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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: el presente marco estratégico agrario es un documento elaborado por el ministerio |
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de agricultura y ganadería, de alcance nacional, relativo al período 2014-2018, |
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cuyo objetivo general es incrementar en forma sostenida la competitividad de la |
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producción agraria en función de las demandas de mercado, con enfoque de sistemas |
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agroalimentarios y agroindustriales sostenibles, socialmente incluyentes, equitativos, |
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territorialmente integradores, de modo de satisfacer el consumo interno de alimentos, |
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así como la demanda del sector externo e impulsando otras producciones rurales |
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no agrarias generadoras de ingreso y empleo, para contribuir a la reducción sustantiva |
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de la pobreza. la estrategia busca ayudar a a eliminar el hambre, la inseguridad |
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alimentaria y la malnutrición, además de reducir la pobreza rural. unos de sus |
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objetivos específicos es concretamente mejorar la calidad de vida con reducción |
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sustantiva de la pobreza en la agricultura familiar, generando las condiciones |
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institucionales adecuadas que posibiliten a sus miembros, acceder a los servicios |
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impulsores del arraigo y del desarrollo, promoviendo la producción competitiva |
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de alimentos y de otros rubros comerciales generadores de ingreso, concurrentes |
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a la inserción equitativa y sostenible del sector en el complejo agroalimentario |
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y agroindustrial.' |
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- text: overall, the strategy will use a livelihoods approach that focuses on the |
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promotion of livelihoods assets by supporting income generation through sustainable |
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employment, asset creation and investments (productive assets and skill transfer |
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- market linkages that increase demand for locally produced food and products |
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- and business/entrepreneurship interventions to support graduation out of extreme |
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poverty) alongside prevention approach for managing risks and shocks and protection |
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measures to ensure that basic needs are met. strategic objectives 2021-2024 1. |
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enable refugees and host communities to acquire and preserve livelihoods assets |
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to construct their living, become self-reliant and build resilience to shocks |
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2. promote socio-economic inclusion of refugees and host communities and their |
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enhanced access to economic opportunities on a sustainable basis 3. expand proven |
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and innovative ways of supporting self-reliance of refugees and host communities |
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in rwanda, especially through the graduation approach and market-based interventions |
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4. promote results and evidence-based programming by improving planning- implementation |
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– monitoring – learning and practice on successful livelihoods approaches |
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- text: To elevate livestock production, the policy will promote integrated breeding |
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programs, strengthened animal health services, and extension support to farmers, |
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enabling higher productivity across cattle, sheep, goats, and poultry while safeguarding |
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animal welfare. |
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- text: Research, development, and demonstration programs will be scaled up to close |
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technology gaps, lower processing costs, and strengthen data on lifecycle environmental |
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impacts; partnerships with public research institutions and the private sector |
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will accelerate deployment of efficient bioenergy technologies and standardized |
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sustainability assessment tools. |
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- text: School and workplace nutrition programs will promote healthier choices by |
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removing sugar-rich products from regular offerings, expanding water access, and |
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integrating nutrition education that addresses SSBs, portion sizes, and overall |
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diet quality. |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: false |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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--- |
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# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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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The model has been trained using an efficient few-shot learning technique that involves: |
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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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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) |
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- **Classification head:** a OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 128 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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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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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("faodl/model_cca_multilabel_MiniLM-L12-50prop") |
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# Run inference |
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preds = model("School and workplace nutrition programs will promote healthier choices by removing sugar-rich products from regular offerings, expanding water access, and integrating nutrition education that addresses SSBs, portion sizes, and overall diet quality.") |
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``` |
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<!-- |
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### Downstream Use |
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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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### Out-of-Scope Use |
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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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## Bias, Risks and Limitations |
