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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:69227
- loss:CosineSimilarityLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: Gliss Hair Repair Conditioner Color Protect & Shine. Description
    :This conditioner is designed for long-lasting colour protection for your coloured
    hair. The ultimate colour conditioner gives up to 10 weeks of colour protection
    and intense luminosity. The effective formula with the repair serum and the UV
    filter repairs the hair, seals and protects the colour perfectly from washing
    out and fading. It provides optimal colour protection for coloured hair up to
    10 weeks with regular use.Hate hair drama? Then try Gliss Hair Repair products
    for beautiful, restored and healthy-looking hair. GLISS Hair Repair products with
    breakthrough patented Hair-Identical Keratin leverage technology to up to 10 layers
    deep. Combined with essential benefits like colour protection, intense hydration,
    long-lasting volume, and weightless nourishment, you get the repair you need without
    having to compromise.!
  sentences:
  - Dairy Milk Honeycomb & Nuts - Imported. Description :Cadbury dairy milk is all
    about regaling in the richness and creaminess of these classic chocolate bars.
    These chocolate bars are available in a number of diverse flavours that offer
    you a reason to celebrate every small and big occasion of happiness.!
  - 'product

    Bio Flame Of The Forest - Fresh Shine Expertise Oil    Bio Flame Of The Forest
    - Fresh Shine Expertis...

    Bio Flame Of The Forest - Fresh Shine Expertise Oil    Bio Flame Of The Forest
    - Fresh Shine Expertis...

    Name: combined, dtype: object'
  - Hygiene Hand Wipes With Anti-bacterial Actives- Skin-Friendly. Description :Have
    you stepped out of your house and wondered if the door that you just pushed open,
    was clean? Are there germs lurking around you, that you wish you could see better?
    Have you wondered if you have been careful in ensuring the best protection against
    bacteria and germs? Is your personal hygiene standard good enough? Personal Hygiene
    is in your hands. Literally. KeepSafe by Marico takes care of your Hygiene needs
    through its range of premium quality sanitizer, disinfectants, wipes, hand wash
    and personal hygiene products.KeepSafe Hygiene Hand Wipes are rich in anti-bacterial
    actives that sanitise and effectively fight germs. These wipes are rich in Aloe
    Vera and Glycerine and are mild and soothing on the skin. These hygiene wipes
    are so soft, that you can use them every day, as many times as you want. Like
    a true Marico product, KeepSafe believes in transparency, superior quality and
    complete essential care. Try out the Multi-purpose Disinfectant and the Instant
    Hand Sanitiser from KeepSafe by Marico range for complete out-of-home hygiene.
    Take No Chances. Keep Safe.!
- source_sentence: Fragrance Body Spray For Men (1000 sprays) - Forever. Description
    :Soothing experience throughout the day, Consists of refreshing & Long lasting
    fragrance.  For Beauty tips, tricks & more visit https://bigbasket.blog/!
  sentences:
  - M2 Perfume Spray - for Men. Description :Engage Perfume Sprays created by International
    Experts  For Beauty tips, tricks & more visit http://lookbeautiful.in/  For Beauty
    tips, tricks & more visit https://bigbasket.blog/!
  - 'product

    Dazzle Opalware Noodle Bowl Set - Tropical Lagoon    Dazzle Opalware Noodle Bowl
    Set - Tropical Lag...

    Dazzle Opalware Noodle Bowl Set - Tropical Lagoon    Dazzle Opalware Noodle Bowl
    Set - Tropical Lag...

    Name: combined, dtype: object'
  - 'product

    Hakka Noodles - Veg    Hakka Noodles - Veg. Description :Ching''s Secr...

    Hakka Noodles - Veg    Hakka Noodles - Veg. Description :It is ready ...

