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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: الطريقة الأفضل لتحصل على ريتويت ولايك هل ايام تقتطع جزء من مقابلة للوزير |
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باسيل طبعا جزء غير مكتمل ثم تغرد به وتقول جبران مع التطبيع |
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- text: ما بعرف كيف بدي خبركن ياها بس غير إنو واطي طلع عرص |
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- text: سد نيعك يا صرمايت بشار |
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- text: بتفهّم فهم وحدة فهما متدنّي هههه 😎 |
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- text: للاسف لدينا في الخليج بعض من الكتاب والدكاتره والمحللين كالبغال والجحاش ينهق |
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ويهرف بما لايعرف |
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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: 0.8544600938967136 |
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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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### 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>'كلامي ان حزب الله الحامي الوحيد للبنان معناه ان في لبنان'</li><li>'يا غايظهن يا جبران باسيل انت حبيب القلب'</li><li>'يسعد صباحك معالي الوزير'</li></ul> | |
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| positive | <ul><li>'الى وزير خارجية لبنان جبران باسيل اذا أردت ان تكون مثل الشيخ بشير الجميل عليك ان تستشهد الآن يا اخي فقط إستشهد و لك'</li><li>'شو ضعيف وشو مقهور ومش قادر تعمل شيطز فيكم كلكن كلكن بتصبوا بخانة الكذب والنفاق بتدعسوا عا الناس لمصالحهم وفسادكم'</li><li>'ولك الله انتو شعب بجم روح جبلي مصاري للشعب بعدين احكي'</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** | 0.8545 | |
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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("سد نيعك يا صرمايت بشار") |
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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 | 12.2912 | 52 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| negative | 2015 | |
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| positive | 2800 | |
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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: 8000 |
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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_25kv8 |
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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 | 0.3359 | - | |
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| 0.025 | 100 | 0.2843 | - | |
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| 0.05 | 200 | 0.2376 | - | |
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| 0.075 | 300 | 0.2067 | - | |
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| 0.1 | 400 | 0.1591 | - | |
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| 0.125 | 500 | 0.108 | - | |
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| 0.15 | 600 | 0.0736 | - | |
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| 0.175 | 700 | 0.0513 | - | |
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| 0.2 | 800 | 0.0384 | - | |
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| 0.225 | 900 | 0.0364 | - | |
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| 0.25 | 1000 | 0.0296 | - | |
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| 0.275 | 1100 | 0.0207 | - | |
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| 0.3 | 1200 | 0.0212 | - | |
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| 0.325 | 1300 | 0.0164 | - | |
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| 0.35 | 1400 | 0.0122 | - | |
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| 0.375 | 1500 | 0.0163 | - | |
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| 0.4 | 1600 | 0.01 | - | |
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| 0.425 | 1700 | 0.0085 | - | |
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| 0.45 | 1800 | 0.0081 | - | |
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| 0.475 | 1900 | 0.0083 | - | |
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| 0.5 | 2000 | 0.0057 | - | |
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| 0.525 | 2100 | 0.0061 | - | |
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| 0.55 | 2200 | 0.0046 | - | |
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| 0.575 | 2300 | 0.0049 | - | |
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| 0.6 | 2400 | 0.007 | - | |
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| 0.625 | 2500 | 0.0048 | - | |
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| 0.65 | 2600 | 0.0057 | - | |
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| 0.675 | 2700 | 0.0058 | - | |
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| 0.7 | 2800 | 0.0046 | - | |
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| 0.725 | 2900 | 0.0044 | - | |
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| 0.75 | 3000 | 0.0042 | - | |
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| 0.775 | 3100 | 0.0042 | - | |
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| 0.8 | 3200 | 0.0057 | - | |
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| 0.825 | 3300 | 0.003 | - | |
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| 0.85 | 3400 | 0.0041 | - | |
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| 0.875 | 3500 | 0.0052 | - | |
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| 0.9 | 3600 | 0.004 | - | |
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| 0.925 | 3700 | 0.0042 | - | |
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| 0.95 | 3800 | 0.0058 | - | |
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| 0.975 | 3900 | 0.0049 | - | |
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| 1.0 | 4000 | 0.0052 | - | |
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| 1.025 | 4100 | 0.0031 | - | |
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| 1.05 | 4200 | 0.0025 | - | |
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| 1.075 | 4300 | 0.003 | - | |
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| 1.1 | 4400 | 0.0018 | - | |
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| 1.125 | 4500 | 0.0015 | - | |
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| 1.15 | 4600 | 0.0038 | - | |
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| 1.175 | 4700 | 0.0033 | - | |
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| 1.2 | 4800 | 0.0031 | - | |
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| 1.225 | 4900 | 0.0022 | - | |
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| 1.25 | 5000 | 0.0023 | - | |
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| 1.275 | 5100 | 0.0022 | - | |
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| 1.3 | 5200 | 0.0027 | - | |
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| 1.325 | 5300 | 0.0017 | - | |
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| 1.35 | 5400 | 0.0027 | - | |
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| 1.375 | 5500 | 0.0019 | - | |
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| 1.4 | 5600 | 0.0024 | - | |
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| 1.425 | 5700 | 0.0015 | - | |
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| 1.45 | 5800 | 0.0023 | - | |
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| 1.475 | 5900 | 0.0021 | - | |
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| 1.5 | 6000 | 0.0009 | - | |
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| 1.525 | 6100 | 0.0015 | - | |
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| 1.55 | 6200 | 0.0009 | - | |
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| 1.575 | 6300 | 0.001 | - | |
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| 1.6 | 6400 | 0.0002 | - | |
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| 1.625 | 6500 | 0.0004 | - | |
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| 1.65 | 6600 | 0.0012 | - | |
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| 1.675 | 6700 | 0.0011 | - | |
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| 1.7 | 6800 | 0.0008 | - | |
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| 1.725 | 6900 | 0.0013 | - | |
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| 1.75 | 7000 | 0.0004 | - | |
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| 1.775 | 7100 | 0.0004 | - | |
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| 1.8 | 7200 | 0.0008 | - | |
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| 1.825 | 7300 | 0.0007 | - | |
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| 1.85 | 7400 | 0.0007 | - | |
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| 1.875 | 7500 | 0.001 | - | |
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| 1.9 | 7600 | 0.001 | - | |
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| 1.925 | 7700 | 0.0002 | - | |
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| 1.95 | 7800 | 0.0005 | - | |
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| 1.975 | 7900 | 0.0009 | - | |
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| 2.0 | 8000 | 0.0002 | - | |
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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: 3.2.1 |
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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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