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faodl
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model_cca_multilabel_mpnet-65max-data-augmented-v03

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
text-embeddings-inference
Model card Files
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Files changed (14) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +792 -0
  4. config.json +28 -0
  5. config_sentence_transformers.json +14 -0
  6. config_setfit.json +101 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. sentencepiece.bpe.model +3 -0
  12. special_tokens_map.json +51 -0
  13. tokenizer.json +3 -0
  14. tokenizer_config.json +62 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ 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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  *tfevents* 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
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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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
@@ -0,0 +1,792 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: Encourage interoperability of farm-management systems with national tax and
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+ regulatory reporting to reduce administrative burden.
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+ - text: Support critical infrastructure investments for rural bioenergy supply chains,
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+ including feedstock storage, processing facilities, and logistics, to reduce post-harvest
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+ losses and strengthen resilience.
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+ - text: Policy coherence will be strengthened by aligning agricultural, forestry,
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+ and fisheries policies with international instruments on biodiversity and sustainable
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+ use of ecosystems, ensuring that area restoration and sustainable fishing goals
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+ are mutually reinforcing. The approach will be backed by sectoral budgets and
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+ performance-based support to encourage early adoption.
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+ - text: Financing windows will be created to de-risk early-stage bioenergy ventures,
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+ including blended finance and concessional lending.
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+ - text: Foster regional integration to broaden market access, reduce dependence on
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+ a narrow product mix, and enhance resilience of the agrifood trade profile in
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+ the face of global price volatility.
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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-mpnet-base-v2
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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:** 96 classes
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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_mpnet-65max-data-augmented-v03")
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+ # Run inference
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+ preds = model("Financing windows will be created to de-risk early-stage bioenergy ventures, including blended finance and concessional lending.")
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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 | 47.2721 | 947 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (8, 8)
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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: 10
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - 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 | 0.3187 | - |
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+ | 0.0032 | 50 | 0.2107 | - |
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+ | 0.0065 | 100 | 0.2079 | - |
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+ | 0.0097 | 150 | 0.2015 | - |
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+ | 0.0130 | 200 | 0.2011 | - |
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+ | 0.0162 | 250 | 0.1917 | - |
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+ | 0.0194 | 300 | 0.187 | - |
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+ | 0.0227 | 350 | 0.1892 | - |
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+ | 0.0259 | 400 | 0.1726 | - |
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+ | 0.0291 | 450 | 0.1776 | - |
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+ | 0.0324 | 500 | 0.1685 | - |
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+ | 0.0356 | 550 | 0.176 | - |
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+ | 0.0389 | 600 | 0.1646 | - |
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+ | 0.0421 | 650 | 0.1689 | - |
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+ | 0.0453 | 700 | 0.1577 | - |
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+ | 0.0486 | 750 | 0.1466 | - |
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+ | 0.0518 | 800 | 0.1534 | - |
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+ | 0.0551 | 850 | 0.1606 | - |
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+ | 0.0583 | 900 | 0.149 | - |
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+ | 0.0615 | 950 | 0.1414 | - |
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+ | 0.0648 | 1000 | 0.1357 | - |
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+ | 0.0680 | 1050 | 0.1483 | - |
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+ | 0.0713 | 1100 | 0.1302 | - |
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+ | 0.0745 | 1150 | 0.14 | - |
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+ | 0.0777 | 1200 | 0.1479 | - |
