Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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 +1,618 @@
+---
+base_model: Snowflake/snowflake-arctic-embed-m
+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
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:600
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: How does the Blueprint for an AI Bill of Rights aim to protect
+    the rights of the American public?
+  sentences:
+  - "and use prohibitions. You and your communities should be free from unchecked\
+    \ surveillance; surveillance \ntechnologies should be subject to heightened oversight\
+    \ that includes at least pre-deployment assessment of their \npotential harms\
+    \ and scope limits to protect privacy and civil liberties. Continuous surveillance\
+    \ and monitoring"
+  - "steps to move these principles into practice and promote common approaches that\
+    \ allow technological \ninnovation to flourish while protecting people from harm.\
+    \ \n9"
+  - "ABOUT THIS FRAMEWORK­­­­­\nThe Blueprint for an AI Bill of Rights is a set of\
+    \ five principles and associated practices to help guide the \ndesign, use, and\
+    \ deployment of automated systems to protect the rights of the American public\
+    \ in the age of \nartificial intel-ligence. Developed through extensive consultation\
+    \ with the American public, these principles are"
+- source_sentence: How can organizations monitor the impact of proxy features on algorithmic
+    discrimination?
+  sentences:
+  - "sociodemographic variables that adjust or “correct” the algorithm’s output on\
+    \ the basis of a patient’s race or\nethnicity, which can lead to race-based health\
+    \ inequities.47\n25\nAlgorithmic \nDiscrimination \nProtections"
+  - "proxy; if needed, it may be possible to identify alternative attributes that\
+    \ can be used instead. At a minimum, \norganizations should ensure a proxy feature\
+    \ is not given undue weight and should monitor the system closely \nfor any resulting\
+    \ algorithmic discrimination.   \n26\nAlgorithmic \nDiscrimination \nProtections"
+  - "velopment, and deployment of automated systems, and from the \ncompounded harm\
+    \ of its reuse. Independent evaluation and report­\ning that confirms that the\
+    \ system is safe and effective, including re­\nporting of steps taken to mitigate\
+    \ potential harms, should be per­\nformed and the results made public whenever\
+    \ possible. \n15"
+- source_sentence: What measures can be taken to ensure that AI systems are designed
+    to be accessible for people with disabilities?
+  sentences:
+  - "potential for meaningful impact on people’s rights, opportunities, or access\
+    \ and include those to impacted \ncommunities that may not be direct users of\
+    \ the automated system, risks resulting from purposeful misuse of \nthe system,\
+    \ and other concerns identified via the consultation process. Assessment and,\
+    \ where possible, mea­"
+  - "and as a lifecycle minimum performance standard. Decision possibilities resulting\
+    \ from performance testing \nshould include the possibility of not deploying the\
+    \ system. \nRisk identification and mitigation. Before deployment, and in a proactive\
+    \ and ongoing manner, poten­\ntial risks of the automated system should be identified\
+    \ and mitigated. Identified risks should focus on the"
+  - "individuals \nand \ncommunities \nfrom algorithmic \ndiscrimination and to use\
+    \ and design systems in an equitable way. This protection should include proactive\
+    \ \nequity assessments as part of the system design, use of representative data\
+    \ and protection against proxies \nfor demographic features, ensuring accessibility\
+    \ for people with disabilities in design and development,"
+- source_sentence: 'How should organizations address concerns raised during public
+    consultations regarding AI data processing and interpretation?  '
+  sentences:
+  - "and testing and evaluation of AI technologies and systems. It is expected to\
+    \ be released in the winter of 2022-23. \n21"
+  - "provide guidance whenever automated systems can meaningfully impact the public’s\
+    \ rights, opportunities, \nor access to critical needs. \n3"
+  - "learning models or for other purposes, including how data sources were processed\
+    \ and interpreted, a \nsummary of what data might be missing, incomplete, or erroneous,\
+    \ and data relevancy justifications; the \nresults of public consultation such\
+    \ as concerns raised and any decisions made due to these concerns; risk"
+- source_sentence: What role do ethical considerations play in the development and
+    implementation of automated systems?
