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faodl
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model_cca_multilabel_MiniLM-L12-75max-data-augmented-v03

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Model card Files
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  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +884 -0
  4. config.json +25 -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. special_tokens_map.json +51 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +65 -0
  14. unigram.json +3 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
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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: Regional and cross-border coordination will harmonize technical standards,
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+ interoperability, and cross-jurisdictional service delivery to unlock broader
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+ agrifood supply-chain benefits.
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+ - text: Regional and cross-border collaboration shall support migratory corridors
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+ by establishing joint inventories, harmonized protection standards, and data-sharing
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+ arrangements.
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+ - text: Develop cross-cutting gender, youth, and disability considerations in market
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+ infrastructure planning.
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+ - text: Regional phytosanitary standards will be harmonized to minimize non-tariff
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+ barriers for input imports that meet safety criteria.
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+ - text: Develop gender responsive input policies that recognize women farmers as key
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+ decision makers in input choices and training.
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: false
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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+ - **Classification head:** a OneVsRestClassifier instance
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Number of Classes:** 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_MiniLM-L12-75max-data-augmented-v03")
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+ # Run inference
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+ preds = model("Develop cross-cutting gender, youth, and disability considerations in market infrastructure planning.")
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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 | 46.8162 | 951 |
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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 | 0.2621 | - |
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+ | 0.0028 | 50 | 0.2218 | - |
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+ | 0.0056 | 100 | 0.2242 | - |
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+ | 0.0084 | 150 | 0.2169 | - |
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+ | 0.0112 | 200 | 0.2096 | - |
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+ | 0.0140 | 250 | 0.2046 | - |
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+ | 0.0168 | 300 | 0.1913 | - |