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+ | 0.0810 | 1250 | 0.1496 | - |
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+ | 0.0842 | 1300 | 0.1308 | - |
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+ | 0.0874 | 1350 | 0.1509 | - |
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+ | 0.0907 | 1400 | 0.15 | - |
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+ | 0.0939 | 1450 | 0.1516 | - |
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+ | 0.0972 | 1500 | 0.1319 | - |
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+ | 0.1004 | 1550 | 0.1349 | - |
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+ | 0.1036 | 1600 | 0.1398 | - |
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+ | 0.1069 | 1650 | 0.1276 | - |
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+ | 0.1101 | 1700 | 0.1309 | - |
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+ | 0.1198 | 1850 | 0.1371 | - |
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+ | 0.1296 | 2000 | 0.1337 | - |
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+ | 0.1360 | 2100 | 0.1412 | - |
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+ | 0.1425 | 2200 | 0.1281 | - |
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+ | 0.1490 | 2300 | 0.1186 | - |
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+ | 0.1522 | 2350 | 0.142 | - |
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+ | 0.1555 | 2400 | 0.1327 | - |
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+ | 0.1587 | 2450 | 0.1356 | - |
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+ | 0.1619 | 2500 | 0.1357 | - |
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+ | 0.1652 | 2550 | 0.1235 | - |
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+ | 0.1684 | 2600 | 0.1448 | - |
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+ | 0.1717 | 2650 | 0.1274 | - |
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+ | 0.1749 | 2700 | 0.1138 | - |
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+ | 0.1781 | 2750 | 0.13 | - |
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+ | 0.1814 | 2800 | 0.1231 | - |
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+ | 0.1846 | 2850 | 0.1258 | - |
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+ | 0.1878 | 2900 | 0.1148 | - |
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+ | 0.1911 | 2950 | 0.1249 | - |
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+ | 0.1943 | 3000 | 0.1281 | - |
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+ | 0.1976 | 3050 | 0.1239 | - |
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+ | 0.2008 | 3100 | 0.1205 | - |
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+ | 0.2040 | 3150 | 0.1265 | - |
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+ | 0.2073 | 3200 | 0.1371 | - |
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+ | 0.2591 | 4000 | 0.1106 | - |
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347
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348
+ | 0.6996 | 10800 | 0.0846 | - |
349
+ | 0.7028 | 10850 | 0.0904 | - |
350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
+ | 0.7482 | 11550 | 0.092 | - |
364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
+ | 1.5838 | 24450 | 0.0597 | - |
622
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623
+ | 1.5902 | 24550 | 0.0537 | - |
624
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625
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626
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627
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628
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629
+ | 1.6097 | 24850 | 0.0584 | - |
630
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631
+ | 1.6161 | 24950 | 0.0593 | - |
632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
+ | 1.7003 | 26250 | 0.0497 | - |
658
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659
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660
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661
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662
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663
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664
+ | 1.7230 | 26600 | 0.0586 | - |
665
+ | 1.7263 | 26650 | 0.0511 | - |
666
+ | 1.7295 | 26700 | 0.0508 | - |
667
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668
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669
+ | 1.7392 | 26850 | 0.0532 | - |
670
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671
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672
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673
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674
+ | 1.7554 | 27100 | 0.0588 | - |
675
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676
+ | 1.7619 | 27200 | 0.0529 | - |
677
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678
+ | 1.7684 | 27300 | 0.0491 | - |
679
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680
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681
+ | 1.7781 | 27450 | 0.0553 | - |
682
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683
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684
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685
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686
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687
+ | 1.7975 | 27750 | 0.0493 | - |
688
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689
+ | 1.8040 | 27850 | 0.0557 | - |
690
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691
+ | 1.8105 | 27950 | 0.0517 | - |
692
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693
+ | 1.8169 | 28050 | 0.0591 | - |
694
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695
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696
+ | 1.8267 | 28200 | 0.0568 | - |
697
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698
+ | 1.8331 | 28300 | 0.0622 | - |
699
+ | 1.8364 | 28350 | 0.0499 | - |
700
+ | 1.8396 | 28400 | 0.0634 | - |
701
