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 +1,752 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:98
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l
+widget:
+- source_sentence: What are some common attitudes and beliefs that can create personal
+    barriers to self-care for family caregivers?
+  sentences:
+  - 'Support for nutrition, breathing, and feeding
+
+    People with ALS may have trouble chewing and swallowing their food, and getting
+    the nutrients they need. Nutritionists and registered dieticians can help plan
+    small, nutritious meals throughout the day and identify foods to avoid. When the
+    person can no longer eat with help, a feeding tube can reduce the person’s risk
+    of choking and pneumonia.'
+  - "Amyotrophic Lateral Sclerosis (ALS) | National Institute of Neurological Disorders\
+    \ and Stroke\n\n\n\n\n\n\n\n\n      Skip to main content\n    \n\n\n\n\n\n\n\n\
+    \n\n\n\n\n\n\nAn official website of the United States government\n\n        \
+    \    Here’s how you know\n\n\n\n\n\n\n\n\n\n\n\nOfficial websites use .gov \n\
+    \                A\n                .gov\n                website belongs to an\
+    \ official government organization in the United States.\n              \n\n\n\
+    \n\n\n\n\n\nSecure .gov websites use HTTPS\n\n                  A lock\n     \
+    \             (\n\n)\n                   or\n                  https://\n    \
+    \               means you’ve safely connected to the .gov website. Share sensitive\
+    \ information only on official, secure websites.\n                \n\n\n\n\n\n\
+    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSearch\n\n\nMenu\n\n\n\n\n\n\n\n\n\nSearch NINDS\n\
+    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSearch NINDS\n\n\n\n\n\n\n\n\n\n\n\
+    \n\n\n\nMain navigation"
+  - "Identifying Personal Barriers \nMany times, attitudes and beliefs form personal\
+    \ barriers that stand in the \nway of caring for yourself.  Not taking care of\
+    \ yourself may be a lifelong \npattern, with taking care of others an easier option.\
+    \  However, as a family \ncaregiver you must ask yourself, \"What good will I\
+    \ be to the person I care \nfor if I become ill? If I die?\"  Breaking old patterns\
+    \ and overcoming \nobstacles is not an easy proposition, but it can be done –\
+    \ regardless of \nyour age or situation.  The first task in removing personal\
+    \ barriers to self-\ncare is to identify what is in your way.  For example, \n\
+    • Do you feel you have to prove that you are worthy of the care recipient's \n\
+    affection?  \n• Do you think you are being selfish if you put your needs first?\
+    \  \n• Is it frightening to think of your own needs? What is the fear about?"
+- source_sentence: What role does the SOD1 gene play in the body?
+  sentences:
+  - "Migraine Trainer® Shareable Resources\n\n\n\nMind Your Risks®\n\n\nNINDS Brain\
+    \ Educational Resources\n\n\nStroke\n\n\n\n\n\n\nStroke Overview\n\n\nPrevention\n\
+    \n\nSigns and Symptoms\n\n\nAssess and Treat\n\n\n\n\n\n\nNIH Stroke Scale\n\n\
+    \n\nRecovery\n\n\nResearch\n\n\nOutreach\n\n\n\n\n\n\n\n\nDid you find the content\
+    \ you were looking for?\n\n\n\n\n\nYes, I did find the content I was looking for\n\
+    \n\n\n\nNo, I did not find the content I was looking for\n\n\n\n\n\n\n\nPlease\
+    \ rate how easy it was to navigate the NINDS website\n\n\n\n\n\nVery easy to navigate\n\
+    \n\n\n\nEasy to navigate\n\n\n\n\nNeutral\n\n\n\n\nDifficult to navigate\n\n\n\
+    \n\nVery difficult to navigate\n\n\n\n\n\n\nThank you for letting us know! Any\
+    \ other feedback?\n\n\n\n\nSubmit\n\n\n\n\n\nThis site is protected by reCAPTCHA\
+    \ and the Google Privacy Policyand Terms of Serviceapply.\n\n\n\n\n\n\n\n\n\n\n\
+    \n                          Last reviewed on July 19, 2024\n    \n\n\n\n\n\n\n\
+    \n\n\n\n\nContact Us"
+  - 'Muscle twitches in the arm, leg, shoulder, or tongue
+
+    Muscle cramps
+
+    Tight and stiff muscles (spasticity)
+
+    Muscle weakness affecting an arm, a leg, or the neck
+
+    Slurred and nasal speech
+
+    Difficulty chewing or swallowing
+
+
+    As the disease progresses, muscle weakness and atrophy spread to other parts of
+    your body. People with ALS may develop problems with:
+
+
+    Chewing food and swallowing (dysphagia)
+
+    Drooling (sialorrhea)
+
+    Speaking or forming words (dysarthria)
+
+    Breathing (dyspnea)
+
+    Unintended crying, laughing, or other emotional displays (pseudobulbar symptoms)
+
+    Constipation
+
+    Maintaining weight and getting enough nutrients'
+  - "About 25-40% of all familial cases (and a small percentage of sporadic cases)\
+    \ are caused by a defect in the C9orf72 gene. C9orf72 makes a protein found in\
+    \ motor neurons and nerve cells in the brain. \nAnother 12-20% of familial cases\
+    \ result from mutations in the SOD1 gene. SOD1 is involved in production of the\
+    \ enzyme copper-zinc superoxide dismutase 1."
