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kperkins411
/
mpnet-base-all-nli-triplet

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:35258
loss:TripletLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
kperkins411 commited on
Commit
2255cc6
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1 Parent(s): a4e7a08

Add new SentenceTransformer model.

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +587 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +64 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,587 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ datasets: []
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+ language: []
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy
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+ - dot_accuracy
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+ - manhattan_accuracy
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+ - euclidean_accuracy
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+ - max_accuracy
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:35258
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+ - loss:TripletLoss
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+ widget:
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+ - source_sentence: 2.5 subcontracting 7
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+ sentences:
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+ - 1.51 "suppliers" has the meaning set forth in section 2.3.
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+ - section 6.4 delegation and contracting 31
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+ - what are the limitations on subcontracting as per clause 6.4?
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+ - source_sentence: 4.3 accrued obligations. in the event that either distributor or
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+ company fails to comply with the terms of this agreement, both distributor and
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+ company acknowledge and agree that in addition to any claim for damages either
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+ party may have arising from the default of the other, they shall have the right
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+ to seek equitable relief by way of a temporary restraining order, preliminary
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+ injunction, permanent injunction and such other equitable relief as may be appropriate.
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+ in the event a party seeks the equitable relief of a temporary restraining order,
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+ preliminary injunction, permanent injunction, mandatory injunction or specific
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+ performance both parties acknowledge that they shall not be required to demonstrate
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+ the absence of an adequate remedy at law, and neither party shall be required
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+ to post bond as a precondition to obtaining a temporary restraining order or preliminary
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+ injunction. the termination of this agreement shall not relieve either party hereto
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+ from obligations which have occurred pursuant to the provisions of this agreement
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+ prior to its termination, nor shall it release either party hereto from any obligations
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+ which have been incurred as a result of operations conducted under this agreement.
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+ sentences:
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+ - 9.4 equitable relief. each party acknowledges that a breach by the -----------------
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+ other party of any confidentiality or proprietary rights provision of this agreement
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+ may cause the non-breaching party irreparable damage, for which the award of damages
44
+ would not be adequate compensation. consequently, the non-breaching party may
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+ institute an action to enjoin the breaching party from any and all acts in violation
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+ of those provisions, which remedy shall be cumulative and not exclusive, and a
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+ party may seek the entry of an injunction enjoining any breach or threatened breach
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+ of those provisions, in addition to any other relief to which the non-breaching
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+ party may be entitled at law or in equity. 4
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+ - what legal relief is available beyond injunctions?
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+ - section 4.02. other covenants 9
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+ - source_sentence: 11.6 no waiver. failure or delay in exercising any right or remedy
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+ under this agreement shall not constitute a waiver of such (or any other) right
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+ or remedy.
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+ sentences:
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+ - is injunctive relief mentioned in section 11.06 as an enforceable remedy?
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+ - section 11.06. specific performance 16
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+ - article 14. general provisions
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+ - source_sentence: 5.3 "nettaxi advertising revenue" means the gross revenue from
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+ advertising invoiced by nettaxi in a calendar quarter for advertising by third
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+ parties on the nettaxi pages, less any commissions.
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+ sentences:
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+ - 7.5.3 workers' compensation coverage plus occupational disease insurance if occupational
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+ disease coverage is required by the laws of the state where the facility is located
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+ or work is to be performed. employers liability $500,000 each accident
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+ - 5.1 "spinrecords.com's advertising revenue" means the gross revenue from advertising
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+ invoiced by spinrecords.com in a calendar quarter for advertising by third parties
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+ on the spinrecords.com pages, less any commissions not to exceed 35%.
69
+ - are commissions exceeding 35% permitted?
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+ - source_sentence: 11.8 no agency. except as expressly stated otherwise, nothing in
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+ this agreement shall create an agency, partnership or joint venture of any kind
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+ between the parties.
73
+ sentences:
74
+ - any agency relationship implied?
