st-karlos-efood commited on
Commit
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1 Parent(s): 1c25503

Add SetFit model

Browse files
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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: παστα ατομικη
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+ - text: mikel mini croissant σοκολατα
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+ - text: tasty nat nut παστελι σουσαμι
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+ - text: σκιουφιχτα σαλτσα ντοματας μυζηθρα σαλτσα ντοματας ελιες καππαρη μυζηθρα
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+ - text: κρασι ροζε λιανος
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+ pipeline_tag: text-classification
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+ inference: false
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+ base_model: lighteternal/stsb-xlm-r-greek-transfer
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+ model-index:
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+ - name: SetFit with lighteternal/stsb-xlm-r-greek-transfer
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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: 0.1588785046728972
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with lighteternal/stsb-xlm-r-greek-transfer
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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 [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) as the Sentence Transformer embedding model. A OneVsRestClassifier 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:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer)
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+ - **Classification head:** a OneVsRestClassifier instance
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+ - **Maximum Sequence Length:** 400 tokens
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+ <!-- - **Number of Classes:** Unknown -->
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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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+ ## 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** | 0.1589 |
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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("st-karlos-efood/setfit-multilabel-one-vs-rest-feb-2024")
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+ # Run inference
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+ preds = model("παστα ατομικη")
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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 | 8.6048 | 116 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (48, 48)
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+ - num_epochs: (5, 5)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 10
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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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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+ | 0.0008 | 1 | 0.2009 | - |
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+ | 0.0377 | 50 | 0.1674 | - |
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+ | 0.0754 | 100 | 0.1593 | - |
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+ | 0.1131 | 150 | 0.1793 | - |
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+ | 0.1508 | 200 | 0.176 | - |
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+ | 0.1885 | 250 | 0.1818 | - |
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+ | 0.2262 | 300 | 0.1209 | - |
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+ | 0.2640 | 350 | 0.1546 | - |
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+ | 0.3017 | 400 | 0.0996 | - |
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+ | 0.3394 | 450 | 0.1108 | - |
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+ | 0.3771 | 500 | 0.1163 | - |
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+ | 0.4148 | 550 | 0.1102 | - |
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+ | 0.4525 | 600 | 0.1477 | - |
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+ | 0.4902 | 650 | 0.0973 | - |
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+ | 0.5279 | 700 | 0.1324 | - |
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+ | 0.5656 | 750 | 0.1792 | - |
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+ | 0.6033 | 800 | 0.1026 | - |
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+ | 0.6410 | 850 | 0.1461 | - |
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+ | 0.6787 | 900 | 0.117 | - |
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+ | 0.7164 | 950 | 0.0907 | - |
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+ | 0.7541 | 1000 | 0.0904 | - |
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+ | 0.7919 | 1050 | 0.1168 | - |
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+ | 0.8296 | 1100 | 0.0831 | - |
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+ | 0.8673 | 1150 | 0.0623 | - |
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+ | 0.9050 | 1200 | 0.0802 | - |
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+ | 0.9427 | 1250 | 0.0802 | - |
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+ | 0.9804 | 1300 | 0.1212 | - |
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+ | 1.0181 | 1350 | 0.0872 | - |
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+ | 1.0558 | 1400 | 0.1068 | - |
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+ | 1.0935 | 1450 | 0.0975 | - |
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+ | 1.1312 | 1500 | 0.096 | - |
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+ | 1.1689 | 1550 | 0.0649 | - |
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+ | 1.2066 | 1600 | 0.1004 | - |
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+ | 4.0347 | 5350 | 0.0888 | - |
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+ | 4.0724 | 5400 | 0.0917 | - |
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+ | 4.1855 | 5550 | 0.0807 | - |
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+ | 4.2232 | 5600 | 0.0997 | - |
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+ | 4.2609 | 5650 | 0.0782 | - |
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+ | 4.2986 | 5700 | 0.1165 | - |
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+ | 4.3363 | 5750 | 0.0837 | - |
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+ | 4.3741 | 5800 | 0.1098 | - |
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+ | 4.4118 | 5850 | 0.0564 | - |
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+ | 4.4495 | 5900 | 0.0715 | - |
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+ | 4.4872 | 5950 | 0.0858 | - |
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+ | 4.5249 | 6000 | 0.0889 | - |
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+ | 4.5626 | 6050 | 0.0719 | - |
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+ | 4.6003 | 6100 | 0.1076 | - |
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+ | 4.6757 | 6200 | 0.0914 | - |
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+ | 4.7134 | 6250 | 0.1078 | - |
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+ | 4.7888 | 6350 | 0.0666 | - |
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+ | 4.9397 | 6550 | 0.1366 | - |
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+ | 4.9774 | 6600 | 0.1009 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.0.3
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+ - Sentence Transformers: 2.3.1
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+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.17.0
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+ - Tokenizers: 0.15.1
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+
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+ ## Citation
286
+
287
+ ### BibTeX
288
+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
290
+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
292
+ 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}
298
+ }
299
+ ```
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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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+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "eos_token": "</s>",
48
+ "mask_token": "<mask>",
49
+ "max_length": 400,
50
+ "model_max_length": 512,
51
+ "pad_to_multiple_of": null,
52
+ "pad_token": "<pad>",
53
+ "pad_token_type_id": 0,
54
+ "padding_side": "right",
55
+ "sep_token": "</s>",
56
+ "stride": 0,
57
+ "tokenizer_class": "XLMRobertaTokenizer",
58
+ "truncation_side": "right",
59
+ "truncation_strategy": "longest_first",
60
+ "unk_token": "<unk>"
61
+ }