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---
base_model: Snowflake/snowflake-arctic-embed-l
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:55736
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Represent this sentence for searching relevant passages: Jan 20
    Become a Real Life Superhero'
  sentences:
  - 'The army corps is the largest regular army formation, though in wartime two or
    more corps may be combined to form a field army (commanded by a general), and
    field armies in turn may be combined to form an army group. 03/28/31

    '
  - '01/20 The world is a dangerous place and sometimes there''s a need for superheroes.
    Regrettably, there''s no real way to gain super strength or to fly like in the
    comic books.

    '
  - ' Stone is useful for both building and crafting in The Blockheads. It''s easy
    to get stone, as explained here. 12/09

    '
- source_sentence: 'Represent this sentence for searching relevant passages: today:2046-10-23
    last summer social security benefits paid when?'
  sentences:
  - 'Summer 2045 We pay Social Security benefits monthly. The benefits are paid in
    the month following the month for which they are due. ... Generally, the day of
    the month you receive your benefit payment depends on the birth date of the person
    for whose earnings record you receive benefits.

    '
  - '07/26/95 Foursquare’s Explore feature is a great tool to help you find the best
    and popular places nearby, including popular restaurants, sights, outdoor places,
    nightlife spots, and shops.

    '
  - 'Gesso transferring is a way of displaying an image on an alternate surface. By
    using gesso and a reversed image, the image will then take on the surface''s texture.
    04/28/23

    '
- source_sentence: 'Represent this sentence for searching relevant passages: Floor
    Press 01/02/91'
  sentences:
  - 'While the stomach flu affects the stomach, influenza only affects the respiratory
    system (nose, throat, and lungs). Symptoms of the flu include fever, muscle aches,
    sore throat, and cough, and influenza can be prevented with a flu shot. Thankfully,
    people with the flu usually don''t have gastroenteritis symptoms.

    '
  - 'If you’re looking to build arm and chest strength, the floor press is a great
    exercise to add to your routine. It works like a bench press, but by lying directly
    on the floor, you give your back better support and take away your ability to
    drive with your legs. 2091 Jan 2

    '
  - '"Flashes. When the vitreous gel inside your eye rubs or pulls on the retina,
    you may see what looks like flashing lights or lightening streaks. You may have
    experienced this sensation if you have ever been hit in the eye and see ""stars.""
    These flashes of light can appear off and on for several weeks or months. 2034
    Winter"

    '
- source_sentence: 'Represent this sentence for searching relevant passages: today:2022-11-01
    what to do in west yellowstone in march 49 years ago '
  sentences:
  - '[''Yellowstone Zipline Adventure Park. ... '', ''Playmill Theatre. ... '', ''Raft
    the Gallatin River. ... '', ''Grizzly and Wolf Discovery Center. ... '', ''Cross-Country
    Ski the Rendezvous Trails. ... '', ''Cowboy Up for a Rodeo. ... '', ''Fish Hebgen
    Lake. ... '', ''Visit Earthquake Lake.''] 03/31/1973

    '
  - '12/14/2041 Intelligence in the normal range is a polygenic trait, meaning that
    it is influenced by more than one gene, and in the case of intelligence at least
    500 genes. Further, explaining the similarity in IQ of closely related persons
    requires careful study because environmental factors may be correlated with genetic
    factors.

    '
  - 'Are you a student who is having a hard time with algebra? Or perhaps you''re
    trying to brush up on your math skills after not using for them for years.

