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--- |
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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:208 |
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- loss:BatchSemiHardTripletLoss |
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base_model: BAAI/bge-base-en |
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widget: |
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- source_sentence: ' |
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Name : Vigilant Protec |
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Category: Consulting Services, Cybersecurity Solutions |
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Department: Legal |
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Location: London, UK |
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Amount: 1987.65 |
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Card: Global Compliance Enhancement |
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Trip Name: unknown |
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' |
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sentences: |
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- ' |
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Name : Rosetta Tech |
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Category: Technology Supplies, Software Solutions |
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Department: Research & Development |
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Location: Hamburg, Germany |
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Amount: 2129.49 |
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Card: Advanced Research Toolkit Acquisition |
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Trip Name: unknown |
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' |
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- ' |
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Name : Ikebana Studio |
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Category: Office Decor Services, Art Supplies |
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Department: All Departments |
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Location: Kyoto, Japan |
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Amount: 789.45 |
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Card: Creative Work Environment Initiative |
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Trip Name: unknown |
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' |
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- ' |
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Name : Analytix Global Solutions |
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Category: Business Intelligence Services, Regulatory Compliance Tools |
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Department: Finance |
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Location: London, UK |
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Amount: 1323.67 |
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Card: Financial Compliance Enhancement |
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Trip Name: unknown |
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' |
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- source_sentence: ' |
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Name : La Gourmanderie Collective |
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Category: Culinary Consulting, Team Building Activities |
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Department: Marketing |
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Location: Paris, France |
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Amount: 1468.77 |
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Card: Innovative Cuisine Workshop |
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Trip Name: unknown |
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' |
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sentences: |
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- ' |
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Name : Gandalf |
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Category: Financial Services, Consulting |
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Department: Finance |
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Location: Singapore |
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Amount: 457.29 |
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Card: Financial Advisory Services |
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Trip Name: unknown |
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' |
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- ' |
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Name : Anthro Insights |
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Category: Talent Acquisition Services, Corporate Education Programs |
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Department: Human Resource |
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Location: London, UK |
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Amount: 1440.75 |
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Card: Diversity & Inclusion |
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Trip Name: unknown |
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' |
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- ' |
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Name : Baku |
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Category: Ride Sharing |
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Department: Sales |
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Location: Baku, Azerbaijan |
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Amount: 1247.88 |
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Card: Client Engagement Activities |
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Trip Name: unknown |
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' |
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- source_sentence: ' |
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Name : Nimbus Networks Inc. |
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Category: Cloud Services, Application Hosting |
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Department: Research & Development |
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Location: Austin, TX |
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Amount: 1134.67 |
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Card: NextGen Application Deployment |
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Trip Name: unknown |
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' |
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sentences: |
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- ' |
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Name : CleverInsight Solutions |
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Category: Business Process Optimization |
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Department: Finance |
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Location: Toronto, Canada |
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Amount: 2127.45 |
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Card: Quarterly Insights & Efficiency Project |
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Trip Name: unknown |
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' |
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- ' |
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Name : SynergyBridge |
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Category: Customer Experience Software, Revenue Growth Tools |
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Department: Sales |
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Location: San Francisco, CA |
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Amount: 1558.72 |
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Card: Customer Relationship Enhancement |
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Trip Name: unknown |
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' |
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- ' |
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Name : CloudArc |
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Category: Cloud Storage Solutions, Internet Services |
