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setfit-multilabel-multioutput-batchsize-128

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +1230 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +9 -0
  6. config_setfit.json +4 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. sentencepiece.bpe.model +3 -0
  12. special_tokens_map.json +51 -0
  13. tokenizer.json +3 -0
  14. tokenizer_config.json +61 -0
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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: σολομος pancake καπνιστο σολομος κρεμα ανθοτυρο μυρωδικα
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+ - text: cheese ball burger μπιφτεκι μοσχαρισιο cheddar cheese πικλες ketchup
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+ - text: σοκολατα βιεννεζικη
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+ - text: ρυζι αρωματικο
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+ - text: σπιτικος χυμος πρασινο μηλο ροδι
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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: 0.15195369030390737
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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 MultiOutputClassifier 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 MultiOutputClassifier 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** | 0.1520 |
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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-multioutput-batchsize-128")
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+ # Run inference
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+ preds = model("ρυζι αρωματικο")
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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 | 8.6201 | 116 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (10, 10)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 25
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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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+ | 0.0002 | 1 | 0.2104 | - |
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+ | 0.0096 | 50 | 0.1979 | - |
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+ | 0.0191 | 100 | 0.1902 | - |
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+ | 0.0287 | 150 | 0.2136 | - |
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+ | 0.0382 | 200 | 0.1932 | - |
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+ | 0.0478 | 250 | 0.2207 | - |
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+ | 0.0574 | 300 | 0.246 | - |
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+ | 0.0669 | 350 | 0.2228 | - |
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+ | 0.0765 | 400 | 0.192 | - |
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+ | 0.0861 | 450 | 0.2274 | - |
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+ | 0.0956 | 500 | 0.2315 | - |
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+ | 0.1052 | 550 | 0.2072 | - |
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+ | 0.1147 | 600 | 0.1461 | - |
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+ | 0.1243 | 650 | 0.1013 | - |
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+ | 0.1339 | 700 | 0.1796 | - |
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+ | 0.1434 | 750 | 0.1801 | - |
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+ | 0.1530 | 800 | 0.1395 | - |
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+ | 0.1626 | 850 | 0.0999 | - |
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+ | 0.1721 | 900 | 0.1589 | - |
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+ | 0.1817 | 950 | 0.1486 | - |
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+ | 0.1912 | 1000 | 0.1214 | - |
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+ | 0.2008 | 1050 | 0.0995 | - |
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+ | 0.2104 | 1100 | 0.1034 | - |
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+ | 0.2199 | 1150 | 0.1347 | - |
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+ | 0.2295 | 1200 | 0.1171 | - |
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+ | 0.2391 | 1250 | 0.1026 | - |