+- source_sentence: What types of resources are available for caregivers of individuals
+    with ALS?
+  sentences:
+  - 'Eventually, people with ALS will not be able to stand or walk, get in or out
+    of bed on their own, use their hands and arms, or breathe on their own. Because
+    they usually remain able to reason, remember, and understand, they are aware of
+    their progressive loss of function. This can cause anxiety and depression in the
+    person with ALS and their loved ones. Although not as common, people with ALS
+    also may experience problems with language or decision-making. Some also develop
+    a form of dementia known as FTD-ALS.
+
+    Most people with ALS die from being unable to breathe on their own (known as respiratory
+    failure,) usually within three to five years from when the symptoms first appear.
+    However, about 10% survive for a decade or more.
+
+    Who is more likely to get amyotrophic lateral sclerosis (ALS)?'
+  - 'Motor Neuron Diseases 
+
+
+
+
+
+
+
+
+
+
+
+
+
+    Order publications from the NINDS Catalog
+
+    The NINDS Publication Catalog offers printed materials on neurological disorders
+    for patients, health professionals, and the general public. All materials are
+    free of charge, and a downloadable PDF version is also available for most publications.
+
+    Order NINDS Publications
+
+
+
+     
+
+
+
+
+
+
+
+
+    Health Information
+
+
+
+
+
+
+
+    Disorders
+
+
+
+
+
+
+
+    Glossary of Neurological Terms
+
+
+
+    Order Publications
+
+
+
+
+    Clinical Trials
+
+
+
+
+
+
+
+    Clinical Trials in the Spotlight
+
+
+
+    Find NINDS Clinical Trials
+
+
+
+
+    Patient & Caregiver Education
+
+
+
+
+
+
+
+    Brain Attack Coalition
+
+
+
+    Brain Donation
+
+
+
+
+    Public Education
+
+
+
+
+
+
+
+    Brain Basics
+
+
+
+
+
+
+
+    Know Your Brain
+
+
+
+    Understanding Sleep
+
+
+
+    Preventing Stroke
+
+
+
+    The Life and Death of a Neuron
+
+
+
+    Genes and the Brain
+
+
+
+
+    Migraine Trainer®
+
+
+
+
+
+
+
+    Migraine Trainer® Shareable Resources'
+  - "Caring for a person living with ALS\nAs the person with ALS progresses in their\
+    \ disease, they will need more and more help with daily activities. Being a caregiver\
+    \ for a person with ALS, while rewarding, can be challenging for the person’s\
+    \ loved ones and caregivers. It is important for caregivers take care of themselves\
+    \ and to seek support when needed. Free and paid resources are available to provide\
+    \ home health care services and support. Visit the organizations listed at the\
+    \ end of this article to find support in your area. \nWhat are the latest updates\
+    \ on amyotrophic lateral sclerosis (ALS)?"
+- source_sentence: How can prospective donors participate in ALS research through
+    brain donation?
+  sentences:
+  - 'Doctors may use the following medications approved by the U.S. Food and Drug
+    Administration (FDA) to support a treatment plan for ALS:'
+  - NINDS also supports the NIH NeuroBioBank, a collaborative effort involving several
+    brain banks across the U.S. that supply investigators with tissue from people
+    with neurological and other disorders. Tissue from individuals with ALS is needed
+    to help advance critical research on the disease. A single donated brain can make
+    a huge impact on ALS research, potentially providing information for hundreds
+    of studies. The goal is to increase the availability of, and access to, high quality
+    specimens for research to understand the neurological basis of the disease. Prospective
+    donors can begin the enrollment process by visiting Learn How to Become a Brain
+    Donor.
+  - The National ALS Registry collects, manages, and analyzes de-identified data about
+    people with ALS in the United States. Developed by the Center for Disease Control
+    and Prevention's Agency for Toxic Substances and Disease Registry (ATSDR), this
+    registry establishes information about the number of ALS cases, collects demographic,
+    occupational, and environmental exposure data from people with ALS to learn about
+    potential risk factors for the disease, and notifies participants about research
+    opportunities. The Registry includes data from national databases as well as de-identified
+    information provided by individuals with ALS. All information is kept confidential.
+    People with ALS can add their information to the registry and sign up to receive
+    for more information.
+- source_sentence: Does having a risk factor guarantee that a person will develop
+    a disorder?
