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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: يا زلمة يلي بيصنع معنا معروف بنتشكره شو ما كان يكون وانتم ادعياء الاخوة العرب |
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هول مش ايرانيين ولا عجم عرب متلنا متلهم |
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- text: لعمي |
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- text: هلق رجع لمن قلو الريس تبعو هش قلو مشمو على عيني ؟ |
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- text: مثل الكليشيه وبشكل يومي في حدا بده يعاير التاني بيقوم بيشبهه بالكلب والله |
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اذا حدا شبهني بالكلب بعتبرها مدح شديد |
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- text: الله لا يحرمك من الهبل ان شاء الله |
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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: 0.8497652582159625 |
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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>'الف تحية لشيخ العقل ومشايخنا الكرام'</li><li>'بتحبو او بتكرهو انشط وزير و رئيس تيار و ديبلوماسيتو بتتدرّس'</li><li>'نعم معاليك ستظل دمشق المدينة التي تغنى بها الشعراء وهذه الكلمات خير شاهد فرشت فوق ثراك الطاهرالهدبا'</li></ul> | |
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| positive | <ul><li>'لسانك حصانك وحسنا فعلت قطر لتلغي مركز الأبحاث لا مرحبا بكم انتم ولا تستاهلون اي عمل لكم ناكرين المعروف'</li><li>'ارنب وبضلك ارنب ابكي بترتاح يا صرماية'</li><li>'سليمان فرنجية عبارة عن كلب مسعور لديه حاسة شم قوية جداً شم ريحة كرسي الرئاسة ولكنه لن يجلس عليها ابداً وتصبحو على خير'</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** | 0.8498 | |
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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("لعمي") |
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``` |
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### Downstream Use |
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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 | 12.2323 | 52 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| negative | 1995 | |
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| positive | 2500 | |
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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: 10000 |
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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_25kv |
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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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| 0.0002 | 1 | 0.3185 | - | |
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| 0.02 | 100 | 0.2901 | - | |
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| 0.04 | 200 | 0.2441 | - | |
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| 0.06 | 300 | 0.2209 | - | |
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| 0.08 | 400 | 0.1715 | - | |
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| 0.1 | 500 | 0.1304 | - | |
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| 0.12 | 600 | 0.0891 | - | |
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| 0.14 | 700 | 0.0604 | - | |
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| 0.16 | 800 | 0.0436 | - | |
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| 0.18 | 900 | 0.0408 | - | |
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| 0.2 | 1000 | 0.0265 | - | |
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| 0.22 | 1100 | 0.0239 | - | |
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| 0.24 | 1200 | 0.0235 | - | |
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| 0.26 | 1300 | 0.0232 | - | |
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| 0.28 | 1400 | 0.0241 | - | |
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| 0.3 | 1500 | 0.019 | - | |
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| 0.32 | 1600 | 0.0168 | - | |
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| 0.34 | 1700 | 0.0172 | - | |
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| 0.36 | 1800 | 0.0136 | - | |
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| 0.38 | 1900 | 0.0099 | - | |
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| 0.4 | 2000 | 0.0117 | - | |
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| 0.42 | 2100 | 0.0091 | - | |
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| 0.44 | 2200 | 0.0067 | - | |
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| 0.46 | 2300 | 0.0074 | - | |
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| 0.48 | 2400 | 0.0055 | - | |
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| 0.5 | 2500 | 0.0053 | - | |
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| 0.52 | 2600 | 0.0054 | - | |
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| 0.54 | 2700 | 0.0058 | - | |
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| 0.56 | 2800 | 0.0059 | - | |
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| 0.58 | 2900 | 0.0055 | - | |
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| 0.6 | 3000 | 0.0043 | - | |
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| 0.62 | 3100 | 0.0045 | - | |
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| 0.64 | 3200 | 0.0055 | - | |
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| 0.66 | 3300 | 0.0042 | - | |
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| 0.68 | 3400 | 0.0024 | - | |
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| 0.7 | 3500 | 0.0025 | - | |