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+ | 0.0197 | 350 | 0.1954 | - |
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+ | 0.0225 | 400 | 0.1884 | - |
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+ | 0.0253 | 450 | 0.1936 | - |
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+ | 0.0281 | 500 | 0.192 | - |
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+ | 0.0309 | 550 | 0.1829 | - |
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+ | 0.0337 | 600 | 0.1939 | - |
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+ | 0.0365 | 650 | 0.1765 | - |
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342
+ | 0.5981 | 10650 | 0.0943 | - |
343
+ | 0.6010 | 10700 | 0.0985 | - |
344
+ | 0.6038 | 10750 | 0.1058 | - |
345
+ | 0.6066 | 10800 | 0.0815 | - |
346
+ | 0.6094 | 10850 | 0.1051 | - |
347
+ | 0.6122 | 10900 | 0.0937 | - |
348
+ | 0.6150 | 10950 | 0.0813 | - |
349
+ | 0.6178 | 11000 | 0.0903 | - |
350
+ | 0.6206 | 11050 | 0.1062 | - |
351
+ | 0.6234 | 11100 | 0.0948 | - |
352
+ | 0.6262 | 11150 | 0.0954 | - |
353
+ | 0.6290 | 11200 | 0.0982 | - |
354
+ | 0.6318 | 11250 | 0.0907 | - |
355
+ | 0.6347 | 11300 | 0.0905 | - |
356
+ | 0.6375 | 11350 | 0.0926 | - |
357
+ | 0.6403 | 11400 | 0.0965 | - |
358
+ | 0.6431 | 11450 | 0.0939 | - |
359
+ | 0.6459 | 11500 | 0.0979 | - |
360
+ | 0.6487 | 11550 | 0.0869 | - |
361
+ | 0.6515 | 11600 | 0.0999 | - |
362
+ | 0.6543 | 11650 | 0.0793 | - |
363
+ | 0.6571 | 11700 | 0.0911 | - |
364
+ | 0.6599 | 11750 | 0.0914 | - |
365
+ | 0.6627 | 11800 | 0.0832 | - |
366
+ | 0.6655 | 11850 | 0.0972 | - |
367
+ | 0.6684 | 11900 | 0.0852 | - |
368
+ | 0.6712 | 11950 | 0.101 | - |
369
+ | 0.6740 | 12000 | 0.0987 | - |
370
+ | 0.6768 | 12050 | 0.0905 | - |
371
+ | 0.6796 | 12100 | 0.0867 | - |
372
+ | 0.6824 | 12150 | 0.0811 | - |
373
+ | 0.6852 | 12200 | 0.0795 | - |
374
+ | 0.6880 | 12250 | 0.0936 | - |
375
+ | 0.6908 | 12300 | 0.0888 | - |
376
+ | 0.6936 | 12350 | 0.0876 | - |
377
+ | 0.6964 | 12400 | 0.1076 | - |
378
+ | 0.6992 | 12450 | 0.0961 | - |
379
+ | 0.7020 | 12500 | 0.0937 | - |
380
+ | 0.7049 | 12550 | 0.0921 | - |
381
+ | 0.7077 | 12600 | 0.095 | - |
382
+ | 0.7105 | 12650 | 0.1013 | - |
383
+ | 0.7133 | 12700 | 0.0896 | - |
384
+ | 0.7161 | 12750 | 0.1058 | - |
385
+ | 0.7189 | 12800 | 0.0883 | - |
386
+ | 0.7217 | 12850 | 0.0814 | - |
387
+ | 0.7245 | 12900 | 0.0889 | - |
388
+ | 0.7273 | 12950 | 0.0965 | - |
389
+ | 0.7301 | 13000 | 0.098 | - |
390
+ | 0.7329 | 13050 | 0.093 | - |
391
+ | 0.7357 | 13100 | 0.0965 | - |
392
+ | 0.7386 | 13150 | 0.0791 | - |
393
+ | 0.7414 | 13200 | 0.0891 | - |
394
+ | 0.7442 | 13250 | 0.0814 | - |
395
+ | 0.7470 | 13300 | 0.0969 | - |
396
+ | 0.7498 | 13350 | 0.0871 | - |
397
+ | 0.7526 | 13400 | 0.0899 | - |
398
+ | 0.7554 | 13450 | 0.0992 | - |
399
+ | 0.7582 | 13500 | 0.0768 | - |
400
+ | 0.7610 | 13550 | 0.0894 | - |
401
+ | 0.7638 | 13600 | 0.0842 | - |
402
+ | 0.7666 | 13650 | 0.0763 | - |
403
+ | 0.7694 | 13700 | 0.0897 | - |
404
+ | 0.7723 | 13750 | 0.0919 | - |
405
+ | 0.7751 | 13800 | 0.0811 | - |
406
+ | 0.7779 | 13850 | 0.0818 | - |
407
+ | 0.7807 | 13900 | 0.076 | - |
408
+ | 0.7835 | 13950 | 0.0833 | - |
409
+ | 0.7863 | 14000 | 0.0888 | - |
410
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411
+ | 0.7919 | 14100 | 0.0983 | - |
412
+ | 0.7947 | 14150 | 0.079 | - |
413
