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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:786
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: How much money was saved through systems automation and process
improvement efforts?
sentences:
- Member","Thought Leadership","E-commerce","Entrepreneurship","Mobile Devices","Product
Management","Start-ups","Strategic Partnerships","Strategy"]
- '- URL":"linkedin.com/company/channel-factory","Description":"• Helped scale
the video advertising startup from 0 to 8-figure revenues and 5 to 40+ employees
in 2.5 years.\n• Managed the company''s day-to-day operations. Saved $100,000+
through systems automation and process improvement efforts.\n• Led sales operations
for a 7-person ad sales team and managed BD partnerships with one of the three
largest online travel agencies, a major online ad management platform, and rep
firms in the United Kingdom, India, Brazil, and Australia.\n• Spearheaded company
recruitment efforts and improved HR budget efficiency to save $350,000+ annually.\n•
Evaluated, implemented, and managed third party business systems, including Salesforce
and'
- and start building trust and camaraderie at work - vital assets in providing psychological
safety, enabling agility and unleashing growth.\n","Company Size":"11-50","Industries":["Administrative
Services","Community and Lifestyle","Government and Military","HR and Recruiting","Health","Information
Technology","Software"],"Title":"Co-Founder and Servant CEO","Departments":["Senior
Leadership"],"Start Date":"2018-01-01","End Date":null,"Location":"Santa Monica,
California, United States, United States","Is Current":true,"Job Order":18},{"Company
Name":"CNCCEF","Specter - Company ID":"5e3b912d137e998b5ae832aa","Domain":"cnccef.org","LinkedIn
-
- source_sentence: What skills do you possess that relate to marketing and brand development?
sentences:
- 'I have been fortunate to have been a part of the creation and/or growth story
for brands including ASYSTEM, Formula Fig, Aritzia, Mr Porter to name a few.
Skills: ["E-commerce","Advertising","Social Media","Strategy","Marketing","Online
Advertising","Fashion","Brand Development","Marketing Strategy","Digital Strategy","Media
Relations","Retail","Business Development","Digital Marketing","Mobile Devices","Digital
Media","Marketing Communications","Strategic Communications","Branding & Identity","Business
Strategy","Product Development","Social media","eCommerce","Art Direction","Brand
Management","Brand Strategy","Consumer Behavior","Creative Strategy","E-Commerce","Media"]'
- is able to do so in near real time.","Company Size":null,"Industries":null,"Title":"ceo","Departments":["Senior
Leadership"],"Start Date":"2005-03-01","End Date":"2007-12-01","Location":null,"Is
Current":false,"Job Order":8},{"Company Name":"SnapNames","Specter - Company ID":"5e3bc17800c8f4c966a8bad6","Domain":"snapnames.com","LinkedIn
- URL":"linkedin.com/company/snapnames-com","Description":"I served as a strategic
advisor to the CEO in the capacity of a Board Director, and briefly as Chairman
of the Board, prior to its acquisition by Oversee","Company Size":"11-50","Industries":["Commerce
and Shopping","Internet Services"],"Title":"Director Board Of Directors","Departments":["Senior
Leadership"],"Start Date":"2002-04-01","End
- "Technology\",\"Software\",\"Transportation\"],\"Title\":\"Co-Founder & CTO\"\
,\"Departments\":[\"Senior Leadership\",\"Engineering\"],\"Start Date\":\"2021-08-01\"\
,\"End Date\":null,\"Location\":\"Los Altos, California, United States, United\
\ States\",\"Is Current\":true,\"Job Order\":6},{\"Company Name\":\"XDLINX Space\
\ Labs\",\"Specter - Company ID\":\"6712477ab8cbb513aaee920e\",\"Domain\":\"xdlinx.space\"\
,\"LinkedIn - URL\":\"linkedin.com/company/xdlinx-labs\",\"Description\":null,\"\
Company Size\":\"51-200\",\"Industries\":[\"Hardware\",\"Transportation\"],\"\
Title\":\"Co-Founder\",\"Departments\":[\"Senior Leadership\"],\"Start Date\"\
:\"2022-07-01\",\"End Date\":null,\"Location\":\"HyderÄ\x81bÄ\x81d, Telangana,\
\ India, Asia\",\"Is Current\":true,\"Job Order\":5},{\"Company Name\":\"Diamanti\"\
,\"Specter - Company"
- source_sentence: In what ways does SignalFire support companies at the seed stage?
