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faodl
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model_g20_multilabel_MiniLM-L12-v2_15_sample

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files
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  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +784 -0
  4. config.json +25 -0
  5. config_sentence_transformers.json +10 -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. special_tokens_map.json +51 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +65 -0
  14. unigram.json +3 -0
.gitattributes CHANGED
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: This expenditure has financed projects in road works, energy, agriculture
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+ and water. Madam Speaker, priority allocations are being made to power generation,
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+ road networks, irrigation schemes, schools and improvement of health infrastructure.
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+ Addressing constraints in transport, energy and health and education and improving
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+ service delivery, will accord Ugandans a better quality of life.
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+ - text: interoperability, acceptance) that are not exclusively related to G2P programs
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+ and that need to be addressed to realize digital payments’ benefits. Unemployment
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+ benefits Social security contributions Labor Markets Activation measures Labor
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+ market regulations Reduced work time Wage subsidies 418 (back to the top) Sudan
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+ Social Assistance Cash-based transfers Cash transfers (conditional and unconditional)
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+ One-off cash transfers Childcare support Social pensions In-kind transfers Food,
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+ vouchers, others • The ministry of labor and social development will provide in
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+ kind support to poor households, informal workers, teachers, and casual workers
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+ (total 2,050,000 households). A total of 100,000 Bahraini will benefit from the
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+ measure (cost of BD 215 million)54 55 Social security contributions Labor Markets
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+ Activation measures Labor market regulations Reduced work time Wage subsidies
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+ 54 https://www.moh.gov.bh/COVID19/Details/3969 55 https://www.moh.gov.bh/COVID19/Details/3982
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+ 70 (back to the top) Bangladesh Social Assistance Cash-based transfers Cash transfers
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+ (conditional and unconditional) • Benefit under key safety net programs will be
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+ increased (amount not determined yet).
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+ - text: National Food and Nutrition Strategic Plan 2011-2015 55 7) Promote practices
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+ that enhance sustainable availability, accessibility and consumption of a variety
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+ of foods at household level. National Food and Nutrition Strategic Plan 2011-2015
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+ 54 5.11 Strategic Direction 11 Expanding and Developing Communication and Advocacy
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+ Support for Food and Nutrition Interventions at Various Levels. National Food
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+ and Nutrition Strategic Plan 2011-2015 18 3.
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+ - text: 13 (Deroga delle norme in materia di riconoscimento delle qualifiche professionali
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+ sanitarie) 1. 93 (Disposizioni in materia di autoservizi pubblici non di linea)
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+ 1. 4 (Disciplina delle aree sanitarie temporanee) 1.
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+ - text: Furthermore, there is a need for improvements in forecasting, distribution
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+ and funding of micronutrient commodities, as well as the provision of adequate
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+ resources to ensure universal coverage. The National Nutrition Program is also
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+ responsible for estimating the demand of nutrition commodities, such as vitamin
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+ A capsules, iron/folic acid tablets, and Mebedazole for deworming. It is therefore
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+ limited in scope to address the full spectrum of causes of undernutrition, which
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+ requires a broad coalition of multisectoral interventions.
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: false
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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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 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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+ - **Classification head:** a OneVsRestClassifier instance
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+ - **Maximum Sequence Length:** 128 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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+ ## 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("faodl/model_g20_multilabel_MiniLM-L12-v2_15_sample")
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+ # Run inference
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+ preds = model("13 (Deroga delle norme in materia di riconoscimento delle qualifiche professionali sanitarie) 1. 93 (Disposizioni in materia di autoservizi pubblici non di linea) 1. 4 (Disciplina delle aree sanitarie temporanee) 1.")