+ | 0.7975 | 14200 | 0.0902 | - |
414
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415
+ | 0.8031 | 14300 | 0.081 | - |
416
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417
+ | 0.8088 | 14400 | 0.089 | - |
418
+ | 0.8116 | 14450 | 0.1018 | - |
419
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420
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421
+ | 0.8200 | 14600 | 0.0888 | - |
422
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423
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424
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425
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426
+ | 0.8340 | 14850 | 0.0781 | - |
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
+ | 0.8593 | 15300 | 0.094 | - |
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
+ | 0.8846 | 15750 | 0.0902 | - |
445
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446
+ | 0.8902 | 15850 | 0.0875 | - |
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
+ | 0.9070 | 16150 | 0.0732 | - |
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
+ | 0.9492 | 16900 | 0.0833 | - |
468
+ | 0.9520 | 16950 | 0.0692 | - |
469
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470
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471
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472
+ | 0.9632 | 17150 | 0.0817 | - |
473
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474
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475
+ | 0.9716 | 17300 | 0.0844 | - |
476
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477
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478
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479
+ | 0.9829 | 17500 | 0.0874 | - |
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
+ | 0.9997 | 17800 | 0.0779 | - |
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
+ | 1.0278 | 18300 | 0.0648 | - |
496
+ | 1.0306 | 18350 | 0.0787 | - |
497
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498
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499
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500
+ | 1.0418 | 18550 | 0.0638 | - |
501
+ | 1.0447 | 18600 | 0.071 | - |
502
+ | 1.0475 | 18650 | 0.0836 | - |
503
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504
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505
+ | 1.0559 | 18800 | 0.0821 | - |
506
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507
+ | 1.0615 | 18900 | 0.0793 | - |
508
+ | 1.0643 | 18950 | 0.0739 | - |
509
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510
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511
+ | 1.0727 | 19100 | 0.0752 | - |
512
+ | 1.0755 | 19150 | 0.0783 | - |
513
+ | 1.0783 | 19200 | 0.0697 | - |
514
+ | 1.0812 | 19250 | 0.0759 | - |
515
+ | 1.0840 | 19300 | 0.0727 | - |
516
+ | 1.0868 | 19350 | 0.0742 | - |
517
+ | 1.0896 | 19400 | 0.0668 | - |
518
+ | 1.0924 | 19450 | 0.0838 | - |
519
+ | 1.0952 | 19500 | 0.0738 | - |
520
+ | 1.0980 | 19550 | 0.0869 | - |
521
+ | 1.1008 | 19600 | 0.072 | - |
522
+ | 1.1036 | 19650 | 0.068 | - |
523
+ | 1.1064 | 19700 | 0.0738 | - |
524
+ | 1.1092 | 19750 | 0.07 | - |
525
+ | 1.1120 | 19800 | 0.0685 | - |
526
+ | 1.1149 | 19850 | 0.0728 | - |
527
+ | 1.1177 | 19900 | 0.0715 | - |
528
+ | 1.1205 | 19950 | 0.0749 | - |
529
+ | 1.1233 | 20000 | 0.0743 | - |
530
+ | 1.1261 | 20050 | 0.0699 | - |
531
+ | 1.1289 | 20100 | 0.0814 | - |
