metadata
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
数字孪生包含三个核心元素：物理系统（产品、流程、网络）、代表它的虚拟模型以及实时更新模型的数据连接。虚拟模型反映了物理系统的当前状态和行为，并与来自传感器和物联网设备的数据持续同步。这种设置允许数字孪生模拟和预测物理系统在各种条件下的性能。将这三个组件结合在一起需要几项关键技术。首先，数据的收集和使用涉及云计算以及用于存储和处理的平台。其次，需要人工智能和机器学习来启用提供高级分析和准确虚拟模型的模拟模型。最后，增强现实和虚拟现实实现了数字模型和物理系统之间的高级可视化和交互。
- text: >-
这项技术使 BMO 能够在模型内完成远程站点评估和操作测试，而不会中断服务。结果：15 个月内节省了 50 多万美元，在 503 个地点收回了
6,000 个调查小时，并集中了分行资源和文档。
- text: >-
在银行业，数字孪生可能看起来像是增强的情景分析。如果您是这么想的，我们不会责怪您。但关键的区别就在这里：数据。传统情景分析依赖于静态数据，而数字孪生则使用实时动态数据并促进双向数据流。这意味着数字孪生可以利用其产生的洞察并触发更改以优化其复制的物理系统，而情景分析仅提供必须单独审查和采取行动的输出。
- text: '## 数字孪生解决了什么问题？'
- text: >-
数字孪生的使用始于 20 世纪 60 年代，当时 NASA 使用孪生模型在太空任务期间监控和调整航天器。最近，拜登政府宣布投资 2.85
亿美元用于半导体制造的数字孪生技术，因为该技术有潜力提高美国的效率、创新和弹性。
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8571428571428571
name: Accuracy
SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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-multilingual-MiniLM-L12-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 14 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| 6 |
|
| 7 |
|
| 9 |
|
| 3 |
|
| 0 | |
| 11 |
|
| 13 |
|
| 10 |
|
| 5 |
|
| 2 |
|
| 4 |
|
| 12 |
|
| 1 |
|
| 8 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.8571 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mikeee/setfit-model")
# Run inference
preds = model("## 数字孪生解决了什么问题？")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 50.7143 | 156 |
| Label | Training Sample Count |
|---|---|
| 0 | 1 |
| 1 | 1 |
| 2 | 1 |
| 3 | 1 |
| 4 | 1 |
| 5 | 1 |
| 6 | 1 |
| 7 | 1 |
| 8 | 1 |
| 9 | 1 |
| 10 | 1 |
| 11 | 1 |
| 12 | 1 |
| 13 | 1 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 4
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.1429 | 1 | 0.0039 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2
Citation
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}
}