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faodl
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model_cca_multilabel_MiniLM-L12-70prop-data-augmented

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Model card Files
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  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +504 -0
  4. config.json +25 -0
  5. config_sentence_transformers.json +14 -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
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,504 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: Strengthen macro-fiscal resilience through risk-informed public investment
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+ planning, including scenario-based budgeting and contingent financing arrangements.
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+ - text: 'finding environmentally sustainable energy solutions is central to the document.
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+ it seeks to facilitate cultural, institutional and technological change in a way
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+ that supports ''''aggressive'''' advances in energy efficiency and conservation,
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+ minimises greenhouse emissions and ultimately provides green growth. these energy
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+ efficiency and conservation goals are seen as ''''no regrets'''' mitigation actions
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+ that can have positive impacts on society and the economy, principally by reducing
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+ costs and dependency on fossil fuel imports. overall the policy propose to reduce
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+ the percentage of petroleum in the country''''s energy supply mix from the current
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+ 95 percent (does not state to what level) and increase the percentage of renewables
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+ in the energy mix with proposed targets of 11 percent by 2012, 12.5 percent by
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+ 2015 and 20 percent by 2030. six sub-policies exist to support the national energy
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+ policy, namely: - a carbon emissions trading policy developed to address jamaica''''s
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+ participation in the clean development mechanism - energy-from-waste policy -
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+ national renewable energy policy 2010-2030 - national energy from waste policy
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+ 2010-2030 - energy conservation and efficiency policy - biofuels policy'
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+ - text: 'objetivos: 1. promover la garantía del derecho a la alimentación para la
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+ población general y en especial para las personas y grupos de mayor vulnerabilidad.
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+ 2. respetar la identidad cultural, las necesidades nutricionales según el ciclo
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+ de vida y la diversidad de formas de producción, de consumo y comercialización
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+ agropecuaria, fortaleciendo los mercados locales, sin contraponerse al comercio
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+ agroalimentario internacional, favoreciéndose la producción nacional en granos
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+ básicos, frutas y vegetales. 3. promover la igualdad entre hombres y mujeres,
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+ dando las mismas posibilidades de acceso a recursos productivos, servicios y oportunidades
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+ para asumir responsabilidades y roles en la seguridad alimentaria y nutricional.
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+ 4.transformar el enfoque de las políticas públicas y sociales, para que pasen
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+ las personas de ser clientela pasiva y vulnerable que requiere de asistencia,
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+ a personas sujetos de derechos.'
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+ - text: Regulatory arrangements will be reformed to accelerate innovation in agriculture,
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+ including pilot programs, regulatory sandboxes for new inputs and services, clear
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+ intellectual property protection, and predictable approval timelines for agrochemical
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+ and digital solutions that meet safety and environmental criteria.
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+ - text: Climate-smart strategies will protect livelihoods by diversifying income sources,
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+ expanding agroforestry and drought-resistant crops, and implementing risk-transfer
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+ mechanisms that shield poor households from shocks, thereby contributing to sustained
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+ declines in poverty levels.
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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_cca_multilabel_MiniLM-L12-70prop-data-augmented")
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+ # Run inference
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+ preds = model("Strengthen macro-fiscal resilience through risk-informed public investment planning, including scenario-based budgeting and contingent financing arrangements.")
