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---
base_model: Snowflake/snowflake-arctic-embed-s
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: 07/16/64
are sour things good for you?'
sentences:
- '07/06/2006 [''Select the cells that you want to copy For more information, see
Select cells, ranges, rows, or columns on a worksheet. ... '', ''Click Home >
Find & Select, and pick Go To Special.'', ''Click Visible cells only > OK.'',
''Click Copy (or press Ctrl+C).'', ''Select the upper-left cell of the paste area
and click Paste (or press Ctrl+V).'']
'
- 'jul 16 1964 Yet, this doesn''t mean that sour foods are always unsafe to eat.
In fact, many sour foods are quite nutritious and rich in plant compounds called
antioxidants, which help protect your cells from damage ( 7 , 8 ).
'
- '"05/19/2044 Chicken tikka masala differs from the recipe, chicken tikka , because
of the various spices (the meaning of ""masala""). Despite the origin being debated
between a British-take on Indian food and an actual Indian dish, it has become
very popular in the U.K. and in other countries."
'
- source_sentence: 'Represent this sentence for searching relevant passages: 07/27/32
Avoid Mononucleosis'
sentences:
- '07/27/32 The Epstein-Barr Virus (EBV) causes mononucleosis, also known as mono.
Transmitted through saliva, mono is most commonly spread by kissing, sharing eating
or drinking utensils, coughing and sneezing.
'
- 'Making your own pencil bag is a great way to use up scrap fabric that you love
but that isn''t large enough for bigger craft projects. It''s also an eco-friendly
option for toting pencils around, and a reflection of your own style. 5/01
'
- '11/24 Being beautiful on the inside requires as much attention as your appearance,
if not more so. With a dose of humility and self-reflection, along with some consideration
for others, becoming beautiful on the inside is possible for every girl.
'
- source_sentence: 'Represent this sentence for searching relevant passages: today:2044-07-28
6 months ago Downtown Getdown'
sentences:
- '"10/04 You can say goodbye in German in nearly any circumstance if you know two
phrases: ""Auf Wiedersehen"" and ""Tschüs."" If you really want to impress native
German speakers, though, there are a few other phrases you can also use when parting
ways."
'
- '2044-01-14 The City of Tallahassee''s Downtown Getdown is a seasonal festival
in fall that happens in the central business district in the City of Tallahassee,
Florida. The festival involves concerts, stands for food, dancing, street entertainers,
make up artist, clowns, grilling, and fun for everyone. The city attracts people
from all over the area, including Gadsden County, Wakulla County, Jefferson County,
Jackson County, Gulf County, Liberty County, Madison County, Taylor County, and
even south Georgia counties, including Thomas and Grady counties. The festival
attracts tens of thousands of people every year and directly benefits United Way
of the Big Bend. The GetDowns are supported by title sponsor Capital City Bank
and supporting sponsors, Bud Light along with Tri-Eagle Sales, Aarons, Inc, Tallahassee
Democrat, WCTV, Clear Channel Radio, Coke and the City of Tallahassee. References
Culture of Tallahassee, Florida
'
- '03/04 Sloppy joes are the kind of food that you never outgrow. But if you don’t
eat meat, you may find yourself wistfully reminiscing over the the childhood joy
of tucking into a messy sandwich while nibbling on a much less exciting snack.
'
- source_sentence: 'Represent this sentence for searching relevant passages: today:2039-12-02
last tuesday what happens after you beat the elite four in sun and moon?'
sentences:
- '11-29-2039 Go catch all the Ultra Beasts It''s pretty hard to miss this mission,
since it starts right after Sun and Moon''s storyline wraps. After beating the
Elite Four and heading home, the player is given a new task: Head to the previously
empty motel on Route 8 for a new mission.
'
- 'Anaphalis acutifolia is a species of flowering plants within the family Asteraceae.
It is found in South Tibet (Yadong). References acutifolia Flora of Tibet February
4
'
- '05/09/02 Math is not as daunting as it seems, it''s all about following simple
rules. Repeated use of these rules builds understanding and confidence. This article
will teach you how to use and understand those rules.
'
- source_sentence: 'Represent this sentence for searching relevant passages: Philadelphia
Business Journal 01/30/83'
sentences:
- '2017/10/17 Circle with Towers is a concrete block 2005/2012 sculpture by American
artist Sol LeWitt, installed outside the Bill and Melinda Gates Computer Science
Complex on the University of Texas at Austin campus in Austin, Texas, United States.
Previously, the artwork was installed in Madison Square Park; the university''s
public art program, Landmarks, purchased the sculpture from the Madison Square
Park Conservancy. References External links Concrete sculptures in the United
States Outdoor sculptures in Austin, Texas University of Texas at Austin campus
'
- 'Have you just bought your brand new Nintendo Wii console? Are you gutted that
you can''t get Wii Connect 24 in your country of residence? This article will
resolve this problem, so you can surf the Internet on your Wii!