532
+ | 1.1317 | 20150 | 0.0754 | - |
533
+ | 1.1345 | 20200 | 0.0633 | - |
534
+ | 1.1373 | 20250 | 0.0842 | - |
535
+ | 1.1401 | 20300 | 0.0693 | - |
536
+ | 1.1429 | 20350 | 0.0775 | - |
537
+ | 1.1457 | 20400 | 0.0658 | - |
538
+ | 1.1486 | 20450 | 0.0747 | - |
539
+ | 1.1514 | 20500 | 0.0606 | - |
540
+ | 1.1542 | 20550 | 0.0673 | - |
541
+ | 1.1570 | 20600 | 0.0679 | - |
542
+ | 1.1598 | 20650 | 0.0783 | - |
543
+ | 1.1626 | 20700 | 0.0728 | - |
544
+ | 1.1654 | 20750 | 0.0787 | - |
545
+ | 1.1682 | 20800 | 0.0688 | - |
546
+ | 1.1710 | 20850 | 0.0693 | - |
547
+ | 1.1738 | 20900 | 0.0717 | - |
548
+ | 1.1766 | 20950 | 0.0765 | - |
549
+ | 1.1794 | 21000 | 0.0702 | - |
550
+ | 1.1823 | 21050 | 0.0743 | - |
551
+ | 1.1851 | 21100 | 0.0813 | - |
552
+ | 1.1879 | 21150 | 0.0649 | - |
553
+ | 1.1907 | 21200 | 0.0799 | - |
554
+ | 1.1935 | 21250 | 0.0695 | - |
555
+ | 1.1963 | 21300 | 0.0747 | - |
556
+ | 1.1991 | 21350 | 0.0685 | - |
557
+ | 1.2019 | 21400 | 0.0703 | - |
558
+ | 1.2047 | 21450 | 0.063 | - |
559
+ | 1.2075 | 21500 | 0.0745 | - |
560
+ | 1.2103 | 21550 | 0.0815 | - |
561
+ | 1.2131 | 21600 | 0.0739 | - |
562
+ | 1.2160 | 21650 | 0.0646 | - |
563
+ | 1.2188 | 21700 | 0.0758 | - |
564
+ | 1.2216 | 21750 | 0.0711 | - |
565
+ | 1.2244 | 21800 | 0.069 | - |
566
+ | 1.2272 | 21850 | 0.0679 | - |
567
+ | 1.2300 | 21900 | 0.0765 | - |
568
+ | 1.2328 | 21950 | 0.0801 | - |
569
+ | 1.2356 | 22000 | 0.0765 | - |
570
+ | 1.2384 | 22050 | 0.0718 | - |
571
+ | 1.2412 | 22100 | 0.0619 | - |
572
+ | 1.2440 | 22150 | 0.0683 | - |
573
+ | 1.2468 | 22200 | 0.061 | - |
574
+ | 1.2496 | 22250 | 0.0813 | - |
575
+ | 1.2525 | 22300 | 0.0732 | - |
576
+ | 1.2553 | 22350 | 0.0757 | - |
577
+ | 1.2581 | 22400 | 0.0591 | - |
578
+ | 1.2609 | 22450 | 0.0574 | - |
579
+ | 1.2637 | 22500 | 0.0729 | - |
580
+ | 1.2665 | 22550 | 0.0682 | - |
581
+ | 1.2693 | 22600 | 0.0679 | - |
582
+ | 1.2721 | 22650 | 0.0732 | - |
583
+ | 1.2749 | 22700 | 0.0801 | - |
584
+ | 1.2777 | 22750 | 0.0682 | - |
585
+ | 1.2805 | 22800 | 0.0642 | - |
586
+ | 1.2833 | 22850 | 0.0563 | - |
587
+ | 1.2862 | 22900 | 0.0652 | - |
588
+ | 1.2890 | 22950 | 0.0723 | - |
589
+ | 1.2918 | 23000 | 0.0692 | - |
590
+ | 1.2946 | 23050 | 0.0673 | - |
591
+ | 1.2974 | 23100 | 0.0778 | - |
592
+ | 1.3002 | 23150 | 0.0615 | - |
593
+ | 1.3030 | 23200 | 0.0673 | - |
594
+ | 1.3058 | 23250 | 0.0739 | - |
595
+ | 1.3086 | 23300 | 0.0755 | - |
596
+ | 1.3114 | 23350 | 0.0682 | - |
597
+ | 1.3142 | 23400 | 0.0645 | - |
598
+ | 1.3170 | 23450 | 0.0701 | - |
599
+ | 1.3199 | 23500 | 0.0673 | - |
600
+ | 1.3227 | 23550 | 0.0767 | - |
601
+ | 1.3255 | 23600 | 0.071 | - |
602
+ | 1.3283 | 23650 | 0.0712 | - |
603
+ | 1.3311 | 23700 | 0.0628 | - |
604
+ | 1.3339 | 23750 | 0.0664 | - |
605
+ | 1.3367 | 23800 | 0.0639 | - |
606
+ | 1.3395 | 23850 | 0.0537 | - |
607
+ | 1.3423 | 23900 | 0.061 | - |
608
+ | 1.3451 | 23950 | 0.0728 | - |
609
+ | 1.3479 | 24000 | 0.0589 | - |
610
+ | 1.3507 | 24050 | 0.0677 | - |
611
+ | 1.3536 | 24100 | 0.0773 | - |
612
+ | 1.3564 | 24150 | 0.0716 | - |
613
+ | 1.3592 | 24200 | 0.0765 | - |
614
+ | 1.3620 | 24250 | 0.0665 | - |
615
+ | 1.3648 | 24300 | 0.0824 | - |
616
+ | 1.3676 | 24350 | 0.0673 | - |
617
+ | 1.3704 | 24400 | 0.0678 | - |
618
+ | 1.3732 | 24450 | 0.0617 | - |
619
+ | 1.3760 | 24500 | 0.0779 | - |
620
+ | 1.3788 | 24550 | 0.0701 | - |
621
+ | 1.3816 | 24600 | 0.0691 | - |
