Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "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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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:210
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l
+widget:
+- source_sentence: 'What does maintenance refer to in the context of providing for
+    another person?  '
+  sentences:
+  - '-M-
+
+    Maintenance:  The f urnishing by one  person to another the means of living, or
+    fo od, clothing,'
+  - 'income and expenses to determine if the debtor may proceed under Chapter 7.
+
+    Chapter 7 trustee
+
+    A person appointed in a Chapter 7 case to represent the interests of the bankruptcy
+    estate
+
+    and the creditors. The trustee''s responsibilities include reviewing the debtor''s
+    petition and
+
+    schedules, liquidating the property of the estate, and making distributions to
+    creditors. The
+
+    trustee may also bring actions against creditors or the debtor to recover property
+    of the
+
+    bankruptcy estate.
+
+    Chapter 9'
+  - '-19-
+
+    Trial De Novo:  A new trial (See 22NYCRR 28.12).
+
+    -U-
+
+    Undertaking:  Deposit of a sum of money or filing of a bond in court, to secure
+    some actual or
+
+    potential obligation.
+
+    -V-
+
+    Vacate:  To set aside or undo a previous action or order.
+
+    Venire:  Technically, a writ summoning persons to court to act as jurors; popularly
+    used as meaning
+
+    the body of names thus summoned.
+
+    Venue:  (a) Geographical place where some legal matter occurs or may be determined.
+    (b) The
+
+    geographical area within which a court has jurisdiction. It relates only to a
+    place or territory within
+
+    which either party may require a case to be tried. A defect in venue may be waived
+    by the parties.'
+- source_sentence: 'What does the term "Pro Se" refer to in a legal context?  '
+  sentences:
+  - 'Process:  A l egal means, such as a s ummons, used to s ubject a de fendant i
+    n a l awsuit to the
+
+    personal jur isdiction o f the c ourt; broa dly, r efers to all writs iss ued
+    i n the c ourse of a  le gal
+
+    proceeding - what is served to obtain jurisdiction.
+
+    Pro Se (aka Self-Represented):  Appearing on one’s own behalf without an attorney.
+
+    Purge:  To atone for or correct an offense, to submit to a court''s mandate (i.e.,
+    to purge oneself
+
+    of contempt of court).
+
+    -Q-
+
+    None.
+
+    -R-
+
+    Recuse:  To disqualify oneself as a judge.
+
+    Redact:  To edit, revise or block out written text.
+
+    Referee:  A person to whom a claim  pending in a court is referred by the court
+    to take testimony,'
+  - '-10-
+
+    Hearing:  A pr eliminary examination where testimony is given and e vidence presented
+    for the
+
+    purpose of determining an issue of fact and reaching a decision on the basis of
+    that evidence.
+
+    Hearsay:  Testimony of a witness who relates not what he/she knows personally,
+    but what others
+
+    have told the witness, or what the witness has heard said by others; may be admissible
+    or
+
+    inadmissible in court depending upon rules of evidence.
+
+    Hung Jury:  A jury whose members cannot reconcile their differences of opinion
+    and thus cannot
+
+    reach a verdict.
+
+    -I-
+
+    Impaneling:  The process by which jurors are selected and sworn to their task.
+
+    Impleader:  An addition of another party to an action by the defendant, a “third
+    party” claim.'
+  - '-12-
+
+    Jurisdiction, Subject Matter:  Whether the court has authority over the thing
+    or right claimed by
+
+    one party against another.
+
+    Jury:  A prescribed number of persons selected according to law and sworn to make
+    findings of
+
+    fact.
+
+    Jury (Advisory):  A body of jurors impaneled to hear a case in which the parties
+    have no right to
+
+    a jury trial - the judge remains solely responsible for the findings and may accept
+    or reject the
+
+    jury''s verdict.
+
+    Jury Instructions:  Directions given by the judge to the jury, at the beginning
+    and end of trial.
+
+    -K-
+
+    None.
+
+    -L-
+
+    Laches:  The failure to diligently assert a right, which results in a refusal
+    to allow the right to be
+
+    asserted later.
+
+    Legal Age:  Eighteen (18) years of age. See CPLR Section 1206.'
