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1_Pooling/config.json

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+{
+  "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

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+---
+base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3192024
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: Must have experience in interdisciplinary collaboration
+  sentences:
+  - Nurse Coordinator specializing in advanced heart failure programs at The Queen's
+    Health System. Skilled in patient care coordination, clinical assessments, and
+    interdisciplinary collaboration. Experienced in managing complex health cases
+    and ensuring compliance with healthcare regulations. Proficient in utilizing advanced
+    medical technologies and technologies to enhance patient outcomes. Strong background
+    in nonprofit healthcare environments, contributing to optimal health and wellness
+    initiatives.
+  - Administrative Assistant in the judiciary with experience at the Minnesota Judicial
+    Branch and Mayo Clinic. Skilled in managing administrative tasks, coordinating
+    schedules, and supporting judicial processes. Proficient in office software and
+    communication tools. Previous roles include bank teller positions, enhancing customer
+    service and financial transactions. Strong organizational skills and attention
+    to detail, contributing to efficient operations in high-pressure environments.
+  - Area Manager in facilities services with expertise in managing public parks, campgrounds,
+    and recreational facilities. Skilled in operational management, team leadership,
+    and customer service. Proven track record in enhancing service delivery and operational
+    efficiency. Previous roles include Management Team and Accounts Payable Manager,
+    demonstrating versatility across various industries. Strong background in office
+    management and office operations, contributing to a well-rounded understanding
+    of facility management practices.
+- source_sentence: Must have a customer service orientation
+  sentences:
+  - Research Assistant in biotechnology with expertise in Molecular Biology, Protein
+    Expression, Purification, and Crystallization. Currently employed at Seagen, contributing
+    to innovative cancer treatments. Holds a B.S. in Biochemistry and minors in Chemistry
+    and Spanish. Previous experience includes roles as a Manufacturing Technician
+    at AGC Biologics and undergraduate research at NG Lab and Mueller Lab, focusing
+    on recombinant human proteins and protein processing. Proficient in leading project
+    cooperation and public speaking.
+  - Instructional Developer with a Master's in Human Resource Development, specializing
+    in learning solutions across various media platforms. Experienced in storyboarding,
+    animation, videography, and post-production. Proven track record in e-learning
+    design and development, team leadership, and creative problem-solving. Currently
+    employed at The University of Texas Health Science Center at Houston, focusing
+    on enhancing organizational value through tailored corporate learning. Previous
+    roles include Learning Consultant at Strategic Ascent and Assistant Manager at
+    Cicis Pizza. Strong background in healthcare and professional training industries.
+  - Human Resource professional with expertise in hiring, compliance, benefits, and
+    compensation within the hospitality and semiconductor industries. Currently a
+    Talent Acquisition Specialist at MKS Instruments, skilled in relationship building
+    and attention to detail. Previous roles include Recruitment Manager at Block by
+    Block and Talent Acquisition Specialist at Manpower. Proficient in advanced computer
+    skills and a customer service orientation. Experienced in staffing management
+    and recruitment strategies, with a strong focus on enhancing workforce capabilities
+    and fostering client relationships.
+- source_sentence: Must be proficient in graphic design software
+  sentences:
+  - Senior Software Engineer with expertise in developing innovative solutions for
+    the aviation and defense industries. Currently at Delta Flight Products, specializing
+    in aircraft cabin interiors and avionics. Proficient in backend ETL processes,
+    REST API development, and software development life cycle. Previous experience
+    includes roles at Cisco, Thales, Safran, and FatPipe Networks, focusing on enhancing
+    operational efficiency and user experience. Holds multiple patents for web application
+    design and deployment. Strong background in collaborating with cross-functional
+    teams to deliver high-quality software solutions.
+  - Client Advisor in financial services with a strong background in luxury goods
+    and retail. Currently at Louis Vuitton, specializing in client relationship management
+    and personalized service. Previously worked at Salvatore Ferragano, enhancing
+    client engagement and driving sales. Experienced in marketing management from
+    SkPros, focusing on brand strategy and market analysis. Proficient in leveraging
+    data to inform decision-making and improve client experiences.
