Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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: jebish7/mpnet-base-all-obliqa_NMR
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:29547
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Are there any ADGM-specific guidelines or best practices for integrating
+    anti-money laundering (AML) compliance into our technology and financial systems
+    to manage operational risks effectively?
+  sentences:
+  - "REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES\
+    \ IN RELATION TO VIRTUAL ASSETS\nAnti-Money Laundering and Countering Financing\
+    \ of Terrorism\nIn order to develop a robust and sustainable regulatory framework\
+    \ for Virtual Assets, FSRA is of the view that a comprehensive application of\
+    \ its AML/CFT framework should be in place, including full compliance with, among\
+    \ other things, the:\n\na)\tUAE AML/CFT Federal Laws, including the UAE Cabinet\
+    \ Resolution No. (10) of 2019 Concerning the Executive Regulation of the Federal\
+    \ Law No. 20 of 2018 concerning Anti-Money Laundering and Combating Terrorism\
+    \ Financing;\n\nb)\tUAE Cabinet Resolution 20 of 2019 concerning the procedures\
+    \ of dealing with those listed under the UN sanctions list and UAE/local terrorist\
+    \ lists issued by the Cabinet, including the FSRA AML and Sanctions Rules and\
+    \ Guidance (“AML Rules”) or such other AML rules as may be applicable in ADGM\
+    \ from time to time; and\n\nc)\tadoption of international best practices (including\
+    \ the FATF Recommendations).\n"
+  - 'DIGITAL SECURITIES SETTLEMENT
+
+    Digital Settlement Facilities (DSFs)
+
+    For the purposes of this Guidance and distinct from RCHs, the FSRA will consider
+    DSFs suitable for the purposes of settlement (MIR Rule 3.8) and custody (MIR Rule
+    2.10) of Digital Securities. A DSF, holding an FSP for Providing Custody, may
+    provide custody and settlement services in Digital Securities for RIEs and MTFs
+    (as applicable).  Therefore, for the purposes of custody and settlement of Digital
+    Securities, the arrangements that a RIE or MTF would normally have in place with
+    a RCH can be replaced with arrangements provided by a DSF, provided that certain
+    requirements, as described in this section, are met.
+
+    '
+  - 'REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES
+    IN RELATION TO VIRTUAL ASSETS
+
+    Security measures and procedures
+
+    IT infrastructures should be strong enough to resist, without significant loss
+    to Clients, a number of scenarios, including but not limited to: accidental destruction
+    or breach of data, collusion or leakage of information by employees/former employees,
+    successful hack of a cryptographic and hardware security module or server, or
+    access by hackers of any single set of encryption/decryption keys that could result
+    in a complete system breach.
+
+    '
+- source_sentence: How does the ADGM enforce the Market Abuse Provisions, such as
+    those outlined in section 92 of the FSMR, especially for Accepted Spot Commodities,
+    and what are the reporting obligations for companies in relation to market abuse
+    and transaction reporting?
+  sentences:
+  - The Regulator shall have the power to require an Institution in Resolution, or
+    any of its Group Entities, to provide any services or facilities (excluding any
+    financial support) that are necessary to enable the Recipient to operate the transferred
+    business effectively, including where the Institution under Resolution or relevant
+    Group Entity has entered into Insolvency Proceedings.
+  - If the Regulator considers that an auditor or actuary has committed a contravention
+    of these Regulations, it may disqualify the auditor or actuary from being the
+    auditor of, or (as the case may be), from acting as an actuary for, any Authorised
+    Person, Recognised Body or Reporting Entity or any particular class thereof.
+  - 'REGULATORY REQUIREMENTS - SPOT COMMODITY ACTIVITIES
+
+    Market Abuse and Transaction Reporting (FSMR)
+
+    Importantly, the Market Abuse Provisions (including section 92) in Part 8 of FSMR
+    specifically cover Market Abuse Behaviour in relation to Accepted Spot Commodities
+    admitted to trading on an RIE, MTF or OTF.  In this regard, the FSRA imposes the
+    same high regulatory standards to Accepted Spot Commodities traded on RIEs, MTFs
+    or OTFs as it does to Financial Instruments traded on RIEs, MTFs or OTFs.
+
+    '
+- source_sentence: Can you provide further clarification on the specific measures
+    deemed adequate for handling conflicts of interest related to the provision and
+    management of credit within an Authorised Person's organization?
+  sentences:
+  - 'Own estimate haircuts . If an Authorised Person fails to comply with Rule A4.3.18,
+    the Regulator may revoke its approval for the Authorised Person to use own estimate
+    haircuts. The Authorised Person may also be required to revise its estimates for
+    the purpose of calculating regulatory Capital Requirements if its estimates of
+    E*, does not adequately reflect its Exposure to Counterparty Credit Risk.
+
+
+    '
+  - Financial risk . All applicants are required to demonstrate they have a sound
+    initial capital base and funding and must be able to meet the relevant prudential
+    requirements of ADGM legislation, on an ongoing basis. This includes holding enough
+    capital resources to cover expenses even if expected revenue takes time to materialise.