622
+ | 1.3844 | 24650 | 0.071 | - |
623
+ | 1.3873 | 24700 | 0.0741 | - |
624
+ | 1.3901 | 24750 | 0.073 | - |
625
+ | 1.3929 | 24800 | 0.0516 | - |
626
+ | 1.3957 | 24850 | 0.0683 | - |
627
+ | 1.3985 | 24900 | 0.0595 | - |
628
+ | 1.4013 | 24950 | 0.0621 | - |
629
+ | 1.4041 | 25000 | 0.064 | - |
630
+ | 1.4069 | 25050 | 0.0642 | - |
631
+ | 1.4097 | 25100 | 0.0702 | - |
632
+ | 1.4125 | 25150 | 0.0736 | - |
633
+ | 1.4153 | 25200 | 0.0656 | - |
634
+ | 1.4181 | 25250 | 0.0687 | - |
635
+ | 1.4209 | 25300 | 0.0635 | - |
636
+ | 1.4238 | 25350 | 0.0748 | - |
637
+ | 1.4266 | 25400 | 0.0728 | - |
638
+ | 1.4294 | 25450 | 0.0616 | - |
639
+ | 1.4322 | 25500 | 0.0695 | - |
640
+ | 1.4350 | 25550 | 0.062 | - |
641
+ | 1.4378 | 25600 | 0.0651 | - |
642
+ | 1.4406 | 25650 | 0.0676 | - |
643
+ | 1.4434 | 25700 | 0.0677 | - |
644
+ | 1.4462 | 25750 | 0.0668 | - |
645
+ | 1.4490 | 25800 | 0.0567 | - |
646
+ | 1.4518 | 25850 | 0.0661 | - |
647
+ | 1.4546 | 25900 | 0.0695 | - |
648
+ | 1.4575 | 25950 | 0.0692 | - |
649
+ | 1.4603 | 26000 | 0.0823 | - |
650
+ | 1.4631 | 26050 | 0.069 | - |
651
+ | 1.4659 | 26100 | 0.0648 | - |
652
+ | 1.4687 | 26150 | 0.0661 | - |
653
+ | 1.4715 | 26200 | 0.0705 | - |
654
+ | 1.4743 | 26250 | 0.0622 | - |
655
+ | 1.4771 | 26300 | 0.0734 | - |
656
+ | 1.4799 | 26350 | 0.0649 | - |
657
+ | 1.4827 | 26400 | 0.0667 | - |
658
+ | 1.4855 | 26450 | 0.0692 | - |
659
+ | 1.4883 | 26500 | 0.0661 | - |
660
+ | 1.4912 | 26550 | 0.0731 | - |
661
+ | 1.4940 | 26600 | 0.0775 | - |
662
+ | 1.4968 | 26650 | 0.0706 | - |
663
+ | 1.4996 | 26700 | 0.0652 | - |
664
+ | 1.5024 | 26750 | 0.0669 | - |
665
+ | 1.5052 | 26800 | 0.0709 | - |
666
+ | 1.5080 | 26850 | 0.0614 | - |
667
+ | 1.5108 | 26900 | 0.0708 | - |
668
+ | 1.5136 | 26950 | 0.0578 | - |
669
+ | 1.5164 | 27000 | 0.0756 | - |
670
+ | 1.5192 | 27050 | 0.0609 | - |
671
+ | 1.5220 | 27100 | 0.0602 | - |
672
+ | 1.5249 | 27150 | 0.0656 | - |
673
+ | 1.5277 | 27200 | 0.0669 | - |
674
+ | 1.5305 | 27250 | 0.086 | - |
675
+ | 1.5333 | 27300 | 0.068 | - |
676
+ | 1.5361 | 27350 | 0.0648 | - |
677
+ | 1.5389 | 27400 | 0.0735 | - |
678
+ | 1.5417 | 27450 | 0.0744 | - |
679
+ | 1.5445 | 27500 | 0.0697 | - |
680
+ | 1.5473 | 27550 | 0.0788 | - |
681
+ | 1.5501 | 27600 | 0.0566 | - |
682
+ | 1.5529 | 27650 | 0.0759 | - |
683
+ | 1.5557 | 27700 | 0.0773 | - |
684
+ | 1.5586 | 27750 | 0.0553 | - |
685
+ | 1.5614 | 27800 | 0.0744 | - |
686
+ | 1.5642 | 27850 | 0.0581 | - |
687
+ | 1.5670 | 27900 | 0.0742 | - |
688
+ | 1.5698 | 27950 | 0.0662 | - |
689
+ | 1.5726 | 28000 | 0.065 | - |
690
+ | 1.5754 | 28050 | 0.0686 | - |
691
+ | 1.5782 | 28100 | 0.0581 | - |
692
+ | 1.5810 | 28150 | 0.0585 | - |
693
+ | 1.5838 | 28200 | 0.0621 | - |
694
+ | 1.5866 | 28250 | 0.0638 | - |
695
+ | 1.5894 | 28300 | 0.0674 | - |
696
+ | 1.5922 | 28350 | 0.0693 | - |
697
+ | 1.5951 | 28400 | 0.07 | - |
698
+ | 1.5979 | 28450 | 0.0756 | - |
699
+ | 1.6007 | 28500 | 0.0584 | - |
700
+ | 1.6035 | 28550 | 0.0731 | - |
701
+ | 1.6063 | 28600 | 0.0737 | - |
702
+ | 1.6091 | 28650 | 0.0579 | - |
703
+ | 1.6119 | 28700 | 0.0677 | - |
704
+ | 1.6147 | 28750 | 0.0648 | - |
705
+ | 1.6175 | 28800 | 0.0662 | - |
706
+ | 1.6203 | 28850 | 0.0725 | - |
707
+ | 1.6231 | 28900 | 0.0648 | - |
708
+ | 1.6259 | 28950 | 0.0691 | - |
709
+ | 1.6288 | 29000 | 0.0647 | - |
710
+ | 1.6316 | 29050 | 0.0597 | - |
711
+ | 1.6344 | 29100 | 0.07 | - |