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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 | 3 | 93.9143 | 1651 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (8, 8)
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+ - num_epochs: (4, 4)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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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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+ - 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: 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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+ | 0.0001 | 1 | 0.2185 | - |
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+ | 0.0068 | 50 | 0.1579 | - |
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+ | 0.0136 | 100 | 0.1625 | - |
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+ | 0.0204 | 150 | 0.1649 | - |
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+ | 0.0272 | 200 | 0.1511 | - |
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+ | 0.0340 | 250 | 0.1263 | - |
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+ | 0.0408 | 300 | 0.1335 | - |
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+ | 0.0476 | 350 | 0.1276 | - |
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+ | 0.0544 | 400 | 0.1143 | - |
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+ | 0.0612 | 450 | 0.1095 | - |
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+ | 0.0680 | 500 | 0.1029 | - |
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+ | 0.0748 | 550 | 0.1161 | - |
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+ | 0.0816 | 600 | 0.114 | - |
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+ | 0.0884 | 650 | 0.0945 | - |
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+ | 0.0952 | 700 | 0.0903 | - |
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+ | 0.1020 | 750 | 0.0793 | - |
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+ | 0.1088 | 800 | 0.0848 | - |
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+ | 0.1156 | 850 | 0.0802 | - |
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+ | 0.1224 | 900 | 0.0819 | - |
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+ | 0.1293 | 950 | 0.0802 | - |
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+ | 0.1361 | 1000 | 0.0879 | - |
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+ | 0.1429 | 1050 | 0.0738 | - |
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+ | 0.1497 | 1100 | 0.0737 | - |
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+ | 0.1565 | 1150 | 0.0761 | - |
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+ | 0.1633 | 1200 | 0.0715 | - |
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+ | 0.1701 | 1250 | 0.0633 | - |
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+ | 0.1769 | 1300 | 0.06 | - |
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+ | 0.1837 | 1350 | 0.06 | - |
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+ | 0.1905 | 1400 | 0.0641 | - |
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+ | 0.1973 | 1450 | 0.057 | - |
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+ | 0.2041 | 1500 | 0.0554 | - |
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+ | 0.2109 | 1550 | 0.0552 | - |
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+ | 0.2177 | 1600 | 0.0447 | - |
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+ | 0.2245 | 1650 | 0.0442 | - |
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+ | 0.2313 | 1700 | 0.0547 | - |
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+ | 0.2381 | 1750 | 0.0358 | - |
