cosine_mrr@10       | 0.8796     |
| cosine_map@100      | 0.8805     |

<!--
## 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: 3,820 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, <code>sentence_3</code>, <code>sentence_4</code>, <code>sentence_5</code>, <code>sentence_6</code>, <code>sentence_7</code>, <code>sentence_8</code>, <code>sentence_9</code>, <code>sentence_10</code>, <code>sentence_11</code>, <code>sentence_12</code>, <code>sentence_13</code>, <code>sentence_14</code>, <code>sentence_15</code>, <code>sentence_16</code>, <code>sentence_17</code>, <code>sentence_18</code>, <code>sentence_19</code>, <code>sentence_20</code>, and <code>sentence_21</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        | sentence_3                                                                        | sentence_4                                                                        | sentence_5                                                                        | sentence_6                                                                        | sentence_7                                                                        | sentence_8                                                                        | sentence_9                                                                        | sentence_10                                                                       | sentence_11                                                                       | sentence_12                                                                       | sentence_13                                                                       | sentence_14                                                                       | sentence_15                                                                       | sentence_16                                                                       | sentence_17                                                                       | sentence_18                                                                       | sentence_19                                                                       | sentence_20                                                                       | sentence_21                                                                       |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 18.41 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.94 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.11 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.15 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.89 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.89 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.98 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.84 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.82 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.05 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.92 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.18 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.93 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.95 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.86 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
  | sentence_0                                                           | sentence_1                              | sentence_2                                   | sentence_3                           | sentence_4                           | sentence_5                            | sentence_6                                    | sentence_7                        | sentence_8                       | sentence_9                                    | sentence_10                                                                  | sentence_11                                  | sentence_12                         | sentence_13                                 | sentence_14                                  | sentence_15                             | sentence_16                                      | sentence_17                    | sentence_18                               | sentence_19                            | sentence_20                                                                     | sentence_21                                                                     |
  |:---------------------------------------------------------------------|:----------------------------------------|:---------------------------------------------|:-------------------------------------|:-------------------------------------|:--------------------------------------|:----------------------------------------------|:----------------------------------|:---------------------------------|:----------------------------------------------|:-----------------------------------------------------------------------------|:---------------------------------------------|:------------------------------------|:--------------------------------------------|:---------------------------------------------|:----------------------------------------|:-------------------------------------------------|:-------------------------------|:------------------------------------------|:---------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | <code>sony kd49xf8505bu 49 4k ultra hd tv</code>                     | <code>Sony Bravia KD-49XF8505</code>    | <code>Intel Core i7-8700K 3.7GHz  Box</code> | <code>Bosch WAN24100GB</code>        | <code>AMD FX-6300 3.5GHz  Box</code> | <code>Bosch WIW28500GB</code>         | <code>Bosch KGN36VL35G Stainless Steel</code> | <code>Indesit XWDE751480XS</code> | <code>CAT S41 Dual SIM</code>    | <code>Sony Xperia XA1 Ultra 32GB</code>       | <code>Samsung Galaxy J6</code>                                               | <code>Samsung QE55Q7FN</code>                | <code>Bosch KGN39VW35G White</code> | <code>Intel Core i5 7400 3.0GHz  Box</code> | <code>Neff C17UR02N0B Stainless Steel</code> | <code>Samsung RR39M7340SA Silver</code> | <code>Samsung RB41J7255SR Stainless Steel</code> | <code>Hoover DXOC 68C3B</code> | <code>Canon PowerShot SX730 HS</code>     | <code>Samsung RR39M7340BC Black</code> | <code>Praktica Luxmedia WP240</code>                                            | <code>HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray</code> |
  | <code>doro 8040 4g sim free mobile phone black</code>                | <code>Doro 8040</code>                  | <code>Bosch HMT75M551 Stainless Steel</code> | <code>Bosch SMI50C15GB Silver</code> | <code>Samsung WW90K5413UX</code>     | <code>Panasonic Lumix DMC-TZ70</code> | <code>Sony KD-49XF7073</code>                 | <code>Nikon CoolPix W100</code>   | <code>Samsung WD90J6A10AW</code> | <code>Bosch CFA634GS1B Stainless Steel</code> | <code>HP AMD Opteron 8425 HE 2.1GHz Socket F 4800MHz bus Upgrade Tray</code> | <code>Canon EOS 800D + 18-55mm IS STM</code> | <code>Samsung UE50NU7400</code>     | <code>Apple iPhone 6S 128GB</code>          | <code>Samsung RS52N3313SA/EU Graphite</code> | <code>Bosch WAW325H0GB</code>           | <code>Sony Bravia KD-55AF8</code>                | <code>Sony Alpha 6500</code>   | <code>Doro 5030</code>                    | <code>LG GSL761WBXV Black</code>       | <code>Bosch SMS67MW00G White</code>                                             | <code>AEG L6FBG942R</code>                                                      |
  | <code>fridgemaster muz4965 undercounter freezer white a rated</code> | <code>Fridgemaster MUZ4965 White</code> | <code>Samsung UE49NU7100</code>              | <code>Nikon CoolPix A10</code>       | <code>Samsung UE55NU7100</code>      | <code>Samsung QE55Q7FN</code>         | <code>Bosch KGN49XL30G Stainless Steel</code> | <code>Samsung UE49NU7500</code>   | <code>LG 55UK6300PLB</code>      | <code>Hoover DXOC 68C3B</code>                | <code>Panasonic Lumix DMC-FZ2000</code>                                      | <code>Panasonic Lumix DMC-TZ80</code>        | <code>Bosch WKD28541GB</code>       | <code>Apple iPhone 6 32GB</code>            | <code>Sony Bravia KDL-32WE613</code>         | <code>Lec TF50152W White</code>         | <code>Bosch KGV36VW32G White</code>              | <code>Bosch WAYH8790GB</code>  | <code>Samsung RS68N8240B1/EU Black</code> | <code>Sony Xperia XZ1</code>           | <code>HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray</code> | <code>Sharp R372WM White</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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 8
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 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`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | Product-Category-Retrieval-Test_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:----------------------------------------------:|
| 1.0    | 120  | -             | 0.7406                                         |
| 2.0    | 240  | -             | 0.8437                                         |
| 3.0    | 360  | -             | 0.8756                                         |
| 4.0    | 480  | -             | 0.8875                                         |
| 4.1667 | 500  | 2.5302        | -                                              |
| 5.0    | 600  | -             | 0.8963                                         |
| 6.0    | 720  | -             | 0.9015                                         |
| 7.0    | 840  | -             | 0.9042                                         |


### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- 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",
}
```

#### 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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