+    Start-ups can encounter greater financial risks as they seek to establish and
+    grow a new business.
+  - An Authorised Person with one or more branches outside the ADGM must implement
+    and maintain Credit Risk policies adapted to each local market and its regulatory
+    conditions.
+- source_sentence: What are the recommended best practices for ensuring that all disclosures
+    are prepared in accordance with the PRMS, and how can we validate that our classification
+    and reporting of Petroleum Resources meet the standards set forth?
+  sentences:
+  - 'DISCLOSURE REQUIREMENTS .
+
+    Material Exploration and drilling results
+
+    Rule 12.5.1 sets out the reporting requirements relevant to disclosures of material
+    Exploration and drilling results in relation to Petroleum Resources.  Such disclosures
+    should be presented in a factual and balanced manner, and contain sufficient information
+    to allow investors and their advisers to make an informed judgement of its materiality.  Care
+    needs to be taken to ensure that a disclosure does not suggest, without reasonable
+    grounds, that commercially recoverable or potentially recoverable quantities of
+    Petroleum have been discovered, in the absence of determining and disclosing estimates
+    of Petroleum Resources in accordance with Chapter 12 and the PRMS.
+
+    '
+  - If appointed, the Trustee must also take reasonable steps to ensure that its Employees
+    comply with IFR ‎6.2.6(a)‎(i)-‎(iv).
+  - Notwithstanding this Rule, an Authorised Person would generally be expected to
+    separate the roles of Compliance Officer and Senior Executive Officer. In addition,
+    the roles of Compliance Officer, Finance Officer and Money Laundering Reporting
+    Officer would not be expected to be combined with any other Controlled Functions
+    unless appropriate monitoring and control arrangements independent of the individual
+    concerned will be implemented by the Authorised Person. This may be possible in
+    the case of a Branch, where monitoring and controlling of the individual (carrying
+    out more than one role in the Branch) is conducted from the Authorised Person's
+    home state by an appropriate individual for each of the relevant Controlled Functions
+    as applicable. However, it is recognised that, on a case by case basis, there
+    may be exceptional circumstances in which this may not always be practical or
+    possible.
+- source_sentence: Can the ADGM provide examples of legal risks associated with securitisation
+    that Authorised Persons should particularly be aware of and manage?
+  sentences:
+  - "When employing an eKYC System to assist with CDD, a Relevant Person should:\n\
+    a.\tensure that it has a thorough understanding of the eKYC System itself and\
+    \ the risks of eKYC, including those outlined by relevant guidance from FATF and\
+    \ other international standard setting bodies;\nb.\tcomply with all the Rules\
+    \ of the Regulator relevant to eKYC including, but not limited to, applicable\
+    \ requirements regarding the business risk assessment, as per Rule ‎6.1, and outsourcing,\
+    \ as per Rule ‎9.3;\nc.\tcombine eKYC with transaction monitoring, anti-fraud\
+    \ and cyber-security measures to support a wider framework preventing applicable\
+    \ Financial Crime; and\nd.\ttake appropriate steps to identify, assess and mitigate\
+    \ the risk of the eKYC system being misused for the purposes of Financial Crime."
+  - This Chapter includes the detailed Rules and associated guidance in respect of
+    a firm's obligation to manage effectively its Exposures to Operational Risk. Operational
+    Risk refers to the risk of incurring losses due to the failure of systems, processes,
+    and personnel to perform expected tasks. Operational Risk losses also include
+    losses arising out of legal risk. This Chapter aims to ensure that an Authorised
+    Person has a robust Operational Risk management framework commensurate with the
+    nature, scale and complexity of its operations and that it holds sufficient regulatory
+    capital against Operational Risk Exposures.
+  - 'An Insurer must calculate the asset management risk component in respect of a
+    Long Term Insurance Fund according to the method set out in Rule ‎A4.13, applied
+    as though all references in that Rule to an Insurer were instead references to
+    that fund.