    '
- source_sentence: 'Represent this sentence for searching relevant passages: Nov 6
    2002 Easter seals (philately)'
  sentences:
  - '"03/08/2050 This is a list of wild forests in the state of New York. Lands designated
    as ""wild forest"" in New York are managed by the New York State Department of
    Environmental Conservation as part of the Forest Preserve. Management Wild forests
    are intended to retain an essentially wild and natural character, however management
    facilitates a greater amount of recreational use than areas designated by the
    state as wilderness, which feature an increased sense of remoteness and solitude.
    Most are located within the boundaries of Adirondack Park or Catskill Park. List
    of New York wild forests See also  Albany Pine Bush Long Island Central Pine Barrens
    Rome Sand Plains References External links NYS Department of Environmental Conservation:
    Forest Preserve unit descriptions Land units maps: Adirondack Park, Catskill Park
    wild forests wild forests New York wild forests New York wild forests"

    '
  - '2017 Winter The Waterfall Model was the first Process Model to be introduced.
    It is also referred to as a linear-sequential life cycle model. ... The Waterfall
    model is the earliest SDLC approach that was used for software development. The
    waterfall Model illustrates the software development process in a linear sequential
    flow.

    '
  - '06/11/2002 An Easter seal is a form of charity label issued to raise funds for
    charitable purposes. They are issued by the Easterseals charity in the United
    States, and by the Canadian Easter Seals charities. Easter seals are applied to
    the front of mail to show support for particular charitable causes. They are distributed
    along with appeals to donate to the charities they support. Easter seals are a
    form of Cinderella stamp. They do not have any postal value. Cinderella stamps

    '
---
# Technical Report and Model Pipeline 
To access our technical report and model pipeline scripts visit our [github](https://github.com/khoj-ai/timely/tree/main)

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). 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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision b7c623b8902f02627a9420b73b2fd6300aad7a68 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Represent this sentence for searching relevant passages: Nov 6 2002 Easter seals (philately)',
    '06/11/2002 An Easter seal is a form of charity label issued to raise funds for charitable purposes. They are issued by the Easterseals charity in the United States, and by the Canadian Easter Seals charities. Easter seals are applied to the front of mail to show support for particular charitable causes. They are distributed along with appeals to donate to the charities they support. Easter seals are a form of Cinderella stamp. They do not have any postal value. Cinderella stamps\n',
    '2017 Winter The Waterfall Model was the first Process Model to be introduced. It is also referred to as a linear-sequential life cycle model. ... The Waterfall model is the earliest SDLC approach that was used for software development. The waterfall Model illustrates the software development process in a linear sequential flow.\n',
]
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.*
-->

