Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,623 @@
+---
+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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-l",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6ba56d84ae47eb51384ecf500b52d0ba4191a67665884bce78bf8d5980e8150
+size 1336413848

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,63 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}