+  - Weld Process Specialist at Airgas with expertise in industrial automation and
+    chemicals. Skilled in Resistance weld gun calibration, schedule database management,
+    and asset locating matrix creation. Previous experience as a Welding Engineer
+    at R&E Automated, providing support in automation systems for manufacturing applications.
+    Proficient in DCEN and various welding techniques, including Fanuc and Motoman.
+    Background includes roles in drafting and welding, enhancing fabrication efficiency
+    and quality. Strong foundation in mechanical design and engineering principles,
+    with a focus on improving performance and performance in manufacturing environments.
+- source_sentence: Must have experience in pharmaceutical marketing
+  sentences:
+  - Brand Influencer specializing in Black Literary, Culture, and Lifestyle. Certified
+    UrbanAg with over 20 years of experience in urban agriculture consulting and retail
+    operations. Currently supervises community gardens at Chicago Botanic Garden,
+    educating residents on organic growing methods and addressing nutrition, food
+    security, and healthy lifestyle options. Previously served as president of Af-Am
+    Bookstore, demonstrating entrepreneurial skills and community engagement. Expertise
+    in marketing and advertising, with a focus on enhancing community engagement and
+    promoting sustainable practices.
+  - Experienced Studio Manager and Executive Producer in media production, specializing
+    in immersive entertainment and virtual environments. Proficient in business planning,
+    team building, fundraising, and management. Co-founder of Dirty Secret, focusing
+    on brand activation and custom worlds. Previous roles at Wevr involved production
+    coordination and project management, with a strong background in arts and design.
+    Holds a degree from California State University-Los Angeles.
+  - Owner and CEO of Cake N Wings, a catering company specializing in food and travel
+    PR. Experienced in public relations across health, technology, and entertainment
+    sectors. Proven track record in developing innovative urban cuisine and enhancing
+    customer experiences. Previous roles include account executive at Development
+    Counsellors International and public relations manager at Creole Restaurant. Skilled
+    in brand development, event management, and community engagement.
+- source_sentence: Must have experience in software development
+  sentences:
+  - Multi-skilled Business Analytics professional with a Master’s in Business Analytics
+    and a dual MBA. Experienced in data analytics, predictive modeling, and project
+    management within the health and wellness sector. Proficient in extracting, summarizing,
+    and analyzing claims databases and healthcare analytics. Skilled in statistical
+    analysis, database management, and data visualization. Previous roles include
+    Business Analytics Advisor at Cigna Healthcare and Informatics Senior Specialist
+    at Cigna Healthcare. Strong leadership and project management abilities, with
+    a solid foundation in healthcare economics and outcomes observational research.
+    Familiar with Base SAS 9.2, SAS EG, SAS EM, SAS JMP, Tableau, and Oracle Crystal
+    Ball.
+  - Assistant Vice President in commercial real estate financing with a strong background
+    in banking. Experienced in business banking and branch management, having held
+    roles as Assistant Vice President and Business Banking Officer. Proven track record
+    in business development and branch operations within a large independent bank.
+    Skilled in building client relationships and driving financial growth. Holds expertise
+    in managing diverse teams and enhancing operational efficiency. Previous experience
+    includes branch management across multiple branches, demonstrating a commitment
+    to community engagement and financial wellness.
+  - CEO of IMPROVLearning, specializing in e-learning and driver education. Founded
+    and managed multiple ventures in training, healthcare, and real estate. Proven
+    track record of expanding product offerings and achieving recognition on the Inc
+    500/5000 list. Active board member of the LA Chapter of the Entrepreneur Organization,
+    contributing to the growth of over 3 million students. Experienced in venture
+    capital and entrepreneurship, with a focus on innovative training solutions and
+    community engagement. Active member of various organizations, including the Entrepreneurs'
+    Organization and the Los Angeles County Business Federation.