sentences:
- '- URL":"linkedin.com/school/%D0%BC%D0%BE%D1%81%D0%BA%D0%BE%D0%B2%D1%81%D0%BA%D0%B0%D1%8F-%D0%BC%D0%B5%D0%B6%D0%B4%D1%83%D0%BD%D0%B0%D1%80%D0%BE%D0%B4%D0%BD%D0%B0%D1%8F-%D0%B2%D1%8B%D1%81%D1%88%D0%B0%D1%8F-%D1%88%D0%BA%D0%BE%D0%BB%D0%B0-%D0%B1%D0%B8%D0%B7%D0%BD%D0%B5%D1%81%D0%B0-%C2%AB%D0%BC%D0%B8%D1%80%D0%B1%D0%B8%D1%81%C2%BB-%D0%B8%D0%BD%D1%81%D1%82%D0%B8%D1%82%D1%83%D1%82-","Field
of Study":"","Degree Title":"Integrated year abroad","Description":null,"Start
Date":"2006-01-01","End Date":"2006-01-01","Location":"Moscow, Moscow, Russian
Federation, Russia"},{"Name":"Hochschule Furtwangen University","LinkedIn - URL":"linkedin.com/school/hochschule-furtwangen-university","Field
of Study":"International Management","Degree Title":"Bachelor'
- I specialize in driving the data algorithms that can predict venture outcomes
and target the top 5% of funding rounds at each stage. I have a product mentality
and a people-first, technology second, point of view. I also have an honorary
doctorate from the University of Kent, where I studied British Constitution and
Sociology. I have lived in Palo Alto, California since 1997, and I am passionate
about anticipating and creating change in the tech industry.
- 'firepower at the seed stage to solve the biggest entrepreneur pain points. Our
distributed network approach provides expert advice from some of the world''s
best entrepreneurs, product & engineering leaders in virtually every key discipline
and industry. We have developed a first of its kind centralized infrastructure
to help with recruiting exceptional talent, business development, customer acquisition
as well as educational & community events. We don’t follow the crowd, and almost
always lead our investment rounds as the first institutional investors in exceptional
companies. You can read more about SignalFire at: https://medium.com/signalfire-fund","Company
Size":"51-200","Industries":["Data and Analytics","Finance","Lending and'
- source_sentence: What role did the individual hold at the company from 1998 to 2002?
sentences:
- Current":true,"Job Order":25},{"Company Name":"BigSpring","Specter - Company ID":"653554dfd1653b1e73051e7c","Domain":"bigspring.ai","LinkedIn
- URL":"linkedin.com/company/bigspringai","Description":null,"Company Size":"11-50","Industries":["Community
and Lifestyle","Data and Analytics","DeepTech","Education","HR and Recruiting","Professional
Services","Software"],"Title":"Advisor","Departments":["Other"],"Start Date":"2019-01-01","End
Date":null,"Location":"San Francisco, California, United States, United States","Is
Current":true,"Job Order":24},{"Company Name":"Clockwise","Specter - Company ID":"5e3a8f1e040ca7b0c6f0bd98","Domain":"getclockwise.com","LinkedIn
- URL":"linkedin.com/company/clockwise-inc.","Description":null,"Company
- a relationship to VeriSIgn to sell Internet Keywords through its channels.\n\nAn
IPO filing.\n\nOver 350 employees.","Company Size":"1-10","Industries":["Internet
Services","Software","Transportation"],"Title":"CEO, President, Chairman","Departments":["Senior
Leadership"],"Start Date":"1998-01-01","End Date":"2002-06-01","Location":"San
Carlos, California, United States, United States","Is Current":false,"Job Order":4},{"Company
Name":"NetNames","Specter - Company ID":"5e3bbde400c8f4c9669d8d4b","Domain":"netnames.com","LinkedIn
- URL":"linkedin.com/company/netnames","Description":"I seed funded NetNames.