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+ | 0.2486 | 1300 | 0.1543 | - |
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+ | 0.2582 | 1350 | 0.1095 | - |
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+ | 0.2677 | 1400 | 0.1475 | - |
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+ | 0.2773 | 1450 | 0.1198 | - |
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+ | 0.2869 | 1500 | 0.1325 | - |
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+ | 0.2964 | 1550 | 0.0955 | - |
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+ | 0.3060 | 1600 | 0.1637 | - |
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+ | 0.3155 | 1650 | 0.1619 | - |
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+ | 0.3251 | 1700 | 0.0789 | - |
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+ | 0.3347 | 1750 | 0.1061 | - |
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+ | 0.3442 | 1800 | 0.1224 | - |
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+ | 0.3538 | 1850 | 0.135 | - |
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+ | 0.3634 | 1900 | 0.0971 | - |
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+ | 0.3729 | 1950 | 0.1111 | - |
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+ | 0.3825 | 2000 | 0.1166 | - |
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+ | 0.3920 | 2050 | 0.1304 | - |
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+ | 0.4016 | 2100 | 0.0551 | - |
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+ | 0.4112 | 2150 | 0.0921 | - |
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+ | 0.4207 | 2200 | 0.1248 | - |
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+ | 0.4303 | 2250 | 0.0814 | - |
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+ | 0.4399 | 2300 | 0.1525 | - |
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+ | 0.4494 | 2350 | 0.0743 | - |
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+ | 0.4590 | 2400 | 0.0894 | - |
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+ | 0.4685 | 2450 | 0.1213 | - |
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+ | 0.4781 | 2500 | 0.1068 | - |
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+ | 0.4877 | 2550 | 0.0573 | - |
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+ | 0.4972 | 2600 | 0.1233 | - |
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+ | 0.5068 | 2650 | 0.0995 | - |
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+ | 0.5164 | 2700 | 0.0659 | - |
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+ | 0.5259 | 2750 | 0.1107 | - |
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+ | 0.5355 | 2800 | 0.0934 | - |
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+ | 0.5450 | 2850 | 0.068 | - |
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+ | 0.5546 | 2900 | 0.077 | - |
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+ | 0.5642 | 2950 | 0.0836 | - |
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+ | 0.5737 | 3000 | 0.1709 | - |
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+ | 0.5833 | 3050 | 0.0766 | - |
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+ | 0.5928 | 3100 | 0.1081 | - |
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+ | 0.6024 | 3150 | 0.1099 | - |
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+ | 0.6120 | 3200 | 0.1611 | - |
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+ | 0.6693 | 3500 | 0.0709 | - |
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+ | 0.6789 | 3550 | 0.1163 | - |
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+ | 0.6885 | 3600 | 0.1985 | - |
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+ | 0.6980 | 3650 | 0.13 | - |
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+ | 0.7076 | 3700 | 0.1039 | - |
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+ | 0.7172 | 3750 | 0.0982 | - |
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+ | 0.7267 | 3800 | 0.1149 | - |
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+ | 0.7363 | 3850 | 0.0983 | - |
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+ | 0.7458 | 3900 | 0.1207 | - |
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+ | 0.7554 | 3950 | 0.1003 | - |
