| | --- |
| | tags: |
| | - setfit |
| | - sentence-transformers |
| | - text-classification |
| | - generated_from_setfit_trainer |
| | widget: |
| | - text: يا حاقد ع الاسلام السياسي |
| | - text: 'بلد مخيف، صار القتل بحجه الشرف متل قتل بعوضة، واللي بيخوف اكتر من اللي واقف |
| | مكتف ايديه ومش مساعد. وين كنآ، ووين وصلنآ، لمتى حنضل عايشين وساكتين! |
| | |
| | ' |
| | - text: "من خلال المتابعة ..يتضح أن أكثر اللاعبين الذين يتم تسويقهم هم لاعبي امريكا\ |
| | \ الجنوبية وأقلهم الافارقة. \nمن خلال الواقع ..أكثر اللاعبين تهاونا ولعب على\ |
| | \ الواقف في آخر ٦ شهور من عقودهم هم لاعبي امريكا الجنوبية ." |
| | - text: ' علم الحزب يا فهمانه ما حطوا لانه عم يحكي وطنيا ومشان ماحدا متلك يعترض. اذا |
| | حطوا بتعترضي واذا ما حطوا كمان بتعترضي.' |
| | - text: "شيوعي \nعلماني \nمسيحي\nانصار سنه \nصوفي \nيمثلك التجمع \nلا يمثلك التجمع\ |
| | \ \nاهلا بكم جميعا فنحن نريد بناء وطن ❤" |
| | metrics: |
| | - accuracy |
| | pipeline_tag: text-classification |
| | library_name: setfit |
| | inference: true |
| | base_model: akhooli/sbert-nli-500k-triplets-MB |
| | model-index: |
| | - name: SetFit with akhooli/sbert-nli-500k-triplets-MB |
| | results: |
| | - task: |
| | type: text-classification |
| | name: Text Classification |
| | dataset: |
| | name: Unknown |
| | type: unknown |
| | split: test |
| | metrics: |
| | - type: accuracy |
| | value: 0.7956709956709956 |
| | name: Accuracy |
| | --- |
| | |
| | # SetFit with akhooli/sbert-nli-500k-triplets-MB |
| |
|
| | This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert-nli-500k-triplets-MB](https://huggingface.co/akhooli/sbert-nli-500k-triplets-MB) 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. |
| |
|
| | The model has been trained using an efficient few-shot learning technique that involves: |
| |
|
| | 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) 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:** [akhooli/sbert-nli-500k-triplets-MB](https://huggingface.co/akhooli/sbert-nli-500k-triplets-MB) |
| | - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| | - **Maximum Sequence Length:** 8192 tokens |
| | - **Number of Classes:** 2 classes |
| | <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
| | - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
| | - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
| |
|
| | ### Model Labels |
| | | Label | Examples | |
| | |:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | positive | <ul><li>' سبحان الله الفلسطينيين شعب خاين في كل مكان \nلاحول ولا قوة إلا بالله'</li><li>'يا بيك عّم تخبرنا عن شي ما فينا تعملو نحن ماًعندنا نواب ولا وزراء بمثلونا بالدولة الا اذا زهقان وعبالك ليك'</li><li>'جوز كذابين منافقين…'</li></ul> | |
| | | negative | <ul><li>'ربي لا تجعلني أسيء الظن بأحد ولا تجعل في قلبي شيئا على أحد ، اللهم أسألك قلباً نقياً صافيا'</li><li>'هشام حداد عامل فيها جون ستيوارت'</li><li>' بحياة اختك من وين بتجيبي اخبارك؟؟ من صغري وانا عبالي كون… LINK'</li></ul> | |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| | | Label | Accuracy | |
| | |:--------|:---------| |
| | | **all** | 0.7957 | |
| |
|
| | ## Uses |
| |
|
| | ### Direct Use for Inference |
| |
|
| | First install the SetFit library: |
| |
|
| | ```bash |
| | 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("akhooli/setfit_ar_hs_mb") |
| | # Run inference |
| | preds = model("يا حاقد ع الاسلام السياسي") |
| | ``` |
| |
|
| | <!-- |
| | ### Downstream Use |
| |
|
| | *List how someone could finetune this model on their own dataset.* |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | <!-- |
| | ## Bias, Risks and Limitations |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Set Metrics |
| | | Training set | Min | Median | Max | |
