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Add SetFit model

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+ ---
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+ library_name: setfit
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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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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: ' i still dont know what we would do though'
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+ - text: ' where`d you go!'
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+ - text: ' Thank you! I`m working on `s'
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+ - text: Terminator Salvation... by myself.
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+ - text: ' lol man i got 2 1 /2 hrs an iont how i woulda made it wit out my ramen noodles
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+ and t.v. Time'
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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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: 0.77
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
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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 [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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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:** 128 tokens
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+ - **Number of Classes:** 3 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 0 | <ul><li>'چه سودایی که سر همینا از دست دادم😂'</li><li>'خو فارسی بنویس بفهمه 😂😂😂😂😂'</li><li>'اینجا ایران همین سایتا هم\u200cزیادی..نیازی به بررسی ندارن...کلا دوسداریم به همچی ایراد بگیریم.'</li></ul> |
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+ | 1 | <ul><li>'کد کارت مشکی NHKDKI'</li><li>'اتفاقا مسیولیت بیشتری برات میاره و درگیریات بیشتر میشه برای هدفی که داری'</li><li>'من میخام شروع کنم،اورج بفروشم یا فیک؟فیک ارزونتره ولی فیکه.اورجینال هم ک گرون تره ؟بنظرت اورج میخرن؟؟'</li></ul> |
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+ | 2 | <ul><li>'🔥🔥🔥🔥'</li><li>'😂😂😂'</li><li>'چه قدر عالی وخفن 🔥🔥'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.77 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("ehsanhallo/setfit-paraphrase-multilingual-MiniLM-L12-v2-ig-fa")
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+ # Run inference
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+ preds = model(" where`d you go!")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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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 | 7.0 | 75 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 31 |
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+ | 1 | 131 |
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+ | 2 | 364 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 16)
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+ - num_epochs: (2, 4)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 5e-06)
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+ - head_learning_rate: 0.002
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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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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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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 | 0.1854 | - |
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+ | 0.0529 | 250 | 0.0626 | - |
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+ | 0.1058 | 500 | 0.0034 | 0.2484 |
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+ | 0.1588 | 750 | 0.0029 | - |
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+ | 0.2117 | 1000 | 0.001 | 0.1899 |
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+ | 0.2646 | 1250 | 0.0001 | - |
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+ | **0.3175** | **1500** | **0.0001** | **0.1849** |
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+ | 0.3704 | 1750 | 0.0001 | - |
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+ | 0.4234 | 2000 | 0.0001 | 0.1876 |
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+ | 0.4763 | 2250 | 0.0001 | - |
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+ | 0.5292 | 2500 | 0.0 | 0.1888 |
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+ | 0.5821 | 2750 | 0.0001 | - |
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+ | 0.6351 | 3000 | 0.0 | 0.1885 |
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+ | 0.6880 | 3250 | 0.0 | - |
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+ | 0.7409 | 3500 | 0.0 | 0.1915 |
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+ | 0.7938 | 3750 | 0.0 | - |
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+ | 0.8467 | 4000 | 0.0 | 0.1947 |
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+ | 0.8997 | 4250 | 0.0 | - |
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+ | 0.9526 | 4500 | 0.0 | 0.1986 |
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+ | 1.0055 | 4750 | 0.0 | - |
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+ | 1.0584 | 5000 | 0.0 | 0.207 |
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+ | 1.1113 | 5250 | 0.0 | - |
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+ | 1.1643 | 5500 | 0.0 | 0.2078 |
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+ | 1.2172 | 5750 | 0.0 | - |
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+ | 1.2701 | 6000 | 0.0 | 0.2096 |
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+ | 1.3230 | 6250 | 0.0 | - |
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+ | 1.3760 | 6500 | 0.0 | 0.2095 |
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+ | 1.4289 | 6750 | 0.0 | - |
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+ | 1.4818 | 7000 | 0.0 | 0.2103 |
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+ | 1.5347 | 7250 | 0.0 | - |
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+ | 1.5876 | 7500 | 0.0 | 0.2133 |
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+ | 1.6406 | 7750 | 0.0 | - |
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+ | 1.6935 | 8000 | 0.0 | 0.2154 |
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+ | 1.7464 | 8250 | 0.0 | - |
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+ | 1.7993 | 8500 | 0.0 | 0.2141 |
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+ | 1.8522 | 8750 | 0.0 | - |
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+ | 1.9052 | 9000 | 0.0 | 0.2141 |
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+ | 1.9581 | 9250 | 0.0 | - |
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+
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+ * The bold row denotes the saved checkpoint.
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.0.1
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+ - Sentence Transformers: 2.2.2
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+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.16.1
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+ - Tokenizers: 0.15.0
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+
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+ ## Citation
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *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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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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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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+ -->
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