+  sentences:
+  - 'Doctors may use the following medications approved by the U.S. Food and Drug
+    Administration (FDA) to support a treatment plan for ALS:'
+  - "possible decline in quality of life. \n \nBut despite these risks, family caregivers\
+    \ of any age are less likely than \nnon-caregivers to practice preventive healthcare\
+    \ and self-care behavior.  \nRegardless of age, sex, and race and ethnicity, caregivers\
+    \ report problems \nattending to their own health and well-being while managing\
+    \ caregiving \nresponsibilities. They report: \n• sleep deprivation  \n• poor\
+    \ eating habits  \n• failure to exercise  \n• failure to stay in bed when ill\
+    \  \n• postponement of or failure to make medical appointments ."
+  - 'A risk factor is a condition or behavior that occurs more frequently in those
+    who have a disease, or who are at greater risk of getting a disease, than in those
+    who don''t have the risk factor. Having a risk factor doesn''t mean a person will
+    develop a disorder, and not having a risk factor doesn''t mean you won’t. Risk
+    factors for ALS include:'
+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: 0.9166666666666666
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 1.0
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9166666666666666
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.20000000000000004
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10000000000000002
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9166666666666666
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 1.0
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9637887397321441
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.951388888888889
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9513888888888888
+      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("ernestobs7/caregiver-ft-v1")
+# Run inference
+sentences = [
+    'Does having a risk factor guarantee that a person will develop a disorder?',
+    "A risk factor is a condition or behavior that occurs more frequently in those who have a disease, or who are at greater risk of getting a disease, than in those who don't have the risk factor. Having a risk factor doesn't mean a person will develop a disorder, and not having a risk factor doesn't mean you won’t. Risk factors for ALS include:",
+    'possible decline in quality of life. \n \nBut despite these risks, family caregivers of any age are less likely than \nnon-caregivers to practice preventive healthcare and self-care behavior.  \nRegardless of age, sex, and race and ethnicity, caregivers report problems \nattending to their own health and well-being while managing caregiving \nresponsibilities. They report: \n• sleep deprivation  \n• poor eating habits  \n• failure to exercise  \n• failure to stay in bed when ill  \n• postponement of or failure to make medical appointments .',
+]
+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   | 0.9167     |
+| cosine_accuracy@3   | 1.0        |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9167     |
+| cosine_precision@3  | 0.3333     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9167     |
+| cosine_recall@3     | 1.0        |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| **cosine_ndcg@10**  | **0.9638** |
+| cosine_mrr@10       | 0.9514     |
+| cosine_map@100      | 0.9514     |
+
+<!--
+## 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: 98 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 98 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 19.21 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 120.29 tokens</li><li>max: 181 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:-----------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are some common symptoms experienced by individuals with ALS related to muscle function?</code> | <code>Muscle twitches in the arm, leg, shoulder, or tongue<br>Muscle cramps<br>Tight and stiff muscles (spasticity)<br>Muscle weakness affecting an arm, a leg, or the neck<br>Slurred and nasal speech<br>Difficulty chewing or swallowing<br><br>As the disease progresses, muscle weakness and atrophy spread to other parts of your body. People with ALS may develop problems with:<br><br>Chewing food and swallowing (dysphagia)<br>Drooling (sialorrhea)<br>Speaking or forming words (dysarthria)<br>Breathing (dyspnea)<br>Unintended crying, laughing, or other emotional displays (pseudobulbar symptoms)<br>Constipation<br>Maintaining weight and getting enough nutrients</code> |
+  | <code>How does ALS affect a person's ability to chew and swallow food?</code>                              | <code>Muscle twitches in the arm, leg, shoulder, or tongue<br>Muscle cramps<br>Tight and stiff muscles (spasticity)<br>Muscle weakness affecting an arm, a leg, or the neck<br>Slurred and nasal speech<br>Difficulty chewing or swallowing<br><br>As the disease progresses, muscle weakness and atrophy spread to other parts of your body. People with ALS may develop problems with:<br><br>Chewing food and swallowing (dysphagia)<br>Drooling (sialorrhea)<br>Speaking or forming words (dysarthria)<br>Breathing (dyspnea)<br>Unintended crying, laughing, or other emotional displays (pseudobulbar symptoms)<br>Constipation<br>Maintaining weight and getting enough nutrients</code> |
+  | <code>What percentage of ALS cases are classified as familial?</code>                                      | <code>About 10% of all ALS cases are familial (also called inherited or genetic). Changes in more than a dozen genes have been found to cause familial ALS.</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 | cosine_ndcg@10 |
+|:-----:|:----:|:--------------:|
+| 1.0   | 10   | 0.9382         |
+| 2.0   | 20   | 0.9539         |
+| 3.0   | 30   | 0.9484         |
+| 4.0   | 40   | 0.9484         |
+| 5.0   | 50   | 0.9638         |
+| 6.0   | 60   | 0.9638         |
+| 7.0   | 70   | 0.9638         |
+| 8.0   | 80   | 0.9638         |
+| 9.0   | 90   | 0.9638         |
+| 10.0  | 100  | 0.9638         |
+
+
+### Framework Versions
+- Python: 3.11.4
+- 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 sha256:9a67fd83e5631a454c6ef787244826ea321100a1188830bec09778b70b117f43
+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]"
+}