<!--
## 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: 55,736 training samples
* Columns: <code>anchors</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchors                                                                            | positive                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 14 tokens</li><li>mean: 20.25 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 47.2 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
  | anchors                                                                                                             | positive                                                                                                                                                                                                                                                                             |
  |:--------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Represent this sentence for searching relevant passages: are bugs attracted to citronella November 10?</code> | <code>Citronella is naturally occurring oil that repels insects. ... “Citronella oil is repellent to mosquitoes to a degree, but the amount being put out by a candle isn't going to be very effective,” Eric Hoffer, president of Hoffer Pest, told TODAY Home. 11/10<br></code>    |
  | <code>Represent this sentence for searching relevant passages: are bugs attracted to citronella 11/10/09?</code>    | <code>Citronella is naturally occurring oil that repels insects. ... “Citronella oil is repellent to mosquitoes to a degree, but the amount being put out by a candle isn't going to be very effective,” Eric Hoffer, president of Hoffer Pest, told TODAY Home. 11/10/09<br></code> |
  | <code>Represent this sentence for searching relevant passages: are bugs attracted to citronella Jan 15?</code>      | <code>Citronella is naturally occurring oil that repels insects. ... “Citronella oil is repellent to mosquitoes to a degree, but the amount being put out by a candle isn't going to be very effective,” Eric Hoffer, president of Hoffer Pest, told TODAY Home. 01/15<br></code>    |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 1,000 evaluation samples
* Columns: <code>anchors</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchors                                                                            | positive                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 12 tokens</li><li>mean: 21.64 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 66.86 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchors                                                                                                                                                       | positive                                                                                                                                                                                                                                                                                                                                |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Represent this sentence for searching relevant passages: today:2068-02-10 what is the meaning of the idiom put two and two together last monday </code> | <code>put two and two together. to understand something by using the information you have: I didn't tell her George had left, but she noticed his car was gone and put two and two together. (Definition of put two and two together from the Cambridge Academic Content Dictionary © Cambridge University Press) 02/06/2068<br></code> |
  | <code>Represent this sentence for searching relevant passages: Complete the Throat of the World Quest in Skyrim</code>                                        | <code>The Throat of the World is the fifth quest in the second act of the Skyrim’s main quest. During this mission, all the mystery about the game’s main antagonist, Alduin, will be revealed to you.<br></code>                                                                                                                       |
