Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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 +1,649 @@
+---
+base_model: TaylorAI/bge-micro-v2
+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
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1814
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: '
+
+    The list you''ve provided contains a variety of medications, including antidepressants,
+    antihistamines, anxiolytics, and more. Here''s a breakdown by category:
+
+
+    ### Antidepressants
+
+    - **Amphetamine**
+
+    - **Cevimeline**
+
+    - **Esmolol**
+
+    - **Bortezomib**
+
+    - **'
+  sentences:
+  - Which body parts are associated with the expression of genes or proteins that
+    impact the transporter responsible for the movement of Cycloserine?
+  - Identify genes or proteins that interact with a protein threonine kinase, participate
+    in the mitotic centrosome proteins and complexes recruitment pathway, and engage
+    in protein-protein interactions with CCT2.
+  - Which medication is effective against simple Plasmodium falciparum infections
+    and functions by engaging with genes or proteins that interact with the minor
+    groove of DNA rich in adenine and thymine?
+- source_sentence: '
+
+    RNASE6, also known by aliases such as RAD1, RNS6, and RNasek6, functions as a
+    member of the ribonuclease A superfamily. Specifically identified via the NCBI
+    gene/protein database, this protein is related to the antimicrobial peptides pathway,
+    showcasing broad-spectrum antimicrobial activity against pathogenic bacteria in
+    the urinary tract. The provided gene summary emphasizes its role in the urinary
+    tract, highlighting its enzymatic function and broad antimicrobial capability.
+
+
+    With a genomic position spanning from 20781268 to 20782467 on chromosome 14, the
+    RNASE6 gene encodes a protein named ribonuclease A family member k6. The protein''s
+    interactions with cellular and molecular functions are integral to its role, including
+    its interaction with molecular functions like ribonuclease activity and endonuclease
+    activity, as well as its involvement in nucleic acid binding.
+
+
+    RNASE6''s involvement in biological'
+  sentences:
+  - Identify genes or proteins linked to encephalopathy that are involved in the Antimicrobial
+    peptides pathway and have interactions with molecular functions associated with
+    ribonuclease activity.
+  - Identify genes or proteins that exhibit interaction with COMMD1 and share an associated
+    phenotype or effect.
+  - What medical conditions are associated with severe combined immunodeficiency and
+    also cause muscle pain and weakness?
+- source_sentence: '
+
+
+    The gene in question is likely involved in multiple biological processes, including:
+
+
+    1. **Transmembrane transport**: It facilitates the entry of substances into or
+    out of a cell through the cell membrane, which is crucial for maintaining cellular
+    homeostasis and responding to environmental stimuli. This includes organic anion
+    and carboxylic acid transport.
+
+
+    2. **ABC-family proteins mediated transport**: ABC (or ATP-binding cassette) proteins
+    are responsible for a variety of transport processes, such as drug efflux, nutrient
+    uptake, and xenobiotic detoxification.
+
+
+    3. **Response to drug**: It likely plays a role in how cells interact with and
+    respond to medication or other foreign substances they encounter. This is important
+    in pharmacology and toxicology.
+
+
+    4. **Regulation of chloride transport**: Chloride ions are crucial for maintaining
+    electrolyte balance and are involved in multiple physiological processes. This
+    gene likely helps regulate their transport in and out of the cell.
+
+
+    5. **Export across plasma membrane**: It is part of pathways that help in the
+    removal of substances from the cell, such as efflux of drug metabolites or other
+    waste products.
+
+
+    ### Expression Contexts:
+
+
+    - **Present**: This gene is expressed in many parts of the body, indicating a
+    broad role. It shows presence in tissues like the islet of Langerhans (involved
+    in insulin regulation), zones of the skin, and various brain regions. It''s also
+    active in organs such as the heart, kidney, and lungs, and in the digestive tract,
+    including the stomach, esophagus, and intestines.
+
+
+    - **Absent or Reduced**: The gene''s expression is notably absent or less pronounced
+    in tissues like the nasal cavity epithelium, suggesting it may not play a significant
+    role in this specific tissue type.
+
+
+    The gene''s multifaceted expression and roles suggest a key function in biological
+    activities related to:
+
+    - **Chemical'
+  sentences:
+  - Could you supply a selection of medications used to treat acute myeloid leukemia
+    with minimal differentiation that have a potential side effect of arrhythmias
+    and work by intercalating DNA and inhibiting topoisomerase II?
+  - Is the ABCB1 protein responsible for the translocation of pharmaceuticals that
+    exhibit synergistic effects when combined with ferric ions?
+  - What potential conditions could I have that are associated with oophoritis and
+    involve ovarian complications?
