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faodl
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model_cca_multilabel_mpnet-65max-full-poorf10-artificial

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
text-embeddings-inference
Model card Files
xet
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lgsilvaesilva commited on
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Files changed (14) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +867 -0
  4. config.json +28 -0
  5. config_sentence_transformers.json +14 -0
  6. config_setfit.json +100 -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
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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
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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: Targeted skills audits will identify gaps in the current rural workforce and
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+ inform training investments.
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+ - text: Policy will promote durable partnerships between public research institutions,
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+ universities, and private sector actors to accelerate the translation of agrifood
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+ R&D into market-ready technologies that improve productivity and resilience.
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+ - text: Interoperability across agencies will be enhanced through shared data platforms,
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+ common data standards, and legally anchored data sharing agreements that protect
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+ privacy while enabling timely access to agrifood data for policy formulation and
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+ M&E.
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+ - text: Digital surveillance will enable near-real-time anomaly detection using machine
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+ learning for pattern recognition.
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+ - text: The policy will support seed multiplications and farmer-led seed networks
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+ to ensure access to locally adapted, climate-resilient varieties.
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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:** 95 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-full-poorf10-artificial")
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+ # Run inference
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+ preds = model("Targeted skills audits will identify gaps in the current rural workforce and inform training investments.")
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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 | 7 | 19.7924 | 100 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (8, 8)
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+ - num_epochs: (1, 1)
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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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+ | 0.0000 | 1 | 0.2267 | - |
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+ | 0.0014 | 50 | 0.2064 | - |
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+ | 0.0029 | 100 | 0.2078 | - |
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+ | 0.0043 | 150 | 0.1999 | - |
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+ | 0.0058 | 200 | 0.1965 | - |
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+ | 0.0072 | 250 | 0.1865 | - |
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+ | 0.0086 | 300 | 0.1831 | - |
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+ | 0.0101 | 350 | 0.1824 | - |
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+ | 0.0115 | 400 | 0.1696 | - |
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+ | 0.0130 | 450 | 0.1635 | - |
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+ | 0.0144 | 500 | 0.1685 | - |
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+ | 0.0158 | 550 | 0.1542 | - |
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+ | 0.0173 | 600 | 0.15 | - |
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+ | 0.0187 | 650 | 0.1511 | - |
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+ | 0.0202 | 700 | 0.16 | - |
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+ | 0.0216 | 750 | 0.1413 | - |
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+ | 0.0230 | 800 | 0.1363 | - |
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+ | 0.0245 | 850 | 0.1527 | - |
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+ | 0.0259 | 900 | 0.1324 | - |
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+ | 0.0273 | 950 | 0.1274 | - |
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+ | 0.0288 | 1000 | 0.1526 | - |
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+ | 0.0302 | 1050 | 0.1182 | - |
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+ | 0.0317 | 1100 | 0.1327 | - |
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+ | 0.0331 | 1150 | 0.1291 | - |
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+ | 0.0345 | 1200 | 0.1285 | - |
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+ | 0.0360 | 1250 | 0.1196 | - |
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+ | 0.0374 | 1300 | 0.1265 | - |
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+ | 0.0389 | 1350 | 0.1167 | - |
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+ | 0.0403 | 1400 | 0.1144 | - |
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+ | 0.0417 | 1450 | 0.1347 | - |
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+ | 0.0432 | 1500 | 0.1258 | - |
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+ | 0.0446 | 1550 | 0.1332 | - |
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+ | 0.0461 | 1600 | 0.1128 | - |
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+ | 0.0475 | 1650 | 0.1168 | - |
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+ | 0.0489 | 1700 | 0.1203 | - |
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+ | 0.0677 | 2350 | 0.1136 | - |
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+ | 0.0691 | 2400 | 0.092 | - |
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+ | 0.0705 | 2450 | 0.099 | - |
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+ | 0.0720 | 2500 | 0.1091 | - |
