mrzaizai2k/model_use_planner

SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:

1. Fine-tuning a Sentence Transformer 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/paraphrase-multilingual-MiniLM-L12-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 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	'Tìm trạm sạc gần nhất và bắt đầu dẫn đường' 'Please open the driver door' 'スポーツモードをオンにしてロック音楽を再生して'
1	'Choose that one and navigate there' '근처 카페 찾고 거기로 안내해 주세요' '그 노래 다시 틀고 볼륨 높여 주세요'

Evaluation

Metrics

Label	Accuracy
all	1.0

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("mrzaizai2k/model_use_planner")
# Run inference
preds = model("彼に電話して住所を送って")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	6.2	17

Label	Training Sample Count
0	30
1	30

Training Hyperparameters

• batch_size: (8, 8)
• 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
0.0043	1	0.2053	-
0.2146	50	0.2011	-
0.4292	100	0.008	-
0.6438	150	0.0031	-
0.8584	200	0.0005	-
1.0	233	-	0.0688
1.0730	250	0.0005	-
1.2876	300	0.0002	-
1.5021	350	0.0002	-
1.7167	400	0.0003	-
1.9313	450	0.0002	-
2.0	466	-	0.1132
2.1459	500	0.0002	-
2.3605	550	0.0003	-
2.5751	600	0.0001	-
2.7897	650	0.0002	-
3.0	699	-	0.1015
3.0043	700	0.0002	-
3.2189	750	0.0002	-
3.4335	800	0.0002	-
3.6481	850	0.0001	-
3.8627	900	0.0001	-
4.0	932	-	0.0928

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.19
• SetFit: 1.0.3
• Sentence Transformers: 5.2.3
• Transformers: 4.41.2
• PyTorch: 2.10.0+cu128
• 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}
}