+- source_sentence: "\n\nThe list you provided seems to be a collection of various\
+    \ chemical compounds, pharmaceuticals, and their synonyms. They span across various\
+    \ categories:\n\n1. **Pharmaceuticals & Synthetic Drug Analogs**:\n    - **Antibiotics**\
+    \ (Ceftazidime, Azithromycin, Ceftodipen, etc.)\n    - **Analgesics** (Fentanyl,\
+    \ Ketorolac, etc.)\n    - **Cephalosporins** (Ceftazidime, Ceftazidime-avibactam,\
+    \ etc.)\n    - **Blood Thinners/Synthetic Anticoagulants** (Enoxaparin, Edoxaban,\
+    \ Rivaroxaban, etc.)\n    - **Analgesic/Aspirin Analogues** (Mefenamic Acid, Indometacin,\
+    \ etc.)\n    - **Adrenergic Agonists** (Isoprenaline, Dopamine, etc.)\n    - **Antiviral\
+    \ Drugs** (Adefovir, Idelalisib, etc.)\n    - **Antibiotic Resistance Modifiers**\
+    \ (Sulbactam, Tazobactam, etc.)\n    - **Calcium Channel Blockers** (Verapamil,\
+    \ Nicardipine, etc.)\n    - **Nutraceuticals/Herbal Extracts** (Ginsenoside, Phloretin,\
+    \ etc.)\n   \n2. **Diagnostic Agents**:\n    - **Radiopharmaceuticals** (F-Fluorodeoxyglucose,\
+    \ Ga-68 DOTATOC, etc.)\n    - **MRI Contrasts** (Gadolinium chelates, etc.)\n\
+    \    - **CT Contrast Agents** (Iodinated contrast agents, etc.)\n   \n3. **Ingredients\
+    \ in Drugs**:\n    - **Excipients** (Hydroxypropylmethylcellulose, Lactose, etc.)\n\
+    \    - **Antifungal Drugs** (Itraconazole, Terconazole, etc.)\n    - **Anticoagulants**\
+    \ (Warfarin, Heparin, etc.)\n        \nThis list represents a broad spectrum of\
+    \ modern medicine, from antibiotics to chemicals used in diagnostic imaging techniques,\
+    \ and from dietary supplements to drug excipients. Each compound typically serves\
+    \ a specific therapeutic purpose in the human body."
+  sentences:
+  - Which investigational compound in solid form that aims at altering membrane lipids,
+    specifically phospholipids and glycerophospholipids, has the additional property
+    of interacting with genes or proteins involved in ubiquitin-specific protease
+    binding?
+  - Could you provide a list of medications that exhibit synergistic effects when
+    used in combination with Choline magnesium trisalicylate to treat the same condition
+    and that also selectively target COX-2 enzymes to alleviate inflammation?
+  - Identify pathways associated with the interaction between TNFs and their physiological
+    receptors that concurrently influence the same gene or protein.
+- source_sentence: "\n\nDiarrhea, a condition characterized by the passage of loose,\
+    \ watery, and often more than five times a day, is a common ailment affecting\
+    \ individuals of all ages. It is typically acute when it lasts for a few days\
+    \ to a week or recurrent when it persists for more than four weeks. While acute\
+    \ diarrhea often resolves on its own and is usually not a cause for concern, recurrent\
+    \ or chronic forms require medical attention due to the risk of dehydration and\
+    \ nutrient deficiencies. \n\n### Causes\n\nDiarrhea can be caused by various factors,\
+    \ including:\n\n1. **Viral"
+  sentences:
+  - Could you describe the specific effects or phenotypes associated with acute hydrops
+    in patients with the subtype of keratoconus?
+  - What is the disease associated with the CPT2 gene that causes severe fasting intolerance
+    leading to metabolic disturbances such as hypoketotic hypoglycemia, risking coma
+    and seizures, and can lead to hepatic encephalopathy and liver failure, and also
+    affects the heart and skeletal muscles, increasing the risk of potentially fatal
+    cardiac arrhythmias?
+  - Could you assist in identifying a condition linked to congenital secretory diarrhea,
+    similar to intractable diarrhea of infancy, given my symptoms of persistent, salty
+    watery diarrhea, hyponatremia, abnormal body pH, and reliance on parenteral nutrition
+    due to chronic dehydration?