+ | 1.8429 | 28450 | 0.0584 | - |
702
+ | 1.8461 | 28500 | 0.0648 | - |
703
+ | 1.8493 | 28550 | 0.0628 | - |
704
+ | 1.8526 | 28600 | 0.057 | - |
705
+ | 1.8558 | 28650 | 0.0528 | - |
706
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707
+ | 1.8623 | 28750 | 0.0527 | - |
708
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709
+ | 1.8688 | 28850 | 0.0505 | - |
710
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711
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712
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713
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714
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715
+ | 1.8882 | 29150 | 0.054 | - |
716
+ | 1.8914 | 29200 | 0.0497 | - |
717
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718
+ | 1.8979 | 29300 | 0.0484 | - |
719
+ | 1.9012 | 29350 | 0.0649 | - |
720
+ | 1.9044 | 29400 | 0.0643 | - |
721
+ | 1.9076 | 29450 | 0.0484 | - |
722
+ | 1.9109 | 29500 | 0.0531 | - |
723
+ | 1.9141 | 29550 | 0.0527 | - |
724
+ | 1.9173 | 29600 | 0.0617 | - |
725
+ | 1.9206 | 29650 | 0.0469 | - |
726
+ | 1.9238 | 29700 | 0.0615 | - |
727
+ | 1.9271 | 29750 | 0.055 | - |
728
+ | 1.9303 | 29800 | 0.055 | - |
729
+ | 1.9335 | 29850 | 0.0658 | - |
730
+ | 1.9368 | 29900 | 0.0483 | - |
731
+ | 1.9400 | 29950 | 0.0559 | - |
732
+ | 1.9433 | 30000 | 0.0481 | - |
733
+ | 1.9465 | 30050 | 0.0719 | - |
734
+ | 1.9497 | 30100 | 0.0589 | - |
735
+ | 1.9530 | 30150 | 0.0498 | - |
736
+ | 1.9562 | 30200 | 0.049 | - |
737
+ | 1.9595 | 30250 | 0.0456 | - |
738
+ | 1.9627 | 30300 | 0.0551 | - |
739
+ | 1.9659 | 30350 | 0.0415 | - |
740
+ | 1.9692 | 30400 | 0.0607 | - |
741
+ | 1.9724 | 30450 | 0.0521 | - |
742
+ | 1.9756 | 30500 | 0.0545 | - |
743
+ | 1.9789 | 30550 | 0.0544 | - |
744
+ | 1.9821 | 30600 | 0.0535 | - |
745
+ | 1.9854 | 30650 | 0.0637 | - |
746
+ | 1.9886 | 30700 | 0.0555 | - |
747
+ | 1.9918 | 30750 | 0.0472 | - |
748
+ | 1.9951 | 30800 | 0.0544 | - |
749
+ | 1.9983 | 30850 | 0.0592 | - |
750
+
751
+ ### Framework Versions
752
+ - Python: 3.12.12
753
+ - SetFit: 1.1.3
754
+ - Sentence Transformers: 5.1.2
755
+ - Transformers: 4.57.1
756
+ - PyTorch: 2.8.0+cu126
757
+ - Datasets: 4.0.0
758
+ - Tokenizers: 0.22.1
759
+
760
+ ## Citation
761
+
762
+ ### BibTeX
763
+ ```bibtex
764
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
765
+ doi = {10.48550/ARXIV.2209.11055},
766
+ url = {https://arxiv.org/abs/2209.11055},
767
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
768
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
769
+ title = {Efficient Few-Shot Learning Without Prompts},
770
+ publisher = {arXiv},
771
+ year = {2022},
772
+ copyright = {Creative Commons Attribution 4.0 International}
773
+ }
774
+ ```
775
+
776
+ <!--
777
+ ## Glossary
778
+
779
+ *Clearly define terms in order to be accessible across audiences.*
780
+ -->
781
+
782
+ <!--
783
+ ## Model Card Authors
784
+
785
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
786
+ -->
787
+
788
+ <!--
789
+ ## Model Card Contact
790
+
791
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
792
+ -->
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+ "1.1.1 Total factor productivity",
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+ "1.1.2 Crop Production",
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+ "1.1.3 Livestock Production",
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+ "1.1.4 Fisheries and Aquaculture",
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+ "1.1.5 Forestry",
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+ "1.1.6 Bioenergy and biofuels production",
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+ "1.1.7 Overall Agrifood Production",
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+ "1.2.1 Phytosanitary and agri-chemicals management (including pesticide and fertilisers)",
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+ "1.2.2 Veterinary services and medicines management",
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+ "1.2.3 Mechanization",
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+ "1.2.5 Seeds (e.g. penetration of modern varieties or GMO, etc.",
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+ "1.2.7 Origin and production of pre-farm gate inputs",
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+ "1.2.8 Water usage: for irrigation, food processing, animal and human consumption, waste water",
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+ "1.2.9 Water efficiency",
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+ "1.3.1 Organic Agriculture",
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+ "1.3.2 Other sustainable practices: Agroecology, Agroforestry; Nature based solutions; Sustainable fishing",
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+ "1.3.3 Climate-Smart Agriculture",
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+ "1.4.1 Storage and post-harvest handling",
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+ "1.4.2 Logistics & Distribution",
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+ "1.4.3 Market infrastructure",
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+ "1.4.4 Food Processing and adding value",
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+ "1.5.1 Food losses",
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+ "2.1.1 Hunger and Food security",
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+ "2.1.2 Nutritional status",
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+ "2.2.1 Non-communicable diseases related to AFS",
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+ "2.2.2 Diversity of diet",
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+ "2.3.1 Hygiene prerequisites",
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+ "2.3.2 Water quality",
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+ "2.3.3 Foodborne diseases monitoring, inspection and reporting - short and long term",
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+ "2.3.4 Traceability, Risk and Process/HACCP-based monitoring and control systems",
