Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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

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+---
+base_model: BAAI/bge-small-en-v1.5
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:29545
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: How should a Trust Service Provider keep the Regulator informed
+    about the status of its professional indemnity insurance?
+  sentences:
+  - "DocumentID: 3 | PassageID: 17.4.1 | Passage: An Authorised Person conducting\
+    \ a Regulated Activity in relation to Virtual Assets, where applicable, should\
+    \ consider any reporting obligations in relation to, among other things –\n(a)\t\
+    FATCA, as set out in the Guidance Notes on the requirements of the Intergovernmental\
+    \ Agreement between the United Arab Emirates and the United States, issued by\
+    \ the UAE Ministry of Finance in 2015 and as amended from time to  time; and\n\
+    (b)\tCommon Reporting Standards, set out in the ADGM Common Reporting Standard\
+    \ Regulations 2017."
+  - "DocumentID: 3 | PassageID: 5.6.2 | Passage: A Trust Service Provider must:\n\
+    (a)\tprovide the Regulator with a copy of its professional indemnity insurance\
+    \ cover; and\n(b)\tnotify the Regulator of any changes to the cover including\
+    \ termination and renewal."
+  - 'DocumentID: 34 | PassageID: 70) | Passage: REGULATORY REQUIREMENTS - SPOT COMMODITY
+    ACTIVITIES
+
+    Market Abuse / Market Surveillance
+
+    MTFs are required to operate an effective market surveillance program to identify,
+    monitor, detect and prevent conduct amounting to market misconduct and/or Financial
+    Crime.  Given the significant risks within Spot Commodity markets, an MTF’s or
+    OTF’s surveillance system will need to be robust, and regularly reviewed and enhanced.
+
+
+    '
+- source_sentence: '- Paragraphs 162-166 of the Virtual Assets Guidance address stablecoins
+    – can you elaborate on the specific regulatory requirements that an entity must
+    meet to use stablecoins in conjunction with digital securities?'
+  sentences:
+  - "DocumentID: 13 | PassageID: APP2.A2.1.12.(2) | Passage: Positions arising from\
+    \ internal hedges are eligible for Trading Book capital treatment, provided that\
+    \ they meet the criteria for trading intent specified in Rule A2.1.5 and the following\
+    \ criteria on prudent valuation:\n(a)\tthe internal hedge is not primarily intended\
+    \ to avoid or reduce Capital Requirements which the Authorised Person would be\
+    \ otherwise required to maintain;\n(b)\tthe internal hedge is properly documented\
+    \ and subject to specific internal approval and audit procedures;\n(c)\tthe internal\
+    \ hedge is dealt with at market conditions;\n(d)\tthe bulk of the Market Risk\
+    \ which is generated by the internal hedge is dynamically managed in the Trading\
+    \ Book within the limits approved by senior management; and\n(e)\tthe internal\
+    \ hedge is carefully monitored with adequate procedures."
+  - "DocumentID: 19 | PassageID: 166).e) | Passage: MTF (using Virtual Assets): using\
+    \ third-party issued fiat tokens as a payment/transaction mechanism:\n\ni.\tIn\
+    \ the context of using third party fiat tokens, the Authorised Person must directly\
+    \ meet the requirements of the Accepted Virtual Assets, Technology Governance\
+    \ and AML/CFT sections of this Guidance.\n\nii.\tFor the related fiat currency\
+    \ custody activities, FSRA preference is to have the MTF utilise a Virtual Asset/Fiat\
+    \ Custodian authorised on the basis of paragraphs 139 - 145 or 166(b) above.\n\
+    \niii.\tIn relation to the issuance of the related fiat token, in circumstances\
+    \ where the issuer is not authorised under paragraph 166(a) above, it is expected\
+    \ that the Authorised Person undertake the same due diligence as that it would\
+    \ apply for the purposes of determining Accepted Virtual Assets (focusing on Technology\
+    \ Governance requirements, the seven factors used to determine an Accepted Virtual\
+    \ Asset, and requirements relating to reporting and reconciliation).\n"
+  - 'DocumentID: 33 | PassageID: 117) | Passage: DIGITAL SECURITIES – SPECIFIC REGULATORY
+    CONSIDERATIONS
+
+    Islamic Finance Rules
+
+    FSRA’s Islamic Finance Rules (IFR) apply to a number of entities that can operate
+    within ADGM, including Authorised Persons and a Person making an Offer of Securities.  As
+    IFR is linked to the use of ‘Specified Investments’, including (Digital) Securities,
+    IFR can apply to Authorised Persons Conducting Islamic Financial Business or offering/distributing
+    Shari’a-compliant Securities.
