|
--- |
|
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: What are the benefits of using cloud storage? |
|
- text: 'Which of the following is a Code-Based Test Coverage Metrics(E. F. Miller, |
|
1977 dissertation)? |
|
|
|
Câu hỏi 1Trả lời |
|
|
|
|
|
a. |
|
|
|
C1c: Every condition outcome |
|
|
|
|
|
b. |
|
|
|
MMCC: Multiple Module condition coverage |
|
|
|
|
|
c. |
|
|
|
Cx - Every "x" statement ("x" can be single, double, triple) |
|
|
|
|
|
d. |
|
|
|
C2: C0 coverage + loop coverage' |
|
- 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: Thủ đô của nước Pháp là gì? |
|
- text: How to prove a problem is NP complete problem |
|
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: 0.6666666666666666 |
|
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>'what is microservices'</li><li>'What is the capital of France?'</li><li>'Write a Python function that calculates the factorial of a number.'</li></ul> | |
|
| 1 | <ul><li>'Tell me the difference between microservice and service based architecture'</li><li>'What is White-box testing?\nCâu hỏi 7Trả lời\n\na.\nAll of the other answers.\n\nb.\nA testing technique in which internal structure, design and coding of software are tested.\n\nc.\nIts foundation is to execute every part of the code at least once.\n\nd.\nIn this technique, code is visible to testers.'</li><li>'Analyze the time complexity of the merge sort algorithm.'</li></ul> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 0.6667 | |
|
|
|
## 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("Thủ đô của nước Pháp là gì?") |
|
``` |
|
|
|
<!-- |
|
### Downstream Use |
|
|
|
*List how someone could finetune this model on their own dataset.* |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Set Metrics |
|
| Training set | Min | Median | Max | |
|
|:-------------|:----|:--------|:----| |
|
| Word count | 3 | 20.1613 | 115 | |
|
|
|
| Label | Training Sample Count | |
|
|:------|:----------------------| |
|
| 0 | 16 | |
|
| 1 | 15 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (4, 4) |
|
- num_epochs: (4, 4) |
|
- 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 | |
|
|:-------:|:-------:|:-------------:|:---------------:| |
|
| 0.0078 | 1 | 0.5129 | - | |
|
| 0.3906 | 50 | 0.2717 | - | |
|
| 0.7812 | 100 | 0.0941 | - | |
|
| **1.0** | **128** | **-** | **0.1068** | |
|
| 1.1719 | 150 | 0.0434 | - | |
|
| 1.5625 | 200 | 0.0075 | - | |
|
| 1.9531 | 250 | 0.005 | - | |
|
| 2.0 | 256 | - | 0.1193 | |
|
| 2.3438 | 300 | 0.0088 | - | |
|
| 2.7344 | 350 | 0.0027 | - | |
|
| 3.0 | 384 | - | 0.1587 | |
|
| 3.125 | 400 | 0.0023 | - | |
|
| 3.5156 | 450 | 0.0013 | - | |
|
| 3.9062 | 500 | 0.0011 | - | |
|
| 4.0 | 512 | - | 0.1103 | |
|
|
|
* 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
