sentence-transformers safetensors new sentence-similarity feature-extraction generated_from_trainer dataset_size:412125 loss:TripletLoss custom_code arxiv:1908.10084 arxiv:1703.07737 base_model:NovaSearch/stella_en_400M_v5 base_model:finetune:NovaSearch/stella_en_400M_v5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: NovaSearch/stella_en_400M_v5
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:412125
- loss:TripletLoss
widget:
- source_sentence: "PRASA is recruiting for railway line maintenance from Soweto to\
    \ Johannesburg. Bring a copy of your certified ID and your original ID book to\
    \ Johannesburg Park Station, PRASA Offices. Please share this information with\
    \ others.\t verdict: ['False information.']"
  sentences:
  - The caregiver in this photo had a miscarriage three days after her Covid-19 vaccination
    No, this photo does not show a caregiver who suffered a miscarriage after being
    vaccinated against Covid-19
  - Photos of abandoned Port Harcourt-Maiduguri rail lines in Nigeria Claim that pictures
    show neglected railway line between two Nigerian cities is misleading
  - South Africa's PRASA hiring workers for railway Facebook posts falsely claim rail
    operator in South Africa has job openings for Soweto-Jo’burg route
- source_sentence: "**Breaking News:**\n\nA reliable government source has confirmed\
    \ a major announcement impending for tomorrow. A complete shutdown is imminent.\
    \ Stay informed—this information stems from a cabinet briefing held this morning.\
    \ The details suggest:\n\n**Country Lockdown:** Effective from Wednesday, 18th\
    \ March, to 31st March.\n\n1. **Gatherings Prohibited:** All religious, sporting,\
    \ social, and cultural gatherings are banned. Business and religious premises\
    \ will close, with exceptions for grocery stores, public markets, and provision\
    \ shops. Mosque access and Islamic events are restricted, pending Muzakarah meeting\
    \ outcomes.\n\n2. **Travel Restrictions:** Nationwide travel restrictions will\
    \ be imposed on all citizens. Those recently returned from abroad must self-quarantine.\n\
    \n3. **Border Control:** All tourists and visitors are barred from entering the\
    \ country.\n\n4. **School Closures:** All public and private schools, including\
    \ kindergartens, nurseries, residential schools, and other educational institutions,\
    \ will shut down.\n\n5. **Higher Education Institutions:** Both public and private\
    \ universities and colleges will close.\n\n6. **Essential Services Exemption:**\
    \ Government and private premises will close, except for essential services like\
    \ water, electricity, telecommunications, postal services, transport, fuel, gas,\
    \ lubrication, broadcasting, banking, health, pharmacies, fire-fighting, ports,\
    \ airports, prisons, security, defense, sanitation, and food supply.\t verdict:\
    \ ['Partly false information']"
  sentences:
  - UNESCO declared Islam ‘most peaceful religion in world' UNESCO denies declaring
    Islam ‘most peaceful religion’ -- and the story originated on a satirical website
  - All schools closing in South Africa this week Only a few schools have shut so
    far in South Africa as third Covid-19 wave looms
  - Australian Prime Minister to announce mandatory lockdown Misinformation circulates
    online that Australia has announced a nationwide 'shut down'
- source_sentence: "Netherlands - Senate - The current \"pandemic\" and all the freedom-restricting\
