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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
- accuracy
widget:
- text: Giải thích sự khác biệt giữa mô hình học có giám sát và không giám sát. Cung
cấp ví dụ cho từng loại. (ít nhất 150 từ)
- text: What is White-box testing?
- text: 'Gọi X là dòng đời (thời gian làm việc tốt) của sản phẩm ổ cứng máy tính (tính
theo năm). Một ổ cứng loại
ABC có xác suất làm việc tốt sau 9 năm là 0.1. Giả sử hàm mật độ xác suất của
X là f(x) = a
(x+1)b cho x ≥ 0
với a > 0 và b > 1. Hãy Tính a, b?'
- text: What are the benefits of using cloud storage?
- text: What are the benefits of using cloud storage?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1.0
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-v2
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.
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/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co./datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'Thủ đô của nước Pháp là gì?'</li><li>'What are the benefits of using cloud storage?'</li><li>'Phần mềm kiểm thử là gì?'</li></ul> |
| 1 | <ul><li>'Gọi X là dòng đời (thời gian làm việc tốt) của sản phẩm ổ cứng máy tính (tính theo năm). Một ổ cứng loại\nABC có xác suất làm việc tốt sau 9 năm là 0.1. Giả sử hàm mật độ xác suất của X là f(x) = a\n(x+1)b cho x ≥ 0\nvới a > 0 và b > 1. Hãy Tính a, b?'</li><li>'For the expression "(a AND (b OR c))", which of the following test-cases is Multiple Condition Coverage (MCC)?\nCâu hỏi 8Trả lời\n\na.\n04 test cases in (a,b,c) format: "(true,true,true)", "(true,true,false)", "(true,false,true)" and "(false,true,true)"\n\nb.\n02 test cases in (a,b,c) format: "(true,true,true)" and "(false,true,false)"\n\nc.\n06 test cases in (a,b,c)format: "(true,true,true)", "(true,true,false)", "(true,false,true)", "(true,false,false)", "(false,true,true)", and "(false,false,false)"\n\nd.\n08 test cases for all combination of a=true/false, b=true/false, c=true/false'</li><li>'explain in detail what is FFT and the complexity of it'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## 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("chibao24/model_routing_few_shot")
# Run inference
preds = model("What is White-box testing?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 24.2414 | 115 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 14 |
| 1 | 15 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0088 | 1 | 0.2899 | - |
| 0.4425 | 50 | 0.2103 | - |
| 0.8850 | 100 | 0.1249 | - |
| **1.0** | **113** | **-** | **0.0799** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## 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}
}
```
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