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+ | 0.7650 | 4000 | 0.0429 | - |
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+ | 0.7745 | 4050 | 0.0658 | - |
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+ | 0.7841 | 4100 | 0.0744 | - |
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+ | 0.7937 | 4150 | 0.1364 | - |
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+ | 0.8032 | 4200 | 0.0325 | - |
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+ | 0.8128 | 4250 | 0.0932 | - |
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+ | 0.8223 | 4300 | 0.0893 | - |
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+ | 0.8319 | 4350 | 0.1125 | - |
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+ | 1.0040 | 5250 | 0.1326 | - |
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+ | 1.0231 | 5350 | 0.1312 | - |
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+ | 1.0327 | 5400 | 0.0447 | - |
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+ | 1.0518 | 5500 | 0.1063 | - |
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+ | 1.0614 | 5550 | 0.0983 | - |
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+ | 1.0710 | 5600 | 0.143 | - |
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+ | 1.0805 | 5650 | 0.0454 | - |
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+ | 1.0901 | 5700 | 0.0949 | - |
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+ | 1.0996 | 5750 | 0.1111 | - |
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+ | 1.1188 | 5850 | 0.0973 | - |
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+ | 1.1283 | 5900 | 0.0814 | - |
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+ | 1.1379 | 5950 | 0.0729 | - |
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+ | 1.1474 | 6000 | 0.0812 | - |
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+ | 1.1570 | 6050 | 0.1238 | - |
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+ | 1.1666 | 6100 | 0.1062 | - |
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+ | 1.1761 | 6150 | 0.1136 | - |
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+ | 1.1857 | 6200 | 0.1197 | - |
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+ | 1.1953 | 6250 | 0.0722 | - |
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+ | 1.2048 | 6300 | 0.0823 | - |
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+ | 1.2144 | 6350 | 0.1198 | - |
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+ | 1.2239 | 6400 | 0.1312 | - |
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+ | 1.2335 | 6450 | 0.1114 | - |
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+ | 1.2431 | 6500 | 0.0502 | - |
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+ | 1.2526 | 6550 | 0.1183 | - |
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+ | 1.2622 | 6600 | 0.0609 | - |
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+ | 1.2718 | 6650 | 0.084 | - |
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+ | 1.2813 | 6700 | 0.1239 | - |
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+ | 1.2909 | 6750 | 0.07 | - |
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+ | 1.3004 | 6800 | 0.0464 | - |
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+ | 1.3100 | 6850 | 0.0907 | - |
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+ | 1.3196 | 6900 | 0.0645 | - |
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+ | 1.3291 | 6950 | 0.0816 | - |
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+ | 1.3387 | 7000 | 0.0636 | - |
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+ | 1.3483 | 7050 | 0.091 | - |
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+ | 1.3578 | 7100 | 0.1056 | - |
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+ | 1.3674 | 7150 | 0.0829 | - |
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+ | 1.3769 | 7200 | 0.0848 | - |
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+ | 1.3865 | 7250 | 0.0748 | - |
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+ | 1.3961 | 7300 | 0.1255 | - |
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+ | 1.4152 | 7400 | 0.0583 | - |
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+ | 1.5299 | 8000 | 0.076 | - |