+model-index:
+- name: SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: validation
+      type: validation
+    metrics:
+    - type: pearson_cosine
+      value: 0.9594453206302572
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.860568334150162
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.9436690128729379
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8604275677997159
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.9443183012069103
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8605683342374743
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.9594453207129489
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8605683341225518
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.9594453207129489
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8605683342374743
+      name: Spearman Max
+---
+
+# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). 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:** [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) <!-- at revision 2430568290bb832d22ad5064f44dd86cf0240142 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': 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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Must have experience in software development',
+    "CEO of IMPROVLearning, specializing in e-learning and driver education. Founded and managed multiple ventures in training, healthcare, and real estate. Proven track record of expanding product offerings and achieving recognition on the Inc 500/5000 list. Active board member of the LA Chapter of the Entrepreneur Organization, contributing to the growth of over 3 million students. Experienced in venture capital and entrepreneurship, with a focus on innovative training solutions and community engagement. Active member of various organizations, including the Entrepreneurs' Organization and the Los Angeles County Business Federation.",
+    'Multi-skilled Business Analytics professional with a Master’s in Business Analytics and a dual MBA. Experienced in data analytics, predictive modeling, and project management within the health and wellness sector. Proficient in extracting, summarizing, and analyzing claims databases and healthcare analytics. Skilled in statistical analysis, database management, and data visualization. Previous roles include Business Analytics Advisor at Cigna Healthcare and Informatics Senior Specialist at Cigna Healthcare. Strong leadership and project management abilities, with a solid foundation in healthcare economics and outcomes observational research. Familiar with Base SAS 9.2, SAS EG, SAS EM, SAS JMP, Tableau, and Oracle Crystal Ball.',
+]
+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
+
+#### Semantic Similarity
+* Dataset: `validation`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.9594     |
+| spearman_cosine    | 0.8606     |
+| pearson_manhattan  | 0.9437     |
+| spearman_manhattan | 0.8604     |
+| pearson_euclidean  | 0.9443     |
+| spearman_euclidean | 0.8606     |
+| pearson_dot        | 0.9594     |
+| spearman_dot       | 0.8606     |
+| pearson_max        | 0.9594     |
+| **spearman_max**   | **0.8606** |
+
+<!--
+## 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: 3,192,024 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                         | label                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | float                                                         |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 9.15 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 53 tokens</li><li>mean: 93.6 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | label            |
+  |:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Must have experience in software development</code>                               | <code>Executive Assistant with a strong background in real estate and financial services. Experienced in managing executive schedules, coordinating communications, and supporting investment banking operations. Proficient in office management software and adept at multitasking in fast-paced environments. Previous roles at Blackstone, Piper Sandler, and Broe Real Estate Group, where responsibilities included supporting high-level executives and enhancing operational efficiency. Skilled in fostering relationships and facilitating smooth transitions in fast-paced settings.</code>                                                              | <code>0.0</code> |
+  | <code>Must have experience in overseeing service delivery for health initiatives</code> | <code>Director of Solution Strategy in health, wellness, and fitness, specializing in relationship building and strategy execution. Experienced in overseeing service delivery and performance management for telehealth and digital health initiatives at Blue Cross Blue Shield of Massachusetts. Proven track record in vendor lifecycle management, contract strategy, and operational leadership. Skilled in developing standardized wellness programs and enhancing client satisfaction through innovative solutions. Strong background in managing cross-functional teams and driving performance metrics in health engagement and wellness services.</code> | <code>1.0</code> |