| | |:-------------|:----|:--------|:----| |
| | | Word count | 1 | 18.8388 | 185 | |
| |
|
| | | Label | Training Sample Count | |
| | |:---------|:----------------------| |
| | | negative | 5200 | |
| | | positive | 4943 | |
| |
|
| | ### Training Hyperparameters |
| | - batch_size: (16, 16) |
| | - num_epochs: (1, 1) |
| | - max_steps: 6000 |
| | - sampling_strategy: undersampling |
| | - 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 |
| | - run_name: setfit_hate_52k_mb_6k |
| | - eval_max_steps: -1 |
| | - load_best_model_at_end: False |
| |
|
| | ### Training Results |
| | | Epoch | Step | Training Loss | Validation Loss | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0003 | 1 | 0.3373 | - | |
| | | 0.0333 | 100 | 0.2955 | - | |
| | | 0.0667 | 200 | 0.2535 | - | |
| | | 0.1 | 300 | 0.2373 | - | |
| | | 0.1333 | 400 | 0.2228 | - | |
| | | 0.1667 | 500 | 0.1956 | - | |
| | | 0.2 | 600 | 0.1768 | - | |
| | | 0.2333 | 700 | 0.1489 | - | |
| | | 0.2667 | 800 | 0.122 | - | |
| | | 0.3 | 900 | 0.1045 | - | |
| | | 0.3333 | 1000 | 0.086 | - | |
| | | 0.3667 | 1100 | 0.0681 | - | |
| | | 0.4 | 1200 | 0.067 | - | |
| | | 0.4333 | 1300 | 0.0477 | - | |
| | | 0.4667 | 1400 | 0.043 | - | |
| | | 0.5 | 1500 | 0.0316 | - | |
| | | 0.5333 | 1600 | 0.0251 | - | |
| | | 0.5667 | 1700 | 0.0236 | - | |
| | | 0.6 | 1800 | 0.0163 | - | |
| | | 0.6333 | 1900 | 0.0148 | - | |
| | | 0.6667 | 2000 | 0.0105 | - | |
| | | 0.7 | 2100 | 0.018 | - | |
| | | 0.7333 | 2200 | 0.013 | - | |
| | | 0.7667 | 2300 | 0.0103 | - | |
| | | 0.8 | 2400 | 0.0107 | - | |
| | | 0.8333 | 2500 | 0.0115 | - | |
| | | 0.8667 | 2600 | 0.0069 | - | |
| | | 0.9 | 2700 | 0.0062 | - | |
| | | 0.9333 | 2800 | 0.0074 | - | |
| | | 0.9667 | 2900 | 0.0063 | - | |
| | | 1.0 | 3000 | 0.0068 | - | |
| | | 1.0333 | 3100 | 0.0048 | - | |
| | | 1.0667 | 3200 | 0.0055 | - | |
| | | 1.1 | 3300 | 0.0047 | - | |
| | | 1.1333 | 3400 | 0.0043 | - | |
| | | 1.1667 | 3500 | 0.0029 | - | |
| | | 1.2 | 3600 | 0.0036 | - | |
| | | 1.2333 | 3700 | 0.0034 | - | |
| | | 1.2667 | 3800 | 0.0024 | - | |
| | | 1.3 | 3900 | 0.0033 | - | |
| | | 1.3333 | 4000 | 0.0042 | - | |
| | | 1.3667 | 4100 | 0.0039 | - | |
| | | 1.4 | 4200 | 0.0019 | - | |
| | | 1.4333 | 4300 | 0.0022 | - | |
| | | 1.4667 | 4400 | 0.0031 | - | |
| | | 1.5 | 4500 | 0.0019 | - | |
| | | 1.5333 | 4600 | 0.0036 | - | |
| | | 1.5667 | 4700 | 0.0017 | - | |
| | | 1.6 | 4800 | 0.0007 | - | |
| | | 1.6333 | 4900 | 0.0006 | - | |
| | | 1.6667 | 5000 | 0.0019 | - | |
| | | 1.7 | 5100 | 0.0022 | - | |
| | | 1.7333 | 5200 | 0.0013 | - | |
| | | 1.7667 | 5300 | 0.0025 | - | |
| | | 1.8 | 5400 | 0.0024 | - | |
| | | 1.8333 | 5500 | 0.0013 | - | |
| | | 1.8667 | 5600 | 0.0022 | - | |
| | | 1.9 | 5700 | 0.0022 | - | |
| | | 1.9333 | 5800 | 0.0019 | - | |
| | | 1.9667 | 5900 | 0.0019 | - | |
| | | 2.0 | 6000 | 0.0031 | - | |
| |
|
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - SetFit: 1.2.0.dev0 |
| | - Sentence Transformers: 3.3.1 |
| | - Transformers: 4.48.0 |
| | - PyTorch: 2.5.1+cu121 |
| | - Datasets: 3.2.0 |
| | - Tokenizers: 0.21.0 |
| |
|
| | ## 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} |
| | } |
| | ``` |
| |
|
| | <!-- |
| | ## Glossary |
| |
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| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
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| | ## Model Card Authors |
| |
|
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
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| | ## Model Card Contact |
| |
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| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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