    Name: combined, dtype: object'
- source_sentence: Amlant Ayurvedic Medicine For Acidity
  sentences:
  - Grapes - Thompson Seedless
  - 'Ice Cream Bowl. Description :Excellent quality crystal clear glass

    Easy to handle

    Ideal for gifting

    Dishwasher safe

    This glass is made from high-quality material & crafted in a new design for easy
    handling.!'
  - Melamine Snack Set - Red. Description :Made of 100% food-grade melamine and food
    contact grade colour, this snack set is heat resistant up to a temp of 140º C.
    It is resistant to breaking, cracking & chipping. Stain-proof, it comes with long-lasting
    designs. It has a glazed finish that makes it aesthetically pleasing. This snack
    set is safe for dishwasher use.!
- source_sentence: Wonder Pants - Small, Combo. Description :Your baby spends a good
    part of their day in a diaper. Therefore, choosing the right diaper for their
    tender and delicate skin is extremely important. And this is where, we introduce
    our next-generation product, India's 1st diaper pants with the unique Bubble-Bed
    technology. There are 3 areas where a diaper surrounds the baby's skin-the baby's
    bottom, the baby's waist, and the baby's thigh. The skin of the baby in all these
    areas is extremely delicate and sensitiveHuggies Wonder Pants Diapers Small Size
    pack with 3D Bubble bed technology with a Cushiony Waistband.!
  sentences:
  - Home Mate Garbage Bag - Green, Oxo-Bio-Degradable Roll, 30X37, 50 Micron. Description
    :These garbage bags are designed to ease the task of garbage disposal, and the
    bio-degradable material, makes it environment friendly and helps spread the word
    of hygiene and cleanliness. They are strong enough to carry waste neatly without
    causing a mess, and large enough to carry it all at once. They also offer great
    flexibility, convenience and ensure a high degree of hygiene, whether at home
    or in office.!
  - 'product

    Baby Diapers & Sanitary Disposal Bag    Baby Diapers & Sanitary Disposal Bag.
    Descript...

    Baby Diapers & Sanitary Disposal Bag    Baby Diapers & Sanitary Disposal Bag.
    Descript...

    Name: combined, dtype: object'
  - 'product

    Organic - Sugar/Sakkare Brown    Organic - Sugar/Sakkare Brown. Description :Pu...

    Organic - Sugar/Sakkare Brown    Organic - Sugar/Sakkare Brown. Description :Tu...

    Name: combined, dtype: object'
- source_sentence: Coffee Filter Papers - Size 02, White. Description :Hario brings
    in Cone-shaped natural paper filter for Pour-over brewing experience for a great
    cup of Coffee. Hario's V60, size 02 White, give you a perfect brew in comparison
    to mesh filters. These paper filters are of great quality and they produce a clean,
    flavorful, sediment-free cup. They are disposable, and thus it makes it convenient
    and easier to use for brewing and cleanup. Perfect choice for coffee enthusiasts
    who like to grind their coffee at home. These papers are safe to use and eco-friendly.
    The Box comes with 100 disposable 02 paper filters.!
  sentences:
  - Tomato Disc 70 g + Cheese Balls 70 g
  - 'product

    4mm Aluminium Induction Base Chapati Roti Tawa - Silver    4mm Aluminium Induction
    Base Chapati Roti Tawa...

    4mm Aluminium Induction Base Chapati Roti Tawa - Silver    4mm Aluminium Induction
    Base Chapati Roti Tawa...

    Name: combined, dtype: object'
  - Steel Rice Serving Spoon - Medium, Classic Diana Series, BBST37. Description :BB
    Home provides fine and classy cooking and serving tools that can make difference
    to your kitchen experience. These cooking/serving tools are made from 100% food
    grade stainless steel. The handle is designed in a way so it does not feel heavy
    while cooking/serving. It is easy to store as it has a bottom hole on the handle
    to hang it on the wall.!
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: bge eval
      type: bge-eval
    metrics:
    - type: pearson_cosine
      value: 0.9791486195203369
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.15795715637146185
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.9798210832808076
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.1632937701650559
      name: Spearman Cosine
---

# SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("mavihsrr/bgeEmbeddingsRetailedFT")
# Run inference
sentences = [
    "Coffee Filter Papers - Size 02, White. Description :Hario brings in Cone-shaped natural paper filter for Pour-over brewing experience for a great cup of Coffee. Hario's V60, size 02 White, give you a perfect brew in comparison to mesh filters. These paper filters are of great quality and they produce a clean, flavorful, sediment-free cup. They are disposable, and thus it makes it convenient and easier to use for brewing and cleanup. Perfect choice for coffee enthusiasts who like to grind their coffee at home. These papers are safe to use and eco-friendly. The Box comes with 100 disposable 02 paper filters.!",
    'Steel Rice Serving Spoon - Medium, Classic Diana Series, BBST37. Description :BB Home provides fine and classy cooking and serving tools that can make difference to your kitchen experience. These cooking/serving tools are made from 100% food grade stainless steel. The handle is designed in a way so it does not feel heavy while cooking/serving. It is easy to store as it has a bottom hole on the handle to hang it on the wall.!',
    'Tomato Disc 70 g + Cheese Balls 70 g',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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

#### Semantic Similarity

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.9791    |
| **spearman_cosine** | **0.158** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9798     |
| **spearman_cosine** | **0.1633** |

<!--
## 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: 69,227 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                           | sentence2                                                                           | score                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | float                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 114.97 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 101.87 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.18</li><li>mean: 0.88</li><li>max: 0.96</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | score                           |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>Breakfast Mix - Masala Idli. Description :Established in 1924, MTR is the contemporary way to authentic tasting food, Our products are backed by culinary expertise honed, over 8 decades of serving wholesome, tasty and high quality vegetarian food, Using authentic Indian recipes, the purest and best quality natural ingredients and traditional methods of preparation, We brings you a range of products of unmatched flavour and taste, to delight your family at every meal and every occasion, MTR Daily Favourites is your dependable partner in the Kitchen that helps you make your family's everyday meals tasty and wholesome, So bring home the confidence of great tasting food everyday with MTR..!</code> | <code>Quinoa Flakes. Description :Keep a good balance of satisfying your taste buds and satiating your hunger pangs. Nutriwish Quinoa Flakes are a “complete” protein containing all eight essential amino acids. The perfect antidote to all that sugar, Nutriwish Quinoa Flakes are delicious cold in a salad, served warm as a side dish or even combined with vegetables and dairy to make a spectacular and filling vegetarian main course. Curb food cravings and start your day yummy with the starchy Nutriwish Quinoa Flakes.!</code> | <code>0.9524586385560029</code> |
  | <code>1 To 1  Baking Flour - Gluten Free. Description :Bob Red Mill gluten-free 1-to-1 baking flour makes it easy to transform traditional recipes to gluten-free. Simply follow your favourite baking recipe, replacing the wheat flour with this blend. It is formulated for baked goods with terrific taste and texture, no additional speciality ingredients or recipes required. It is suitable for cookies, cakes, brownies, muffins, and more.!</code>                                                                                                                                                                                                                                                                        | <code>Chocolate - Drink Powder. Description :Hintz cocoa powder is not just ideal for making biscuits, ice cream and deserts. It is also dissolved in hot milk - a delicious chocolate beverage.!</code>                                                                                                                                                                                                                                                                                                                                       | <code>0.8764388983469142</code> |