    \ measures were detailed in a scenario proposed by the Rockefeller Foundation\
    \ in 2010! It's like a dream!\t verdict: ['Missing context']"
  sentences:
  - The current pandemic and all liberticidal measures were detailed in a scenario
    proposed by the Rockefeller Foundation in 2010. The Rockefeller Foundation imagined
    a fictional scenario, not the current pandemic
  - A photo shows tractors "today" on a highway in "Holland". There is nothing to
    read about it in the German press. This photo of peasant protests was taken in
    2019 - and the media reported on the road blockade
  - This photo shows elderly social activist joining India's ruling Bharatiya Janata
    Party. This image has been digitally manipulated to insert the face of an Indian
    activist
- source_sentence: "These blue masks are the most common in Uganda due to their availability\
    \ and the ease of purchasing them from street vendors at minimal cost. However,\
    \ the Canadian government has banned their use, stating that these Chinese-made\
    \ masks contain graphene, a material shown to cause cellular lung damage. \"Health\
    \ Canada's preliminary risk assessment revealed a potential for early lung damage\
    \ from inhaling microscopic graphene,\" they said. The Quebec provincial government\
    \ directed people to stop distributing and using these masks, keeping them in\
    \ a safe place. If you regularly wear masks, choose an alternative, preferably\
    \ reusable cloth masks. They are a safer option. As our medical authorities may\
    \ not conduct their own research on what Ugandans constantly inhale, we should\
    \ adopt the findings of other countries and also ban the sale and use of these\
    \ blue masks. Share this information to protect others' lung health.\t verdict:\
    \ ['Partly false information.']"
  sentences:
  - 'Long-term mask-wearing may cause advanced-stage lung cancer, one study shows
    Fact check: Long-term face mask use will not cause lung cancer'
  - Video showing the call to prayer at a famous church in Spain This video shows
    the call to prayer during a concert at Eton College in England
  - All ‘blue masks’ contain graphene Only a small percentage of all blue surgical
    masks contain graphene
- source_sentence: "**Person of the Year: TIME names JAIR BOLSONARO, President of\
    \ Brazil, and Leader of the Free World.**\n\n**TIME Magazine's Personality of\
    \ the Year! President Jair Messias Bolsonaro! Second place, Trump! A Victory for\
    \ Conservatism! Eat your hearts out, leftists. Bite your nails in envy, you pack\
    \ of fools! It's Bolsonaro, damn it! \U0001F926\U0001F602\U0001F4AA\U0001F1E7\U0001F1F7\
    **\t verdict: ['False information']"
  sentences:
  - Jair Bolsonaro at Times as Personality of the Year The cover of Time magazine
    that shows Bolsonaro as "personality of the year" is a montage
  - Wearing face masks can cause carbon dioxide toxicity; can weaken immune system
    Wearing face masks does not cause hypercapnia or affect the immune system
  - 'Donald Trump said: "The Brazilian press fears the candidacy of a military man
    more than that of one convicted of corruption" There is no record that Trump said
    that the Brazilian press fears the candidacy of a military man'
model-index:
- name: SentenceTransformer based on NovaSearch/stella_en_400M_v5
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.976250097057225
      name: Cosine Accuracy
---