+- source_sentence: What is the purpose of a Chapter 11 bankruptcy filing?
+  sentences:
+  - 'condemnation, i.e., the legal process by which real estate of a private owner
+    is taken for public use
+
+    without the owner''s consent, but upon the award and payment of just compensation.
+
+    Enjoin:  To require a person, by writ of injunction from a court of equity, to
+    perform or to refrain
+
+    from  or cease doing some act.
+
+    Entry:  The formal filing of an order of judgment with the County Clerk.
+
+    Equitable Action (Equity Matter):  An action which may be brought for the purpose
+    of restraining'
+  - 'A legal claim.
+
+    Chambers
+
+    The offices of a judge and his or her staff.
+
+    Chapter 11
+
+    A reorganization bankruptcy, usually involving a corporation or partnership. A
+    Chapter 11
+
+    debtor usually proposes a plan of reorganization to keep its business alive and
+    pay creditors
+
+    over time. Individuals or people in business can also seek relief in Chapter 11.
+
+    Chapter 12
+
+    The chapter of the Bankruptcy Code providing for adjustment of debts of a "family
+    farmer"
+
+    or "family fisherman," as the terms are defined in the Bankruptcy Code.
+
+    Chapter 13
+
+    The chapter of the Bankruptcy Code providing for the adjustment of debts of an
+    individual
+
+    with regular income, often referred to as a "wage-earner" plan. Chapter 13 allows
+    a debtor'
+  - 'Conviction
+
+    A judgment of guilt against a criminal defendant.
+
+    Counsel
+
+    Legal advice; a term also used to refer to the lawyers in a case.
+
+    Count
+
+    An allegation in an indictment or information, charging a defendant with a crime.
+    An
+
+    indictment or information may contain allegations that the defendant committed
+    more
+
+    than one crime. Each allegation is referred to as a count.
+
+    Court
+
+    Government entity authorized to resolve legal disputes. Judges sometimes use "court"
+    to
+
+    refer to themselves in the third person, as in "the court has read the briefs."
+
+    Court reporter
+
+    A person who makes a word-for-word record of what is said in court, generally
+    by using a
+
+    stenographic machine, shorthand or audio recording, and then produces a transcript
+    of the'
+- source_sentence: 'What types of property may a debtor be able to exempt under the
+    homestead exemption?  '
+  sentences:
+  - '-2-
+
+    Affidavit of Service:  An affidavit intended to certify or prove that  service
+    of a writ, notice, or other
+
+    document has been made.
+
+    Affirm:  An act of declaring something to be true under the penalty of perjury
+    by a person who
+
+    conscientiously declines to take an oath for religious or other pertinent reasons;
+    also attorneys are
+
+    permitted to affirm rather than swear under oath.
+
+    Affirmation:  A solemn and formal declaration under penalties of perjury that
+    a statement is true,
+
+    without an oath.
+
+    Affirmed:  Upheld, agreed with (e.g.,The Appellate Court affirmed the judgment
+    of the City Court);
+
+    also means a challenge to a court decision or order was rejected.'
+  - 'A formal request for the protection of the federal bankruptcy laws. (There is
+    an official form
+
+    for bankruptcy petitions.)
+
+    Bankruptcy trustee
+
+    A private individual or corporation appointed in all Chapter 7 and Chapter 13
+    cases to
+
+    represent the interests of the bankruptcy estate and the debtor''s creditors.
+
+    Bench trial
+
+    A trial without a jury, in which the judge serves as the fact-finder.
+
+    Brief
+
+    A written statement submitted in a trial or appellate proceeding that explains
+    one side''s
+
+    legal and factual arguments.
+
+    Burden of proof
+
+    The duty to prove disputed facts. In civil cases, a plaintiff generally has the
+    burden of
+
+    proving his or her case. In criminal cases, the government has the burden of proving
+    the
+
+    defendant''s guilt. (See standard of proof.)'
+  - 'residence (homestead exemption), or some or all "tools of the trade" used by
+    the debtor to
+
+    make a living (i.e., auto tools for an auto mechanic or dental tools for a dentist).
+    The
+
+    availability and amount of property the debtor may exempt depends on the state
+    the debtor
+
+    lives in.