712
+ | 1.6372 | 29150 | 0.0754 | - |
713
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714
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+
843
+ ### Framework Versions
844
+ - Python: 3.12.12
845
+ - SetFit: 1.1.3
846
+ - Sentence Transformers: 5.1.2
847
+ - Transformers: 4.57.1
848
+ - PyTorch: 2.8.0+cu126
849
+ - Datasets: 4.0.0
850
+ - Tokenizers: 0.22.1
851
+
852
+ ## Citation
853
+
854
+ ### BibTeX
855
+ ```bibtex
856
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
857
+ doi = {10.48550/ARXIV.2209.11055},
858
+ url = {https://arxiv.org/abs/2209.11055},
859
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
860
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
861
+ title = {Efficient Few-Shot Learning Without Prompts},
862
+ publisher = {arXiv},
863
+ year = {2022},
864
+ copyright = {Creative Commons Attribution 4.0 International}
865
+ }
866
+ ```
867
+
868
+ <!--
869
+ ## Glossary
870
+
871
+ *Clearly define terms in order to be accessible across audiences.*
872
+ -->
873
+
874
+ <!--
875
+ ## Model Card Authors
876
+
877
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
878
+ -->
879
+
880
+ <!--
881
+ ## Model Card Contact
882
+
883
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
884
+ -->
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+ "1.1.1 Total factor productivity",
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+ "1.1.2 Crop Production",
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+ "1.1.4 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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+ "1.2.8 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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+ "1.4.4 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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+ "3.3.1 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",
56
+ "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",
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+ "4.3.2 Earnings and Income Inequality",
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+ "4.3.3 Landholdings structure and tenure rights",
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+ "4.3.4 Social protection",
62
+ "4.4.1 Bioenergy",
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+ "4.4.2 Circular Economy",
64
+ "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",
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+ "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)",
68
+ "5.1.5 Protracted crises (including population displacements and migrations)",
69
+ "5.2.1 Animal and plant health surveillance, early warning and protection systems",
70
+ "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
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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",
87
+ "6.5.1 Agrifood education and advisory services",
88
+ "6.5.2 Cooperation of science and R&D with the private sector",
89
+ "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
+ "6.6.1 Trade profile",
94
+ "6.6.2 Export performance and import dependency",
95
+ "6.6.3 Market Access and Trade facilitation",
96
+ "6.6.4 Quality Standards and Certification",
97
+ "6.6.5 Export potential"
98
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99
+ "normalize_embeddings": false
100
+ }
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