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+ | 0.0734 | 2550 | 0.1192 | - |
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+ | 0.0749 | 2600 | 0.1148 | - |
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+ | 0.0763 | 2650 | 0.0921 | - |
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+ | 0.0777 | 2700 | 0.0917 | - |
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+ | 0.0792 | 2750 | 0.1148 | - |
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+ | 0.0806 | 2800 | 0.1055 | - |
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+ | 0.0820 | 2850 | 0.0943 | - |
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+ | 0.0835 | 2900 | 0.0926 | - |
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+ | 0.0849 | 2950 | 0.115 | - |
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+ | 0.0864 | 3000 | 0.0928 | - |
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+ | 0.0892 | 3100 | 0.0917 | - |
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344
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345
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346
+ | 0.3109 | 10800 | 0.0317 | - |
347
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348
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349
+ | 0.3152 | 10950 | 0.0324 | - |
350
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351
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352
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353
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354
+ | 0.3224 | 11200 | 0.0352 | - |
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
+ | 0.3354 | 11650 | 0.0295 | - |
364
+ | 0.3368 | 11700 | 0.0251 | - |
365
+ | 0.3383 | 11750 | 0.027 | - |
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
+ | 0.5916 | 20550 | 0.014 | - |
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
+ | 0.6996 | 24300 | 0.0171 | - |
617
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618
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619
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620
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621
+ | 0.7068 | 24550 | 0.0142 | - |
622
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623
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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
+ | 0.7183 | 24950 | 0.0204 | - |
630
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631
+ | 0.7212 | 25050 | 0.0096 | - |
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
+ | 0.7327 | 25450 | 0.0066 | - |
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
+ | 0.7471 | 25950 | 0.0101 | - |
650
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651
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652
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653
+ | 0.7528 | 26150 | 0.0126 | - |
654
+ | 0.7543 | 26200 | 0.0122 | - |
655
+ | 0.7557 | 26250 | 0.0049 | - |
656
+ | 0.7572 | 26300 | 0.0126 | - |
657
+ | 0.7586 | 26350 | 0.0133 | - |
658
+ | 0.7600 | 26400 | 0.0035 | - |
659
+ | 0.7615 | 26450 | 0.018 | - |
660
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661
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662
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663
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664
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665
+ | 0.7701 | 26750 | 0.0113 | - |
666
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667
+ | 0.7730 | 26850 | 0.0251 | - |
668
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669
+ | 0.7759 | 26950 | 0.0135 | - |
670
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671
+ | 0.7788 | 27050 | 0.0081 | - |
672
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673
+ | 0.7816 | 27150 | 0.0014 | - |
674
+ | 0.7831 | 27200 | 0.0134 | - |
675
+ | 0.7845 | 27250 | 0.0058 | - |
676
+ | 0.7860 | 27300 | 0.0071 | - |
677
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678
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679
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680
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681
+ | 0.7931 | 27550 | 0.0129 | - |
682
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683
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684
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685
+ | 0.7989 | 27750 | 0.0054 | - |
686
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687
+ | 0.8018 | 27850 | 0.0124 | - |
688
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689
+ | 0.8047 | 27950 | 0.0035 | - |
690
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691
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692
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693
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694
+ | 0.8119 | 28200 | 0.0075 | - |
695
+ | 0.8133 | 28250 | 0.0114 | - |
696
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697
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698
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699
+ | 0.8191 | 28450 | 0.0094 | - |
700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
+ | 0.8421 | 29250 | 0.0121 | - |
716
+ | 0.8435 | 29300 | 0.0132 | - |
717
+ | 0.8450 | 29350 | 0.009 | - |
718
+ | 0.8464 | 29400 | 0.0097 | - |
719
+ | 0.8478 | 29450 | 0.0059 | - |
720
+ | 0.8493 | 29500 | 0.0192 | - |
721
+ | 0.8507 | 29550 | 0.0093 | - |
722
+ | 0.8522 | 29600 | 0.011 | - |
723
+ | 0.8536 | 29650 | 0.0153 | - |
724
+ | 0.8550 | 29700 | 0.0157 | - |
725
+ | 0.8565 | 29750 | 0.0113 | - |
726
+ | 0.8579 | 29800 | 0.0062 | - |
727
+ | 0.8594 | 29850 | 0.008 | - |
728
+ | 0.8608 | 29900 | 0.007 | - |
729
+ | 0.8622 | 29950 | 0.0099 | - |
730
+ | 0.8637 | 30000 | 0.0059 | - |
731
+ | 0.8651 | 30050 | 0.0103 | - |
732
+ | 0.8666 | 30100 | 0.0115 | - |
733
+ | 0.8680 | 30150 | 0.0155 | - |
734
+ | 0.8694 | 30200 | 0.0104 | - |
735
+ | 0.8709 | 30250 | 0.0073 | - |
736
+ | 0.8723 | 30300 | 0.0112 | - |
737
+ | 0.8738 | 30350 | 0.0059 | - |
738
+ | 0.8752 | 30400 | 0.0069 | - |
739
+ | 0.8766 | 30450 | 0.0109 | - |
740
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741
+ | 0.8795 | 30550 | 0.0074 | - |
742
+ | 0.8810 | 30600 | 0.012 | - |
743
+ | 0.8824 | 30650 | 0.0057 | - |
744
+ | 0.8838 | 30700 | 0.0106 | - |
745
+ | 0.8853 | 30750 | 0.0014 | - |
746
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747
+ | 0.8882 | 30850 | 0.0119 | - |
748
+ | 0.8896 | 30900 | 0.0071 | - |
749
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750