+model-index:
+- name: SentenceTransformer based on TaylorAI/bge-micro-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 384
+      type: dim_384
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.36633663366336633
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.45544554455445546
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.4801980198019802
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.504950495049505
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.36633663366336633
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1518151815181518
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.09603960396039603
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05049504950495049
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.36633663366336633
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.45544554455445546
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.4801980198019802
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.504950495049505
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.4371640266541694
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4153524280999529
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.42164032403755497
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on TaylorAI/bge-micro-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) on the json dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) <!-- at revision 3edf6d7de0faa426b09780416fe61009f26ae589 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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("FareedKhan/TaylorAI_bge-micro-v2_FareedKhan_prime_synthetic_data_2k_10_64")
+# Run inference
+sentences = [
+    '\n\nDiarrhea, a condition characterized by the passage of loose, watery, and often more than five times a day, is a common ailment affecting individuals of all ages. It is typically acute when it lasts for a few days to a week or recurrent when it persists for more than four weeks. While acute diarrhea often resolves on its own and is usually not a cause for concern, recurrent or chronic forms require medical attention due to the risk of dehydration and nutrient deficiencies. \n\n### Causes\n\nDiarrhea can be caused by various factors, including:\n\n1. **Viral',
+    'Could you assist in identifying a condition linked to congenital secretory diarrhea, similar to intractable diarrhea of infancy, given my symptoms of persistent, salty watery diarrhea, hyponatremia, abnormal body pH, and reliance on parenteral nutrition due to chronic dehydration?',
+    'Could you describe the specific effects or phenotypes associated with acute hydrops in patients with the subtype of keratoconus?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+* Dataset: `dim_384`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.3663     |
+| cosine_accuracy@3   | 0.4554     |
+| cosine_accuracy@5   | 0.4802     |
+| cosine_accuracy@10  | 0.505      |
+| cosine_precision@1  | 0.3663     |
+| cosine_precision@3  | 0.1518     |
+| cosine_precision@5  | 0.096      |
+| cosine_precision@10 | 0.0505     |
+| cosine_recall@1     | 0.3663     |
+| cosine_recall@3     | 0.4554     |
+| cosine_recall@5     | 0.4802     |
+| cosine_recall@10    | 0.505      |
+| cosine_ndcg@10      | 0.4372     |
+| cosine_mrr@10       | 0.4154     |
+| **cosine_map@100**  | **0.4216** |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 1,814 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                           | anchor                                                                              |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 249.7 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 35.54 tokens</li><li>max: 135 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | anchor                                                                                                                                                                                                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code><br>The list you provided appears to be a collection of various substances and medications, each with its own unique properties and uses. Here's a brief overview of each:<br><br>1. **Abacavir**<br>   - Used in HIV treatment, it inhibits reverse transcriptase.<br><br>2. **Abate**<br>   - Often refers to fenpyroximate, used as an insecticide.<br><br>3. **Abidaquine**<br>   - An antimalarial drug used to treat and prevent malaria.<br><br>4. **Abiraterone**<br>   - Used in treating prostate cancer, specifically to block the production of testosterone.<br><br>5. **Abiraterone alfa**<br>   - Similar to abiraterone, used in prostate cancer treatment.<br><br>6. **Abiraterone acetate**<br>   - An active form of abiraterone.<br><br>7. **Abiraterone citrate**<br>   - Another form of abiraterone.<br><br>8. **Acelprozil**<br>   - A medication commonly used as an anti-epileptic drug.<br><br>9. **Acenocoumarol**<br>   - Used as a blood thinner, also known as a vitamin K antagonist.<br><br>10. **Acenocoumarol citrate**<br>    - Same as acenocoumarol but with citrate, functioning similarly as a</code>                                                                                                                                                                                                                           | <code>Which pharmacological agents with antioxidant properties have the potential to disrupt the PCSK9-LDLR interaction by affecting the gene or protein players in this pathway?</code>                          |
+  | <code><br>Bartholin duct cyst is a gynecological condition characterized by the distension of Bartholin glands due to mucus accumulation within the ducts, typically resulting from an obstructed orifice. This issue, categorized under women's reproductive health, falls directly under the umbrella of both integumentary system diseases and female reproductive system diseases. Originating from the Bartholin glands, which play a pivotal role in lubrication and arousal of the vulva during intercourse, the blockage or obstruction leads to cyst formation, affecting the overall female reproductive health landscape.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>What is the name of the gynecological condition that arises due to blocked Bartholin's glands and involves cyst formation, falling under the broader category of women's reproductive health issues?</code> |