  | <code>Joy Round Kids Glass. Description :This glass, made of plastic material, is specially designed for your kid. It is lightweight and easy to use. This glass is ideal for drinking water, milk, juices, health drinks etc.!</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>Plastic Lunch Box/Tiffin Box - Disney Mickey Mouse, BPA Free, HMHILB 199-MK. Description :HMI brings this 4 side lock and lock style. This is airtight, leak-proof and microwave safe. It comes with a small container, fork & spoon.!</code>                                                                                                                                                                                                                                                                                            | <code>0.9289614489097255</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: 8,654 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                           | sentence2                                                                          | score                                                            |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | float                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 110.58 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 97.13 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.19</li><li>mean: 0.87</li><li>max: 0.96</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                            | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | score                           |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>1947 Flora Natural Aroma Incense Sticks - Economy Pack. Description :A Traditional formula that is handed over by the founder, incense sticks is made the traditional way with a ‘masala’ or mixture of 100% natural aromatic botanicals. During your rituals, these incense sticks will bring about a fresh and fragrant breath of conscious soothing bliss.!</code>                                                                                          | <code>Designer Jyot - Green. Description :This is made in India Initiative and create a meditative and peaceful ambience in your puja room with the handmade Brass Mandir Jyot. It extremely durable and crack-resistant, which allows you to use it with ease on a daily basis. This Jyot is very attractive and worth purchasing for personal use or for gifting purpose. Easy to Use and Clean. This Glass brass diya is designed for ease in inserting whip, refilling oil and cleaning. It emits brighter light due to the increased clarity provided by the superior quality glass. The flame of this brass diya does not go off or cause any danger even when the fan is on as the diya comes with a lid.!</code> | <code>0.9030882765047124</code> |
  | <code>Mexican Seasoning. Description :The rich tapestry of sweet and spicy flavours that Mexican cuisine is loved for - now captured in a magic blend. This international seasoning product is inbuilt with unique 2-way flip cap to sprinkle it or scoop it. On1y is a new way of rediscovering the power of herbs and spices. On1y can conveniently become a part of your daily diet for the irresistible benefits that it brings.!</code>                         | <code>Rainbow Strands. Description :Colourful jimmies/sprinkles make decorating your cakes, cupcakes and cookies fun and easy. Great as an ice cream topping too.!</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>0.9584305870004965</code> |
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* 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
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: 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`: 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`: 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`: True
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | bge-eval_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|
| 0      | 0    | -             | -               | 0.0923                   |
| 0.0231 | 100  | 0.0657        | 0.0386          | 0.1450                   |
| 0.0462 | 200  | 0.0248        | 0.0133          | 0.1661                   |
| 0.0693 | 300  | 0.0118        | -               | -                        |
| 0.0231 | 100  | 0.0069        | 0.0070          | 0.1644                   |
| 0.0462 | 200  | 0.0037        | 0.0040          | 0.1634                   |
| 0.0693 | 300  | 0.0016        | 0.0038          | 0.1619                   |
| 0.0924 | 400  | 0.0013        | 0.0042          | 0.1603                   |
| 0.1156 | 500  | 0.0011        | 0.0049          | 0.1579                   |
| 0.1387 | 600  | 0.0012        | 0.0052          | 0.1593                   |
| 0.1618 | 700  | 0.0011        | 0.0053          | 0.1608                   |
| 0.1849 | 800  | 0.0011        | 0.0055          | 0.1612                   |
| 0.2080 | 900  | 0.0011        | 0.0063          | 0.1606                   |
| 0.2311 | 1000 | 0.0011        | 0.0061          | 0.1585                   |
| 0.2542 | 1100 | 0.0012        | 0.0061          | 0.1566                   |
| 0.2773 | 1200 | 0.0011        | 0.0062          | 0.1557                   |
| 0.3004 | 1300 | 0.0012        | 0.0062          | 0.1570                   |
| 0.3235 | 1400 | 0.001         | 0.0058          | 0.1557                   |
| 0.3467 | 1500 | 0.001         | 0.0063          | 0.1554                   |
| 0.3698 | 1600 | 0.0011        | 0.0062          | 0.1572                   |
| 0.3929 | 1700 | 0.0011        | 0.0061          | 0.1580                   |
| 0.4160 | 1800 | 0.001         | -               | 0.1598                   |
| 0.2311 | 1000 | 0.0008        | 0.0063          | 0.1532                   |
| 0.4622 | 2000 | 0.0008        | 0.0064          | 0.1651                   |
| 0.6933 | 3000 | 0.001         | 0.0067          | 0.1627                   |
| 0.9244 | 4000 | 0.001         | 0.0067          | 0.1633                   |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.1.0+cu118
- 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",
}
```

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