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+ | 0.2449 | 1800 | 0.0503 | - |
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+ | 0.2517 | 1850 | 0.0366 | - |
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+ | 0.2585 | 1900 | 0.0421 | - |
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+ | 0.2653 | 1950 | 0.0332 | - |
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+ | 0.2721 | 2000 | 0.0429 | - |
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+ | 0.2789 | 2050 | 0.0316 | - |
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+ | 0.2857 | 2100 | 0.0382 | - |
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+ | 0.2925 | 2150 | 0.0456 | - |
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+ | 0.2993 | 2200 | 0.0327 | - |
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+ | 0.3061 | 2250 | 0.0286 | - |
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+ | 0.3129 | 2300 | 0.0295 | - |
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+ | 0.3197 | 2350 | 0.0305 | - |
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+ | 0.3265 | 2400 | 0.0223 | - |
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+ | 0.3333 | 2450 | 0.0228 | - |
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+ | 0.3401 | 2500 | 0.0305 | - |
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+ | 0.3469 | 2550 | 0.0294 | - |
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+ | 0.3537 | 2600 | 0.0342 | - |
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+ | 0.3605 | 2650 | 0.0275 | - |
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+ | 0.3673 | 2700 | 0.0181 | - |
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+ | 0.3741 | 2750 | 0.0267 | - |
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+ | 0.3810 | 2800 | 0.0229 | - |
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+ | 0.3878 | 2850 | 0.0213 | - |
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+ | 0.3946 | 2900 | 0.0203 | - |
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+ | 0.4014 | 2950 | 0.0281 | - |
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+ | 0.4082 | 3000 | 0.025 | - |
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+ | 0.4150 | 3050 | 0.0233 | - |
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+ | 0.4218 | 3100 | 0.0306 | - |
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+ | 0.4286 | 3150 | 0.0159 | - |
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+ | 0.4354 | 3200 | 0.0246 | - |
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+ | 0.7211 | 5300 | 0.0106 | - |
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+ | 0.7279 | 5350 | 0.0086 | - |
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+ | 0.7483 | 5500 | 0.0085 | - |
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+ | 0.7619 | 5600 | 0.0143 | - |
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+ | 0.7755 | 5700 | 0.0185 | - |
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+ | 0.7823 | 5750 | 0.0064 | - |
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+ | 0.7959 | 5850 | 0.0116 | - |
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+ | 0.8027 | 5900 | 0.0063 | - |
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+ | 0.8095 | 5950 | 0.0166 | - |
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+ | 0.8299 | 6100 | 0.0121 | - |
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+ | 0.8367 | 6150 | 0.0214 | - |
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+ | 0.8435 | 6200 | 0.009 | - |
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+ | 0.8503 | 6250 | 0.0133 | - |
