metadata
base_model: akhooli/sbert_ar_nli_500k_norm
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
هل كان من الممكن أن أكون مهووسًا أكثر عندما سمعت أن أبولو 13 سيتم إصداره
بتنسيق آيماكس؟
- text: 'يبدو أنه تم إعادة تجميعه من أرضية غرفة التقطيع لأي صابون نهاري. '
- text: 'تخريبي، تأملي، سريري وشاعري، مدرس البيانو هو عمل جريء من العبقرية. '
- text: >-
يثبت نولان أنه يستطيع أن يتعارض مع أفضلهم ويدير فيلم تشويق تقليدي أكثر
بينما يتخلى عن القليل من صرامته الفكرية أو رباطة جأشه الإبداعية.
- text: >-
فيلم بائس يختزل الحرب العالمية الثانية إلى سعي رجل واحد للعثور على شعلة
قديمة.
inference: true
model-index:
- name: SetFit with akhooli/sbert_ar_nli_500k_norm
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.56
name: Accuracy
SetFit with akhooli/sbert_ar_nli_500k_norm
This is a SetFit model that can be used for Text Classification. This SetFit model uses akhooli/sbert_ar_nli_500k_norm 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: akhooli/sbert_ar_nli_500k_norm
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 4 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 |
|
| 0 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.56 |
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("akhooli/setfit")
# Run inference
preds = model("يبدو أنه تم إعادة تجميعه من أرضية غرفة التقطيع لأي صابون نهاري. ")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 5 | 16.5769 | 31 |
| Label | Training Sample Count |
|---|---|
| سلبي | 0 |
| إيجابي | 0 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.125 | 1 | 0.278 | - |
| 1.0 | 8 | - | 0.2514 |
| 2.0 | 16 | - | 0.2524 |
| 3.0 | 24 | - | 0.2543 |
Framework Versions
- Python: 3.10.14
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0
- Datasets: 3.0.1
- Tokenizers: 0.20.0
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}
}