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+ - 9.2 relationship of parties. the parties are independent contractors -------------------------
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+ under this agreement and no other relationship is intended, including a partnership,
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+ franchise, joint venture, agency, employer/employee, fiduciary, master/servant
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+ relationship, or other special relationship. neither party shall act in a manner
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+ which expresses or implies a relationship other than that of independent contractor,
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+ nor bind the other party.
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+ - 3.4 scope of governance
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+ model-index:
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+ - name: SentenceTransformer
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+ results:
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: all nli dev
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+ type: all-nli-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.4891758450436764
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.5157614887960501
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.4823395366502089
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.4808203570072161
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.4891758450436764
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+ name: Max Accuracy
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: all nli test
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+ type: all-nli-test
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.4891758450436764
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.5157614887960501
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.4823395366502089
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.4808203570072161
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.4891758450436764
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+ name: Max Accuracy
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+ ---
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+
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+ # SentenceTransformer
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
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+
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+ ## Model Details
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+
137
+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 384 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
147
+ ### Model Sources
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+
149
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
150
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
153
+ ### Full Model Architecture
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+
155
+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ )
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+ ```
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+
162
+ ## Usage
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+
164
+ ### Direct Usage (Sentence Transformers)
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+
166
+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
172
+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("kperkins411/mpnet-base-all-nli-triplet")
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+ # Run inference
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+ sentences = [
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+ '11.8 no agency. except as expressly stated otherwise, nothing in this agreement shall create an agency, partnership or joint venture of any kind between the parties.',
181
+ '9.2 relationship of parties. the parties are independent contractors ------------------------- under this agreement and no other relationship is intended, including a partnership, franchise, joint venture, agency, employer/employee, fiduciary, master/servant relationship, or other special relationship. neither party shall act in a manner which expresses or implies a relationship other than that of independent contractor, nor bind the other party.',