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Department: Engineering |
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Location: Toronto, Canada |
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Amount: 1573.63 |
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Card: Infrastructure Scaling |
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Trip Name: unknown |
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' |
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- source_sentence: ' |
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Name : GigaTrend |
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Category: Data Services, Cloud Software Solutions |
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Department: Research & Development |
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Location: London, UK |
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Amount: 1345.67 |
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Card: Data-Driven Innovation Project |
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Trip Name: unknown |
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' |
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sentences: |
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- ' |
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Name : Global Wellness Network |
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Category: Corporate Wellness Programs, Employee Engagement |
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Department: HR |
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Location: Berlin, Germany |
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Amount: 1285.75 |
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Card: Wellness and Engagement Program |
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Trip Name: unknown |
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' |
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- ' |
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Name : TechXperts Global |
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Category: IT Services, Consulting |
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Department: IT Operations |
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Location: Berlin, Germany |
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Amount: 987.49 |
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Card: Quarterly System Assessment |
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Trip Name: unknown |
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' |
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- ' |
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Name : InterStep Insight Reports |
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Category: Data Services, Research Publications |
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Department: Marketing |
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Location: Toronto, Canada |
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Amount: 1248.76 |
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Card: Strategic Market Research |
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Trip Name: unknown |
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' |
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- source_sentence: ' |
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Name : Viacom Solutions |
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Category: Telecom Hardware, Network Architecture |
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Department: Engineering |
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Location: Tokyo, Japan |
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Amount: 1450.67 |
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Card: Global Network Optimization Project |
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Trip Name: unknown |
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' |
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sentences: |
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- ' |
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Name : CloudMetric Solutions |
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Category: Data Analytics, Virtual Infrastructure Management |
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Department: Engineering |
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Location: Toronto, Canada |
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Amount: 1644.75 |
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Card: Real-Time Resource Monitoring |
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Trip Name: unknown |
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' |
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- ' |
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Name : Il Vino e L''Arte |
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Category: Culinary Experience, Cultural Event Venue |
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Department: Marketing |
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Location: Rome, Italy |
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Amount: 748.32 |
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Card: Cultural Engagement Dinner |
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Trip Name: unknown |
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' |
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- ' |
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Name : Pardalis Digital |
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Category: Data Analytics Platform, Professional Networking Service |
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Department: Sales |
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Location: Dublin, Ireland |
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Amount: 1456.75 |
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Card: Sales Intelligence & Networking Platform |
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Trip Name: unknown |
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' |
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pipeline_tag: sentence-similarity |
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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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model-index: |
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- name: SentenceTransformer based on BAAI/bge-base-en |
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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: bge base en train |
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type: bge-base-en-train |
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metrics: |
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- type: cosine_accuracy |
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value: 0.8461538461538461 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.15384615384615385 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.8557692307692307 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.8461538461538461 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.8557692307692307 |
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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: bge base en eval |
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type: bge-base-en-eval |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9545454545454546 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.045454545454545456 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.9545454545454546 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.9545454545454546 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.9545454545454546 |
|
name: Max Accuracy |
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--- |
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|
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# SentenceTransformer based on BAAI/bge-base-en |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 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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|
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **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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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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### Direct Usage (Sentence Transformers) |