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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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### Recommendations |
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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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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 78.4753 | 951 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:-----:|:-------------:|:---------------:| |
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| 0.0002 | 1 | 0.3075 | - | |
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| 0.0087 | 50 | 0.2066 | - | |
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| 0.0173 | 100 | 0.1932 | - | |
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| 0.0260 | 150 | 0.1878 | - | |
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| 0.0347 | 200 | 0.1824 | - | |
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| 0.0434 | 250 | 0.1682 | - | |
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| 0.0520 | 300 | 0.1566 | - | |
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| 0.0607 | 350 | 0.1487 | - | |
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| 0.0694 | 400 | 0.1542 | - | |
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| 0.0781 | 450 | 0.1553 | - | |
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| 0.0867 | 500 | 0.1513 | - | |
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| 0.0954 | 550 | 0.1329 | - | |
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| 0.1041 | 600 | 0.1551 | - | |
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| 0.1127 | 650 | 0.1428 | - | |
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| 0.1214 | 700 | 0.1414 | - | |
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| 0.1301 | 750 | 0.1152 | - | |
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| 0.1388 | 800 | 0.1283 | - | |
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| 0.1474 | 850 | 0.1305 | - | |
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| 0.1561 | 900 | 0.1303 | - | |
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| 0.1648 | 950 | 0.1257 | - | |
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| 0.1735 | 1000 | 0.1103 | - | |
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| 0.1821 | 1050 | 0.1183 | - | |
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| 0.1908 | 1100 | 0.1151 | - | |
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| 0.1995 | 1150 | 0.1129 | - | |
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| 0.2082 | 1200 | 0.1039 | - | |
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| 0.2168 | 1250 | 0.1126 | - | |
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| 0.2255 | 1300 | 0.1188 | - | |
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| 0.2342 | 1350 | 0.114 | - | |
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| 0.2428 | 1400 | 0.1094 | - | |
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| 0.2515 | 1450 | 0.1078 | - | |
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| 0.2602 | 1500 | 0.1018 | - | |
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| 0.2689 | 1550 | 0.1136 | - | |
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| 0.2775 | 1600 | 0.1004 | - | |
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| 0.2862 | 1650 | 0.1018 | - | |
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| 0.2949 | 1700 | 0.0929 | - | |
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| 0.3036 | 1750 | 0.0986 | - | |
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| 0.3122 | 1800 | 0.0951 | - | |
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| 0.3209 | 1850 | 0.0939 | - | |
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| 0.3296 | 1900 | 0.0898 | - | |
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| 0.3382 | 1950 | 0.095 | - | |
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| 0.3469 | 2000 | 0.0885 | - | |
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| 0.3556 | 2050 | 0.0941 | - | |
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| 0.3643 | 2100 | 0.1028 | - | |
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| 0.3729 | 2150 | 0.0945 | - | |
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| 0.3816 | 2200 | 0.0924 | - | |
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| 0.3903 | 2250 | 0.0846 | - | |
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| 0.3990 | 2300 | 0.0839 | - | |
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| 0.4076 | 2350 | 0.0927 | - | |
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| 0.4163 | 2400 | 0.0839 | - | |
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| 0.4250 | 2450 | 0.0799 | - | |
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| 0.4337 | 2500 | 0.0862 | - | |
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| 0.4423 | 2550 | 0.0872 | - | |
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| 0.4510 | 2600 | 0.0905 | - | |
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| 0.4597 | 2650 | 0.0857 | - | |
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| 0.4683 | 2700 | 0.0791 | - | |
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| 0.4770 | 2750 | 0.0829 | - | |
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| 0.4857 | 2800 | 0.0776 | - | |
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| 0.4944 | 2850 | 0.0775 | - | |
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| 0.5030 | 2900 | 0.088 | - | |
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| 0.5117 | 2950 | 0.0824 | - | |
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| 0.5204 | 3000 | 0.0871 | - | |
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| 0.5291 | 3050 | 0.0731 | - | |
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| 0.5377 | 3100 | 0.0799 | - | |
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| 0.5464 | 3150 | 0.0763 | - | |
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| 0.5551 | 3200 | 0.0725 | - | |
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| 0.5637 | 3250 | 0.0789 | - | |
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| 0.5724 | 3300 | 0.0893 | - | |
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| 0.5811 | 3350 | 0.0714 | - | |
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| 0.5898 | 3400 | 0.0802 | - | |
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| 0.5984 | 3450 | 0.0725 | - | |
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| 0.6071 | 3500 | 0.0756 | - | |
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| 0.6158 | 3550 | 0.0778 | - | |
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| 0.6245 | 3600 | 0.0735 | - | |
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| 0.6331 | 3650 | 0.0738 | - | |
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| 0.6418 | 3700 | 0.0733 | - | |
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| 0.6505 | 3750 | 0.0696 | - | |
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| 0.6592 | 3800 | 0.0732 | - | |
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| 0.6678 | 3850 | 0.0757 | - | |
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| 0.6765 | 3900 | 0.0652 | - | |
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| 0.6852 | 3950 | 0.0662 | - | |
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| 0.6938 | 4000 | 0.0796 | - | |
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| 0.7025 | 4050 | 0.0709 | - | |
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| 0.7112 | 4100 | 0.0678 | - | |
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| 0.7199 | 4150 | 0.0698 | - | |
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| 0.7285 | 4200 | 0.0636 | - | |
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| 0.7372 | 4250 | 0.0679 | - | |
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| 0.7459 | 4300 | 0.073 | - | |
