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faodl
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model_cca_multilabel_MiniLM-L12-50prop

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 +435 -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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  *.zip 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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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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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
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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: el presente marco estratégico agrario es un documento elaborado por el ministerio
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+ de agricultura y ganadería, de alcance nacional, relativo al período 2014-2018,
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+ cuyo objetivo general es incrementar en forma sostenida la competitividad de la
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+ producción agraria en función de las demandas de mercado, con enfoque de sistemas
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+ agroalimentarios y agroindustriales sostenibles, socialmente incluyentes, equitativos,
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+ territorialmente integradores, de modo de satisfacer el consumo interno de alimentos,
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+ así como la demanda del sector externo e impulsando otras producciones rurales
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+ no agrarias generadoras de ingreso y empleo, para contribuir a la reducción sustantiva
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+ de la pobreza. la estrategia busca ayudar a a eliminar el hambre, la inseguridad
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+ alimentaria y la malnutrición, además de reducir la pobreza rural. unos de sus
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+ objetivos específicos es concretamente mejorar la calidad de vida con reducción
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+ sustantiva de la pobreza en la agricultura familiar, generando las condiciones
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+ institucionales adecuadas que posibiliten a sus miembros, acceder a los servicios
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+ impulsores del arraigo y del desarrollo, promoviendo la producción competitiva
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+ de alimentos y de otros rubros comerciales generadores de ingreso, concurrentes
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+ a la inserción equitativa y sostenible del sector en el complejo agroalimentario
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+ y agroindustrial.'
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+ - text: overall, the strategy will use a livelihoods approach that focuses on the
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+ promotion of livelihoods assets by supporting income generation through sustainable
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+ employment, asset creation and investments (productive assets and skill transfer
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+ - market linkages that increase demand for locally produced food and products
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+ - and business/entrepreneurship interventions to support graduation out of extreme
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+ poverty) alongside prevention approach for managing risks and shocks and protection
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+ measures to ensure that basic needs are met. strategic objectives 2021-2024 1.
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+ enable refugees and host communities to acquire and preserve livelihoods assets
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+ to construct their living, become self-reliant and build resilience to shocks
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+ 2. promote socio-economic inclusion of refugees and host communities and their
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+ enhanced access to economic opportunities on a sustainable basis 3. expand proven
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+ and innovative ways of supporting self-reliance of refugees and host communities
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+ in rwanda, especially through the graduation approach and market-based interventions
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+ 4. promote results and evidence-based programming by improving planning- implementation
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+ – monitoring – learning and practice on successful livelihoods approaches
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+ - text: To elevate livestock production, the policy will promote integrated breeding
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+ programs, strengthened animal health services, and extension support to farmers,
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+ enabling higher productivity across cattle, sheep, goats, and poultry while safeguarding
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+ animal welfare.
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+ - text: Research, development, and demonstration programs will be scaled up to close
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+ technology gaps, lower processing costs, and strengthen data on lifecycle environmental
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+ impacts; partnerships with public research institutions and the private sector
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+ will accelerate deployment of efficient bioenergy technologies and standardized
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+ sustainability assessment tools.
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+ - text: School and workplace nutrition programs will promote healthier choices by
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+ removing sugar-rich products from regular offerings, expanding water access, and
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+ integrating nutrition education that addresses SSBs, portion sizes, and overall
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+ diet quality.
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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-L12-50prop")
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+ # Run inference
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+ preds = model("School and workplace nutrition programs will promote healthier choices by removing sugar-rich products from regular offerings, expanding water access, and integrating nutrition education that addresses SSBs, portion sizes, and overall diet quality.")
