--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: The HR Learning & Development Specialist designs and delivers workforce upskilling programmes that align with Singapore’s SkillsFuture initiatives, focusing on employee growth, well-being, and career progression. He/She facilitates training workshops, coaches managers on supportive leadership, and counsels staff on learning pathways, ensuring inclusive and accessible development opportunities across diverse teams. He/She collaborates with department heads to identify skill gaps and mobilises internal and external resources to deliver targeted interventions, including digital learning platforms and micro-credentialing solutions. He/She maintains accurate records of training participation and compliance with MOM’s Tripartite Guidelines, applying structured processes to track outcomes and report impact. He is empathetic, a natural communicator, and adept at building trust with employees at all levels. He thrives in Singapore’s dynamic labour landscape, where continuous learning is prioritised and HR practices must reflect both human-centric values and regulatory precision. - text: The Systems Compliance Analyst is tasked with investigating anomalies in digital workflow logs across enterprise systems, identifying root causes through data pattern analysis, and developing corrective models to align operational processes with Singapore’s PDPA and MAS regulatory standards. He/She manually audits hardware and network infrastructure configurations, verifying physical server integrity, cable terminations, and firmware compatibility using diagnostic tools, while ensuring all system changes are documented in structured compliance repositories. He must interpret complex audit findings, translate technical deviations into actionable remediation plans, and enforce protocol adherence through documented control frameworks. He operates within Singapore’s tightly regulated financial technology and logistics sectors, where real-time system uptime and data integrity are critical. He is detail-oriented with strong analytical reasoning, proficient in SQL, SIEM tools, and endpoint monitoring systems, and demonstrates precision in record-keeping under strict audit timelines. His work demands rigorous logical deduction, hands-on technical troubleshooting, and unwavering adherence to procedural precision. - text: The Industrial Process Auditor evaluates the efficiency and safety of manufacturing equipment on Singapore’s automated production floors, investigating deviations in machine performance data to pinpoint mechanical or calibration inconsistencies. He/She performs physical inspections of conveyor systems, pneumatic actuators, and robotic arms, using precision measurement tools to verify tolerances and record maintenance logs, while correlating observed wear patterns with digital sensor outputs to forecast failures. He designs standardized inspection checklists and updates machine-specific compliance templates to align with SCCS and MOM occupational safety directives. He operates within Singapore’s high-density electronics and pharmaceutical manufacturing hubs, where minute deviations impact batch quality and regulatory certification. He is technically adept in PLC diagnostics, metrology instruments, and CMMS platforms, with a methodical approach to data logging and root-cause analysis. He combines intuitive mechanical insight with disciplined documentation practices, ensuring operational integrity through empirical observation and structured reporting. - text: The Talent Strategy Analyst designs and evaluates human capital initiatives by interrogating workforce data to uncover patterns in performance, mobility, and attrition, providing strategic recommendations that shape hiring, development, and retention policies. He/She collaborates with business units to identify emerging skill demands in Singapore’s high-growth sectors, using advanced analytics to model future workforce needs and align training investments with SkillsFuture priorities. He/She ensures all talent frameworks comply with statutory reporting requirements and internal audit controls, maintaining precise records of deployment outcomes and metrics. He influences c-suite decisions through compelling narratives grounded in statistical insights and market benchmarking, often negotiating resource allocation for high-impact programmes. He operates with precision in structured reporting environments yet thrives in ambiguity, turning ambiguous data into clear strategic pathways that balance innovation with governance. His leadership is trusted for its clarity, consistency, and commitment to evidence-based decision-making. - text: The HR Transformation Lead drives organisational change by aligning talent strategies with business objectives, leveraging data-driven insights to redesign workforce models and influence senior leadership on people initiatives. He/She leads cross-functional teams to implement HR technology platforms, piloting digital tools such as AI-powered talent analytics and workforce planning systems prevalent in Singapore’s tech-forward industries. He/She investigates talent trends, interprets workforce metrics, and constructs predictive models to anticipate skill gaps and retention risks in sectors like finance and logistics. He ensures strict adherence to Singapore’s Tripartite Guidelines and Tripartite Standards while managing budgets and stakeholder expectations to secure buy-in across departments. He is a persuasive communicator who translates complex HR analytics into actionable business cases, consistently delivering scalable solutions that enhance operational efficiency and employee engagement. His approach is methodical, grounded in evidence, and rigorously compliant with employment regulations. metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.25 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 4 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | IRC | | | EIC | | | ESC | | | SEC | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.25 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("dnth/setfit-riasec-classifier-subset") # Run inference preds = model("The HR Learning & Development Specialist designs and delivers workforce upskilling programmes that align with Singapore’s SkillsFuture initiatives, focusing on employee growth, well-being, and career progression. He/She facilitates training workshops, coaches managers on supportive leadership, and counsels staff on learning pathways, ensuring inclusive and accessible development opportunities across diverse teams. He/She collaborates with department heads to identify skill gaps and mobilises internal and external resources to deliver targeted interventions, including digital learning platforms and micro-credentialing solutions. He/She maintains accurate records of training participation and compliance with MOM’s Tripartite Guidelines, applying structured processes to track outcomes and report impact. He is empathetic, a natural communicator, and adept at building trust with employees at all levels. He thrives in Singapore’s dynamic labour landscape, where continuous learning is prioritised and HR practices must reflect both human-centric values and regulatory precision.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 114 | 126.625 | 146 | | Label | Training Sample Count | |:------|:----------------------| | IRC | 10 | | EIC | 10 | | ESC | 10 | | SEC | 10 | ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (5, 5) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0067 | 1 | 0.1555 | - | | 0.3333 | 50 | 0.1366 | - | | 0.6667 | 100 | 0.0117 | - | | 1.0 | 150 | 0.001 | 0.0772 | | 1.3333 | 200 | 0.0006 | - | | 1.6667 | 250 | 0.0003 | - | | 2.0 | 300 | 0.0003 | 0.0802 | | 2.3333 | 350 | 0.0002 | - | | 2.6667 | 400 | 0.0002 | - | | 3.0 | 450 | 0.0001 | 0.0805 | | 3.3333 | 500 | 0.0001 | - | | 3.6667 | 550 | 0.0001 | - | | 4.0 | 600 | 0.0001 | 0.0810 | | 4.3333 | 650 | 0.0001 | - | | 4.6667 | 700 | 0.0001 | - | | 5.0 | 750 | 0.0001 | 0.0814 | ### Framework Versions - Python: 3.12.8 - SetFit: 1.1.3 - Sentence Transformers: 5.1.1 - Transformers: 4.56.2 - PyTorch: 2.8.0+cu128 - Datasets: 4.1.1 - Tokenizers: 0.22.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```