+
+
+    '
+- source_sentence: How does the FSRA define a "suitably senior level" within a Mining
+    Reporting Entity for the sign-off of Production Targets, and what qualifications
+    or experience is required for individuals at this level?
+  sentences:
+  - 'DocumentID: 6 | PassageID: PART 5.13A.1.1 | Passage: Chapter 13A applies in its
+    entirety to the Fund Manager and, if appointed, the Trustee of a Private Credit
+    Fund, unless otherwise expressly provided for in this Chapter.'
+  - 'DocumentID: 11 | PassageID: 2.7.4.Guidance.1. | Passage: A Listed Entity should
+    provide the Regulator with at least ten Business Days in which to review a proposal
+    for the purchase of its own Shares. The more complex a proposal, the more time
+    that will be required by the Regulator to review and approve the proposal.'
+  - 'DocumentID: 30 | PassageID: 67) | Passage: PRODUCTION TARGETS .
+
+    Rule 11.8 sets out the requirements for disclosing certain types of Production
+    Targets.  The FSRA emphasises that Production Targets are forward looking statements.
+    A Production Target must, therefore, be based on reasonable grounds or it will
+    otherwise be deemed misleading.  An appropriate level of due diligence must, as
+    a result, be applied to the preparation of a Production Target.  The assumptions
+    and underlying figures used in preparing a Production Target need to be carefully
+    vetted and signed off at a suitably senior level within the Mining Reporting Entity
+    before it is disclosed.
+
+    '
+- source_sentence: In managing PSIAs, what specific prudential requirements must be
+    adhered to in relation to Trading Book and Non-Trading Book activities to ensure
+    compliance with the PRU Rule 1.3?
+  sentences:
+  - "DocumentID: 13 | PassageID: APP11.A11.1.Guidance.11. | Passage: Guidance on risks\
+    \ to be covered as part of the IRAP. An Authorised Person should consider the\
+    \ following risks, where relevant, in its IRAP:\na.\tCredit Risk, including Large\
+    \ Exposures and concentration risks;\nb.\tMarket Risk;\nc.\tLiquidity Risk;\n\
+    d.\tfor Islamic Financial Business involving PSIAs, displaced commercial risk;\n\
+    e.\tinterest rate risk in the Non Trading Book;\nf.\tOperational Risk;\ng.\tinternal\
+    \ controls and systems; and\nh.\treputational risk."
+  - 'DocumentID: 1 | PassageID: 7.2.4.Guidance on Restricted Scope Companies.2. |
+    Passage: Relevant Persons will know that Restricted Scope Companies are subject
+    to less onerous corporate disclosure requirements than other forms of corporate
+    entities due to the requirement to have "(Restricted)" in a company''s name. Given
+    that only the constitution and details of the registered office of a Restricted
+    Scope Company will be available in a public register, a Relevant Person will be
+    required to have a bilateral dialogue with the Restricted Scope Company, in accordance
+    with the RBA, to obtain any other relevant information which it needs to assess
+    the money laundering risks to which it is exposed.'
+  - "DocumentID: 12 | PassageID: 2.3.3 | Passage: An Insurer must develop, implement\
+    \ and maintain a risk management system to identify the operational risks faced\
+    \ by the Insurer, including but not limited to:\n(a)\ttechnology risk (including\
+    \ processing risks);\n(b)\treputational risk;\n(c)\tfraud and other fiduciary\
+    \ risks;\n(d)\tcompliance risk;\n(e)\toutsourcing risk;\n(f)\tbusiness continuity\
+    \ planning risk;\n(g)\tlegal risk; and\n(h)\tkey person risk."
+- source_sentence: Can a Captive Insurer's concentration positions be considered a
+    reason for establishing reserves for less liquid positions?
+  sentences:
+  - 'DocumentID: 19 | PassageID: 23) | Passage: REGULATORY REQUIREMENTS FOR AUTHORISED
+    PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS
+
+    Conducting a Regulated Activity in relation to Virtual Assets
+
+    Chapter 17 of COBS applies to all Authorised Persons conducting a Regulated Activity
+    in relation to Virtual Assets, requiring compliance with all requirements set
+    out in COBS Rules 17.1 – 17.6. Authorised Persons that are Operating a Multilateral
+    Trading Facility or Providing Custody in relation to Virtual Assets are also required
+    to comply with the additional requirements set out in COBS Rules 17.7 or 17.8
+    respectively.
+
+    '
+  - 'DocumentID: 2 | PassageID: 6.8.3 | Passage: A Captive Insurer must consider the
+    need for establishing reserves for less liquid positions and, on an on-going basis,
+    review their continued appropriateness in accordance with the requirements set
+    out in this Rule. Less liquid positions could arise from both market events and
+    institution-related situations e.g. concentration positions and/or stale positions.'