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| 0.72 | 3600 | 0.0047 | - | |
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| 0.74 | 3700 | 0.0036 | - | |
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| 0.76 | 3800 | 0.0029 | - | |
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| 0.78 | 3900 | 0.0043 | - | |
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| 0.8 | 4000 | 0.0036 | - | |
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| 0.82 | 4100 | 0.0025 | - | |
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| 0.84 | 4200 | 0.0033 | - | |
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| 0.86 | 4300 | 0.0018 | - | |
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| 0.88 | 4400 | 0.0016 | - | |
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| 0.9 | 4500 | 0.0018 | - | |
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| 0.92 | 4600 | 0.0023 | - | |
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| 0.94 | 4700 | 0.0027 | - | |
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| 0.96 | 4800 | 0.0023 | - | |
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| 0.98 | 4900 | 0.0012 | - | |
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| 1.0 | 5000 | 0.0021 | - | |
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| 1.02 | 5100 | 0.0026 | - | |
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| 1.04 | 5200 | 0.0019 | - | |
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| 1.06 | 5300 | 0.002 | - | |
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| 1.08 | 5400 | 0.0022 | - | |
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| 1.1 | 5500 | 0.0025 | - | |
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| 1.12 | 5600 | 0.0033 | - | |
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| 1.1400 | 5700 | 0.001 | - | |
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| 1.16 | 5800 | 0.0016 | - | |
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| 1.18 | 5900 | 0.0015 | - | |
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| 1.2 | 6000 | 0.0008 | - | |
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| 1.22 | 6100 | 0.0011 | - | |
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| 1.24 | 6200 | 0.0012 | - | |
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| 1.26 | 6300 | 0.0009 | - | |
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| 1.28 | 6400 | 0.0012 | - | |
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| 1.3 | 6500 | 0.001 | - | |
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| 1.32 | 6600 | 0.0014 | - | |
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| 1.34 | 6700 | 0.0002 | - | |
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| 1.3600 | 6800 | 0.0005 | - | |
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| 1.38 | 6900 | 0.0003 | - | |
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| 1.4 | 7000 | 0.0001 | - | |
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| 1.42 | 7100 | 0.0007 | - | |
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| 1.44 | 7200 | 0.0003 | - | |
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| 1.46 | 7300 | 0.0002 | - | |
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| 1.48 | 7400 | 0.0005 | - | |
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| 1.5 | 7500 | 0.0001 | - | |
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| 1.52 | 7600 | 0.0003 | - | |
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| 1.54 | 7700 | 0.001 | - | |
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| 1.56 | 7800 | 0.0003 | - | |
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| 1.58 | 7900 | 0.0 | - | |
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| 1.6 | 8000 | 0.0002 | - | |
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| 1.62 | 8100 | 0.0 | - | |
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| 1.6400 | 8200 | 0.0002 | - | |
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| 1.6600 | 8300 | 0.0002 | - | |
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| 1.6800 | 8400 | 0.0 | - | |
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| 1.7 | 8500 | 0.0 | - | |
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| 1.72 | 8600 | 0.0002 | - | |
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| 1.74 | 8700 | 0.0002 | - | |
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| 1.76 | 8800 | 0.0002 | - | |
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| 1.78 | 8900 | 0.0002 | - | |
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| 1.8 | 9000 | 0.0 | - | |
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| 1.8200 | 9100 | 0.0004 | - | |
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| 1.8400 | 9200 | 0.0 | - | |
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| 1.8600 | 9300 | 0.0002 | - | |
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| 1.88 | 9400 | 0.0002 | - | |
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| 1.9 | 9500 | 0.0 | - | |
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| 1.92 | 9600 | 0.0003 | - | |
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| 1.94 | 9700 | 0.0 | - | |
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| 1.96 | 9800 | 0.0 | - | |
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| 1.98 | 9900 | 0.0 | - | |
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| 2.0 | 10000 | 0.0 | - | |
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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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