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+ | 1.6925 | 8850 | 0.09 | - |
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+ | 1.7020 | 8900 | 0.0853 | - |
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+ | 1.7116 | 8950 | 0.1042 | - |
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+ | 1.7212 | 9000 | 0.0708 | - |
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+ | 1.7307 | 9050 | 0.093 | - |
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+ | 1.7403 | 9100 | 0.0688 | - |
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+ | 1.7594 | 9200 | 0.0564 | - |
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+ | 1.7690 | 9250 | 0.0877 | - |
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+ | 1.7785 | 9300 | 0.1199 | - |
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+ | 1.7881 | 9350 | 0.0805 | - |
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+ | 1.7977 | 9400 | 0.0825 | - |
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+ | 1.8072 | 9450 | 0.0904 | - |
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+ | 1.8168 | 9500 | 0.0504 | - |
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+ | 1.8264 | 9550 | 0.0942 | - |
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+ | 1.8359 | 9600 | 0.1052 | - |
335
+ | 1.8455 | 9650 | 0.0874 | - |
336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
+ | 2.0272 | 10600 | 0.0935 | - |
355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
+ | 3.7196 | 19450 | 0.0818 | - |
532
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533
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534
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535
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536
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537
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538
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539
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540
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541
+ | 3.8153 | 19950 | 0.0556 | - |
542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
+ | 3.9204 | 20500 | 0.0746 | - |
553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
+ | 4.1595 | 21750 | 0.0466 | - |
578
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579
+ | 4.1786 | 21850 | 0.1084 | - |
580
+ | 4.1882 | 21900 | 0.0701 | - |
581
+ | 4.1977 | 21950 | 0.0643 | - |
582
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583
+ | 4.2169 | 22050 | 0.1027 | - |
584
+ | 4.2264 | 22100 | 0.08 | - |
585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
+ | 4.3412 | 22700 | 0.0714 | - |
597
+ | 4.3507 | 22750 | 0.0686 | - |
598
+ | 4.3603 | 22800 | 0.0292 | - |
599
+ | 4.3699 | 22850 | 0.1188 | - |
600
+ | 4.3794 | 22900 | 0.0339 | - |
601
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602
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603
+ | 4.4081 | 23050 | 0.0619 | - |
604
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605
+ | 4.4272 | 23150 | 0.0432 | - |
606
+ | 4.4368 | 23200 | 0.1005 | - |
607
+ | 4.4464 | 23250 | 0.1181 | - |
608
+ | 4.4559 | 23300 | 0.0453 | - |
609
+ | 4.4655 | 23350 | 0.091 | - |
610
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611
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612
+ | 4.4942 | 23500 | 0.0541 | - |
613
+ | 4.5037 | 23550 | 0.0619 | - |
614
+ | 4.5133 | 23600 | 0.0893 | - |
615
+ | 4.5229 | 23650 | 0.1241 | - |
616
+ | 4.5324 | 23700 | 0.093 | - |
617
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618
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619
+ | 4.5611 | 23850 | 0.0874 | - |
620
+ | 4.5707 | 23900 | 0.0804 | - |
621
+ | 4.5802 | 23950 | 0.0728 | - |
622
+ | 4.5898 | 24000 | 0.0719 | - |
623
+ | 4.5993 | 24050 | 0.0584 | - |
624
+ | 4.6089 | 24100 | 0.0617 | - |
625
+ | 4.6185 | 24150 | 0.1071 | - |
626
+ | 4.6280 | 24200 | 0.0658 | - |
627
+ | 4.6376 | 24250 | 0.0439 | - |
628
+ | 4.6472 | 24300 | 0.098 | - |
629
+ | 4.6567 | 24350 | 0.1485 | - |
630
+ | 4.6663 | 24400 | 0.1198 | - |
631
+ | 4.6758 | 24450 | 0.1392 | - |
632
+ | 4.6854 | 24500 | 0.0829 | - |
633
+ | 4.6950 | 24550 | 0.0814 | - |
634
+ | 4.7045 | 24600 | 0.055 | - |
635
+ | 4.7141 | 24650 | 0.1045 | - |