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+ | 0.8571 | 6300 | 0.0062 | - |
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+ | 0.8639 | 6350 | 0.0077 | - |
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+ | 0.8707 | 6400 | 0.0201 | - |
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+ | 0.8776 | 6450 | 0.0163 | - |
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+ | 0.8844 | 6500 | 0.0071 | - |
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+ | 0.8912 | 6550 | 0.0138 | - |
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+ | 0.8980 | 6600 | 0.0131 | - |
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+ | 0.9048 | 6650 | 0.0126 | - |
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+ | 0.9116 | 6700 | 0.0042 | - |
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+ | 0.9524 | 7000 | 0.009 | - |
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+ | 1.1156 | 8200 | 0.0055 | - |
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+ | 1.1361 | 8350 | 0.0038 | - |
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+ | 1.2177 | 8950 | 0.0109 | - |
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+ | 1.2245 | 9000 | 0.008 | - |
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+ | 1.2313 | 9050 | 0.0122 | - |
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+ | 1.2381 | 9100 | 0.0081 | - |
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+ | 1.2449 | 9150 | 0.0014 | - |
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+ | 1.2517 | 9200 | 0.0046 | - |
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+ | 1.2585 | 9250 | 0.0049 | - |
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+ | 1.2653 | 9300 | 0.0147 | - |
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+ | 1.2721 | 9350 | 0.0105 | - |
341
+ | 1.2789 | 9400 | 0.0126 | - |
342
+ | 1.2857 | 9450 | 0.0031 | - |
343
+ | 1.2925 | 9500 | 0.0039 | - |
344
+ | 1.2993 | 9550 | 0.0038 | - |
345
+ | 1.3061 | 9600 | 0.0047 | - |
346
+ | 1.3129 | 9650 | 0.0037 | - |
347
+ | 1.3197 | 9700 | 0.0103 | - |
348
+ | 1.3265 | 9750 | 0.0007 | - |
349
+ | 1.3333 | 9800 | 0.0053 | - |
350
+ | 1.3401 | 9850 | 0.0018 | - |
351
+ | 1.3469 | 9900 | 0.0057 | - |
352
+ | 1.3537 | 9950 | 0.0044 | - |
353
+ | 1.3605 | 10000 | 0.0109 | - |
354
+ | 1.3673 | 10050 | 0.0056 | - |
355
+ | 1.3741 | 10100 | 0.0081 | - |
356
+ | 1.3810 | 10150 | 0.008 | - |
357
+ | 1.3878 | 10200 | 0.0081 | - |
358
+ | 1.3946 | 10250 | 0.0033 | - |
359
+ | 1.4014 | 10300 | 0.0055 | - |
360
+ | 1.4082 | 10350 | 0.0019 | - |
361
+ | 1.4150 | 10400 | 0.0033 | - |
362
+ | 1.4218 | 10450 | 0.0033 | - |
363
+ | 1.4286 | 10500 | 0.0058 | - |
364
+ | 1.4354 | 10550 | 0.0047 | - |
365
+ | 1.4422 | 10600 | 0.0068 | - |
366
+ | 1.4490 | 10650 | 0.0052 | - |
367
+ | 1.4558 | 10700 | 0.0033 | - |
368
+ | 1.4626 | 10750 | 0.001 | - |
369
+ | 1.4694 | 10800 | 0.0101 | - |
370
+ | 1.4762 | 10850 | 0.0011 | - |
371
+ | 1.4830 | 10900 | 0.008 | - |
372
+ | 1.4898 | 10950 | 0.0038 | - |
373
+ | 1.4966 | 11000 | 0.0033 | - |
374
+ | 1.5034 | 11050 | 0.0031 | - |
375
+ | 1.5102 | 11100 | 0.0107 | - |
376
+ | 1.5170 | 11150 | 0.004 | - |
377
+ | 1.5238 | 11200 | 0.0009 | - |
378
+ | 1.5306 | 11250 | 0.0034 | - |
379
+ | 1.5374 | 11300 | 0.0033 | - |
380
+ | 1.5442 | 11350 | 0.0011 | - |
381
+ | 1.5510 | 11400 | 0.0081 | - |
382
+ | 1.5578 | 11450 | 0.0025 | - |
383
+ | 1.5646 | 11500 | 0.0065 | - |
384
+ | 1.5714 | 11550 | 0.0069 | - |
385
+ | 1.5782 | 11600 | 0.0053 | - |
386
+ | 1.5850 | 11650 | 0.0031 | - |
387
+ | 1.5918 | 11700 | 0.0059 | - |
388