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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 | 78.4753 | 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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+ | 0.0002 | 1 | 0.3075 | - |
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+ | 0.0087 | 50 | 0.2066 | - |
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+ | 0.0173 | 100 | 0.1932 | - |
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+ | 0.0260 | 150 | 0.1878 | - |
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+ | 0.0347 | 200 | 0.1824 | - |
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+ | 0.0434 | 250 | 0.1682 | - |
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+ | 0.0520 | 300 | 0.1566 | - |
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+ | 0.0607 | 350 | 0.1487 | - |
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+ | 0.0694 | 400 | 0.1542 | - |
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+ | 0.0781 | 450 | 0.1553 | - |
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+ | 0.0867 | 500 | 0.1513 | - |
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+ | 0.0954 | 550 | 0.1329 | - |
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+ | 0.1041 | 600 | 0.1551 | - |
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+ | 0.1127 | 650 | 0.1428 | - |
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+ | 0.1214 | 700 | 0.1414 | - |
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+ | 0.1301 | 750 | 0.1152 | - |
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+ | 0.1388 | 800 | 0.1283 | - |
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+ | 0.1474 | 850 | 0.1305 | - |
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+ | 0.1561 | 900 | 0.1303 | - |
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+ | 0.1648 | 950 | 0.1257 | - |
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+ | 0.1735 | 1000 | 0.1103 | - |
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+ | 0.1821 | 1050 | 0.1183 | - |
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+ | 0.1908 | 1100 | 0.1151 | - |
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+ | 0.1995 | 1150 | 0.1129 | - |
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+ | 0.2082 | 1200 | 0.1039 | - |
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+ | 0.2168 | 1250 | 0.1126 | - |
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+ | 0.2255 | 1300 | 0.1188 | - |
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+ | 0.2342 | 1350 | 0.114 | - |
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+ | 0.2428 | 1400 | 0.1094 | - |
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+ | 0.2515 | 1450 | 0.1078 | - |
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+ | 0.2602 | 1500 | 0.1018 | - |
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+ | 0.2689 | 1550 | 0.1136 | - |
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+ | 0.2775 | 1600 | 0.1004 | - |
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+ | 0.2862 | 1650 | 0.1018 | - |
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+ | 0.2949 | 1700 | 0.0929 | - |
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+ | 0.3036 | 1750 | 0.0986 | - |
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+ | 0.3122 | 1800 | 0.0951 | - |
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+ | 0.3209 | 1850 | 0.0939 | - |
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+ | 0.3296 | 1900 | 0.0898 | - |
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+ | 0.3382 | 1950 | 0.095 | - |
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+ | 0.3469 | 2000 | 0.0885 | - |
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+ | 0.3556 | 2050 | 0.0941 | - |
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+ | 0.3643 | 2100 | 0.1028 | - |
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+ | 0.3729 | 2150 | 0.0945 | - |
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+ | 0.3816 | 2200 | 0.0924 | - |
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+ | 0.3903 | 2250 | 0.0846 | - |
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+ | 0.3990 | 2300 | 0.0839 | - |
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+ | 0.4076 | 2350 | 0.0927 | - |
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+ | 0.4163 | 2400 | 0.0839 | - |
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+ | 0.4250 | 2450 | 0.0799 | - |
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+ | 0.4337 | 2500 | 0.0862 | - |
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+ | 0.4423 | 2550 | 0.0872 | - |
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+ | 0.4510 | 2600 | 0.0905 | - |
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+ | 0.4597 | 2650 | 0.0857 | - |
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+ | 0.4683 | 2700 | 0.0791 | - |
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+ | 0.4770 | 2750 | 0.0829 | - |
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+ | 0.4857 | 2800 | 0.0776 | - |
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+ | 0.4944 | 2850 | 0.0775 | - |
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+ | 0.5030 | 2900 | 0.088 | - |
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+ | 0.5204 | 3000 | 0.0871 | - |
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+
394
+ ### Framework Versions
395
+ - Python: 3.12.12
396
+ - SetFit: 1.1.3
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+ - Sentence Transformers: 5.1.1
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+ - 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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+
403
+ ## Citation
404
+
405
+ ### BibTeX
406
+ ```bibtex
407
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
408
+ doi = {10.48550/ARXIV.2209.11055},
409
+ url = {https://arxiv.org/abs/2209.11055},
410
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
411
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
414
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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