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+ 'any agency relationship implied?',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 384]
187
+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
197
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
199
+ </details>
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+ -->
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+
202
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
209
+ </details>
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+ -->
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+
212
+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
222
+ #### Triplet
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+ * Dataset: `all-nli-dev`
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+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
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+ |:-------------------|:-----------|
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+ | cosine_accuracy | 0.4892 |
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+ | dot_accuracy | 0.5158 |
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+ | manhattan_accuracy | 0.4823 |
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+ | euclidean_accuracy | 0.4808 |
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+ | **max_accuracy** | **0.4892** |
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+
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+ #### Triplet
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+ * Dataset: `all-nli-test`
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+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
239
+ |:-------------------|:-----------|
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+ | cosine_accuracy | 0.4892 |
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+ | dot_accuracy | 0.5158 |
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+ | manhattan_accuracy | 0.4823 |
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+ | euclidean_accuracy | 0.4808 |
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+ | **max_accuracy** | **0.4892** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
249
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
252
+ <!--
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+ ### Recommendations
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+
255
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
256
+ -->
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+
258
+ ## Training Details
259
+
260
+ ### Training Dataset
261
+
262
+ #### Unnamed Dataset
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+
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+
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+ * Size: 35,258 training samples
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+ * Columns: <code>positive</code>, <code>negative</code>, and <code>anchor</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | positive | negative | anchor |
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+ |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 101.64 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 80.74 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.19 tokens</li><li>max: 167 tokens</li></ul> |
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+ * Samples:
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+ | positive | negative | anchor |
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+ |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>information we collect from other sources we may also receive information from other sources and combine that with information we collect through our services. for example: if you choose to link, create, or log in to your uber account with a payment provider (e.g., google wallet) or social media service (e.g., facebook), or if you engage with a separate app or website that uses our api (or whose api we use), we may receive information about you or your connections from that site or app.</code> | <code>c. the obligations specified in this article shall not apply to information for which the receiving party can reasonably demonstrate that such information: iii. becomes known to the receiving party through disclosure by sources other than the disclosing party, having a right to disclose such information,</code> | <code>what safeguards are in place to protect the information obtained from third-party sources?</code> |