+ | 1.5986 | 11750 | 0.006 | - |
389
+ | 1.6054 | 11800 | 0.0007 | - |
390
+ | 1.6122 | 11850 | 0.0027 | - |
391
+ | 1.6190 | 11900 | 0.003 | - |
392
+ | 1.6259 | 11950 | 0.0052 | - |
393
+ | 1.6327 | 12000 | 0.0065 | - |
394
+ | 1.6395 | 12050 | 0.0032 | - |
395
+ | 1.6463 | 12100 | 0.0054 | - |
396
+ | 1.6531 | 12150 | 0.0063 | - |
397
+ | 1.6599 | 12200 | 0.0155 | - |
398
+ | 1.6667 | 12250 | 0.0105 | - |
399
+ | 1.6735 | 12300 | 0.0067 | - |
400
+ | 1.6803 | 12350 | 0.0034 | - |
401
+ | 1.6871 | 12400 | 0.0076 | - |
402
+ | 1.6939 | 12450 | 0.0042 | - |
403
+ | 1.7007 | 12500 | 0.003 | - |
404
+ | 1.7075 | 12550 | 0.0096 | - |
405
+ | 1.7143 | 12600 | 0.0054 | - |
406
+ | 1.7211 | 12650 | 0.005 | - |
407
+ | 1.7279 | 12700 | 0.0039 | - |
408
+ | 1.7347 | 12750 | 0.0061 | - |
409
+ | 1.7415 | 12800 | 0.0027 | - |
410
+ | 1.7483 | 12850 | 0.0033 | - |
411
+ | 1.7551 | 12900 | 0.0028 | - |
412
+ | 1.7619 | 12950 | 0.0038 | - |
413
+ | 1.7687 | 13000 | 0.0083 | - |
414
+ | 1.7755 | 13050 | 0.0074 | - |
415
+ | 1.7823 | 13100 | 0.0015 | - |
416
+ | 1.7891 | 13150 | 0.0037 | - |
417
+ | 1.7959 | 13200 | 0.0041 | - |
418
+ | 1.8027 | 13250 | 0.0007 | - |
419
+ | 1.8095 | 13300 | 0.0046 | - |
420
+ | 1.8163 | 13350 | 0.0007 | - |
421
+ | 1.8231 | 13400 | 0.0019 | - |
422
+ | 1.8299 | 13450 | 0.0051 | - |
423
+ | 1.8367 | 13500 | 0.0007 | - |
424
+ | 1.8435 | 13550 | 0.0013 | - |
425
+ | 1.8503 | 13600 | 0.0045 | - |
426
+ | 1.8571 | 13650 | 0.0006 | - |
427
+ | 1.8639 | 13700 | 0.0028 | - |
428
+ | 1.8707 | 13750 | 0.0028 | - |
429
+ | 1.8776 | 13800 | 0.001 | - |
430
+ | 1.8844 | 13850 | 0.001 | - |
431
+ | 1.8912 | 13900 | 0.0075 | - |
432
+ | 1.8980 | 13950 | 0.0041 | - |
433
+ | 1.9048 | 14000 | 0.0115 | - |
434
+ | 1.9116 | 14050 | 0.0007 | - |
435
+ | 1.9184 | 14100 | 0.0069 | - |
436
+ | 1.9252 | 14150 | 0.0017 | - |
437
+ | 1.9320 | 14200 | 0.005 | - |
438
+ | 1.9388 | 14250 | 0.0028 | - |
439
+ | 1.9456 | 14300 | 0.0029 | - |
440
+ | 1.9524 | 14350 | 0.0052 | - |
441
+ | 1.9592 | 14400 | 0.0023 | - |
442
+ | 1.9660 | 14450 | 0.0046 | - |
443
+ | 1.9728 | 14500 | 0.001 | - |
444
+ | 1.9796 | 14550 | 0.0009 | - |
445
+ | 1.9864 | 14600 | 0.0059 | - |
446
+ | 1.9932 | 14650 | 0.0075 | - |
447
+ | 2.0 | 14700 | 0.003 | - |
448
+ | 2.0068 | 14750 | 0.0088 | - |
449
+ | 2.0136 | 14800 | 0.0073 | - |
450
+ | 2.0204 | 14850 | 0.0023 | - |
451
+ | 2.0272 | 14900 | 0.0104 | - |
452
+ | 2.0340 | 14950 | 0.0024 | - |
453
+ | 2.0408 | 15000 | 0.0059 | - |
454
+ | 2.0476 | 15050 | 0.0041 | - |
455
+ | 2.0544 | 15100 | 0.0079 | - |
456
+ | 2.0612 | 15150 | 0.0011 | - |
457
+ | 2.0680 | 15200 | 0.0038 | - |
458
+ | 2.0748 | 15250 | 0.0009 | - |
459
+ | 2.0816 | 15300 | 0.0057 | - |
460
+ | 2.0884 | 15350 | 0.0025 | - |
461
+ | 2.0952 | 15400 | 0.0033 | - |
462
+ | 2.1020 | 15450 | 0.0093 | - |
463
+ | 2.1088 | 15500 | 0.0006 | - |
464
+ | 2.1156 | 15550 | 0.0024 | - |
465
+ | 2.1224 | 15600 | 0.0044 | - |
466
+ | 2.1293 | 15650 | 0.0069 | - |
467
+ | 2.1361 | 15700 | 0.0051 | - |
468
+ | 2.1429 | 15750 | 0.008 | - |
469
+ | 2.1497 | 15800 | 0.0047 | - |
470
+ | 2.1565 | 15850 | 0.0012 | - |
471
+ | 2.1633 | 15900 | 0.001 | - |
472
+ | 2.1701 | 15950 | 0.0019 | - |
473
+ | 2.1769 | 16000 | 0.0024 | - |
474
+ | 2.1837 | 16050 | 0.0066 | - |
475
+ | 2.1905 | 16100 | 0.0025 | - |
476
+ | 2.1973 | 16150 | 0.0037 | - |
477
+ | 2.2041 | 16200 | 0.0033 | - |
478
+ | 2.2109 | 16250 | 0.0023 | - |
479
+ | 2.2177 | 16300 | 0.0013 | - |
480
+ | 2.2245 | 16350 | 0.0033 | - |
481
+ | 2.2313 | 16400 | 0.0029 | - |
482
+ | 2.2381 | 16450 | 0.0038 | - |
483