+  - "DocumentID: 3 | PassageID: 22.3.2 | Passage: An Authorised Person must –\n(a)\t\
+    have arrangements in place to ensure that it, and its market participants, are\
+    \ certified as compliant with:\n(i) \tISO 14001 (Environmental Management Systems\
+    \ (EMS));\n(ii)\tOHSAS 18001 / ISO 45001 (Health & Safety Management); or\n(iii)\t\
+    equivalent certification standards; and\n(b)\tensure its arrangements are aligned\
+    \ with the OECD’s Due Diligence Guidance for Responsible Mineral Supply Chains\
+    \ (as applicable)."
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the csv 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **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': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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("jebish7/MedEmbed-small-v0.1_MNR_5_Det")
+# Run inference
+sentences = [
+    "Can a Captive Insurer's concentration positions be considered a reason for establishing reserves for less liquid positions?",
+    'DocumentID: 2 | PassageID: 6.8.3 | Passage: A Captive Insurer must consider the need for establishing reserves for less liquid positions and, on an on-going basis, review their continued appropriateness in accordance with the requirements set out in this Rule. Less liquid positions could arise from both market events and institution-related situations e.g. concentration positions and/or stale positions.',
+    'DocumentID: 19 | PassageID: 23) | Passage: REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS\nConducting a Regulated Activity in relation to Virtual Assets\nChapter 17 of COBS applies to all Authorised Persons conducting a Regulated Activity in relation to Virtual Assets, requiring compliance with all requirements set out in COBS Rules 17.1 – 17.6. Authorised Persons that are Operating a Multilateral Trading Facility or Providing Custody in relation to Virtual Assets are also required to comply with the additional requirements set out in COBS Rules 17.7 or 17.8 respectively.\n',
+]
+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.*
+-->
+
+<!--
+## 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
+
+#### csv
+
+* Dataset: csv
+* Size: 29,545 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 18 tokens</li><li>mean: 34.86 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 131.72 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                         | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the threshold decline in the economic value of a firm, as a result of changes in interest rates, that necessitates immediate notification to the Regulator according to Rule 7.2.2?</code>                                       | <code>DocumentID: 13 | PassageID: 7.2.3 | Passage: An Authorised Person must immediately notify the Regulator if any evaluation under this Section suggests that, as a result of the change in interest rates described in Rule 7.2.2, the economic value of the firm would decline by more than 20% of its Capital Resources.</code>                                                                                                                                                                                                                                                                 |
+  | <code>What level of board and senior management involvement does the ADGM expect in the oversight of the incorporation of climate-related financial risks into capital and liquidity adequacy processes?</code>                                | <code>DocumentID: 36 | PassageID: D.6. | Passage: Principle 6 – Incorporation of climate-related financial risks into capital and liquidity adequacy processes. Relevant financial firms should incorporate material climate-related financial risks in their internal capital and liquidity adequacy assessment processes.<br></code>                                                                                                                                                                                                                                                                |
+  | <code>Can you provide guidance on the specific indicators or factors that should be considered by a Relevant Person when conducting a risk assessment to identify higher money laundering risks within the framework of the ADGM's RBA?</code> | <code>DocumentID: 1 | PassageID: 5.1.1.Guidance.4. | Passage: In adopting an RBA, a Relevant Person should continue to meet the requirements that are mandated under the AML Rulebook including:<br>(a)	assessing the relevant money laundering risks in accordance with Chapter ‎6 or Chapter ‎7 of AML (as applicable);<br>(b)	undertaking CDD in accordance with Rule ‎8.3.1;<br>(c)	undertaking Enhanced CDD pursuant to Rule ‎8.1.1(3) in accordance with Rule ‎8.4.1; and<br>(d)	undertaking Simplified CDD in accordance with Rule ‎8.5.1 where permissible pursuant to Rule ‎8.1.1(4).</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `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`: 2e-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`: 5
+- `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`: 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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.4329 | 100  | 1.743         |
+| 0.8658 | 200  | 1.2012        |
+| 1.0346 | 300  | 0.5543        |
+| 1.4675 | 400  | 1.1161        |
+| 1.9004 | 500  | 1.0257        |
+| 2.0693 | 600  | 0.4671        |
+| 2.5022 | 700  | 0.998         |
+| 2.9351 | 800  | 0.973         |
+| 3.1039 | 900  | 0.4108        |
+| 3.5368 | 1000 | 0.9453        |
+| 3.9697 | 1100 | 0.9343        |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.0
+- 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",
+}
+```
+
+#### 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": "BAAI/bge-small-en-v1.5",
+  "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"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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": "4.45.2",
+  "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": "4.45.2",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
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+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d3bb2e47b37a0715df99c2adb2795c6270c817d349a3b0c2079303c27b57e9c5
+size 133462128

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}