+
+    F
+
+    Face sheet filing
+
+    A bankruptcy case filed either without schedules or with incomplete schedules
+    listing few
+
+    creditors and debts. (Face sheet filings are often made for the purpose of delaying
+    an'
+- source_sentence: How does a fraudulent transfer relate to a debtor's intent in bankruptcy
+    cases?
+  sentences:
+  - 'Glossary of Legal Terms
+
+    Find definitions of legal terms to help understand the federal
+
+    court system.
+
+    A
+
+    Acquittal
+
+    A jury verdict that a criminal defendant is not guilty, or the finding of a judge
+    that the
+
+    evidence is insufficient to support a conviction.
+
+    Active judge
+
+    A judge in the full-time service of the court. Compare to senior judge.
+
+    Administrative Office of the United States Courts (AO)
+
+    Enter legal term to search for definition
+
+    Search'
+  - 'A serious crime, usually punishable by at least one year in prison.
+
+    File
+
+    To place a paper in the official custody of the clerk of court to enter into the
+    files or records
+
+    of a case.
+
+    Fraudulent transfer
+
+    A transfer of a debtor''s property made with intent to defraud or for which the
+    debtor
+
+    receives less than the transferred property''s value.
+
+    Fresh start
+
+    The characterization of a debtor''s status after bankruptcy, i.e., free of most
+    debts. (Giving
+
+    debtors a fresh start is one purpose of the Bankruptcy Code.)
+
+    G
+
+    Grand jury
+
+    A body of 16-23 citizens who listen to evidence of criminal allegations, which
+    is presented by
+
+    the prosecutors, and determine whether there is probable cause to believe an individual'
+  - '-3-
+
+    Argument:  A reason given in proof or rebuttal to persuade a judge or jury.
+
+    At Issue:  Whenever the parties to an action come to a point in the pleadings
+    or argument which
+
+    is affirmed on one side and denied on the other, the points  are said to be "at
+    issue".
+
+    Attachment:  The taking of property into legal custody by an enforcement officer
+    (See specialty
+
+    section: Recovery of Chattel).
+
+    Attestation:  The act of witnessing an instrument in writing at the request of
+    the party making the
+
+    instrument and signing  it as a witness.
+
+    Attorney of Record:  Attorney whose name appears in the court’s  records or files
+    of a case.
+
+    Award:  A decision of an Arbitrator, judge or jury.
+
+    -B-'
+pipeline_tag: sentence-similarity
+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
+model-index:
+- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9318181818181818
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9318181818181818
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9545454545454546
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9318181818181818
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3106060606060606
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1909090909090909
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999996
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9318181818181818
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9318181818181818
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9545454545454546
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9565434941101226
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9438131313131314
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9438131313131314
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **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': 1024, '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("vin00d/snowflake-arctic-legal-ft-1")
+# Run inference
+sentences = [
+    "How does a fraudulent transfer relate to a debtor's intent in bankruptcy cases?",
+    "A serious crime, usually punishable by at least one year in prison.\nFile\nTo place a paper in the official custody of the clerk of court to enter into the files or records\nof a case.\nFraudulent transfer\nA transfer of a debtor's property made with intent to defraud or for which the debtor\nreceives less than the transferred property's value.\nFresh start\nThe characterization of a debtor's status after bankruptcy, i.e., free of most debts. (Giving\ndebtors a fresh start is one purpose of the Bankruptcy Code.)\nG\nGrand jury\nA body of 16-23 citizens who listen to evidence of criminal allegations, which is presented by\nthe prosecutors, and determine whether there is probable cause to believe an individual",