636
+ | 4.7237 | 24700 | 0.0523 | - |
637
+ | 4.7332 | 24750 | 0.1202 | - |
638
+ | 4.7428 | 24800 | 0.0814 | - |
639
+ | 4.7523 | 24850 | 0.1224 | - |
640
+ | 4.7619 | 24900 | 0.0496 | - |
641
+ | 4.7715 | 24950 | 0.0659 | - |
642
+ | 4.7810 | 25000 | 0.0763 | - |
643
+ | 4.7906 | 25050 | 0.0639 | - |
644
+ | 4.8002 | 25100 | 0.0493 | - |
645
+ | 4.8097 | 25150 | 0.0723 | - |
646
+ | 4.8193 | 25200 | 0.1155 | - |
647
+ | 4.8288 | 25250 | 0.0522 | - |
648
+ | 4.8384 | 25300 | 0.0732 | - |
649
+ | 4.8480 | 25350 | 0.0477 | - |
650
+ | 4.8575 | 25400 | 0.0836 | - |
651
+ | 4.8671 | 25450 | 0.091 | - |
652
+ | 4.8766 | 25500 | 0.0625 | - |
653
+ | 4.8862 | 25550 | 0.0853 | - |
654
+ | 4.8958 | 25600 | 0.1145 | - |
655
+ | 4.9053 | 25650 | 0.08 | - |
656
+ | 4.9149 | 25700 | 0.1172 | - |
657
+ | 4.9245 | 25750 | 0.0475 | - |
658
+ | 4.9340 | 25800 | 0.1089 | - |
659
+ | 4.9436 | 25850 | 0.0955 | - |
660
+ | 4.9531 | 25900 | 0.0351 | - |
661
+ | 4.9627 | 25950 | 0.0804 | - |
662
+ | 4.9723 | 26000 | 0.0783 | - |
663
+ | 4.9818 | 26050 | 0.0868 | - |
664
+ | 4.9914 | 26100 | 0.0653 | - |
665
+ | 5.0010 | 26150 | 0.0568 | - |
666
+ | 5.0105 | 26200 | 0.0286 | - |
667
+ | 5.0201 | 26250 | 0.0626 | - |
668
+ | 5.0296 | 26300 | 0.0424 | - |
669
+ | 5.0392 | 26350 | 0.0575 | - |
670
+ | 5.0488 | 26400 | 0.0654 | - |
671
+ | 5.0583 | 26450 | 0.1176 | - |
672
+ | 5.0679 | 26500 | 0.0918 | - |
673
+ | 5.0775 | 26550 | 0.1348 | - |
674
+ | 5.0870 | 26600 | 0.0897 | - |
675
+ | 5.0966 | 26650 | 0.0772 | - |
676
+ | 5.1061 | 26700 | 0.0896 | - |
677
+ | 5.1157 | 26750 | 0.0416 | - |
678
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679
+ | 5.1348 | 26850 | 0.0539 | - |
680
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681
+ | 5.1539 | 26950 | 0.092 | - |
682
+ | 5.1635 | 27000 | 0.0724 | - |
683
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684
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685
+ | 5.1922 | 27150 | 0.0731 | - |
686
+ | 5.2018 | 27200 | 0.0701 | - |
687
+ | 5.2113 | 27250 | 0.0503 | - |
688
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689
+ | 5.2304 | 27350 | 0.0583 | - |
690
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691
+ | 5.2496 | 27450 | 0.068 | - |
692
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693
+ | 5.2687 | 27550 | 0.0714 | - |
694
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695
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696
+ | 5.2974 | 27700 | 0.0608 | - |
697
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698
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699
+ | 5.3261 | 27850 | 0.063 | - |
700
+ | 5.3356 | 27900 | 0.0603 | - |
701
+ | 5.3452 | 27950 | 0.0834 | - |
702
+ | 5.3548 | 28000 | 0.0525 | - |
703
+ | 5.3643 | 28050 | 0.0873 | - |
704
+ | 5.3739 | 28100 | 0.0727 | - |
705
+ | 5.3834 | 28150 | 0.0659 | - |
706
+ | 5.3930 | 28200 | 0.0483 | - |
707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
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767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
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820
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821
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822
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823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
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850
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851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
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979
+ | 8.0034 | 41850 | 0.0868 | - |
980
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981
+ | 8.0226 | 41950 | 0.0741 | - |
982
+ | 8.0321 | 42000 | 0.057 | - |
983
+ | 8.0417 | 42050 | 0.0896 | - |
984
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985
+ | 8.0608 | 42150 | 0.0415 | - |
986
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987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
+ | 8.2042 | 42900 | 0.0919 | - |
1001
+ | 8.2138 | 42950 | 0.0889 | - |
1002
+ | 8.2234 | 43000 | 0.0565 | - |
1003
+ | 8.2329 | 43050 | 0.085 | - |
1004
+ | 8.2425 | 43100 | 0.0575 | - |
1005
+ | 8.2521 | 43150 | 0.1073 | - |