276
+ | <code>each of the suppliers warrants that the products shall comply with the specifications and documentation agreed by the relevant supplier and the company in writing that is applicable to such products for the warranty period.</code> | <code>3.2 manufacturing standards the manufacturer covenants that it is and will remain for the term of this agreement in compliance with all international standards in production and manufacturing.</code> | <code>is there a guarantee from the manufacturers regarding the conformity of the items to the mutually approved written standards for a certain duration?</code> |
277
+ | <code>skype hereby grants to online bvi and the company a limited, non-exclusive, non-sublicensable (except as set forth herein), non-transferable, non-assignable (except as provided in section 14.4), royalty-free (but subject to the provisions of section 5), license during the term to use, market, provide access to, promote, reproduce and display the skype intellectual property solely (i) as incorporated in the company-skype branded application and/or the company-skype toolbar, and (ii) as incorporated in, for the development of, and for transmission pursuant to this agreement of, the company-skype branded content and the company-skype branded web site, in each case for the sole purposes (unless otherwise mutually agreed by the parties) of promoting and distributing, pursuant to this agreement, the company-skype branded application, the company-skype toolbar, the company-skype branded content and the company-skype branded web site in the territory; (a) provided, that it is understood that the company-skype branded customers will have the right under the eula to use the company- skype branded application and the company-skype toolbar and will have the right to access the company-skype branded content, the company-skype branded web site and the online bvi web site through the internet and to otherwise receive support from the company anywhere in the world, and that the company shall be permitted to provide access to and reproduce and display the skype intellectual property through the internet anywhere in the world, and (b) provided further, that online bvi and the company shall ensure that no company-skype branded customer (or potential company-skype branded customer) shall be permitted to access, using the company-skype branded application or the company-skype toolbar or through the company-skype branded web site, any skype premium features requiring payment by the company-skype branded customer (or potential company-skype branded customer), including, but not limited to, skypein, skypeout, or skype plus, unless such company-skype branded customer (or potential company-skype branded customer) uses the payment methods made available by the company pursuant to section 2.5 for the purchase of such premium features.</code> | <code>planetcad hereby grants to dassault systemes a fully-paid, non-exclusive, worldwide, revocable limited license to the server software and infrastructure for the sole purpose of (i) hosting the co-branded service and (ii) fulfilling its<omitted>obligations under this agreement.</code> | <code>what type of authorization has the video conferencing service provided to the british virgin islands-based entity and its associated organization regarding their intellectual property, with respect to the customized software and web platform, including the conditions for customer access to enhanced functionalities that incur additional charges?</code> |
278
+ * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
279
+ ```json
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+ {
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+ "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
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+ "triplet_margin": 5
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+ }
284
+ ```
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+
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+ ### Evaluation Dataset
287
+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 2,633 evaluation samples