+ | 2.2449 | 16500 | 0.0015 | - |
484
+ | 2.2517 | 16550 | 0.0007 | - |
485
+ | 2.2585 | 16600 | 0.0031 | - |
486
+ | 2.2653 | 16650 | 0.0061 | - |
487
+ | 2.2721 | 16700 | 0.0011 | - |
488
+ | 2.2789 | 16750 | 0.0049 | - |
489
+ | 2.2857 | 16800 | 0.0012 | - |
490
+ | 2.2925 | 16850 | 0.0036 | - |
491
+ | 2.2993 | 16900 | 0.004 | - |
492
+ | 2.3061 | 16950 | 0.0005 | - |
493
+ | 2.3129 | 17000 | 0.0019 | - |
494
+ | 2.3197 | 17050 | 0.003 | - |
495
+ | 2.3265 | 17100 | 0.0006 | - |
496
+ | 2.3333 | 17150 | 0.0009 | - |
497
+ | 2.3401 | 17200 | 0.0013 | - |
498
+ | 2.3469 | 17250 | 0.0018 | - |
499
+ | 2.3537 | 17300 | 0.0007 | - |
500
+ | 2.3605 | 17350 | 0.001 | - |
501
+ | 2.3673 | 17400 | 0.0054 | - |
502
+ | 2.3741 | 17450 | 0.0004 | - |
503
+ | 2.3810 | 17500 | 0.0028 | - |
504
+ | 2.3878 | 17550 | 0.0005 | - |
505
+ | 2.3946 | 17600 | 0.0003 | - |
506
+ | 2.4014 | 17650 | 0.0004 | - |
507
+ | 2.4082 | 17700 | 0.0031 | - |
508
+ | 2.4150 | 17750 | 0.0004 | - |
509
+ | 2.4218 | 17800 | 0.0013 | - |
510
+ | 2.4286 | 17850 | 0.0017 | - |
511
+ | 2.4354 | 17900 | 0.0013 | - |
512
+ | 2.4422 | 17950 | 0.0025 | - |
513
+ | 2.4490 | 18000 | 0.0004 | - |
514
+ | 2.4558 | 18050 | 0.0029 | - |
515
+ | 2.4626 | 18100 | 0.0023 | - |
516
+ | 2.4694 | 18150 | 0.0027 | - |
517
+ | 2.4762 | 18200 | 0.0015 | - |
518
+ | 2.4830 | 18250 | 0.0006 | - |
519
+ | 2.4898 | 18300 | 0.0024 | - |
520
+ | 2.4966 | 18350 | 0.0021 | - |
521
+ | 2.5034 | 18400 | 0.0005 | - |
522
+ | 2.5102 | 18450 | 0.0004 | - |
523
+ | 2.5170 | 18500 | 0.0042 | - |
524
+ | 2.5238 | 18550 | 0.0005 | - |
525
+ | 2.5306 | 18600 | 0.0012 | - |
526
+ | 2.5374 | 18650 | 0.005 | - |
527
+ | 2.5442 | 18700 | 0.0032 | - |
528
+ | 2.5510 | 18750 | 0.0079 | - |
529
+ | 2.5578 | 18800 | 0.001 | - |
530
+ | 2.5646 | 18850 | 0.0008 | - |
531
+ | 2.5714 | 18900 | 0.0042 | - |
532
+ | 2.5782 | 18950 | 0.001 | - |
533
+ | 2.5850 | 19000 | 0.001 | - |
534
+ | 2.5918 | 19050 | 0.0009 | - |
535
+ | 2.5986 | 19100 | 0.0003 | - |
536
+ | 2.6054 | 19150 | 0.0003 | - |
537
+ | 2.6122 | 19200 | 0.0003 | - |
538
+ | 2.6190 | 19250 | 0.0035 | - |
539
+ | 2.6259 | 19300 | 0.0006 | - |
540
+ | 2.6327 | 19350 | 0.0035 | - |
541
+ | 2.6395 | 19400 | 0.0003 | - |
542
+ | 2.6463 | 19450 | 0.0021 | - |
543
+ | 2.6531 | 19500 | 0.0005 | - |
544
+ | 2.6599 | 19550 | 0.004 | - |
545
+ | 2.6667 | 19600 | 0.0023 | - |
546
+ | 2.6735 | 19650 | 0.0006 | - |
547
+ | 2.6803 | 19700 | 0.004 | - |
548
+ | 2.6871 | 19750 | 0.0015 | - |
549
+ | 2.6939 | 19800 | 0.0008 | - |
550
+ | 2.7007 | 19850 | 0.0022 | - |
551
+ | 2.7075 | 19900 | 0.001 | - |
552
+ | 2.7143 | 19950 | 0.0007 | - |
553
+ | 2.7211 | 20000 | 0.0013 | - |
554
+ | 2.7279 | 20050 | 0.0004 | - |
555
+ | 2.7347 | 20100 | 0.001 | - |
556
+ | 2.7415 | 20150 | 0.0013 | - |
557
+ | 2.7483 | 20200 | 0.0004 | - |
558
+ | 2.7551 | 20250 | 0.0035 | - |
559
+ | 2.7619 | 20300 | 0.0006 | - |
560
+ | 2.7687 | 20350 | 0.001 | - |
561
+ | 2.7755 | 20400 | 0.0003 | - |
562
+ | 2.7823 | 20450 | 0.0006 | - |
563
+ | 2.7891 | 20500 | 0.0012 | - |
564
+ | 2.7959 | 20550 | 0.0003 | - |
565
+ | 2.8027 | 20600 | 0.0031 | - |
566
+ | 2.8095 | 20650 | 0.0005 | - |
567
+ | 2.8163 | 20700 | 0.0008 | - |
568
+ | 2.8231 | 20750 | 0.0006 | - |
569
+ | 2.8299 | 20800 | 0.0005 | - |
570
+ | 2.8367 | 20850 | 0.0004 | - |
571
+ | 2.8435 | 20900 | 0.0002 | - |
572
+ | 2.8503 | 20950 | 0.0011 | - |
573
+ | 2.8571 | 21000 | 0.0002 | - |
574
+ | 2.8639 | 21050 | 0.0033 | - |
575
+ | 2.8707 | 21100 | 0.0024 | - |
576
+ | 2.8776 | 21150 | 0.0004 | - |
577
+ | 2.8844 | 21200 | 0.0002 | - |
578
+ | 2.8912 | 21250 | 0.0029 | - |