1006
+ | 8.2616 | 43200 | 0.0981 | - |
1007
+ | 8.2712 | 43250 | 0.1363 | - |
1008
+ | 8.2807 | 43300 | 0.0663 | - |
1009
+ | 8.2903 | 43350 | 0.0849 | - |
1010
+ | 8.2999 | 43400 | 0.0559 | - |
1011
+ | 8.3094 | 43450 | 0.0584 | - |
1012
+ | 8.3190 | 43500 | 0.0935 | - |
1013
+ | 8.3286 | 43550 | 0.0727 | - |
1014
+ | 8.3381 | 43600 | 0.1045 | - |
1015
+ | 8.3477 | 43650 | 0.068 | - |
1016
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1017
+ | 8.3668 | 43750 | 0.0782 | - |
1018
+ | 8.3764 | 43800 | 0.0819 | - |
1019
+ | 8.3859 | 43850 | 0.0492 | - |
1020
+ | 8.3955 | 43900 | 0.0751 | - |
1021
+ | 8.4050 | 43950 | 0.0545 | - |
1022
+ | 8.4146 | 44000 | 0.1116 | - |
1023
+ | 8.4242 | 44050 | 0.0788 | - |
1024
+ | 8.4337 | 44100 | 0.0556 | - |
1025
+ | 8.4433 | 44150 | 0.0551 | - |
1026
+ | 8.4529 | 44200 | 0.0367 | - |
1027
+ | 8.4624 | 44250 | 0.0612 | - |
1028
+ | 8.4720 | 44300 | 0.1028 | - |
1029
+ | 8.4815 | 44350 | 0.0747 | - |
1030
+ | 8.4911 | 44400 | 0.0523 | - |
1031
+ | 8.5007 | 44450 | 0.1209 | - |
1032
+ | 8.5102 | 44500 | 0.1103 | - |
1033
+ | 8.5198 | 44550 | 0.0639 | - |
1034
+ | 8.5294 | 44600 | 0.0834 | - |
1035
+ | 8.5389 | 44650 | 0.1181 | - |
1036
+ | 8.5485 | 44700 | 0.1078 | - |
1037
+ | 8.5580 | 44750 | 0.0594 | - |
1038
+ | 8.5676 | 44800 | 0.0596 | - |
1039
+ | 8.5772 | 44850 | 0.0902 | - |
1040
+ | 8.5867 | 44900 | 0.0898 | - |
1041
+ | 8.5963 | 44950 | 0.0505 | - |
1042
+ | 8.6059 | 45000 | 0.0741 | - |
1043
+ | 8.6154 | 45050 | 0.0853 | - |
1044
+ | 8.6250 | 45100 | 0.0464 | - |
1045
+ | 8.6345 | 45150 | 0.0432 | - |
1046
+ | 8.6441 | 45200 | 0.0986 | - |
1047
+ | 8.6537 | 45250 | 0.0816 | - |
1048
+ | 8.6632 | 45300 | 0.119 | - |
1049
+ | 8.6728 | 45350 | 0.0837 | - |
1050
+ | 8.6823 | 45400 | 0.0619 | - |
1051
+ | 8.6919 | 45450 | 0.0676 | - |
1052
+ | 8.7015 | 45500 | 0.0667 | - |
1053
+ | 8.7110 | 45550 | 0.1157 | - |
1054
+ | 8.7206 | 45600 | 0.054 | - |
1055
+ | 8.7302 | 45650 | 0.0834 | - |
1056
+ | 8.7397 | 45700 | 0.0373 | - |
1057
+ | 8.7493 | 45750 | 0.0975 | - |
1058
+ | 8.7588 | 45800 | 0.0558 | - |
1059
+ | 8.7684 | 45850 | 0.0595 | - |
1060
+ | 8.7780 | 45900 | 0.0826 | - |
1061
+ | 8.7875 | 45950 | 0.1136 | - |
1062
+ | 8.7971 | 46000 | 0.073 | - |
1063
+ | 8.8067 | 46050 | 0.0438 | - |
1064
+ | 8.8162 | 46100 | 0.1021 | - |
1065
+ | 8.8258 | 46150 | 0.0637 | - |
1066
+ | 8.8353 | 46200 | 0.1057 | - |
1067
+ | 8.8449 | 46250 | 0.0562 | - |
1068
+ | 8.8545 | 46300 | 0.0528 | - |
1069
+ | 8.8640 | 46350 | 0.0778 | - |
1070
+ | 8.8736 | 46400 | 0.0417 | - |
1071
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1072
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1078
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1080
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1081
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1082
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1102
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+
1189
+ ### Framework Versions
1190
+ - Python: 3.10.12
1191
+ - SetFit: 1.0.3
1192
+ - Sentence Transformers: 2.4.0
1193
+ - Transformers: 4.37.2
1194
+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.17.1
1196
+ - Tokenizers: 0.15.2
1197
+
1198
+ ## Citation
1199
+
1200
+ ### BibTeX
1201
+ ```bibtex
1202
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1203
+ doi = {10.48550/ARXIV.2209.11055},
1204
+ url = {https://arxiv.org/abs/2209.11055},
1205
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1206
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1207
+ title = {Efficient Few-Shot Learning Without Prompts},
1208
+ publisher = {arXiv},
1209
+ year = {2022},
1210
+ copyright = {Creative Commons Attribution 4.0 International}
1211
+ }
1212
+ ```
1213
+
1214
+ <!--
1215
+ ## Glossary
1216
+
1217
+ *Clearly define terms in order to be accessible across audiences.*
1218
+ -->
1219
+
1220
+ <!--
1221
+ ## Model Card Authors
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+
1223
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1224
+ -->
1225
+
1226
+ <!--
1227
+ ## Model Card Contact
1228
+
1229
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1230
+ -->
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