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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 | 69.0403 | 951 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (2, 2)
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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 | 0.2247 | - |
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+ | 0.0065 | 50 | 0.2105 | - |
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+ | 0.0130 | 100 | 0.1984 | - |
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+ | 0.0195 | 150 | 0.1899 | - |
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+ | 0.0260 | 200 | 0.1916 | - |
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+ | 0.0325 | 250 | 0.1769 | - |
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+ | 0.0390 | 300 | 0.1679 | - |
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+ | 0.0455 | 350 | 0.1677 | - |
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+ | 0.0520 | 400 | 0.1591 | - |
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+ | 0.0585 | 450 | 0.1521 | - |
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+ | 0.0650 | 500 | 0.1522 | - |
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+ | 0.0715 | 550 | 0.1497 | - |
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+ | 0.0780 | 600 | 0.1494 | - |
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+ | 0.0845 | 650 | 0.1457 | - |
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+ | 0.0910 | 700 | 0.1503 | - |
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+ | 0.0975 | 750 | 0.1328 | - |
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+ | 0.1040 | 800 | 0.1251 | - |
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+ | 0.1105 | 850 | 0.1395 | - |
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+ | 0.1170 | 900 | 0.1298 | - |
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+ | 0.1235 | 950 | 0.1221 | - |
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+ | 0.1300 | 1000 | 0.1313 | - |
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+ | 0.1365 | 1050 | 0.1267 | - |
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+ | 0.1429 | 1100 | 0.1367 | - |
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+ | 0.1494 | 1150 | 0.1324 | - |
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+ | 0.1559 | 1200 | 0.1201 | - |
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+ | 0.1624 | 1250 | 0.1244 | - |
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+ | 0.1689 | 1300 | 0.1231 | - |
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+ | 0.1754 | 1350 | 0.1214 | - |
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+ | 0.1819 | 1400 | 0.1098 | - |
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+ | 0.1884 | 1450 | 0.1152 | - |
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+ | 0.1949 | 1500 | 0.1149 | - |
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+ | 0.2014 | 1550 | 0.1185 | - |
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+ | 0.2079 | 1600 | 0.1123 | - |
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+ | 0.2144 | 1650 | 0.1092 | - |
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+ | 0.2209 | 1700 | 0.1097 | - |
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+ | 0.2274 | 1750 | 0.1159 | - |
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+ | 0.2339 | 1800 | 0.1076 | - |
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+ | 0.2404 | 1850 | 0.114 | - |
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+ | 0.2469 | 1900 | 0.1055 | - |
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+ | 0.2534 | 1950 | 0.1033 | - |
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+ | 0.2599 | 2000 | 0.1016 | - |
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+ | 0.2664 | 2050 | 0.1004 | - |
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+ | 0.2729 | 2100 | 0.0973 | - |
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+ | 0.2794 | 2150 | 0.1051 | - |
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+ | 0.2859 | 2200 | 0.0954 | - |
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+ | 0.2924 | 2250 | 0.0998 | - |
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+ | 0.2989 | 2300 | 0.0984 | - |
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+ | 0.3054 | 2350 | 0.0906 | - |
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+ | 0.3119 | 2400 | 0.0939 | - |
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+ | 0.3184 | 2450 | 0.1023 | - |
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+ | 0.3249 | 2500 | 0.0983 | - |
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+ | 0.3314 | 2550 | 0.0952 | - |
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+ | 0.3379 | 2600 | 0.099 | - |
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+ | 0.3444 | 2650 | 0.0994 | - |
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+ | 0.3509 | 2700 | 0.0975 | - |
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+ | 0.3574 | 2750 | 0.0871 | - |
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+ | 0.3639 | 2800 | 0.0969 | - |
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+ | 0.3704 | 2850 | 0.0845 | - |
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+ | 0.3769 | 2900 | 0.1007 | - |
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+ | 0.3834 | 2950 | 0.0887 | - |
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+ | 0.3899 | 3000 | 0.0807 | - |
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+ | 0.3964 | 3050 | 0.0859 | - |
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+ | 0.4029 | 3100 | 0.0826 | - |
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+ | 0.4094 | 3150 | 0.0784 | - |
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+ | 0.4159 | 3200 | 0.0851 | - |
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+ | 0.4224 | 3250 | 0.0834 | - |
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+ | 0.4353 | 3350 | 0.0862 | - |
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+ | 0.5328 | 4100 | 0.0693 | - |
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+ | 0.5393 | 4150 | 0.0856 | - |
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+ | 0.5458 | 4200 | 0.072 | - |
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+ | 0.5523 | 4250 | 0.0805 | - |
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+ | 0.5588 | 4300 | 0.0741 | - |
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+ | 0.5718 | 4400 | 0.0753 | - |
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+ | 0.8187 | 6300 | 0.068 | - |
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+
463
+ ### Framework Versions
464
+ - Python: 3.12.12
465
+ - SetFit: 1.1.3
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+ - Sentence Transformers: 5.1.1
467
+ - Transformers: 4.57.1
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+ - PyTorch: 2.8.0+cu126
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+ - Datasets: 4.0.0
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+ - Tokenizers: 0.22.1
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+
472
+ ## Citation
473
+
474
+ ### BibTeX
475
+ ```bibtex
476
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
477
+ doi = {10.48550/ARXIV.2209.11055},
478
+ url = {https://arxiv.org/abs/2209.11055},
479
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
480
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
481
+ title = {Efficient Few-Shot Learning Without Prompts},
482
+ publisher = {arXiv},
483
+ year = {2022},
484
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
486
+ ```
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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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