579
+ | 2.8980 | 21300 | 0.0004 | - |
580
+ | 2.9048 | 21350 | 0.0003 | - |
581
+ | 2.9116 | 21400 | 0.0024 | - |
582
+ | 2.9184 | 21450 | 0.0027 | - |
583
+ | 2.9252 | 21500 | 0.0003 | - |
584
+ | 2.9320 | 21550 | 0.0006 | - |
585
+ | 2.9388 | 21600 | 0.0002 | - |
586
+ | 2.9456 | 21650 | 0.0021 | - |
587
+ | 2.9524 | 21700 | 0.0011 | - |
588
+ | 2.9592 | 21750 | 0.0006 | - |
589
+ | 2.9660 | 21800 | 0.0002 | - |
590
+ | 2.9728 | 21850 | 0.0004 | - |
591
+ | 2.9796 | 21900 | 0.0008 | - |
592
+ | 2.9864 | 21950 | 0.0028 | - |
593
+ | 2.9932 | 22000 | 0.0004 | - |
594
+ | 3.0 | 22050 | 0.0002 | - |
595
+ | 3.0068 | 22100 | 0.0002 | - |
596
+ | 3.0136 | 22150 | 0.0026 | - |
597
+ | 3.0204 | 22200 | 0.0002 | - |
598
+ | 3.0272 | 22250 | 0.0004 | - |
599
+ | 3.0340 | 22300 | 0.0005 | - |
600
+ | 3.0408 | 22350 | 0.0005 | - |
601
+ | 3.0476 | 22400 | 0.0022 | - |
602
+ | 3.0544 | 22450 | 0.0006 | - |
603
+ | 3.0612 | 22500 | 0.0009 | - |
604
+ | 3.0680 | 22550 | 0.0004 | - |
605
+ | 3.0748 | 22600 | 0.0002 | - |
606
+ | 3.0816 | 22650 | 0.0003 | - |
607
+ | 3.0884 | 22700 | 0.0002 | - |
608
+ | 3.0952 | 22750 | 0.0002 | - |
609
+ | 3.1020 | 22800 | 0.0002 | - |
610
+ | 3.1088 | 22850 | 0.0041 | - |
611
+ | 3.1156 | 22900 | 0.0014 | - |
612
+ | 3.1224 | 22950 | 0.0019 | - |
613
+ | 3.1293 | 23000 | 0.0023 | - |
614
+ | 3.1361 | 23050 | 0.0003 | - |
615
+ | 3.1429 | 23100 | 0.0005 | - |
616
+ | 3.1497 | 23150 | 0.0003 | - |
617
+ | 3.1565 | 23200 | 0.0009 | - |
618
+ | 3.1633 | 23250 | 0.0023 | - |
619
+ | 3.1701 | 23300 | 0.0003 | - |
620
+ | 3.1769 | 23350 | 0.0002 | - |
621
+ | 3.1837 | 23400 | 0.0003 | - |
622
+ | 3.1905 | 23450 | 0.0003 | - |
623
+ | 3.1973 | 23500 | 0.0015 | - |
624
+ | 3.2041 | 23550 | 0.0002 | - |
625
+ | 3.2109 | 23600 | 0.0004 | - |
626
+ | 3.2177 | 23650 | 0.0004 | - |
627
+ | 3.2245 | 23700 | 0.0009 | - |
628
+ | 3.2313 | 23750 | 0.0002 | - |
629
+ | 3.2381 | 23800 | 0.0003 | - |
630
+ | 3.2449 | 23850 | 0.0002 | - |
631
+ | 3.2517 | 23900 | 0.0001 | - |
632
+ | 3.2585 | 23950 | 0.0003 | - |
633
+ | 3.2653 | 24000 | 0.0002 | - |
634
+ | 3.2721 | 24050 | 0.0019 | - |
635
+ | 3.2789 | 24100 | 0.0002 | - |
636
+ | 3.2857 | 24150 | 0.0002 | - |
637
+ | 3.2925 | 24200 | 0.0002 | - |
638
+ | 3.2993 | 24250 | 0.0002 | - |
639
+ | 3.3061 | 24300 | 0.0003 | - |
640
+ | 3.3129 | 24350 | 0.0007 | - |
641
+ | 3.3197 | 24400 | 0.0009 | - |
642
+ | 3.3265 | 24450 | 0.0006 | - |
643
+ | 3.3333 | 24500 | 0.0003 | - |
644
+ | 3.3401 | 24550 | 0.0008 | - |
645
+ | 3.3469 | 24600 | 0.0007 | - |
646
+ | 3.3537 | 24650 | 0.0003 | - |
647
+ | 3.3605 | 24700 | 0.0002 | - |
648
+ | 3.3673 | 24750 | 0.0001 | - |
649
+ | 3.3741 | 24800 | 0.0001 | - |
650
+ | 3.3810 | 24850 | 0.0002 | - |
651
+ | 3.3878 | 24900 | 0.0009 | - |
652
+ | 3.3946 | 24950 | 0.0005 | - |
653
+ | 3.4014 | 25000 | 0.0001 | - |
654
+ | 3.4082 | 25050 | 0.0003 | - |
655
+ | 3.4150 | 25100 | 0.0001 | - |
656
+ | 3.4218 | 25150 | 0.0002 | - |
657
+ | 3.4286 | 25200 | 0.0002 | - |
658
+ | 3.4354 | 25250 | 0.0003 | - |
659
+ | 3.4422 | 25300 | 0.0002 | - |
660
+ | 3.4490 | 25350 | 0.0004 | - |
661
+ | 3.4558 | 25400 | 0.0005 | - |
662
+ | 3.4626 | 25450 | 0.0005 | - |
663
+ | 3.4694 | 25500 | 0.0002 | - |
664
+ | 3.4762 | 25550 | 0.0003 | - |
665
+ | 3.4830 | 25600 | 0.0001 | - |
666
+ | 3.4898 | 25650 | 0.0003 | - |
667
+ | 3.4966 | 25700 | 0.0006 | - |
668
+ | 3.5034 | 25750 | 0.0002 | - |
669
+ | 3.5102 | 25800 | 0.0003 | - |
670
+ | 3.5170 | 25850 | 0.0005 | - |
671
+ | 3.5238 | 25900 | 0.0002 | - |
672
+ | 3.5306 | 25950 | 0.0003 | - |
673
+ | 3.5374 | 26000 | 0.0002 | - |
674
