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--- |
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base_model: akhooli/sbert_ar_nli_500k_norm |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: كيف كل زمن وإله أتباعه الزقيفة بإيام الإحتلال السوري للبنان، كان أشهرهم ناصر |
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قنديل زال الوجود العسكري وتبخ... |
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- text: اداي في كتير مشحرين وقلال عقل في لبنان بقصد الذين يمدحون ويمجدون شخص مخلوق |
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من لحم ونسوا ان الله هو من ي... |
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- text: كول هوا و سد نيعك |
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- text: لا تحبسنا على غدر الزمان طالما نحن لها في المرصاد |
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- text: العمر الو حق مبين عليكي الكبر |
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inference: true |
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model-index: |
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- name: SetFit with akhooli/sbert_ar_nli_500k_norm |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8838334946757018 |
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name: Accuracy |
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--- |
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# SetFit with akhooli/sbert_ar_nli_500k_norm |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| negative | <ul><li>'جبران باسيل يغرّد عن رحيل مي منسى لبنان سيفتقد روحك الجميلة'</li><li>'نشرتكم مقابلة لوزير الخارجية اللبناني جبران باسيل مع سي أن أن تثير الجدل على منصات التواصل ما السبب؟'</li><li>' حسا السنه حسا الشيعه حسا الحضران والبدوان\nتساوينا بمحبتها وساوتنا أراضيها\n🌴\nعساني يالحسا فدوة فديت الداار والسكان\nمن اول بيت لآخر بيت بقراها وحواريها\n🌴\nمكانك يالحسا عالي وقدرك فوق مهما كان\nومن حاول يقارن بك بيخسر في تواليها\n🌴وسلامتكم🌴'</li></ul> | |
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| positive | <ul><li>'جبران باسيل اكثر الساسة نفاقا'</li><li>'انا اقترح عليك طبيب بيطري'</li><li>'انتي شكلك مو سورية بس عم تزعمي'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.8838 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("akhooli/setfit_ar_hs") |
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# Run inference |
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preds = model("كول هوا و سد نيعك") |
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``` |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 16.5047 | 102 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| negative | 3709 | |
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| positive | 3800 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (1, 1) |
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- max_steps: 6000 |
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- sampling_strategy: undersampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- run_name: setfit_hate_38k_aub_6k |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0003 | 1 | 0.342 | - | |
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| 0.0333 | 100 | 0.2881 | - | |
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| 0.0667 | 200 | 0.2468 | - | |
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| 0.1 | 300 | 0.1991 | - | |
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| 0.1333 | 400 | 0.1548 | - | |
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| 0.1667 | 500 | 0.113 | - | |
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| 0.2 | 600 | 0.0861 | - | |
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| 0.2333 | 700 | 0.0638 | - | |
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| 0.2667 | 800 | 0.0534 | - | |
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| 0.3 | 900 | 0.0448 | - | |
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| 0.3333 | 1000 | 0.0364 | - | |
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| 0.3667 | 1100 | 0.0278 | - | |
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| 0.4 | 1200 | 0.0306 | - | |
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| 0.4333 | 1300 | 0.0251 | - | |
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| 0.4667 | 1400 | 0.021 | - | |
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| 0.5 | 1500 | 0.0213 | - | |
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| 0.5333 | 1600 | 0.0175 | - | |
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| 0.5667 | 1700 | 0.0181 | - | |
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| 0.6 | 1800 | 0.0175 | - | |
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| 0.6333 | 1900 | 0.0144 | - | |
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| 0.6667 | 2000 | 0.012 | - | |
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| 0.7 | 2100 | 0.0097 | - | |
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| 0.7333 | 2200 | 0.0119 | - | |
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| 0.7667 | 2300 | 0.0112 | - | |
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| 0.8 | 2400 | 0.0106 | - | |
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| 0.8333 | 2500 | 0.0103 | - | |
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| 0.8667 | 2600 | 0.008 | - | |
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| 0.9 | 2700 | 0.0084 | - | |
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| 0.9333 | 2800 | 0.0078 | - | |
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| 0.9667 | 2900 | 0.0063 | - | |
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| 1.0 | 3000 | 0.006 | - | |
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| 1.0333 | 3100 | 0.0057 | - | |
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| 1.0667 | 3200 | 0.0067 | - | |
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| 1.1 | 3300 | 0.0037 | - | |
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| 1.1333 | 3400 | 0.005 | - | |
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| 1.1667 | 3500 | 0.0047 | - | |
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| 1.2 | 3600 | 0.0054 | - | |
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| 1.2333 | 3700 | 0.0038 | - | |
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| 1.2667 | 3800 | 0.0059 | - | |
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| 1.3 | 3900 | 0.0042 | - | |
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| 1.3333 | 4000 | 0.0028 | - | |
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| 1.3667 | 4100 | 0.004 | - | |
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| 1.4 | 4200 | 0.0028 | - | |
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| 1.4333 | 4300 | 0.0032 | - | |
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| 1.4667 | 4400 | 0.003 | - | |
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| 1.5 | 4500 | 0.002 | - | |
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| 1.5333 | 4600 | 0.0022 | - | |
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| 1.5667 | 4700 | 0.0019 | - | |
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| 1.6 | 4800 | 0.0027 | - | |
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| 1.6333 | 4900 | 0.0019 | - | |
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| 1.6667 | 5000 | 0.0021 | - | |
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| 1.7 | 5100 | 0.0024 | - | |
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| 1.7333 | 5200 | 0.0015 | - | |
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| 1.7667 | 5300 | 0.0017 | - | |
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| 1.8 | 5400 | 0.0018 | - | |
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| 1.8333 | 5500 | 0.0015 | - | |
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| 1.8667 | 5600 | 0.0017 | - | |
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| 1.9 | 5700 | 0.0007 | - | |
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| 1.9333 | 5800 | 0.0012 | - | |
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| 1.9667 | 5900 | 0.0014 | - | |
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| 2.0 | 6000 | 0.0018 | - | |
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### Framework Versions |
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- Python: 3.10.14 |
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- SetFit: 1.2.0.dev0 |
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- Sentence Transformers: 3.3.0 |
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- Transformers: 4.45.1 |
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- PyTorch: 2.4.0 |
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- Datasets: 3.0.1 |
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- Tokenizers: 0.20.0 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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