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	'what is microservices' 'What is the capital of France?' 'Write a Python function that calculates the factorial of a number.'
1	'Tell me the difference between microservice and service based architecture' '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.' 'Analyze the time complexity of the merge sort algorithm.'

Label

Examples

'what is microservices'
'What is the capital of France?'
'Write a Python function that calculates the factorial of a number.'

'Tell me the difference between microservice and service based architecture'
'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.'
'Analyze the time complexity of the merge sort algorithm.'

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}
}