We sold it to NetBenefit in 2000. I was a board member of the merged entity through
2001. NetNames was the world's first domain name
- '- Company ID":"64f802e6538115f141f4063a","Domain":"trynectar.io","LinkedIn -
URL":"linkedin.com/company/nectar-ai","Description":null,"Company Size":"11-50","Industries":["Advertising","Commerce
and Shopping","Data and Analytics","DeepTech","Sales and Marketing","Software"],"Title":"Investor","Departments":["Senior
Leadership"],"Start Date":"2023-10-01","End Date":null,"Location":"Seattle, Washington,
United States, United States","Is Current":true,"Job Order":32},{"Company Name":"BinStar","Specter
- Company ID":"6411d185abe7c1e313b62b4a","Domain":"bin-star.com","LinkedIn - URL":"linkedin.com/company/binstar","Description":null,"Company
Size":"1-10","Industries":["Commerce and Shopping"],"Title":"Investor","Departments":["Senior'
- source_sentence: What is the primary focus of Fluence as a continuing education
organization?
sentences:
- Name":"Fluence","Specter - Company ID":"621f973f972ef7e5d69c8085","Domain":"fluencetraining.com","LinkedIn
- URL":"linkedin.com/company/fluencetraining","Description":"Fluence is a leading
continuing education organization in psychedelic therapy.","Company Size":"11-50","Industries":["Education","HR
and Recruiting","Health","Software"],"Title":"Advisor","Departments":["Other"],"Start
Date":"2023-07-01","End Date":null,"Location":"New York City, New York, United
States, United States","Is Current":true,"Job Order":17},{"Company Name":"VentureKit","Specter
- Company ID":null,"Domain":"venturekit.com","LinkedIn - URL":"linkedin.com/company/venturekit","Description":"VentureKit
publishes free guides to help entrepreneurs get things
- Order":7},{"Company Name":"Jelastic","Specter - Company ID":"5e3bbee700c8f4c966a06981","Domain":"jelastic.com","LinkedIn
- URL":"linkedin.com/company/jelastic","Description":"Jelastic is a cloud platform
that provides multi-cloud Platform as a Service (PaaS) based on container technology.
It supports a wide range of programming languages and frameworks, and is easy
to scale up or down to meet your changing needs. Acquired by Virtoozo in 2021.\n\nRole
and results:\n- Managed an engineering team\n- Managed R&D projects\n- Jelastic
won several international startup awards \n- Acquired by Virtozzo","Company Size":"11-50","Industries":["Information
Technology","Internet Services","Software"],"Title":"Co-Founder","Departments":["Senior
- 'Education Level: Bachelor''s Degree
Current Position Title: CTO, Head of Research
Current Position Company Name: Mursion
Current Position Company Website: mursion.com
Past Position Title: CEO and Co-founder
Past Position Company Name: DNABLOCK
Past Position Company Website: dnablock.com
Current Tenure: 85.0
Average Tenure: 34.0
Languages: [{"Name":"Spanish","Proficiency Level":"Limited Working Proficiency"},{"Name":"Arabic","Proficiency
Level":"Limited Working Proficiency"}]
LinkedIn - Followers: 5022.0
LinkedIn - Connections: 2997.0'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7916666666666666
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9666666666666667
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.975
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9833333333333333
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7916666666666666
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32222222222222213
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19500000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09833333333333334
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7916666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9666666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.975
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9833333333333333
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.901899634958155
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.874107142857143
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8748790726817042
name: Cosine Map@100
---
# 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 d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': 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("ngiometti/legal-ft-3")
# Run inference
sentences = [
'What is the primary focus of Fluence as a continuing education organization?',
'Name":"Fluence","Specter - Company ID":"621f973f972ef7e5d69c8085","Domain":"fluencetraining.com","LinkedIn - URL":"linkedin.com/company/fluencetraining","Description":"Fluence is a leading continuing education organization in psychedelic therapy.","Company Size":"11-50","Industries":["Education","HR and Recruiting","Health","Software"],"Title":"Advisor","Departments":["Other"],"Start Date":"2023-07-01","End Date":null,"Location":"New York City, New York, United States, United States","Is Current":true,"Job Order":17},{"Company Name":"VentureKit","Specter - Company ID":null,"Domain":"venturekit.com","LinkedIn - URL":"linkedin.com/company/venturekit","Description":"VentureKit publishes free guides to help entrepreneurs get things',