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+ * Columns: <code>negative</code>, <code>positive</code>, and <code>anchor</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | negative | positive | anchor |
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+ |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 96.02 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 107.62 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.05 tokens</li><li>max: 512 tokens</li></ul> |
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+ * Samples:
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+ | negative | positive | anchor |
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+ |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
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+ | <code>9.1. confidentiality obligations. except as permitted elsewhere under this agreement, each party agrees to take reasonable steps (as defined below) (a) to receive and maintain the confidential information of the other party in confidence and (b) not to disclose such confidential information to any third parties, provided, the receiving party may disclose such confidential information to its employees, representatives and agents who have a need to know such information for purposes of carrying out the terms of this agreement. neither party hereto shall use all or any part of the confidential information of the other party for any purpose other than to perform its obligations under this agreement. the parties will take reasonable steps (as defined below) to ensure that their employees, representatives and agents comply with this provision. as used herein, "reasonable steps" means at least the same degree of care that the receiving party uses to protect its own confidential information, and, in any event, no less than reasonable care.</code> | <code>8.1. each party acknowledges the other's confidential information is unique and valuable and was developed or otherwise acquired by the other at great expense, and that any unauthorized disclosure or use of the other's confidential information would cause the other irreparable injury loss for which damages would be an inadequate remedy. the party agrees to hold such confidential information in strictest confidence, to use all efforts reasonable under the circumstances to maintain the secrecy thereof, and not to make use thereof other than in accordance with this agreement, and not to release or disclose confidential information to any third party without the other's prior written consent, subject to a court order, or subject to a sublicense consistent with this agreement and requiring the sublicensee to maintain the confidential information in strictest confidence, to use all efforts reasonable under the circumstances to maintain the secrecy thereof, not to make use thereof other than in accordance with the sublicense agreement, and not to release or disclose confidential information to any third party without the other's prior written consent. 6 source: legacy education alliance, inc., 10-k, 3/30/2020 certain identified information has been excluded from this exhibit because it is both (i) not material and (ii) would be competitively harmful if publicly disclosed.</code> | <code>what efforts are deemed 'reasonable under the circumstances' to protect confidential information?</code> |
302
+ | <code>14.9 no assignment. neither party may assign this agreement without the other party's prior written consent. notwithstanding the foregoing, either party may assign this agreement without the other party's prior written consent in the event of a merger, acquisition, reorganization, change in control, or sale of substantially all of the assets or business of such assigning party. any assignment in conflict with this provision shall be void.</code> | <code>2.2.1 this agreement does not limit our right, or the right of the entities, to own, license or operate any other business of any nature, whether in the lodging or hospitality industry or not, and whether under the brand, a competing brand, or otherwise. we and the entities have the right to engage in any other businesses, even if they compete with the hotel, the system, or the brand, and whether we or the entities start those businesses, or purchase, merge with, acquire, are acquired by, come under common ownership with, or associate with, such other businesses.</code> | <code>are there any restrictions on mergers or acquisitions involving other businesses?</code> |