+  | <code>Must have experience collaborating with Fortune 500 companies</code>              | <code>Senior Sales and Business Development Manager in the energy sector, specializing in increasing profitable sales for small to large companies. Proven track record in relationship building, team management, and strategy development. Experienced in collaborating with diverse stakeholders, including Fortune 500 companies and small to large privately held companies. Previous roles include Vice President of Operations at NovaStar LP and Director of Sales at NovaStar Mortgage and Athlon Solutions. Strong communicator and team player, with a focus on customer needs and operational efficiency.</code>                                        | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `num_train_epochs`: 1.0
+- `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`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 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`: 1.0
+- `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`: 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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | validation_spearman_max |
+|:------:|:-----:|:-------------:|:-----------------------:|
+| 0.0200 | 500   | 0.1362        | -                       |
+| 0.0401 | 1000  | 0.0533        | -                       |
+| 0.0601 | 1500  | 0.0433        | -                       |
+| 0.0802 | 2000  | 0.0386        | -                       |
+| 0.1002 | 2500  | 0.0356        | -                       |
+| 0.1203 | 3000  | 0.0345        | -                       |
+| 0.1403 | 3500  | 0.0326        | -                       |
+| 0.1604 | 4000  | 0.0323        | -                       |
+| 0.1804 | 4500  | 0.0313        | -                       |
+| 0.2005 | 5000  | 0.0305        | -                       |
+| 0.2205 | 5500  | 0.0298        | -                       |
+| 0.2406 | 6000  | 0.0296        | -                       |
+| 0.2606 | 6500  | 0.0291        | -                       |
+| 0.2807 | 7000  | 0.0286        | -                       |
+| 0.3007 | 7500  | 0.0286        | -                       |
+| 0.3208 | 8000  | 0.0281        | -                       |
+| 0.3408 | 8500  | 0.0278        | -                       |
+| 0.3609 | 9000  | 0.0273        | -                       |
+| 0.3809 | 9500  | 0.0276        | -                       |
+| 0.4010 | 10000 | 0.0274        | -                       |
+| 0.4210 | 10500 | 0.0266        | -                       |
+| 0.4411 | 11000 | 0.0261        | -                       |
+| 0.4611 | 11500 | 0.0264        | -                       |
+| 0.4812 | 12000 | 0.0256        | -                       |
+| 0.5012 | 12500 | 0.0254        | -                       |
+| 0.5213 | 13000 | 0.0251        | -                       |
+| 0.5413 | 13500 | 0.0249        | -                       |
+| 0.5614 | 14000 | 0.0253        | -                       |
+| 0.5814 | 14500 | 0.0247        | -                       |
+| 0.6015 | 15000 | 0.0254        | -                       |
+| 0.6215 | 15500 | 0.0246        | -                       |
+| 0.6416 | 16000 | 0.0251        | -                       |
+| 0.6616 | 16500 | 0.0248        | -                       |
+| 0.6817 | 17000 | 0.0247        | -                       |
+| 0.7017 | 17500 | 0.0246        | -                       |
+| 0.7218 | 18000 | 0.0242        | -                       |
+| 0.7418 | 18500 | 0.024         | -                       |
+| 0.7619 | 19000 | 0.0247        | -                       |
+| 0.7819 | 19500 | 0.0238        | -                       |
+| 0.8020 | 20000 | 0.0244        | 0.8603                  |
+| 0.8220 | 20500 | 0.024         | -                       |
+| 0.8421 | 21000 | 0.0244        | -                       |
+| 0.8621 | 21500 | 0.0242        | -                       |
+| 0.8822 | 22000 | 0.0239        | -                       |
+| 0.9022 | 22500 | 0.0237        | -                       |
+| 0.9223 | 23000 | 0.0241        | -                       |
+| 0.9423 | 23500 | 0.0242        | -                       |
+| 0.9624 | 24000 | 0.0238        | -                       |
+| 0.9824 | 24500 | 0.0236        | -                       |
+| 1.0    | 24938 | -             | 0.8606                  |
+
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.1
+- PyTorch: 2.4.0+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## 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 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ea3b246feacea31caad190a4de120ddd4979dc82af0f8239e3dbbb562585460
+size 90864192

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 +1,64 @@
+{
+  "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_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 250,
+  "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]"
+}