'Education Level: Bachelor\'s Degree\nCurrent Position Title: CTO, Head of Research\nCurrent Position Company Name: Mursion\nCurrent Position Company Website: mursion.com\nPast Position Title: CEO and Co-founder\nPast Position Company Name: DNABLOCK\nPast Position Company Website: dnablock.com\nCurrent Tenure: 85.0\nAverage Tenure: 34.0\nLanguages: [{"Name":"Spanish","Proficiency Level":"Limited Working Proficiency"},{"Name":"Arabic","Proficiency Level":"Limited Working Proficiency"}]\nLinkedIn - Followers: 5022.0\nLinkedIn - Connections: 2997.0',
]
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
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7917 |
| cosine_accuracy@3 | 0.9667 |
| cosine_accuracy@5 | 0.975 |
| cosine_accuracy@10 | 0.9833 |
| cosine_precision@1 | 0.7917 |
| cosine_precision@3 | 0.3222 |
| cosine_precision@5 | 0.195 |
| cosine_precision@10 | 0.0983 |
| cosine_recall@1 | 0.7917 |
| cosine_recall@3 | 0.9667 |
| cosine_recall@5 | 0.975 |
| cosine_recall@10 | 0.9833 |
| **cosine_ndcg@10** | **0.9019** |
| cosine_mrr@10 | 0.8741 |
| cosine_map@100 | 0.8749 |
<!--
## 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: 786 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 786 samples:
| | sentence_0 | sentence_1 |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 17.2 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 218.92 tokens</li><li>max: 464 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What types of products has the individual built experience in, according to the context?</code> | <code>experience in building world class hardware and software products for consumer electronics, aerospace and enterprise software solutions. Proven track record of building big-data cloud computing software and analytic software platform with AI, Computer Vision and Machine Learning. Progressive, innovative and highly valued for aligning corporate strategies with market opportunities, translating goals into actionable plans, and providing leadership to multi-discipline, cross cultural teams.</code> |
| <code>How does the individual align corporate strategies with market opportunities?</code> | <code>experience in building world class hardware and software products for consumer electronics, aerospace and enterprise software solutions. Proven track record of building big-data cloud computing software and analytic software platform with AI, Computer Vision and Machine Learning. Progressive, innovative and highly valued for aligning corporate strategies with market opportunities, translating goals into actionable plans, and providing leadership to multi-discipline, cross cultural teams.</code> |
| <code>What is the company size of Diamanti?</code> | <code>- Company ID":"5e3a8f19040ca7b0c6f031bf","Domain":"diamanti.com","LinkedIn - URL":"linkedin.com/company/diamanti","Description":null,"Company Size":"51-200","Industries":["Consumer Products","Hardware","Information Technology","Internet Services","Software"],"Title":"Chief Operating Officer","Departments":["Senior Leadership","Operations"],"Start Date":"2018-11-01","End Date":"2021-07-01","Location":"San Jose, California, United States, United States","Is Current":false,"Job Order":4},{"Company Name":"Planet","Specter - Company ID":"5e3bc13c00c8f4c966a7da4c","Domain":"planet.com","LinkedIn - URL":"linkedin.com/company/planet-labs","Description":"Planet operates the world's largest fleet of Earth imaging satellites to daily image the entire</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 10
- `per_device_eval_batch_size`: 10
- `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`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|:------:|:----:|:-------------:|:--------------:|
| 0.6329 | 50 | - | 0.8917 |
| 1.0 | 79 | - | 0.9080 |
| 1.2658 | 100 | - | 0.9265 |
| 1.8987 | 150 | - | 0.9091 |
| 2.0 | 158 | - | 0.9100 |
| 2.5316 | 200 | - | 0.9214 |
| 3.0 | 237 | - | 0.9110 |
| 3.1646 | 250 | - | 0.9161 |
| 3.7975 | 300 | - | 0.9108 |
| 4.0 | 316 | - | 0.9145 |
| 4.4304 | 350 | - | 0.8955 |
| 5.0 | 395 | - | 0.9019 |
| 5.0633 | 400 | - | 0.9008 |
| 5.6962 | 450 | - | 0.8980 |
| 6.0 | 474 | - | 0.9036 |
| 6.3291 | 500 | 0.7603 | 0.9021 |
| 6.9620 | 550 | - | 0.8977 |
| 7.0 | 553 | - | 0.8976 |
| 7.5949 | 600 | - | 0.9059 |
| 8.0 | 632 | - | 0.9005 |
| 8.2278 | 650 | - | 0.9039 |
| 8.8608 | 700 | - | 0.9050 |
| 9.0 | 711 | - | 0.9052 |
| 9.4937 | 750 | - | 0.9021 |
| 10.0 | 790 | - | 0.9019 |
### Framework Versions
- Python: 3.13.1
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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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