# SentenceTransformer based on NovaSearch/stella_en_400M_v5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) on the csv dataset. It maps sentences & paragraphs to a 1024-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:** [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) <!-- at revision 32b4baf84d02a1b1beb2df8952e875232e8ebe1d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': 512, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```

## 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("Bharatdeep-H/stella_finetuned_en_dataset_stella_400_20_translated_query_v3_w_v_MAX_100")
# Run inference
sentences = [
    "**Person of the Year: TIME names JAIR BOLSONARO, President of Brazil, and Leader of the Free World.**\n\n**TIME Magazine's Personality of the Year! President Jair Messias Bolsonaro! Second place, Trump! A Victory for Conservatism! Eat your hearts out, leftists. Bite your nails in envy, you pack of fools! It's Bolsonaro, damn it! 🤦😂💪🇧🇷**\t verdict: ['False information']",
    'Jair Bolsonaro at Times as Personality of the Year The cover of Time magazine that shows Bolsonaro as "personality of the year" is a montage',
    'Donald Trump said: "The Brazilian press fears the candidacy of a military man more than that of one convicted of corruption" There is no record that Trump said that the Brazilian press fears the candidacy of a military man',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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

#### Triplet

* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9763** |

<!--
## 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

#### csv

* Dataset: csv
* Size: 412,125 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                               | positive                                                                            | negative                                                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 119.88 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 35.22 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 36.1 tokens</li><li>max: 191 tokens</li></ul> |
* Samples:
  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | positive                                                                                                                                                                                           | negative                                                                                                                                                                                                                                        |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Breaking news: Four Americans and a pilot were killed. A helicopter was attacked by a missile near the Iraqi-Syrian border by the Islamic resistance in Iraq.	 verdict: []</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>US military helicopter shot down over Iraqi-Syrian border in 2020 This photo has circulated since 2003 in reports about a US army helicopter crash in Iraq</code>                            | <code>Very urgent, now violent clashes in the vicinity of Baghdad International Airport Have recent clashes occurred in the vicinity of Baghdad International Airport?</code>                                                                   |
  | <code>Oh God, please understand... Samaa Mahfouz with Amos Yadlin, the head of the Israeli Mossad.	 verdict: ['False information']</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>Revolutionary fighter Asmaa Mahfouz with Amos Yadlin, head of Israeli intelligence This photo shows Asmaa Mahfouz with a German politician, not with the head of Israeli intelligence</code> | <code>Affirmations about Fiocruz's national vaccine in partnership with Israel, and authorization from Vice President Hamilton Mourão to enter a factory The message about an alleged vaccine production by Fiocruz with Israel is false</code> |
  | <code>© Sott.net 2 REDUCING OXYGEN INTAKE<br>1. Breathing through a mask reduces the amount of oxygen we need to live and be healthy, increases blood acidity, and makes breathing difficult.<br><br>3 & 4 WEARING A MASK FOR PROTECTION? <br>5 & 6 WHAT YOU NEED TO KNOW BEFORE WEARING ONE<br><br>INCREASES TOXIN INHALATION: Toxins we normally exhale are trapped in the mask and re-inhaled into the lungs, worsening symptoms.<br><br>IMMUNE SYSTEM DETRIMENT: Reduced oxygen intake, increased carbon dioxide and toxin intake stress the body, releasing cortisol and deteriorating the immune process.<br><br>INCREASES VIRAL RISK: It triggers and infects dormant retroviruses already present in the body, taking advantage of the weakened immune system.<br><br>SCIENTIFICALLY INCORRECT: Virologists measure COVID-19 between 80 and 140 nanometers, making mask fabric comparable to mosquito netting.<br><br>UNPROVEN EFFECTIVENESS: No peer-reviewed study has been conducted on the effectiveness of masks in a social environment to control, prevent, or eliminate ...</code> | <code>Wearing a mask is dangerous for your health These claims about the dangers of wearing a mask are false</code>                                                                                | <code>A microbiologist explains the mask is useless against the new coronavirus "A mask is useless": beware, this "explanatory" video contains false information</code>                                                                         |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 412,125 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                                | positive                                                                            | negative                                                                            |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 115.98 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 35.22 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.64 tokens</li><li>max: 148 tokens</li></ul> |
* Samples:
  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | positive                                                                                                                                                                                                                        | negative                                                                                                                                                          |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>[OFF-TOPIC] ⭐️📸A spectacular and impressive photo of the fire ravaging part of California taken from 30,000 feet (9,144 km) 🙏Good luck to those fighting the fire.	 verdict: ['False information']</code>                                                                                                                                                                                                                                                                                             | <code>Netizens claim this photo shows clouds made incandescent by the fires currently ravaging California No, this viral photo does not show the California skies made red hot by the fires</code>                              | <code>Satellite image shows fires in Australia No, this map does not show a "satellite image" of the catastrophic fires in Australia.</code>                      |
  | <code>This is not Venezuela, this is France, folks!!! It's not Maduro's "dictatorship", it's Macron's "democratic" government. You won't see this on CNN either. #DefendingTheRevolution #WeAllWantPeace #AlwaysAtTheFront #OnToVictoriousFebruary #NicaraguaWantsPeace #TheyCouldNotAndWillNot #FSLNHeroesOfLoveForever #NicaraguaEverBeautiful #NicaraguaWantsFreedom #LongLiveTheYouth	 verdict: ['False information']</code>                                                                            | <code>This is not Venezuela, this is France gentlemen. It is not the dictatorship of Maduro, it is the democratic government of Macron Photos of yellow vests, taken out of context to criticize Macron's "dictatorship"</code> | <code>This video shows the murder of a woman who opposed the government in Venezuela Images of a woman shot at a rally were shot in Mexico, not Venezuela.</code> |
  | <code>## HUEN Airport: Entebbe International Airport (EBB/HUEN)<br><br>Entebbe International Airport (IATA: EBB, ICAO: HUEN) is Uganda's primary international airport. Constructed in 1972-1973, it is located in Entebbe, Uganda and serves as a hub for Eagle Air and Uganda Airlines.<br><br>**Welcome to HUEN International Airport, Entebbe, Uganda 🇺🇬 <br>No further questions. Case closed permanently. Visit Golola Moses (Google) for more information.**	 verdict: ['False information.']</code> | <code>Uganda’s Entebbe Airport changes name to HUEN Airport Name of Uganda's Entebbe International Airport has not been changed to HUEN</code>                                                                                  | <code>The plane seen in a video from Kabul Airport is a dummy. Afghanistan: The plane in Kabul is not a dummy</code>                                              |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 1
- `learning_rate`: 3e-05
- `max_steps`: 4000
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `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`: 1
- `per_device_eval_batch_size`: 8
- `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`: 3e-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`: 3.0
- `max_steps`: 4000
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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 | cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:---------------:|
| 0.0003 | 100  | 1.4568        | -               | -               |
| 0.0006 | 200  | 0.8644        | -               | -               |
| 0.0010 | 300  | 0.783         | -               | -               |
| 0.0013 | 400  | 0.3973        | -               | -               |
| 0.0016 | 500  | 0.3754        | -               | -               |
| 0.0019 | 600  | 0.4358        | -               | -               |
| 0.0023 | 700  | 0.3606        | -               | -               |
| 0.0026 | 800  | 0.7334        | -               | -               |
| 0.0029 | 900  | 0.4401        | -               | -               |
| 0.0032 | 1000 | 0.4936        | 0.5628          | 0.9659          |
| 0.0036 | 1100 | 0.5887        | -               | -               |
| 0.0039 | 1200 | 0.2433        | -               | -               |
| 0.0042 | 1300 | 0.5472        | -               | -               |
| 0.0045 | 1400 | 0.7924        | -               | -               |
| 0.0049 | 1500 | 0.3093        | -               | -               |
| 0.0052 | 1600 | 0.3813        | -               | -               |
| 0.0055 | 1700 | 0.9388        | -               | -               |
| 0.0058 | 1800 | 0.6047        | -               | -               |
| 0.0061 | 1900 | 0.7951        | -               | -               |
| 0.0065 | 2000 | 0.6723        | 0.5892          | 0.9628          |
| 0.0068 | 2100 | 0.6463        | -               | -               |
| 0.0071 | 2200 | 0.5497        | -               | -               |
| 0.0074 | 2300 | 0.6908        | -               | -               |
| 0.0078 | 2400 | 0.1948        | -               | -               |
| 0.0081 | 2500 | 0.6195        | -               | -               |
| 0.0084 | 2600 | 0.3858        | -               | -               |
| 0.0087 | 2700 | 0.2356        | -               | -               |
| 0.0091 | 2800 | 0.134         | -               | -               |
| 0.0094 | 2900 | 0.358         | -               | -               |
| 0.0097 | 3000 | 0.3793        | 0.4350          | 0.9722          |
| 0.0100 | 3100 | 0.4525        | -               | -               |
| 0.0104 | 3200 | 0.6491        | -               | -               |
| 0.0107 | 3300 | 0.4244        | -               | -               |
| 0.0110 | 3400 | 0.6785        | -               | -               |
| 0.0113 | 3500 | 0.4849        | -               | -               |
| 0.0116 | 3600 | 0.4263        | -               | -               |
| 0.0120 | 3700 | 0.5156        | -               | -               |
| 0.0123 | 3800 | 0.6192        | -               | -               |
| 0.0126 | 3900 | 0.3091        | -               | -               |
| 0.0129 | 4000 | 0.3161        | 0.3879          | 0.9763          |


### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.3.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.4.0
- Datasets: 3.3.2
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->