+    '-3-\nArgument:  A reason given in proof or rebuttal to persuade a judge or jury.\nAt Issue:  Whenever the parties to an action come to a point in the pleadings or argument which\nis affirmed on one side and denied on the other, the points  are said to be "at issue".\nAttachment:  The taking of property into legal custody by an enforcement officer (See specialty\nsection: Recovery of Chattel).\nAttestation:  The act of witnessing an instrument in writing at the request of the party making the\ninstrument and signing  it as a witness.\nAttorney of Record:  Attorney whose name appears in the court’s  records or files of a case.\nAward:  A decision of an Arbitrator, judge or jury.\n-B-',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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   | 0.9318     |
+| cosine_accuracy@3   | 0.9318     |
+| cosine_accuracy@5   | 0.9545     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9318     |
+| cosine_precision@3  | 0.3106     |
+| cosine_precision@5  | 0.1909     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9318     |
+| cosine_recall@3     | 0.9318     |
+| cosine_recall@5     | 0.9545     |
+| cosine_recall@10    | 1.0        |
+| **cosine_ndcg@10**  | **0.9565** |
+| cosine_mrr@10       | 0.9438     |
+| cosine_map@100      | 0.9438     |
+
+<!--
+## 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: 210 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 210 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 17.36 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 122.9 tokens</li><li>max: 192 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                     | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  |:---------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the purpose of the glossary of common legal terms provided in the context?  </code>              | <code>GLOSSARY ‐ COMMON LEGAL TERMS<br>NOTE:  The following definitions are not legal definitions.  Rather, these definitions are<br>intended to give you a general idea of the meanings of common legal words.  For <br>comprehensive Definitions of legal terms, you may wish to consult a legal dictionary<br> “Black’s Law Dictionary” is one such legal dictionary which is usually available at<br> most law libraries.<br>This glossary of common legal terms is also available on‐line at:<br>http://www.nycourts.gov/lawlibraries/glossary.shtml<br> <br>ADDITIONAL ON‐LINE RESOURCES:<br>http://www.nolo.com/glossary.cfm <br>Nolo’s on‐line legal dictionary.<br>http://www.law‐dictionary.org/<br>Free on‐line legal dictionary search engine.<br>http://www.law.cornell.edu/wex</code> |
+  | <code>Where can one find a comprehensive legal dictionary for more detailed definitions of legal terms?</code> | <code>GLOSSARY ‐ COMMON LEGAL TERMS<br>NOTE:  The following definitions are not legal definitions.  Rather, these definitions are<br>intended to give you a general idea of the meanings of common legal words.  For <br>comprehensive Definitions of legal terms, you may wish to consult a legal dictionary<br> “Black’s Law Dictionary” is one such legal dictionary which is usually available at<br> most law libraries.<br>This glossary of common legal terms is also available on‐line at:<br>http://www.nycourts.gov/lawlibraries/glossary.shtml<br> <br>ADDITIONAL ON‐LINE RESOURCES:<br>http://www.nolo.com/glossary.cfm <br>Nolo’s on‐line legal dictionary.<br>http://www.law‐dictionary.org/<br>Free on‐line legal dictionary search engine.<br>http://www.law.cornell.edu/wex</code> |
+  | <code>What organization maintains the legal dictionary and encyclopedia mentioned in the context?  </code>     | <code>Legal dictionary and encyclopedia maintained by the<br>Legal Information Institute at Cornell Law School.</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`: 10
+- `per_device_eval_batch_size`: 10
+- `num_train_epochs`: 10
+- `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`: 10
+- `per_device_eval_batch_size`: 10
+- `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`: 10
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | cosine_ndcg@10 |
+|:------:|:----:|:--------------:|
+| 1.0    | 21   | 0.9240         |
+| 2.0    | 42   | 0.9628         |
+| 2.3810 | 50   | 0.9628         |
+| 3.0    | 63   | 0.9502         |
+| 4.0    | 84   | 0.9569         |
+| 4.7619 | 100  | 0.9563         |
+| 5.0    | 105  | 0.9556         |
+| 6.0    | 126  | 0.9569         |
+| 7.0    | 147  | 0.9555         |
+| 7.1429 | 150  | 0.9555         |
+| 8.0    | 168  | 0.9565         |
+| 9.0    | 189  | 0.9565         |
+| 9.5238 | 200  | 0.9565         |
+| 10.0   | 210  | 0.9565         |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-l",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1555b48c1bf42a197ec1d33b0f0f5da39f4ad202b3a5a5372cabf87f6d845c18
+size 1336413848

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 +1,63 @@
+{
+  "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,
+  "extra_special_tokens": {},
+  "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]"
+}