sentence-transformers safetensors new sentence-similarity feature-extraction generated_from_trainer dataset_size:400345 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:400345
- loss:TripletLoss
widget:
- source_sentence: "KAZAKHSTAN IN THE HANDS OF THE PEOPLE\n\nThe president and prime\
    \ minister have fled the country. The government has collapsed. Soldiers and military\
    \ personnel are also trying to escape the country or risk arrest by protesters.\
    \ Demonstrators have seized control of airport roads and are also detaining police\
    \ officers who do not join them. Politicians are being arrested. Healthcare workers\
    \ at vaccination centers (nurses and doctors) are also being detained. Protesters\
    \ are armed and have seized arsenals. Politicians' homes are on fire.\n\nThe unrest\
    \ began when people were no longer able to withdraw money from banks without a\
    \ vaccination passport and QR code.\n\n**INTERNATIONAL MEDIA IS LYING, CLAIMING\
    \ PEOPLE WERE OUTRAGED BY FUEL PRICE HIKE**\t verdict: ['False information.']"
  sentences:
  - The requirement of the vaccination certificate originated the protests of Kazakhstan
    The protests in Kazakhstan were due to the increase in the price of gas, not because
    of the health pass
  - Environmentalists changed the term "global warming" to "climate change" to justify
    intense cold in Antarctica and Brazil Cold waves in Antarctica and Brazil do not
    prove that global warming does not exist
  - Chaos and demonstrations hit Turkey, and Erdogan arrests his interior minister
    This video depicts clashes between police and counterfeit sellers in South Africa
- source_sentence: "Corona Foundation. Committee \"1 Test Reports Lying instead of\
    \ transparent File Edit Image Options View Help EXX G Morality or its absence\
    \ Vw p me.jpg=frainview 100.0 ,,A caregiver from a nursing home in Überlingen\
    \ relates: \"There are 20 seniors in the home living in quarantine, of whom 18\
    \ are infected. All 20 are tested daily. Each time, there are 18 positive cases\
    \ recorded and reported... This amounts to 140 tests in a week with 126 positive\
    \ reports. In reality, there are only 18.... When asked why this is done, the\
    \ response is simply: 'On orders.'\"\n\n**Without Words**\t verdict: ['False information']"
  sentences:
  - Corona infected people are counted several times to increase the number of cases
    Corona infected people are not counted more than once in the statistics
  - 600,000 Canadians illegally live in the United States Posts criticizing US immigration
    policy exaggerate number of undocumented Canadians
  - 'The Covid-19 epidemic is over in Belgium, the virus has lost its virulence, PCR
    tests are too sensitive... "Fear is over": this visual spreads several false claims
    around the Covid-19 epidemic'
- source_sentence: "And when you think things can't get more absurd, we get this:\
    \ A feminist group practicing for the \"Vagina Diversity Day\" celebration. If\
    \ you want to join, don't say I didn't warn you in advance. 20.\n\n\U0001F926\
    ‍♀️\U0001F926‍♀️\U0001F926‍♀️ Follow us on our second account [USER].\n\n#Spain\
    \ #SanchezResign #SanchezGoNow #SpainUnited #OutWithCommunism #Hypocrisy #Madrid\
    \ #Barcelona #Sevilla #Today #InstaSpain #Like #ETA #Partners #Censorship #Dictatorship\
    \ #Chavismo #Ruination #Poverty #SpainDoesntWantYou #SanchezToPrison #IglesiasToPrison\
    \ #SocialismIsRuination #CommunismIsRuination #LetTheMajoritySpeak #DemocraticSyrup\
    \ #LongLiveSpain\U0001F1EA\U0001F1F8\t verdict: []"
  sentences:
  - Government-approved immunity booster for COVID-19 sold online Philippine regulators
    deny the supplement is approved and warn against ‘deceptive marketing’ related
    to COVID-19
  - Polish public television says that Spain lives under a dictatorship The news of
    the public television of Poland spoke of “LGBT dictatorship” in Spain
  - The 'day of the diversity of the vaginas', the new occurrence of Equality to reinforce
    feminist empowerment The Spanish Ministry of Equality has not promoted in 2022
    "the day of the diversity of vaginas"
- source_sentence: "7:53 AM - 66% of the text: Elementary school teacher Bernie Gores\
    \ passed away while shielding his students from gunfire at Robb Elementary School\
    \ in Uvalde, Texas. *Alysa Graves, 21 minutes ago*\n\n7:53 AM - 6:49 AM: Search\
    \ results from Bing, Yandex, and Google lead to Encyclopedia Dramatica's \"Picture\
    \ of the Now\" from November 11, 2021, featuring a CNN report on Afghanistan.\n\
    \n**CNN Breaking News:** Bernie Gores, a CNN journalist, has been executed in\
    \ Kabul by Taliban soldiers. We are collaborating with US officials to evacuate\
    \ several more journalists from Afghanistan. Our deepest condolences to Bernie's\
    \ family. *12:40 AM - August 14, 201[unknown year]* *Alysa Graves, 19 minutes\
    \ ago*\n\n40010101 - TinEye OL: 66%\n\n*I'll leave this information here.*\t verdict:\
    \ []"
  sentences:
  - This video shows Chinese nationals holidaying on the Indian side of a lake that
    straddles the countries’ border Chinese tourists holiday in Indian hotspot? No,
    they’re actually in Tibet
  - CNN made up a dead man in Ukraine. This CNN tweet about a dead man in Ukraine
    is fake
  - 'CNN reported same man''s death in Afghanistan and Uvalde school shooting Fact
    check: False claim that CNN reported man''s death in Uvalde and Afghanistan'
- source_sentence: "<OCR_START> BRASILIA time [URL] CDC Director says 75% of COVID\
    \ deaths had at least 4 comorbidities [USER] SUPPORT INDEPENDENT JOURNALISM VIA\
    \ PIX: [EMAIL] <OCR_END> <TEXT_START> What's your take on this? Follow [USER]\
