metadata
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 model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 256 tokens
- Number of Classes: 2 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 |
---|---|
0 |
|
1 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.6667 |
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("chibao24/model_routing_few_shot")
# Run inference
preds = model("Thủ đô của nước Pháp là gì?")
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
@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}
}