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751
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752
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753
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754
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755
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756
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757
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758
+ | 0.9040 | 31400 | 0.0079 | - |
759
+ | 0.9054 | 31450 | 0.011 | - |
760
+ | 0.9069 | 31500 | 0.013 | - |
761
+ | 0.9083 | 31550 | 0.0092 | - |
762
+ | 0.9097 | 31600 | 0.0092 | - |
763
+ | 0.9112 | 31650 | 0.011 | - |
764
+ | 0.9126 | 31700 | 0.0061 | - |
765
+ | 0.9141 | 31750 | 0.0043 | - |
766
+ | 0.9155 | 31800 | 0.0114 | - |
767
+ | 0.9169 | 31850 | 0.0105 | - |
768
+ | 0.9184 | 31900 | 0.0017 | - |
769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
+ | 0.9788 | 34000 | 0.0091 | - |
811
+ | 0.9803 | 34050 | 0.0061 | - |
812
+ | 0.9817 | 34100 | 0.0144 | - |
813
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814
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815
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816
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817
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818
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819
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820
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821
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822
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823
+ | 0.9976 | 34650 | 0.0074 | - |
824
+ | 0.9990 | 34700 | 0.0062 | - |
825
+
826
+ ### Framework Versions
827
+ - Python: 3.12.12
828
+ - SetFit: 1.1.3
829
+ - Sentence Transformers: 5.1.2
830
+ - Transformers: 4.57.1
831
+ - PyTorch: 2.8.0+cu126
832
+ - Datasets: 4.0.0
833
+ - Tokenizers: 0.22.1
834
+
835
+ ## Citation
836
+
837
+ ### BibTeX
838
+ ```bibtex
839
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
840
+ doi = {10.48550/ARXIV.2209.11055},
841
+ url = {https://arxiv.org/abs/2209.11055},
842
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
843
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
844
+ title = {Efficient Few-Shot Learning Without Prompts},
845
+ publisher = {arXiv},
846
+ year = {2022},
847
+ copyright = {Creative Commons Attribution 4.0 International}
848
+ }
849
+ ```
850
+
851
+ <!--
852
+ ## Glossary
853
+
854
+ *Clearly define terms in order to be accessible across audiences.*
855
+ -->
856
+
857
+ <!--
858
+ ## Model Card Authors
859
+
860
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
861
+ -->
862
+
863
+ <!--
864
+ ## Model Card Contact
865
+
866
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
867
+ -->
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+ "1.1.1 Total factor productivity",
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+ "1.1.2 Crop Production",
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+ "1.1.5 Forestry",
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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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+ "1.5.1 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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+ "2.2.2 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",
38
+ "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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+ "2.5.1 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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+ "3.3.1 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",
52
+ "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:",
55
+ "4.1.2 Availability of human resources (quantitity ) and adapted skills (quality)",
56
+ "4.1.3 Migration",
57
+ "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)",
58
+ "4.2.2 Access to basic service, incl. health, education",
59
+ "4.3.1 Poverty",
60
+ "4.3.2 Earnings and Income Inequality",
61
+ "4.3.3 Landholdings structure and tenure rights",
62
+ "4.3.4 Social protection",
63
+ "4.4.1 Bioenergy",
64
+ "4.4.2 Circular Economy",
65
+ "5.1.1 Environmental and climate stresses (droughts and flooding, typhoons/cyclones or natural disasters etc)",
66
+ "5.1.2 Economic shocks/ stresses",
67
+ "5.1.3 Conflict/ political unrest",
68
+ "5.1.4 Health shocks: human (e.g., avian influenza, COVID-19) or animal (e.g. desert locust, fall armyworm)",
69
+ "5.1.5 Protracted crises (including population displacements and migrations)",
70
+ "5.2.1 Animal and plant health surveillance, early warning and protection systems",
71
+ "5.2.2 Food Diversity (proxies food supply resilience)",
72
+ "5.2.3 Agrodiversity (proxies production resilience)",
73
+ "5.2.4 Social capital",
74
+ "5.2.5 Diversification of income in rural areas",
75
+ "6.1.1 Rights of women, children, youth, indigenous groups and other vulnerable groups",
76
+ "6.1.2 Mainstreaming gender equality, child protection, empowerment, and fairness",
77
+ "6.1.3 Mainstreaming of Environmental protection",
78
+ "6.1.4 Power relationships: Smallholders, individual / small suppliers to large or monopolistic buyers",
79
+ "6.2.1 Availability and quality of agrifood data, targets and indicators",
80
+ "6.3.1 Inclusiveness of cross-sectoral Consensus-Based Policy-Making ensuring LNOB",
81
+ "6.3.2 Creation of supportive regulatory framework",
82
+ "6.3.3 Awareness and use of the evidence-based / agrifood systems approach",
83
+ "6.3.4 Effectiveness of Policy Implementation",
84
+ "6.3.5 Accountability and Transparency in Agrifood Policymaking",
85
+ "6.4.1 Scope and effectiveness of Government budgetary support",
86
+ "6.4.2 Access to Finance and Investment Climate",
87
+ "6.4.3 Insurance / forecast based financing Mechanisms",
88
+ "6.5.1 Agrifood education and advisory services",
89
+ "6.5.2 Cooperation of science and R&D with the private sector",
90
+ "6.5.3 Innovation and technology for adaptation and competitiveness",
91
+ "6.5.4 Digitalisation of agriculture",
92
+ "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.",
93
+ "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.",
94
+ "6.6.1 Trade profile",
95
+ "6.6.2 Export performance and import dependency",
96
+ "6.6.3 Market Access and Trade facilitation",
97
+ "6.6.4 Quality Standards and Certification",
98
+ "6.6.5 Export potential"
99
+ ]
100
+ }
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