    \ for the full article link in stories. #cdc #covid_19 <TEXT_END>\t verdict: []"
  sentences:
  - The US CDC has reduced its COVID-19 death toll US health authorities have not
    cut reported COVID-19 death toll
  - The government of Mexico requests to register by WhatsApp to be a beneficiary
    of its Credits to the Word The Mexican government does not request to register
    by WhatsApp to obtain your Credits to the Word
  - 'Rochelle Walensky CDC Director: "Of the overwhelming number of deaths, more than
    75% occurred in people who had at least four comorbidities" CDC director spoke
    about study that found comorbidities in vaccinees killed by covid-19'
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: 0.9789782888886669
      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_50")
# Run inference
sentences = [
    "<OCR_START> BRASILIA time [URL] CDC Director says 75% of COVID deaths had at least 4 comorbidities [USER] SUPPORT INDEPENDENT JOURNALISM VIA PIX: [EMAIL] <OCR_END> <TEXT_START> What's your take on this? Follow [USER] for the full article link in stories. #cdc #covid_19 <TEXT_END>\t verdict: []",
    'Rochelle Walensky CDC Director: "Of the overwhelming number of deaths, more than 75% occurred in people who had at least four comorbidities" CDC director spoke about study that found comorbidities in vaccinees killed by covid-19',
    'The US CDC has reduced its COVID-19 death toll US health authorities have not cut reported COVID-19 death toll',
]
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** | **0.979** |

<!--
## 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: 400,345 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: 12 tokens</li><li>mean: 126.05 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 35.3 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.44 tokens</li><li>max: 191 tokens</li></ul> |
* Samples:
  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | positive                                                                                                                                                              | negative                                                                                                                                                                |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The rupee's depreciation, along with the exit of multi-national corporations like Pepsi, KFC, Pizza Hut, and Coca-Cola, signifies challenging times for the country.	 verdict: ['Partly false information']</code>                                                                                                                                                                                                                                                                                                                 | <code>Four multinational firms are winding up operations in Sri Lanka Four multinational firms reject claims they are 'winding up operations' in Sri Lanka</code>     | <code>Duque's economic balance is disastrous compared to Santos' A tweet erroneously compares economic data from the governments of Santos and Duque in Colombia</code> |
  | <code>KF94 masks and various particle sizes enlarged 50,000 times: Virus <0.125um, 2.5km ultrafine dust 2.5m.<br><br>With the mandatory mask-wearing policy, everyone is diligently wearing masks... Yet, there are more confirmed cases now compared to when mask-wearing wasn't mandatory... This is evidence that masks cannot block the Wuhan pneumonia virus... Still, everywhere you go... masks, masks everywhere... A time when you're considered crazy if you think rationally...	 verdict: ['Partly false information']</code> | <code>Virus that causes Covid-19 cannot be blocked by face masks Misleading face mask graphic shared in incorrect virus posts</code>                                  | <code>“Stanford study results” show face masks are ineffective and dangerous. Paper about mask wearing was not from Stanford and makes false claims</code>              |
  | <code>RAF 18 Olaf Scholz Source: "Aktuelle Kamera" 1984 Source Actual...<br><br>No, ...right?! Please tell me that's not true!	 verdict: ['Altered photo']</code>                                                                                                                                                                                                                                                                                                                                                                        | <code>In 1984, Olaf Scholz appeared under an RAF logo in the GDR news program “Aktuelle Kamera”. This picture of Olaf Scholz in front of an RAF logo is a fake</code> | <code>The photo showing a dilapidated airport is from Kabul in August 2021 The image of an airport in ruins is not of Kabul in 2021, but of Karachi in 2014</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: 400,345 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: 12 tokens</li><li>mean: 117.77 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 35.32 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 35.57 tokens</li><li>max: 150 tokens</li></ul> |
* Samples:
  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | positive                                                                                                                                                          | negative                                                                                                                                                                        |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>#Ecuador An impressive explosion was recorded this afternoon at the Sangay Volcano. A dense ash cloud keeps the population on alert. At least 22 cantons in five provinces of Ecuador have reported ash fall.	 verdict: ['False']</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>Video of the eruption of the Sangay volcano in Ecuador in September 2020 The video of an erupting volcano is not recent nor of the Sangay in Ecuador</code> | <code>After Taal, another volcano eruption in the Philippines This video is from a 2015 report about the Kanlaon volcano eruption in the Philippines</code>                     |
  | <code>Did you notice, friends, that the number of COVID infections and deaths in India has dramatically decreased over the past two weeks? Previously, they were recording 3-4 million new infections and 3-4 thousand deaths per day, with bodies piling up and being disposed of in rivers. I noticed this and was shocked. I searched for news in Thailand but couldn't find anything. So, I called friends in Singapore and Malaysia, and they told me that India has discovered an extraordinary drug called Ivermectin, which can easily cure COVID. Originally discovered in 1970, it was used to treat eye infections in underdeveloped countries and later for animal diseases. It's easily accessible and very affordable. India started using Ivermectin to treat COVID in various states, including the hard-hit city of Mumbai. The local government even distributed Ivermectin to all residents for self-medication. The results were astonishing, with new infections and deaths plummeting.<br><br>When news spread that Ivermectin...</code> | <code>Ivermectin can be used to treat COVID-19. Ivermectin anthelmintic drug It has not been approved for use in the treatment of COVID-19.</code>                | <code>Molnupiravir cures Covid-19 within 24 hours Posts misleadingly claim anti-viral drug could replace Covid-19 vaccines</code>                                               |
  | <code>Why is the stench from the Trudeaus always buried? Alexandre Trudeau, brother of Canadian Prime Minister Justin Trudeau, was arrested on charges including sexual misconduct with a minor and possession of child pornography. He was booked into jail on Tuesday, as confirmed by the Montreal police (SPMV) in a statement to The Beaver. Additional charges include possession of a small quantity of crystal meth. Why was his bail set at only 50,000 CAD? Was his passport confiscated?	 verdict: ['False information']</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>Alexandre Trudeau arrested by the Montreal police Justin Trudeau's brother was not arrested for sexual misconduct</code>                                    | <code>A new method of kidnapping in the Montreal area A new kidnapping technique in Montreal? Police in Quebec deny and photos have been circulating since at least 2019</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  | 1.5655        | -               | -               |
| 0.0007 | 200  | 0.7517        | -               | -               |
| 0.0010 | 300  | 0.4132        | -               | -               |
| 0.0013 | 400  | 0.5225        | -               | -               |
| 0.0017 | 500  | 0.5147        | -               | -               |
| 0.0020 | 600  | 0.2795        | -               | -               |
| 0.0023 | 700  | 0.5319        | -               | -               |
| 0.0027 | 800  | 0.4263        | -               | -               |
| 0.0030 | 900  | 0.766         | -               | -               |
| 0.0033 | 1000 | 0.6864        | 0.6357          | 0.9585          |
| 0.0037 | 1100 | 0.7172        | -               | -               |
| 0.0040 | 1200 | 0.5172        | -               | -               |
| 0.0043 | 1300 | 1.0088        | -               | -               |
| 0.0047 | 1400 | 0.9853        | -               | -               |
| 0.0050 | 1500 | 0.5999        | -               | -               |
| 0.0053 | 1600 | 0.8267        | -               | -               |
| 0.0057 | 1700 | 0.6835        | -               | -               |
| 0.0060 | 1800 | 0.6136        | -               | -               |
| 0.0063 | 1900 | 1.0621        | -               | -               |
| 0.0067 | 2000 | 0.6602        | 0.6893          | 0.9597          |
| 0.0070 | 2100 | 0.6659        | -               | -               |
| 0.0073 | 2200 | 0.4748        | -               | -               |
| 0.0077 | 2300 | 0.717         | -               | -               |
| 0.0080 | 2400 | 0.6511        | -               | -               |
| 0.0083 | 2500 | 0.3336        | -               | -               |
| 0.0087 | 2600 | 0.45          | -               | -               |
| 0.0090 | 2700 | 0.4516        | -               | -               |
| 0.0093 | 2800 | 0.6044        | -               | -               |
| 0.0097 | 2900 | 0.3644        | -               | -               |
| 0.0100 | 3000 | 0.5219        | 0.4026          | 0.9752          |
| 0.0103 | 3100 | 0.4302        | -               | -               |
| 0.0107 | 3200 | 0.4322        | -               | -               |
| 0.0110 | 3300 | 0.46          | -               | -               |
| 0.0113 | 3400 | 0.5696        | -               | -               |
| 0.0117 | 3500 | 0.383         | -               | -               |
| 0.0120 | 3600 | 0.3649        | -               | -               |
| 0.0123 | 3700 | 0.4096        | -               | -               |
| 0.0127 | 3800 | 0.3919        | -               | -               |
| 0.0130 | 3900 | 0.3003        | -               | -               |
| 0.0133 | 4000 | 0.2425        | 0.3615          | 0.9790          |


### 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.*
-->