303
+ | <code>1.2 pnc or its tpms will place specific orders for ingredients from supplier by issuing a purchase order that specifies, at minimum, the item, quantities, price, delivery dates, and delivery and payment terms (each a "purchase order").</code> | <code>1.5 supplier represents and warrants that at the time and date of delivery, the ingredients will comply with all specifications ("specifications"), a copy of which will be attached to the relevant master purchase commitment or purchase order accordingly. a specification may be updated from time to time by pnc in its sole discretion, provided pnc provides supplier with reasonable prior notice on any updates ("change notification"). within [***] from receipt of the change notification, supplier will either: (1) accept the specification change at the current price and terms; or (2) submit to pnc a proposal ("proposal") setting forth the conditions of acceptance that may include a change in price and/or other terms, including documentation to support same. within [***] the parties will discuss the proposal in good faith and exercise their best efforts to agree on the appropriate adjustment if any. pnc will not issue any purchase orders, nor be required to issue any purchase orders to supplier until pnc and supplier have agreed on required ingredient specifications and any associated price and/or term adjustment. in the event the parties fail to agree on required ingredient specifications or price and/or term adjustments despite their best good faith efforts, neither party will have any further obligation with regard to purchase or supply of those ingredients under any master purchase commitments except that pnc shall take and pay for [***] of ingredient inventory manufactured according to the then-current specification.</code> | <code>are purchase orders mandatory before agreeing on updated ingredient specifications and adjustments?</code> |
304
+ * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
305
+ ```json
306
+ {
307
+ "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
308
+ "triplet_margin": 5
309
+ }
310
+ ```
311
+
312
+ ### Training Hyperparameters
313
+ #### Non-Default Hyperparameters
314
+
315
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
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+ - `learning_rate`: 2e-05
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+ - `num_train_epochs`: 4
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `batch_sampler`: no_duplicates
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+
324
+ #### All Hyperparameters
325
+ <details><summary>Click to expand</summary>
326
+
327
+ - `overwrite_output_dir`: False
328
+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
333
+ - `per_gpu_train_batch_size`: None
334
+ - `per_gpu_eval_batch_size`: None
335
+ - `gradient_accumulation_steps`: 1
336
+ - `eval_accumulation_steps`: None
337
+ - `learning_rate`: 2e-05
338
+ - `weight_decay`: 0.0
339
+ - `adam_beta1`: 0.9
340
+ - `adam_beta2`: 0.999
341
+ - `adam_epsilon`: 1e-08
342
+ - `max_grad_norm`: 1.0
343
+ - `num_train_epochs`: 4
344
+ - `max_steps`: -1
345
+ - `lr_scheduler_type`: linear
346
+ - `lr_scheduler_kwargs`: {}
347
+ - `warmup_ratio`: 0.1
348
+ - `warmup_steps`: 0
349
+ - `log_level`: passive
350
+ - `log_level_replica`: warning
351
+ - `log_on_each_node`: True
352
+ - `logging_nan_inf_filter`: True
353
+ - `save_safetensors`: True
354
+ - `save_on_each_node`: False
355
+ - `save_only_model`: False
356
+ - `restore_callback_states_from_checkpoint`: False
357
+ - `no_cuda`: False
358
+ - `use_cpu`: False
359
+ - `use_mps_device`: False
360
+ - `seed`: 42
361
+ - `data_seed`: None
362
+ - `jit_mode_eval`: False
363
+ - `use_ipex`: False
364
+ - `bf16`: False
365
+ - `fp16`: True
366
+ - `fp16_opt_level`: O1
367
+ - `half_precision_backend`: auto
368
+ - `bf16_full_eval`: False
369
+ - `fp16_full_eval`: False
370
+ - `tf32`: None
371
+ - `local_rank`: 0
372
+ - `ddp_backend`: None
373
+ - `tpu_num_cores`: None
374
+ - `tpu_metrics_debug`: False
375
+ - `debug`: []
376