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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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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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# Download from the 🤗 Hub |
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model = SentenceTransformer("ivanleomk/finetuned-bge-base-en") |
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# Run inference |
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sentences = [ |
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'\nName : Viacom Solutions\nCategory: Telecom Hardware, Network Architecture\nDepartment: Engineering\nLocation: Tokyo, Japan\nAmount: 1450.67\nCard: Global Network Optimization Project\nTrip Name: unknown\n', |
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'\nName : Pardalis Digital\nCategory: Data Analytics Platform, Professional Networking Service\nDepartment: Sales\nLocation: Dublin, Ireland\nAmount: 1456.75\nCard: Sales Intelligence & Networking Platform\nTrip Name: unknown\n', |
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"\nName : Il Vino e L'Arte\nCategory: Culinary Experience, Cultural Event Venue\nDepartment: Marketing\nLocation: Rome, Italy\nAmount: 748.32\nCard: Cultural Engagement Dinner\nTrip Name: unknown\n", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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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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#### Triplet |
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* Dataset: `bge-base-en-train` |
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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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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.8462 | |
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| dot_accuracy | 0.1538 | |
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| manhattan_accuracy | 0.8558 | |
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| euclidean_accuracy | 0.8462 | |
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| **max_accuracy** | **0.8558** | |
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|
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#### Triplet |
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* Dataset: `bge-base-en-eval` |
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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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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.9545 | |
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| dot_accuracy | 0.0455 | |
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| manhattan_accuracy | 0.9545 | |
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| euclidean_accuracy | 0.9545 | |
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| **max_accuracy** | **0.9545** | |
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|
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<!-- |
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## Bias, Risks and Limitations |
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*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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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 208 training samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 208 samples: |
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| | sentence | label | |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| type | string | int | |
|
| details | <ul><li>min: 33 tokens</li><li>mean: 39.66 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~4.81%</li><li>1: ~5.29%</li><li>2: ~6.25%</li><li>3: ~2.40%</li><li>4: ~3.85%</li><li>5: ~4.33%</li><li>6: ~3.85%</li><li>7: ~2.40%</li><li>8: ~4.81%</li><li>9: ~3.37%</li><li>10: ~3.85%</li><li>11: ~3.85%</li><li>12: ~4.81%</li><li>13: ~4.81%</li><li>14: ~5.29%</li><li>15: ~3.37%</li><li>16: ~4.81%</li><li>17: ~4.33%</li><li>18: ~3.85%</li><li>19: ~1.92%</li><li>20: ~2.88%</li><li>21: ~2.88%</li><li>22: ~3.37%</li><li>23: ~0.96%</li><li>24: ~4.33%</li><li>25: ~2.40%</li><li>26: ~0.96%</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code><br>Name : Global Insights Group<br>Category: Subscriptions & Memberships, Data Services & Analytics<br>Department: Marketing<br>Location: London, UK<br>Amount: 1245.67<br>Card: Marketing Intelligence Fund<br>Trip Name: unknown<br></code> | <code>0</code> | |
|
| <code><br>Name : CyberGuard Provisions<br>Category: Security Software Solutions, Data Protection Services<br>Department: Information Security<br>Location: San Francisco, CA<br>Amount: 879.92<br>Card: Digital Fortress Action Plan<br>Trip Name: unknown<br></code> | <code>1</code> | |
|
| <code><br>Name : Apex Innovations Group<br>Category: Business Consulting, Training Services<br>Department: Executive<br>Location: Sydney, Australia<br>Amount: 1575.34<br>Card: Leadership Development Program<br>Trip Name: unknown<br></code> | <code>2</code> | |
|
* Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) |
|
|
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### Evaluation Dataset |
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|
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#### Unnamed Dataset |
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|
|
|
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* Size: 52 evaluation samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 52 samples: |
|
| | sentence | label | |
|
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| type | string | int | |
|
| details | <ul><li>min: 32 tokens</li><li>mean: 40.13 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~5.77%</li><li>1: ~1.92%</li><li>2: ~3.85%</li><li>3: ~1.92%</li><li>4: ~1.92%</li><li>5: ~1.92%</li><li>6: ~5.77%</li><li>8: ~3.85%</li><li>9: ~7.69%</li><li>10: ~5.77%</li><li>12: ~3.85%</li><li>13: ~5.77%</li><li>14: ~3.85%</li><li>15: ~1.92%</li><li>16: ~9.62%</li><li>17: ~1.92%</li><li>18: ~1.92%</li><li>19: ~3.85%</li><li>20: ~1.92%</li><li>21: ~3.85%</li><li>22: ~5.77%</li><li>23: ~3.85%</li><li>24: ~5.77%</li><li>25: ~5.77%</li></ul> | |
|
* Samples: |
|
| sentence | label | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------| |
|
| <code><br>Name : Viacom Solutions<br>Category: Telecom Hardware, Network Architecture<br>Department: Engineering<br>Location: Tokyo, Japan<br>Amount: 1450.67<br>Card: Global Network Optimization Project<br>Trip Name: unknown<br></code> | <code>9</code> | |
|
| <code><br>Name : Vista Cascades Resort<br>Category: Hospitality, Event Hosting<br>Department: Sales<br>Location: Orlando, FL<br>Amount: 1823.45<br>Card: Annual Sales Retreat<br>Trip Name: Q3 Strategy Session<br></code> | <code>12</code> | |
|
| <code><br>Name : ActiveHealth CoLab<br>Category: Health Services, Wellness Solutions<br>Department: HR<br>Location: Amsterdam, Netherlands<br>Amount: 745.32<br>Card: Corporate Wellness Partnership<br>Trip Name: unknown<br></code> | <code>23</code> | |
|
* Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) |
|
|
|
### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `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`: 5 |
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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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|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `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`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | bge-base-en-eval_max_accuracy | bge-base-en-train_max_accuracy | |
|
|:-----:|:----:|:-----------------------------:|:------------------------------:| |
|
| 0 | 0 | - | 0.8558 | |
|
| 5.0 | 65 | 0.9545 | - | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.11.10 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.45.2 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.1.1 |
|
- Datasets: 3.1.0 |
|
- Tokenizers: 0.20.3 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### BatchSemiHardTripletLoss |
|
```bibtex |
|
@misc{hermans2017defense, |
|
title={In Defense of the Triplet Loss for Person Re-Identification}, |
|
author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
|
year={2017}, |
|
eprint={1703.07737}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
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