初始化项目，由ModelHub XC社区提供模型
Model: sudheerdunga/llm-traffic-controller Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text: What are the deadlines and deliverables listed in this project plan summary?
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- text: Quickly, just give me the dates and locations mentioned in this travel itinerary.
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- text: Convert this list of configuration parameters into a JSON object. Keys are
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parameter names, values are their settings.
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- text: My GitLab CI/CD pipeline fails at `npm install`. The error log is `[log snippet]`.
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What's wrong?
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- text: 'Three friends, Alice, Bob, and Carol, each have a favorite color: red, blue,
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or green. Alice doesn''t like red. Bob doesn''t like green. The person who likes
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blue is not Carol. Who likes which color?'
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metrics:
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- accuracy
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pipeline_tag: text-classification
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library_name: setfit
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inference: true
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base_model: sentence-transformers/all-MiniLM-L6-v2
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---
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# SetFit with sentence-transformers/all-MiniLM-L6-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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.
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The model has been trained using an efficient few-shot learning technique that involves:
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **Maximum Sequence Length:** 256 tokens
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- **Number of Classes:** 4 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples |
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|:------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| simple_chat | <ul><li>'What precisely is your nature?'</li><li>'What is your primary function?'</li><li>'Good morning.'</li></ul> |
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| extraction | <ul><li>'Summarize the main benefits of this service based on the provided marketing copy.'</li><li>"Can you just summarize the key findings from this research data list? I don't need all the numbers."</li><li>'Convert this list of configuration parameters into a JSON object. Keys are parameter names, values are their settings.'</li></ul> |
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| reasoning | <ul><li>"What's the best way to pivot our struggling brick-and-mortar bookstore to survive in the digital age?"</li><li>'Develop a decision tree for purchasing a new company car, considering budget, fuel efficiency, maintenance costs, and resale value.'</li><li>"What's 15% of 250?"</li></ul> |
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| coding | <ul><li>'Refactor this C++ legacy code to use `std::unique_ptr` and `std::shared_ptr` instead of raw pointers.'</li><li>'Refactor this spaghetti PHP script to separate business logic, presentation, and data access layers.'</li><li>"What's the fundamental difference between SQL and NoSQL databases, and when should I use each?"</li></ul> |
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## Uses
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### Direct Use for Inference
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First install the SetFit library:
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```bash
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pip install setfit
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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 setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("setfit_model_id")
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# Run inference
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preds = model("What are the deadlines and deliverables listed in this project plan summary?")
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```
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<!--
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### Downstream Use
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*List how someone could finetune this model on their own dataset.*
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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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## 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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## Training Details
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 1 | 13.5101 | 41 |
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| Label | Training Sample Count |
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|:------------|:----------------------|
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| simple_chat | 48 |
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| extraction | 50 |
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| reasoning | 50 |
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| coding | 50 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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- num_epochs: (1, 1)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 10
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- body_learning_rate: (2e-05, 1e-05)
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- head_learning_rate: 0.01
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
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- use_amp: True
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- warmup_proportion: 0.1
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- l2_weight: 0.01
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- seed: 42
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- evaluation_strategy: no
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- eval_max_steps: -1
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- load_best_model_at_end: False
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0040 | 1 | 0.5538 | - |
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| 0.2016 | 50 | 0.2712 | - |
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| 0.4032 | 100 | 0.1337 | - |
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| 0.6048 | 150 | 0.0604 | - |
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| 0.8065 | 200 | 0.0284 | - |
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### Framework Versions
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- Python: 3.9.6
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- SetFit: 1.1.3
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- Sentence Transformers: 5.1.2
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- Transformers: 4.57.6
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- PyTorch: 2.8.0
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- Datasets: 4.5.0
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- Tokenizers: 0.22.1
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## Citation
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### BibTeX
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```bibtex
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@article{https://doi.org/10.48550/arxiv.2209.11055,
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doi = {10.48550/ARXIV.2209.11055},
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url = {https://arxiv.org/abs/2209.11055},
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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title = {Efficient Few-Shot Learning Without Prompts},
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publisher = {arXiv},
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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<!--
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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<!--
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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-->
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