+  | <code><br>Neuralgia, as defined by the MONDO ontology, refers to a pain disorder characterized by pain in the distribution of a nerve or nerves. This condition could be associated with the use of Capsaicin cream, given its known capability to alleviate symptoms by causing a temporary sensation of pain that interferes with the perception of more severe pain. Peripheral neuropathy, another symptom, is often manifest in cases where nerve damage occurs, frequently affecting multiple nerves. This condition can result in symptoms similar to sciatica, which is characterized by pain that starts in the lower back, often radiating down the leg, a common route for the sciatic nerve. The document indicates that diseases related to neuralgia include pudendal neuralgia, peripheral neuropathy, disorders involving pain, cranial neuralgia, post-infectious neuralgia, and sciatica. Furthermore, the document mentions several drugs that can be used for the purpose of managing symptoms related to neuralgia, including Lidocaine, as well as a wide array of off-label uses for treatments like Phenytoin, Morphine, Amitriptyline, Imipramine, Oxycodone, Nortriptyline, Lamotrigine, Maprotiline, Desipramine, Gabapentin, Carbamazepine, Phenobarbital, Tramadol, Venlafaxine, Trimipramine, Desvenlafaxine, Primidone, and Naltrexone.</code> | <code>What condition could be associated with the use of Capsaicin cream, peripheral neuropathy, and symptoms similar to sciatica?</code>                                                                         |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          384
+      ],
+      "matryoshka_weights": [
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: False
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: False
+- `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`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step    | Training Loss | dim_384_cosine_map@100 |
+|:-------:|:-------:|:-------------:|:----------------------:|
+| 0       | 0       | -             | 0.3737                 |
+| 0.3448  | 10      | 2.4936        | -                      |
+| 0.6897  | 20      | 2.4873        | -                      |
+| 1.0     | 29      | -             | 0.3917                 |
+| 1.0345  | 30      | 2.1624        | -                      |
+| 1.3793  | 40      | 2.0774        | -                      |
+| 1.7241  | 50      | 1.973         | -                      |
+| 2.0     | 58      | -             | 0.4065                 |
+| 2.0690  | 60      | 1.8545        | -                      |
+| 2.4138  | 70      | 1.8635        | -                      |
+| 2.7586  | 80      | 1.8483        | -                      |
+| 3.0     | 87      | -             | 0.4167                 |
+| 3.1034  | 90      | 1.764         | -                      |
+| 3.4483  | 100     | 1.744         | -                      |
+| 3.7931  | 110     | 1.8287        | -                      |
+| 4.0     | 116     | -             | 0.4212                 |
+| 4.1379  | 120     | 1.574         | -                      |
+| 4.4828  | 130     | 1.6807        | -                      |
+| 4.8276  | 140     | 1.7146        | -                      |
+| 5.0     | 145     | -             | 0.4222                 |
+| 5.1724  | 150     | 1.5898        | -                      |
+| 5.5172  | 160     | 1.6352        | -                      |
+| 5.8621  | 170     | 1.6344        | -                      |
+| 6.0     | 174     | -             | 0.4183                 |
+| 6.2069  | 180     | 1.5556        | -                      |
+| 6.5517  | 190     | 1.6743        | -                      |
+| 6.8966  | 200     | 1.5934        | -                      |
+| 7.0     | 203     | -             | 0.4199                 |
+| 7.2414  | 210     | 1.4956        | -                      |
+| 7.5862  | 220     | 1.5644        | -                      |
+| 7.9310  | 230     | 1.5856        | -                      |
+| **8.0** | **232** | **-**         | **0.4215**             |
+| 8.2759  | 240     | 1.4328        | -                      |
+| 8.6207  | 250     | 1.6208        | -                      |
+| 8.9655  | 260     | 1.57          | -                      |
+| 9.0     | 261     | -             | 0.4216                 |
+| 9.3103  | 270     | 1.6354        | -                      |
+| 9.6552  | 280     | 1.5414        | -                      |
+| 10.0    | 290     | 1.3757        | 0.4216                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.1
+- PyTorch: 2.2.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+
+## 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 +1,31 @@
+{
+  "_name_or_path": "/teamspace/studios/this_studio/TaylorAI_bge-micro-v2_FareedKhan_prime_synthetic_data_2k_10_64/finetuned_model",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
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+  "intermediate_size": 1536,
+  "label2id": {
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.1",
+    "pytorch": "2.2.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:a2f32d0a289cbe37ee9e4ca37e6da0d69d45d4a30db271e5eb87abf79d3a5459
+size 69565312

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,44 @@
+{
+  "additional_special_tokens": [
+    "[PAD]",
+    "[UNK]",
+    "[CLS]",
+    "[SEP]",
+    "[MASK]"
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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "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 +1,71 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+    "[UNK]",
+    "[CLS]",
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
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+  "model_max_length": 512,
+  "never_split": null,
+  "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]"
+}