+  sentences:
+  - "tial to meaningfully impact rights, opportunities, or access. Additionally, this\
+    \ framework does not analyze or \ntake a position on legislative and regulatory\
+    \ proposals in municipal, state, and federal government, or those in \nother countries.\
+    \ \nWe have seen modest progress in recent years, with some state and local governments\
+    \ responding to these prob­"
+  - '•
+
+    Searches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39
+    sexualized content, rather
+
+    than role models, toys, or activities.40 Some search engines have been working
+    to reduce the prevalence of
+
+    these results, but the problem remains.41
+
+    •
+
+    Advertisement delivery systems that predict who is most likely to click on a job
+    advertisement end up deliv-'
+  - "particularly relevant to automated systems, without articulating a specific set\
+    \ of FIPPs or scoping \napplicability or the interests served to a single particular\
+    \ domain, like privacy, civil rights and civil liberties, \nethics, or risk management.\
+    \ The Technical Companion builds on this prior work to provide practical next"
+model-index:
+- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.83
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.98
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.99
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.83
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.31999999999999995
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19599999999999995
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09899999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.83
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.96
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.98
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.99
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9195971547817925
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8960000000000001
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8966666666666666
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.83
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.96
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.98
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.99
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.83
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.31999999999999995
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19599999999999995
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09899999999999999
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.83
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.96
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.98
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.99
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9195971547817925
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.8960000000000001
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.8966666666666666
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-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-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 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': 768, '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("niting089/finetuned_arctic")
+# Run inference
+sentences = [
+    'What role do ethical considerations play in the development and implementation of automated systems?',
+    'particularly relevant to automated systems, without articulating a specific set of FIPPs or scoping \napplicability or the interests served to a single particular domain, like privacy, civil rights and civil liberties, \nethics, or risk management. The Technical Companion builds on this prior work to provide practical next',
+    '•\nSearches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39 sexualized content, rather\nthan role models, toys, or activities.40 Some search engines have been working to reduce the prevalence of\nthese results, but the problem remains.41\n•\nAdvertisement delivery systems that predict who is most likely to click on a job advertisement end up deliv-',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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   | 0.83       |
+| cosine_accuracy@3   | 0.96       |
+| cosine_accuracy@5   | 0.98       |
+| cosine_accuracy@10  | 0.99       |
+| cosine_precision@1  | 0.83       |
+| cosine_precision@3  | 0.32       |
+| cosine_precision@5  | 0.196      |
+| cosine_precision@10 | 0.099      |
+| cosine_recall@1     | 0.83       |
+| cosine_recall@3     | 0.96       |
+| cosine_recall@5     | 0.98       |
+| cosine_recall@10    | 0.99       |
+| cosine_ndcg@10      | 0.9196     |
+| cosine_mrr@10       | 0.896      |
+| **cosine_map@100**  | **0.8967** |
+| dot_accuracy@1      | 0.83       |
+| dot_accuracy@3      | 0.96       |
+| dot_accuracy@5      | 0.98       |
+| dot_accuracy@10     | 0.99       |
+| dot_precision@1     | 0.83       |
+| dot_precision@3     | 0.32       |
+| dot_precision@5     | 0.196      |
+| dot_precision@10    | 0.099      |
+| dot_recall@1        | 0.83       |
+| dot_recall@3        | 0.96       |
+| dot_recall@5        | 0.98       |
+| dot_recall@10       | 0.99       |
+| dot_ndcg@10         | 0.9196     |
+| dot_mrr@10          | 0.896      |
+| dot_map@100         | 0.8967     |
+
+<!--
+## 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: 600 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 600 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 19.86 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 60.47 tokens</li><li>max: 94 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                           | sentence_1                                                                                                                                                                                                                                                                                                                                                            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are the key principles outlined in the AI Bill of Rights aimed at ensuring automated systems benefit the American people?  </code>        | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                            |
+  | <code>How does the AI Bill of Rights address potential ethical concerns related to automated decision-making systems?</code>                         | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                            |
+  | <code>What is the purpose of the Blueprint for an AI Bill of Rights as outlined by the White House Office of Science and Technology Policy?  </code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered</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`: 20
+- `per_device_eval_batch_size`: 20
+- `num_train_epochs`: 5
+- `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`: 20
+- `per_device_eval_batch_size`: 20
+- `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`: 5
+- `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 | cosine_map@100 |
+|:------:|:----:|:--------------:|
+| 1.0    | 30   | 0.8731         |
+| 1.6667 | 50   | 0.89           |
+| 2.0    | 60   | 0.895          |
+| 3.0    | 90   | 0.8959         |
+| 3.3333 | 100  | 0.8967         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- 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",
+}
+```
+
+#### 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 +1,26 @@
+{
+  "_name_or_path": "finetuned_arctic",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5de40de0b87a0fa4602678a65b38d93016dd2b71825d08129ceabce8c675f6b5
+size 437951328

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 +1,62 @@
+{
+  "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,
+  "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]"
+}