'
- 'The Philadelphia Business Journal is a diversified business media company in
Philadelphia, Pennsylvania, publishing daily stories on its website and social
networks, and a weekly edition available in print and online. It is published
by the American City Business Journals. See also List of newspapers in Pennsylvania
References External links Business newspapers published in the United States Newspapers
published in Philadelphia Jan 30 1983
'
---
# 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-s
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-s](https://huggingface.co./Snowflake/snowflake-arctic-embed-s). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-s](https://huggingface.co./Snowflake/snowflake-arctic-embed-s) <!-- at revision 7dce73c9e586e64e7d7d0a21bf72f50bc5a67e19 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Represent this sentence for searching relevant passages: Philadelphia Business Journal 01/30/83',
'The Philadelphia Business Journal is a diversified business media company in Philadelphia, Pennsylvania, publishing daily stories on its website and social networks, and a weekly edition available in print and online. It is published by the American City Business Journals. See also List of newspapers in Pennsylvania References External links Business newspapers published in the United States Newspapers published in Philadelphia Jan 30 1983\n',
"Have you just bought your brand new Nintendo Wii console? Are you gutted that you can't get Wii Connect 24 in your country of residence? This article will resolve this problem, so you can surf the Internet on your Wii!\n",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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.*
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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: 11 tokens</li><li>mean: 21.67 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 67.0 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchors | positive |
|:---------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Represent this sentence for searching relevant passages: 4/23 Socialize With Someone Who Is Losing Interest in You</code> | <code>04/23 It can hurt to realize that someone you care about is losing interest in you. If a friend has stopped calling you and no longer makes plans to hang out, your first instinct might be to contact them more frequently or to ignore them in return.<br></code> |
| <code>Represent this sentence for searching relevant passages: Alathur taluk, Perambalur 04/19/29</code> | <code>Alathur taluk is a taluk in Perambalur district in the Indian state of Tamil Nadu. It was created by former chief minister J.Jayalalithaa for issues of population increase. Kunnam taluk was bifurcated to form this new taluk. Villages There are 39 villages in Alathur taluk excluding the headquarters Alathur. References Perambalur district Taluks of Perambalur district 2029 Apr 19<br></code> |
| <code>Represent this sentence for searching relevant passages: 01/04 how much weight does a baby gain in the first month?</code> | <code>01/04 During their first month, most newborns gain weight at a rate of about 1 ounce (30 grams) per day. They generally grow in height about 1 to 1½ inches (2.54 to 3.81 centimeters) during the first month. Many newborns go through a period of rapid growth when they are 7 to 10 days old and again at 3 and 6 weeks.<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`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `warmup_steps`: 400
- `bf16`: True
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `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`: True
- `torch_compile_backend`: inductor
- `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
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.0023 | 1 | 2.3154 | - |
| 0.0229 | 10 | 2.3237 | - |
| 0.0459 | 20 | 2.4036 | - |
| 0.0688 | 30 | 2.3314 | - |
| 0.0917 | 40 | 2.3171 | - |
| 0.1147 | 50 | 2.2891 | - |
| 0.0023 | 1 | 2.2343 | - |
| 0.0229 | 10 | 2.2256 | - |
| 0.0459 | 20 | 2.2924 | - |
| 0.0688 | 30 | 2.2354 | - |
| 0.0917 | 40 | 2.2281 | - |
| 0.1147 | 50 | 2.2018 | - |
| 0.1376 | 60 | 2.2377 | - |
| 0.1606 | 70 | 2.2001 | - |
| 0.1835 | 80 | 2.158 | - |
| 0.2064 | 90 | 2.1405 | - |
| 0.2294 | 100 | 2.0916 | - |
| 0.2523 | 110 | 2.0374 | - |
| 0.2752 | 120 | 2.0492 | - |
| 0.2982 | 130 | 1.9824 | - |
| 0.3211 | 140 | 1.9571 | - |
| 0.3440 | 150 | 1.8317 | - |
| 0.3670 | 160 | 1.7183 | - |
| 0.3899 | 170 | 1.5928 | - |
| 0.4128 | 180 | 1.5695 | - |
| 0.4358 | 190 | 1.4592 | - |
| 0.4587 | 200 | 1.2667 | 0.2031 |
| 0.4817 | 210 | 1.3865 | - |
| 0.5046 | 220 | 1.2924 | - |
| 0.5275 | 230 | 1.3042 | - |
| 0.5505 | 240 | 1.4393 | - |
| 0.5734 | 250 | 1.3402 | - |
| 0.5963 | 260 | 1.1939 | - |
| 0.6193 | 270 | 1.1795 | - |
| 0.6422 | 280 | 1.1012 | - |
| 0.6651 | 290 | 1.0379 | - |
| 0.6881 | 300 | 0.9865 | - |
| 0.7110 | 310 | 0.9088 | - |
| 0.7339 | 320 | 0.9132 | - |
| 0.7569 | 330 | 0.8819 | - |
| 0.7798 | 340 | 0.8631 | - |
| 0.8028 | 350 | 1.4084 | - |
| 0.8257 | 360 | 1.325 | - |
| 0.8486 | 370 | 1.2373 | - |
| 0.8716 | 380 | 1.1881 | - |
| 0.8945 | 390 | 1.1656 | - |
| 0.9174 | 400 | 0.7767 | 0.0607 |
| 0.9404 | 410 | 0.1511 | - |
| 0.9633 | 420 | 0.1439 | - |
| 0.9862 | 430 | 0.1216 | - |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.43.3
- 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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