+ - `dataloader_drop_last`: False
377
+ - `dataloader_num_workers`: 0
378
+ - `dataloader_prefetch_factor`: None
379
+ - `past_index`: -1
380
+ - `disable_tqdm`: False
381
+ - `remove_unused_columns`: True
382
+ - `label_names`: None
383
+ - `load_best_model_at_end`: False
384
+ - `ignore_data_skip`: False
385
+ - `fsdp`: []
386
+ - `fsdp_min_num_params`: 0
387
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
388
+ - `fsdp_transformer_layer_cls_to_wrap`: None
389
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
390
+ - `deepspeed`: None
391
+ - `label_smoothing_factor`: 0.0
392
+ - `optim`: adamw_torch
393
+ - `optim_args`: None
394
+ - `adafactor`: False
395
+ - `group_by_length`: False
396
+ - `length_column_name`: length
397
+ - `ddp_find_unused_parameters`: None
398
+ - `ddp_bucket_cap_mb`: None
399
+ - `ddp_broadcast_buffers`: False
400
+ - `dataloader_pin_memory`: True
401
+ - `dataloader_persistent_workers`: False
402
+ - `skip_memory_metrics`: True
403
+ - `use_legacy_prediction_loop`: False
404
+ - `push_to_hub`: False
405
+ - `resume_from_checkpoint`: None
406
+ - `hub_model_id`: None
407
+ - `hub_strategy`: every_save
408
+ - `hub_private_repo`: False
409
+ - `hub_always_push`: False
410
+ - `gradient_checkpointing`: False
411
+ - `gradient_checkpointing_kwargs`: None
412
+ - `include_inputs_for_metrics`: False
413
+ - `eval_do_concat_batches`: True
414
+ - `fp16_backend`: auto
415
+ - `push_to_hub_model_id`: None
416
+ - `push_to_hub_organization`: None
417
+ - `mp_parameters`:
418
+ - `auto_find_batch_size`: False
419
+ - `full_determinism`: False
420
+ - `torchdynamo`: None
421
+ - `ray_scope`: last
422
+ - `ddp_timeout`: 1800
423
+ - `torch_compile`: False
424
+ - `torch_compile_backend`: None
425
+ - `torch_compile_mode`: None
426
+ - `dispatch_batches`: None
427
+ - `split_batches`: None
428
+ - `include_tokens_per_second`: False
429
+ - `include_num_input_tokens_seen`: False
430
+ - `neftune_noise_alpha`: None
431
+ - `optim_target_modules`: None
432
+ - `batch_eval_metrics`: False
433
+ - `batch_sampler`: no_duplicates
434
+ - `multi_dataset_batch_sampler`: proportional
435
+
436
+ </details>
437
+
438
+ ### Training Logs
439
+ | Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
440
+ |:------:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
441
+ | 0 | 0 | - | - | 0.7235 | - |
442
+ | 0.0454 | 100 | 4.2756 | 3.5710 | 0.7091 | - |
443
+ | 0.0907 | 200 | 1.7605 | 0.2244 | 0.6005 | - |
444
+ | 0.1361 | 300 | 0.0792 | 0.1934 | 0.5856 | - |
445
+ | 0.1815 | 400 | 0.0783 | 0.1707 | 0.5636 | - |
446
+ | 0.2269 | 500 | 0.067 | 0.1520 | 0.5534 | - |
447
+ | 0.2722 | 600 | 0.0748 | 0.1315 | 0.5518 | - |
448
+ | 0.3176 | 700 | 0.0673 | 0.1061 | 0.5313 | - |
449
+ | 0.3630 | 800 | 0.0348 | 0.0989 | 0.5063 | - |
450
+ | 0.4083 | 900 | 0.0614 | 0.0783 | 0.5025 | - |
451
+ | 0.4537 | 1000 | 0.0195 | 0.0735 | 0.5241 | - |
452
+ | 0.4991 | 1100 | 0.0279 | 0.0670 | 0.5093 | - |
453
+ | 0.5445 | 1200 | 0.0318 | 0.0537 | 0.5158 | - |
454
+ | 0.5898 | 1300 | 0.0281 | 0.0511 | 0.5074 | - |
455
+ | 0.6352 | 1400 | 0.0162 | 0.0520 | 0.5063 | - |
456
+ | 0.6806 | 1500 | 0.0072 | 0.0508 | 0.5028 | - |
457
+ | 0.7260 | 1600 | 0.0227 | 0.0561 | 0.4861 | - |
458
+ | 0.7713 | 1700 | 0.0162 | 0.0465 | 0.4911 | - |
459
+ | 0.8167 | 1800 | 0.0185 | 0.0440 | 0.5192 | - |
460
+ | 0.8621 | 1900 | 0.03 | 0.0452 | 0.5180 | - |
461
+ | 0.9074 | 2000 | 0.0281 | 0.0450 | 0.4839 | - |
462
+ | 0.9528 | 2100 | 0.0133 | 0.0443 | 0.4994 | - |
463
+ | 0.9982 | 2200 | 0.0154 | 0.0363 | 0.4968 | - |
464
+ | 1.0436 | 2300 | 0.0198 | 0.0355 | 0.4869 | - |
465
+ | 1.0889 | 2400 | 0.0083 | 0.1174 | 0.5222 | - |
466
+ | 1.1343 | 2500 | 0.0108 | 0.0430 | 0.4911 | - |
467
+ | 1.1797 | 2600 | 0.0079 | 0.0411 | 0.4873 | - |
468
+ | 1.2250 | 2700 | 0.0077 | 0.0437 | 0.4804 | - |
469
+ | 1.2704 | 2800 | 0.017 | 0.0331 | 0.4812 | - |
470
+ | 1.3158 | 2900 | 0.0126 | 0.0310 | 0.4979 | - |
471
+ | 1.3612 | 3000 | 0.0105 | 0.0555 | 0.4918 | - |
472
+ | 1.4065 | 3100 | 0.0161 | 0.0425 | 0.4801 | - |
473
+ | 1.4519 | 3200 | 0.0017 | 0.0274 | 0.4865 | - |
474
+ | 1.4973 | 3300 | 0.0062 | 0.0265 | 0.4808 | - |
475
+ | 1.5426 | 3400 | 0.0069 | 0.0338 | 0.4854 | - |
476
+ | 1.5880 | 3500 | 0.0038 | 0.0304 | 0.5120 | - |
477
+ | 1.6334 | 3600 | 0.0067 | 0.0320 | 0.4941 | - |
478