+ | 3.5442 | 26050 | 0.0004 | - |
675
+ | 3.5510 | 26100 | 0.0001 | - |
676
+ | 3.5578 | 26150 | 0.0001 | - |
677
+ | 3.5646 | 26200 | 0.0002 | - |
678
+ | 3.5714 | 26250 | 0.0001 | - |
679
+ | 3.5782 | 26300 | 0.0005 | - |
680
+ | 3.5850 | 26350 | 0.0002 | - |
681
+ | 3.5918 | 26400 | 0.0001 | - |
682
+ | 3.5986 | 26450 | 0.0001 | - |
683
+ | 3.6054 | 26500 | 0.0003 | - |
684
+ | 3.6122 | 26550 | 0.0002 | - |
685
+ | 3.6190 | 26600 | 0.0002 | - |
686
+ | 3.6259 | 26650 | 0.0001 | - |
687
+ | 3.6327 | 26700 | 0.0001 | - |
688
+ | 3.6395 | 26750 | 0.0001 | - |
689
+ | 3.6463 | 26800 | 0.0005 | - |
690
+ | 3.6531 | 26850 | 0.0001 | - |
691
+ | 3.6599 | 26900 | 0.0002 | - |
692
+ | 3.6667 | 26950 | 0.0001 | - |
693
+ | 3.6735 | 27000 | 0.0001 | - |
694
+ | 3.6803 | 27050 | 0.0002 | - |
695
+ | 3.6871 | 27100 | 0.0002 | - |
696
+ | 3.6939 | 27150 | 0.0001 | - |
697
+ | 3.7007 | 27200 | 0.0001 | - |
698
+ | 3.7075 | 27250 | 0.0002 | - |
699
+ | 3.7143 | 27300 | 0.0002 | - |
700
+ | 3.7211 | 27350 | 0.0001 | - |
701
+ | 3.7279 | 27400 | 0.0008 | - |
702
+ | 3.7347 | 27450 | 0.0002 | - |
703
+ | 3.7415 | 27500 | 0.0008 | - |
704
+ | 3.7483 | 27550 | 0.0005 | - |
705
+ | 3.7551 | 27600 | 0.0002 | - |
706
+ | 3.7619 | 27650 | 0.0003 | - |
707
+ | 3.7687 | 27700 | 0.0002 | - |
708
+ | 3.7755 | 27750 | 0.0007 | - |
709
+ | 3.7823 | 27800 | 0.0003 | - |
710
+ | 3.7891 | 27850 | 0.0001 | - |
711
+ | 3.7959 | 27900 | 0.0006 | - |
712
+ | 3.8027 | 27950 | 0.0002 | - |
713
+ | 3.8095 | 28000 | 0.0001 | - |
714
+ | 3.8163 | 28050 | 0.0001 | - |
715
+ | 3.8231 | 28100 | 0.0002 | - |
716
+ | 3.8299 | 28150 | 0.0001 | - |
717
+ | 3.8367 | 28200 | 0.0001 | - |
718
+ | 3.8435 | 28250 | 0.0004 | - |
719
+ | 3.8503 | 28300 | 0.0001 | - |
720
+ | 3.8571 | 28350 | 0.0001 | - |
721
+ | 3.8639 | 28400 | 0.0001 | - |
722
+ | 3.8707 | 28450 | 0.0005 | - |
723
+ | 3.8776 | 28500 | 0.0004 | - |
724
+ | 3.8844 | 28550 | 0.0001 | - |
725
+ | 3.8912 | 28600 | 0.0002 | - |
726
+ | 3.8980 | 28650 | 0.0002 | - |
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+ | 3.9048 | 28700 | 0.0003 | - |
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+ | 3.9116 | 28750 | 0.0001 | - |
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+ | 3.9184 | 28800 | 0.0002 | - |
730
+ | 3.9252 | 28850 | 0.0001 | - |
731
+ | 3.9320 | 28900 | 0.0001 | - |
732
+ | 3.9388 | 28950 | 0.0002 | - |
733
+ | 3.9456 | 29000 | 0.0002 | - |
734
+ | 3.9524 | 29050 | 0.0001 | - |
735
+ | 3.9592 | 29100 | 0.0001 | - |
736
+ | 3.9660 | 29150 | 0.0002 | - |
737
+ | 3.9728 | 29200 | 0.0002 | - |
738
+ | 3.9796 | 29250 | 0.0003 | - |
739
+ | 3.9864 | 29300 | 0.0001 | - |
740
+ | 3.9932 | 29350 | 0.0007 | - |
741
+ | 4.0 | 29400 | 0.0007 | - |
742
+
743
+ ### Framework Versions
744
+ - Python: 3.11.13
745
+ - SetFit: 1.1.2
746
+ - Sentence Transformers: 4.1.0
747
+ - Transformers: 4.52.4
748
+ - PyTorch: 2.6.0+cu124
749
+ - Datasets: 3.6.0
750
+ - Tokenizers: 0.21.2
751
+
752
+ ## Citation
753
+
754
+ ### BibTeX
755
+ ```bibtex
756
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
757
+ doi = {10.48550/ARXIV.2209.11055},
758
+ url = {https://arxiv.org/abs/2209.11055},
759
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
760
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
761
+ title = {Efficient Few-Shot Learning Without Prompts},
762
+ publisher = {arXiv},
763
+ year = {2022},
764
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
766
+ ```
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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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