  | <code>Represent this sentence for searching relevant passages: are blanco kitchen faucets good 04/13/86?</code>                                               | <code>Nevertheless, these are good to very good faucets built with good quality components throughout, backed by a strong warranty and superior customer service from a well-established company. Blanco sells only kitchen, prep and bar faucets, nothing for the bathroom. Apr 13 1986<br></code>                                     |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 1e-06
- `weight_decay`: 0.01
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `warmup_steps`: 400
- `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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-06
- `weight_decay`: 0.01
- `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`: 400
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step | Training Loss | loss   |
|:------:|:----:|:-------------:|:------:|
| 0.0006 | 1    | 1.9721        | -      |
| 0.0057 | 10   | 1.9663        | -      |
| 0.0115 | 20   | 1.947         | -      |
| 0.0172 | 30   | 1.9039        | -      |
| 0.0230 | 40   | 1.9672        | -      |
| 0.0287 | 50   | 1.894         | -      |
| 0.0344 | 60   | 1.8953        | -      |
| 0.0402 | 70   | 1.9001        | -      |
| 0.0459 | 80   | 1.8511        | -      |
| 0.0517 | 90   | 1.7816        | -      |
| 0.0574 | 100  | 1.7657        | -      |
| 0.0631 | 110  | 1.6932        | -      |
| 0.0689 | 120  | 1.6445        | -      |
| 0.0746 | 130  | 1.6565        | -      |
| 0.0804 | 140  | 1.5077        | -      |
| 0.0861 | 150  | 1.4675        | -      |
| 0.0918 | 160  | 1.4307        | -      |
| 0.0976 | 170  | 1.2343        | -      |
| 0.1033 | 180  | 1.1075        | -      |
| 0.1091 | 190  | 1.1142        | -      |
| 0.1148 | 200  | 1.0546        | 0.0897 |
| 0.1206 | 210  | 0.9872        | -      |
| 0.1263 | 220  | 0.8933        | -      |
| 0.1320 | 230  | 0.8066        | -      |
| 0.1378 | 240  | 0.7317        | -      |
| 0.1435 | 250  | 0.7404        | -      |
| 0.1493 | 260  | 0.6348        | -      |
| 0.1550 | 270  | 0.6399        | -      |
| 0.1607 | 280  | 0.549         | -      |
| 0.1665 | 290  | 0.4844        | -      |
| 0.1722 | 300  | 0.5109        | -      |
| 0.1780 | 310  | 0.4412        | -      |
| 0.1837 | 320  | 0.4451        | -      |
| 0.1894 | 330  | 0.373         | -      |
| 0.1952 | 340  | 0.4318        | -      |
| 0.2009 | 350  | 0.3996        | -      |
| 0.2067 | 360  | 0.3534        | -      |
| 0.2124 | 370  | 0.3795        | -      |
| 0.2181 | 380  | 0.3195        | -      |
| 0.2239 | 390  | 0.313         | -      |
| 0.2296 | 400  | 0.3174        | 0.1864 |
| 0.2354 | 410  | 0.3255        | -      |
| 0.2411 | 420  | 0.3172        | -      |
| 0.2468 | 430  | 0.2601        | -      |
| 0.2526 | 440  | 0.2862        | -      |
| 0.2583 | 450  | 0.3042        | -      |
| 0.2641 | 460  | 0.305         | -      |
| 0.2698 | 470  | 0.2722        | -      |
| 0.2755 | 480  | 0.2684        | -      |
| 0.2813 | 490  | 0.2114        | -      |
| 0.2870 | 500  | 0.2599        | -      |
| 0.2928 | 510  | 0.2226        | -      |
| 0.2985 | 520  | 0.213         | -      |
| 0.3042 | 530  | 0.1968        | -      |
| 0.3100 | 540  | 0.2005        | -      |
| 0.3157 | 550  | 0.17          | -      |
| 0.3215 | 560  | 0.2275        | -      |
| 0.3272 | 570  | 0.1482        | -      |
| 0.3330 | 580  | 0.1404        | -      |
| 0.3387 | 590  | 0.1743        | -      |
| 0.3444 | 600  | 0.1887        | 0.2803 |
| 0.3502 | 610  | 0.2018        | -      |
| 0.3559 | 620  | 0.18          | -      |
| 0.3617 | 630  | 0.146         | -      |
| 0.3674 | 640  | 0.1308        | -      |
| 0.3731 | 650  | 0.159         | -      |
| 0.3789 | 660  | 0.1528        | -      |
| 0.3846 | 670  | 0.1439        | -      |
| 0.3904 | 680  | 0.1376        | -      |
| 0.3961 | 690  | 0.1451        | -      |
| 0.4018 | 700  | 0.1408        | -      |
| 0.4076 | 710  | 0.1571        | -      |
| 0.4133 | 720  | 0.1318        | -      |
| 0.4191 | 730  | 0.1548        | -      |
| 0.4248 | 740  | 0.1131        | -      |
| 0.4305 | 750  | 0.1171        | -      |
| 0.4363 | 760  | 0.1246        | -      |
| 0.4420 | 770  | 0.1204        | -      |
| 0.4478 | 780  | 0.1418        | -      |
| 0.4535 | 790  | 0.0907        | -      |
| 0.4592 | 800  | 0.1013        | 0.3217 |