+ | 1.6788 | 3700 | 0.0013 | 0.0300 | 0.5013 | - |
479
+ | 1.7241 | 3800 | 0.0047 | 0.0265 | 0.5154 | - |
480
+ | 1.7695 | 3900 | 0.0068 | 0.0245 | 0.4956 | - |
481
+ | 1.8149 | 4000 | 0.005 | 0.0203 | 0.5127 | - |
482
+ | 1.8603 | 4100 | 0.0137 | 0.0240 | 0.5158 | - |
483
+ | 1.9056 | 4200 | 0.0095 | 0.0404 | 0.5028 | - |
484
+ | 1.9510 | 4300 | 0.0102 | 0.0312 | 0.4808 | - |
485
+ | 1.9964 | 4400 | 0.0056 | 0.0339 | 0.4823 | - |
486
+ | 2.0417 | 4500 | 0.0124 | 0.0250 | 0.4839 | - |
487
+ | 2.0871 | 4600 | 0.0131 | 0.0230 | 0.4945 | - |
488
+ | 2.1325 | 4700 | 0.0024 | 0.0180 | 0.5025 | - |
489
+ | 2.1779 | 4800 | 0.0078 | 0.0216 | 0.5066 | - |
490
+ | 2.2232 | 4900 | 0.0022 | 0.0181 | 0.5013 | - |
491
+ | 2.2686 | 5000 | 0.013 | 0.0200 | 0.4759 | - |
492
+ | 2.3140 | 5100 | 0.009 | 0.0175 | 0.4926 | - |
493
+ | 2.3593 | 5200 | 0.0046 | 0.0206 | 0.4880 | - |
494
+ | 2.4047 | 5300 | 0.0034 | 0.0225 | 0.4972 | - |
495
+ | 2.4501 | 5400 | 0.0006 | 0.0206 | 0.4956 | - |
496
+ | 2.4955 | 5500 | 0.0009 | 0.0275 | 0.4865 | - |
497
+ | 2.5408 | 5600 | 0.0098 | 0.0246 | 0.4873 | - |
498
+ | 2.5862 | 5700 | 0.0017 | 0.0203 | 0.4861 | - |
499
+ | 2.6316 | 5800 | 0.0004 | 0.0219 | 0.4930 | - |
500
+ | 2.6770 | 5900 | 0.001 | 0.0172 | 0.4892 | - |
501
+ | 2.7223 | 6000 | 0.002 | 0.0254 | 0.4850 | - |
502
+ | 2.7677 | 6100 | 0.0002 | 0.0242 | 0.4888 | - |
503
+ | 2.8131 | 6200 | 0.0039 | 0.0237 | 0.4877 | - |
504
+ | 2.8584 | 6300 | 0.0148 | 0.0310 | 0.5078 | - |
505
+ | 2.9038 | 6400 | 0.0 | 0.0234 | 0.4865 | - |
506
+ | 2.9492 | 6500 | 0.0036 | 0.0187 | 0.4899 | - |
507
+ | 2.9946 | 6600 | 0.0 | 0.0196 | 0.4823 | - |
508
+ | 3.0399 | 6700 | 0.0015 | 0.0166 | 0.4850 | - |
509
+ | 3.0853 | 6800 | 0.0058 | 0.0165 | 0.4877 | - |
510
+ | 3.1307 | 6900 | 0.0 | 0.0165 | 0.4869 | - |
511
+ | 3.1760 | 7000 | 0.0023 | 0.0169 | 0.4873 | - |
512
+ | 3.2214 | 7100 | 0.0 | 0.0169 | 0.4877 | - |
513
+ | 3.2668 | 7200 | 0.004 | 0.0163 | 0.4850 | - |
514
+ | 3.3122 | 7300 | 0.0015 | 0.0155 | 0.4926 | - |
515
+ | 3.3575 | 7400 | 0.0007 | 0.0136 | 0.4918 | - |
516
+ | 3.4029 | 7500 | 0.0 | 0.0128 | 0.4892 | - |
517
+ | 3.4483 | 7600 | 0.0 | 0.0128 | 0.4888 | - |
518
+ | 3.4936 | 7700 | 0.0002 | 0.0132 | 0.4964 | - |
519
+ | 3.5390 | 7800 | 0.0062 | 0.0167 | 0.4869 | - |
520
+ | 3.5844 | 7900 | 0.0008 | 0.0194 | 0.4907 | - |
521
+ | 3.6298 | 8000 | 0.0 | 0.0194 | 0.4907 | - |
522
+ | 3.6751 | 8100 | 0.0 | 0.0179 | 0.4869 | - |
523
+ | 3.7205 | 8200 | 0.0 | 0.0178 | 0.4865 | - |
524
+ | 3.7659 | 8300 | 0.0002 | 0.0155 | 0.4827 | - |
525
+ | 3.8113 | 8400 | 0.0019 | 0.0155 | 0.4842 | - |
526
+ | 3.8566 | 8500 | 0.0008 | 0.0171 | 0.4880 | - |
527
+ | 3.9020 | 8600 | 0.0026 | 0.0177 | 0.4888 | - |
528
+ | 3.9474 | 8700 | 0.0 | 0.0179 | 0.4892 | - |
529
+ | 3.9927 | 8800 | 0.0 | 0.0179 | 0.4892 | - |
530
+ | 4.0 | 8816 | - | - | - | 0.4892 |
531
+
532
+
533
+ ### Framework Versions
534
+ - Python: 3.11.9
535
+ - Sentence Transformers: 3.0.1
536
+ - Transformers: 4.41.2
537
+ - PyTorch: 2.1.2+cu121
538
+ - Accelerate: 0.31.0
539
+ - Datasets: 2.19.1
540
+ - Tokenizers: 0.19.1
541
+
542
+ ## Citation
543
+
544
+ ### BibTeX
545
+
546
+ #### Sentence Transformers
547
+ ```bibtex
548
+ @inproceedings{reimers-2019-sentence-bert,
549
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
550
+ author = "Reimers, Nils and Gurevych, Iryna",
551
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
552
+ month = "11",
553
+ year = "2019",
554
+ publisher = "Association for Computational Linguistics",
555
+ url = "https://arxiv.org/abs/1908.10084",
556
+ }
557
+ ```
558
+
559
+ #### TripletLoss
560
+ ```bibtex
561
+ @misc{hermans2017defense,
562
+ title={In Defense of the Triplet Loss for Person Re-Identification},
563
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
564
+ year={2017},
565
+ eprint={1703.07737},
566
+ archivePrefix={arXiv},
567
+ primaryClass={cs.CV}
568
+ }
569
+ ```
570
+
571
+ <!--
572
+ ## Glossary
573
+
574
+ *Clearly define terms in order to be accessible across audiences.*
575
+ -->
576
+
577
+ <!--
578
+ ## Model Card Authors
579
+
580
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
581
+ -->
582
+
583
+ <!--
584
+ ## Model Card Contact
585
+
586
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
587
+ -->
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