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+ "2.4.1 Physical Access to Food (Food Entry Points and Built Environment)",
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+ "2.4.2 Availability of healthy foods",
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+ "2.4.3 Economic Access to Food (Affordability)",
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+ "2.4.4 Political, Social, and Cultural Norms influencing dietary practices",
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+ "2.4.5 Food Marketing - labelling/ information, promotion and advertising",
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+ "2.5.1 Food waste",
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+ "2.5.2 Micronutrients food loss",
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+ "3.1.1 Land Use and Expansion",
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+ "3.1.2 Land and Pasture quality management",
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+ "3.1.3 Soil quality (health) and Nutrient Management",
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+ "3.2.1 Water stress",
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+ "3.2.2 Water pollution",
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+ "3.3.1 Habitat protection",
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+ "3.3.2 Forest Health and Management",
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+ "3.3.3 Fisheries Health",
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+ "3.3.4 Environmental and Biodiversity",
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+ "3.4.1 Greenhouse Gas Emissions management",
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+ "3.4.2 Air pollution",
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+ "4.1.1 Rural and Agrifood System Employment in the country:",
54
+ "4.1.2 Availability of human resources (quantitity ) and adapted skills (quality)",
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+ "4.1.3 Migration",
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+ "4.2.1 Access to basic infrastructure, incl. energy supply (e.g. electricity), communication networks (e.g. roads and other means of transportation, internet, mobile phones)",
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+ "4.2.2 Access to basic service, incl. health, education",
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+ "4.3.1 Poverty",
59
+ "4.3.2 Earnings and Income Inequality",
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+ "4.3.3 Landholdings structure and tenure rights",
61
+ "4.3.4 Social protection",
62
+ "4.4.1 Bioenergy",
63
+ "4.4.2 Circular Economy",
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+ "5.1.1 Environmental and climate stresses (droughts and flooding, typhoons/cyclones or natural disasters etc)",
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+ "5.1.2 Economic shocks/ stresses",
66
+ "5.1.3 Conflict/ political unrest",
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+ "5.1.4 Health shocks: human (e.g., avian influenza, COVID-19) or animal (e.g. desert locust, fall armyworm)",
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+ "5.1.5 Protracted crises (including population displacements and migrations)",
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+ "5.2.1 Animal and plant health surveillance, early warning and protection systems",
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+ "5.2.2 Food Diversity (proxies food supply resilience)",
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+ "5.2.3 Agrodiversity (proxies production resilience)",
72
+ "5.2.4 Social capital",
73
+ "5.2.5 Diversification of income in rural areas",
74
+ "6.1.1 Rights of women, children, youth, indigenous groups and other vulnerable groups",
75
+ "6.1.2 Mainstreaming gender equality, child protection, empowerment, and fairness",
76
+ "6.1.3 Mainstreaming of Environmental protection",
77
+ "6.1.4 Power relationships: Smallholders, individual / small suppliers to large or monopolistic buyers",
78
+ "6.2.1 Availability and quality of agrifood data, targets and indicators",
79
+ "6.3.1 Inclusiveness of cross-sectoral Consensus-Based Policy-Making ensuring LNOB",
80
+ "6.3.2 Creation of supportive regulatory framework",
81
+ "6.3.3 Awareness and use of the evidence-based / agrifood systems approach",
82
+ "6.3.4 Effectiveness of Policy Implementation",
83
+ "6.3.5 Accountability and Transparency in Agrifood Policymaking",
84
+ "6.4.1 Scope and effectiveness of Government budgetary support",
85
+ "6.4.2 Access to Finance and Investment Climate",
86
+ "6.4.3 Insurance / forecast based financing Mechanisms",
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+ "6.5.1 Agrifood education and advisory services",
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+ "6.5.2 Cooperation of science and R&D with the private sector",
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+ "6.5.3 Innovation and technology for adaptation and competitiveness",
90
+ "6.5.4 Digitalisation of agriculture",
91
+ "6.5.5 Role of private sector in developing market agricultural inputs, technologies, and services that can enhance productivity and sustainability. Suggestion to be replaced with Enabling business in agriculture, Agrifood startups.",
92
+ "6.5.6 Role of NGOs and Civil Society in advocating for farmers' rights and sustainable practices, contributing to the dissemination of knowledge and technology.",
93
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94
+ "6.6.2 Export performance and import dependency",
95
+ "6.6.3 Market Access and Trade facilitation",
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+ "6.6.4 Quality Standards and Certification",
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+ "6.6.5 Export potential",
98
+ "labels"
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+ "normalize_embeddings": false
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+ }
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