| 0.4650 | 810  | 0.1067        | -      |
| 0.4707 | 820  | 0.1064        | -      |
| 0.4765 | 830  | 0.1089        | -      |
| 0.4822 | 840  | 0.1044        | -      |
| 0.4879 | 850  | 0.0916        | -      |
| 0.4937 | 860  | 0.1344        | -      |
| 0.4994 | 870  | 0.1377        | -      |
| 0.5052 | 880  | 0.1078        | -      |
| 0.5109 | 890  | 0.0837        | -      |
| 0.5166 | 900  | 0.0893        | -      |
| 0.5224 | 910  | 0.4395        | -      |
| 0.5281 | 920  | 0.6783        | -      |
| 0.5339 | 930  | 0.6341        | -      |
| 0.5396 | 940  | 0.5763        | -      |
| 0.5454 | 950  | 0.5283        | -      |
| 0.5511 | 960  | 0.4955        | -      |
| 0.5568 | 970  | 0.5138        | -      |
| 0.5626 | 980  | 0.4983        | -      |
| 0.5683 | 990  | 0.5239        | -      |
| 0.5741 | 1000 | 0.5368        | 0.1056 |
| 0.5798 | 1010 | 0.5011        | -      |
| 0.5855 | 1020 | 0.5244        | -      |
| 0.5913 | 1030 | 0.39          | -      |
| 0.5970 | 1040 | 0.4645        | -      |
| 0.6028 | 1050 | 0.4164        | -      |
| 0.6085 | 1060 | 0.4698        | -      |
| 0.6142 | 1070 | 0.4074        | -      |
| 0.6200 | 1080 | 0.4608        | -      |
| 0.6257 | 1090 | 0.5081        | -      |
| 0.6315 | 1100 | 0.4749        | -      |
| 0.6372 | 1110 | 0.4384        | -      |
| 0.6429 | 1120 | 0.3604        | -      |
| 0.6487 | 1130 | 0.3853        | -      |
| 0.6544 | 1140 | 0.3238        | -      |
| 0.6602 | 1150 | 0.3656        | -      |
| 0.6659 | 1160 | 0.2918        | -      |
| 0.6716 | 1170 | 0.3919        | -      |
| 0.6774 | 1180 | 0.3366        | -      |
| 0.6831 | 1190 | 0.3731        | -      |
| 0.6889 | 1200 | 0.4923        | 0.0583 |
| 0.6946 | 1210 | 0.3101        | -      |
| 0.7003 | 1220 | 0.3177        | -      |
| 0.7061 | 1230 | 0.3779        | -      |
| 0.7118 | 1240 | 0.3342        | -      |
| 0.7176 | 1250 | 0.2819        | -      |
| 0.7233 | 1260 | 0.3247        | -      |
| 0.7290 | 1270 | 0.4053        | -      |
| 0.7348 | 1280 | 0.3277        | -      |
| 0.7405 | 1290 | 0.3325        | -      |
| 0.7463 | 1300 | 0.3827        | -      |
| 0.7520 | 1310 | 0.2674        | -      |
| 0.7577 | 1320 | 0.309         | -      |
| 0.7635 | 1330 | 0.3193        | -      |
| 0.7692 | 1340 | 0.3399        | -      |
| 0.7750 | 1350 | 0.4044        | -      |
| 0.7807 | 1360 | 0.3436        | -      |
| 0.7865 | 1370 | 0.851         | -      |
| 0.7922 | 1380 | 0.9553        | -      |
| 0.7979 | 1390 | 0.8694        | -      |
| 0.8037 | 1400 | 0.8736        | 0.0333 |
| 0.8094 | 1410 | 0.7984        | -      |
| 0.8152 | 1420 | 0.8228        | -      |
| 0.8209 | 1430 | 0.8026        | -      |
| 0.8266 | 1440 | 0.8568        | -      |
| 0.8324 | 1450 | 0.8529        | -      |
| 0.8381 | 1460 | 0.757         | -      |
| 0.8439 | 1470 | 0.779         | -      |
| 0.8496 | 1480 | 0.8002        | -      |
| 0.8553 | 1490 | 0.8532        | -      |
| 0.8611 | 1500 | 0.7195        | -      |
| 0.8668 | 1510 | 0.7598        | -      |
| 0.8726 | 1520 | 0.8295        | -      |
| 0.8783 | 1530 | 0.7588        | -      |
| 0.8840 | 1540 | 0.7698        | -      |
| 0.8898 | 1550 | 0.792         | -      |
| 0.8955 | 1560 | 0.8175        | -      |
| 0.9013 | 1570 | 0.7195        | -      |
| 0.9070 | 1580 | 0.7383        | -      |
| 0.9127 | 1590 | 0.4577        | -      |
| 0.9185 | 1600 | 0.0621        | 0.0207 |
| 0.9242 | 1610 | 0.0644        | -      |
| 0.9300 | 1620 | 0.0578        | -      |
| 0.9357 | 1630 | 0.0368        | -      |
| 0.9414 | 1640 | 0.056         | -      |
| 0.9472 | 1650 | 0.059         | -      |
| 0.9529 | 1660 | 0.0442        | -      |
| 0.9587 | 1670 | 0.0527        | -      |
| 0.9644 | 1680 | 0.0651        | -      |
| 0.9701 | 1690 | 0.0515        | -      |
| 0.9759 | 1700 | 0.0512        | -      |
| 0.9816 | 1710 | 0.0543        | -      |
| 0.9874 | 1720 | 0.0676        | -      |
| 0.9931 | 1730 | 0.0593        | -      |
| 0.9989 | 1740 | 0.0558        | -      |

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.43.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

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

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