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livinNector/
m-minilm-l12-h384-dra-tam-mal-aw-setfit-finetune

Name: m-minilm-l12-h384-dra-tam-mal-aw-setfit-finetune
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

microsoft/Multilingual-MiniLM-L12-H384

finetune

model-index

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SetFit with microsoft/Multilingual-MiniLM-L12-H384

This is a SetFit model that can be used for Text Classification. This SetFit model uses microsoft/Multilingual-MiniLM-L12-H384 as the Sentence Transformer embedding model. A SetFitHead 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: microsoft/Multilingual-MiniLM-L12-H384
Classification head: a SetFitHead instance
Maximum Sequence Length: 512 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

'Madam divyaக்கு 1கிலோ colgate paste வாங்கி கொடுங்க videoவில் வாய் நாற்றம் தாங்கல'
'ഇനി ഇതുപോലുള്ള സാദനം ആയി വന്നാൽ ഞാൻ ഡിസ്ക്രൈബ് ചെയ്യും'
'ஏன்பா behindwoods தயவு செய்து இப்படி கேவலமான programme ஐ telecast பண்ணாதீங்க ராஜா'

'கம்பிய பழுக்க வச்சு சூத்துல வைங்க சார்'
'ഇനി റെഡ് സ്ട്രീറ്റ്റിലും കൂടി പോയി ഇന്റർവ്യൂ എടുക്ക് ചേച്ചി'
'നിങ്ങൾ പണ്ടേ വിവരക്കേടാണ്. ബോധം ഇല്ലായ്മ കാണിക്കാതെ സ്ത്രീ. മറ്റുള്ളവരുടെ കിഡ്ണി കളയിപ്പിച്ചിട്ടുവേണോ നിന്റെ കഞ്ഞി കുടിക്കൽ.'

Evaluation

Metrics

Label	Accuracy
all	0.6875

Uses

Direct Use for Inference

First install the SetFit library:

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("livinNector/m-minilm-l12-h384-dra-tam-mal-aw-setfit-finetune")
# Run inference
preds = model("\"ഒരുപാട് ഇഷ്ട്ടപെട്ട പോലെ ഒരുപാട് വെറുത്ത് പോയി, ഡോക്ടറെ കിട്ടാനുള്ള ഭാഗ്യം ഇല്ല\"")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	15.4375	123

Label	Training Sample Count
0	132
1	124

Training Hyperparameters

batch_size: (64, 64)
num_epochs: (10, 10)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 2
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.01
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: True
use_amp: False
warmup_proportion: 0.1
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0625	1	0.422	-
0.625	10	-	0.4029
1.25	20	-	0.2799
1.875	30	-	0.2464
2.5	40	-	0.2480
3.125	50	0.2964	0.2451
3.75	60	-	0.2368
4.375	70	-	0.2444
5.0	80	-	0.2393
5.625	90	-	0.2382
6.25	100	0.1825	0.2395
6.875	110	-	0.2405
7.5	120	-	0.2424
8.125	130	-	0.2468
8.75	140	-	0.2432
9.375	150	0.1308	0.2451
10.0	160	-	0.2454

Framework Versions

Python: 3.10.12
SetFit: 1.1.0
Sentence Transformers: 3.3.1
Transformers: 4.45.2
PyTorch: 2.5.1+cu121
Datasets: 3.2.0
Tokenizers: 0.20.3

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}
}