+
+    '
+---
+
+# SentenceTransformer based on jebish7/mpnet-base-all-obliqa_NMR
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jebish7/mpnet-base-all-obliqa_NMR](https://huggingface.co/jebish7/mpnet-base-all-obliqa_NMR) on the csv dataset. 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:** [jebish7/mpnet-base-all-obliqa_NMR](https://huggingface.co/jebish7/mpnet-base-all-obliqa_NMR) <!-- at revision 1e5dd5450bf7c54409b5ac5bba0a8336c233418d -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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("jebish7/mpnet-base-all-obliqa_NMR_3")
+# Run inference
+sentences = [
+    'Can the ADGM provide examples of legal risks associated with securitisation that Authorised Persons should particularly be aware of and manage?',
+    "This Chapter includes the detailed Rules and associated guidance in respect of a firm's obligation to manage effectively its Exposures to Operational Risk. Operational Risk refers to the risk of incurring losses due to the failure of systems, processes, and personnel to perform expected tasks. Operational Risk losses also include losses arising out of legal risk. This Chapter aims to ensure that an Authorised Person has a robust Operational Risk management framework commensurate with the nature, scale and complexity of its operations and that it holds sufficient regulatory capital against Operational Risk Exposures.",
+    'When employing an eKYC System to assist with CDD, a Relevant Person should:\na.\tensure that it has a thorough understanding of the eKYC System itself and the risks of eKYC, including those outlined by relevant guidance from FATF and other international standard setting bodies;\nb.\tcomply with all the Rules of the Regulator relevant to eKYC including, but not limited to, applicable requirements regarding the business risk assessment, as per Rule \u200e6.1, and outsourcing, as per Rule \u200e9.3;\nc.\tcombine eKYC with transaction monitoring, anti-fraud and cyber-security measures to support a wider framework preventing applicable Financial Crime; and\nd.\ttake appropriate steps to identify, assess and mitigate the risk of the eKYC system being misused for the purposes of Financial Crime.',
+]
+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.*
+-->
+
+<!--
+## 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: 29,547 training samples
+* Columns: <code>Question</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | Question                                                                           | positive                                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 15 tokens</li><li>mean: 34.89 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 115.11 tokens</li><li>max: 384 tokens</li></ul> |
+* Samples:
+  | Question                                                                                                                                                                                                      | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Under Rules 7.3.2 and 7.3.3, what are the two specific conditions related to the maturity of a financial instrument that would trigger a disclosure requirement?</code>                                 | <code>Events that trigger a disclosure. For the purposes of Rules 7.3.2 and 7.3.3, a Person is taken to hold Financial Instruments in or relating to a Reporting Entity, if the Person holds a Financial Instrument that on its maturity will confer on him:<br>(1)	an unconditional right to acquire the Financial Instrument; or<br>(2)	the discretion as to his right to acquire the Financial Instrument.<br></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  | <code>**Best Execution and Transaction Handling**: What constitutes 'Best Execution' under Rule 6.5 in the context of virtual assets, and how should Authorised Persons document and demonstrate this?</code> | <code>The following COBS Rules should be read as applying to all Transactions undertaken by an Authorised Person conducting a Regulated Activity in relation to Virtual Assets, irrespective of any restrictions on application or any exception to these Rules elsewhere in COBS -<br>(a)	Rule 3.4 (Suitability);<br>(b)	Rule 6.5 (Best Execution);<br>(c)	Rule 6.7 (Aggregation and Allocation);<br>(d)	Rule 6.10 (Confirmation Notes);<br>(e)	Rule 6.11 (Periodic Statements); and<br>(f)	Chapter 12 (Key Information and Client Agreement).</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  | <code>How does the FSRA define and evaluate "principal risks and uncertainties" for a Petroleum Reporting Entity, particularly for the remaining six months of the financial year?</code>                     | <code>A Reporting Entity must:<br>(a)	prepare such report:<br>(i)	for the first six months of each financial year or period, and if there is a change to the accounting reference date, prepare such report in respect of the period up to the old accounting reference date; and<br>(ii)	in accordance with the applicable IFRS standards or other standards acceptable to the Regulator;<br>(b)	ensure the financial statements have either been audited or reviewed by auditors, and the audit or review by the auditor is included within the report; and<br>(c)	ensure that the report includes:<br>(i)	except in the case of a Mining Exploration Reporting Entity or a Petroleum Exploration Reporting Entity, an indication of important events that have occurred during the first six months of the financial year, and their impact on the financial statements;<br>(ii)	except in the case of a Mining Exploration Reporting Entity or a Petroleum Exploration Reporting Entity, a description of the principal risks and uncertainties for the remaining six months of the financial year; and<br>(iii)	a condensed set of financial statements, an interim management report and associated responsibility statements.</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`: 24
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `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`: 24
+- `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`: 2
+- `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 |
+|:------:|:----:|:-------------:|
+| 0.1623 | 100  | 0.4433        |
+| 0.3247 | 200  | 0.3978        |
+| 0.4870 | 300  | 0.4173        |
+| 0.6494 | 400  | 0.4892        |
+| 0.8117 | 500  | 0.5729        |
+| 0.9740 | 600  | 0.5901        |
+| 1.1331 | 700  | 0.4664        |
+| 1.2955 | 800  | 0.3703        |
+| 1.4578 | 900  | 0.3813        |
+| 1.6201 | 1000 | 0.3964        |
+| 1.7825 | 1100 | 0.4536        |
+| 1.9448 | 1200 | 0.4513        |
+
+
+### 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 +1,24 @@
+{
+  "_name_or_path": "jebish7/mpnet-base-all-obliqa_NMR",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.0"
+  },
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+}

model.safetensors

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+oid sha256:de254c1429df2815811bdfac92c0b1f0d1d90cf13af5d1190cdf9748f4e9f9c6
+size 437967672

modules.json

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+    "name": "0",
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+    "type": "sentence_transformers.models.Transformer"
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+{
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+  "pad_token": "<pad>",
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+  "padding_side": "right",
+  "sep_token": "</s>",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}