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| 0.7546 | 4350 | 0.0685 | - | |
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| 0.7632 | 4400 | 0.074 | - | |
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| 0.7719 | 4450 | 0.0717 | - | |
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| 0.7806 | 4500 | 0.0615 | - | |
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| 0.7892 | 4550 | 0.0671 | - | |
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| 0.7979 | 4600 | 0.0655 | - | |
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| 0.8066 | 4650 | 0.0658 | - | |
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| 0.8153 | 4700 | 0.0585 | - | |
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| 0.8239 | 4750 | 0.0619 | - | |
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| 0.8326 | 4800 | 0.0615 | - | |
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| 0.8413 | 4850 | 0.0593 | - | |
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| 0.8500 | 4900 | 0.0596 | - | |
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| 0.8586 | 4950 | 0.063 | - | |
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| 0.8673 | 5000 | 0.0591 | - | |
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| 0.8760 | 5050 | 0.0685 | - | |
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| 0.8846 | 5100 | 0.0651 | - | |
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| 0.8933 | 5150 | 0.0623 | - | |
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| 0.9020 | 5200 | 0.0605 | - | |
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| 0.9107 | 5250 | 0.0618 | - | |
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| 0.9193 | 5300 | 0.0683 | - | |
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| 0.9280 | 5350 | 0.0631 | - | |
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| 0.9367 | 5400 | 0.0651 | - | |
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| 0.9454 | 5450 | 0.0578 | - | |
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| 0.9540 | 5500 | 0.0646 | - | |
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| 0.9627 | 5550 | 0.054 | - | |
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| 0.9714 | 5600 | 0.0638 | - | |
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| 0.9801 | 5650 | 0.0592 | - | |
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| 0.9887 | 5700 | 0.0632 | - | |
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| 0.9974 | 5750 | 0.0573 | - | |
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| 1.0061 | 5800 | 0.0568 | - | |
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| 1.0147 | 5850 | 0.0554 | - | |
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| 1.0234 | 5900 | 0.0519 | - | |
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| 1.0321 | 5950 | 0.0555 | - | |
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| 1.0408 | 6000 | 0.0487 | - | |
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| 1.0494 | 6050 | 0.0659 | - | |
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| 1.0581 | 6100 | 0.0463 | - | |
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| 1.0668 | 6150 | 0.0604 | - | |
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| 1.0755 | 6200 | 0.0553 | - | |
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| 1.0841 | 6250 | 0.0484 | - | |
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| 1.0928 | 6300 | 0.0475 | - | |
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| 1.1015 | 6350 | 0.0489 | - | |
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| 1.1101 | 6400 | 0.0544 | - | |
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| 1.1188 | 6450 | 0.051 | - | |
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| 1.1275 | 6500 | 0.05 | - | |
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| 1.1362 | 6550 | 0.0578 | - | |
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| 1.1448 | 6600 | 0.0518 | - | |
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| 1.1535 | 6650 | 0.0499 | - | |
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| 1.1622 | 6700 | 0.0512 | - | |
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| 1.1709 | 6750 | 0.054 | - | |
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| 1.1795 | 6800 | 0.0596 | - | |
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| 1.1882 | 6850 | 0.0445 | - | |
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| 1.1969 | 6900 | 0.0546 | - | |
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| 1.2056 | 6950 | 0.0605 | - | |
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| 1.2142 | 7000 | 0.0518 | - | |
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| 1.2229 | 7050 | 0.0535 | - | |
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| 1.2316 | 7100 | 0.0643 | - | |
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| 1.2402 | 7150 | 0.0509 | - | |
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| 1.2489 | 7200 | 0.0477 | - | |
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| 1.2576 | 7250 | 0.0421 | - | |
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| 1.2663 | 7300 | 0.0558 | - | |
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| 1.2749 | 7350 | 0.0431 | - | |
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| 1.2836 | 7400 | 0.0527 | - | |
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| 1.2923 | 7450 | 0.0512 | - | |
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| 1.3010 | 7500 | 0.049 | - | |
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| 1.3096 | 7550 | 0.0489 | - | |
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| 1.3183 | 7600 | 0.0515 | - | |
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| 1.3270 | 7650 | 0.0537 | - | |
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| 1.3356 | 7700 | 0.0556 | - | |
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| 1.3443 | 7750 | 0.0445 | - | |
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| 1.3530 | 7800 | 0.0509 | - | |
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| 1.3617 | 7850 | 0.0571 | - | |
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| 1.3703 | 7900 | 0.0582 | - | |
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| 1.3790 | 7950 | 0.0488 | - | |
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| 1.3877 | 8000 | 0.0482 | - | |
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| 1.3964 | 8050 | 0.0564 | - | |
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| 1.4050 | 8100 | 0.0487 | - | |
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| 1.4137 | 8150 | 0.0605 | - | |
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| 1.4224 | 8200 | 0.0539 | - | |
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| 1.4310 | 8250 | 0.0463 | - | |
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| 1.4397 | 8300 | 0.0468 | - | |
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| 1.4484 | 8350 | 0.0485 | - | |
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| 1.4571 | 8400 | 0.0569 | - | |
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| 1.4657 | 8450 | 0.0601 | - | |
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| 1.4744 | 8500 | 0.0545 | - | |
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| 1.4831 | 8550 | 0.0471 | - | |
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| 1.4918 | 8600 | 0.0472 | - | |
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| 1.5004 | 8650 | 0.0464 | - | |
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| 1.5091 | 8700 | 0.0511 | - | |
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| 1.5178 | 8750 | 0.0477 | - | |
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| 1.5265 | 8800 | 0.0464 | - | |
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| 1.5351 | 8850 | 0.0497 | - | |
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| 1.5438 | 8900 | 0.0493 | - | |
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| 1.5525 | 8950 | 0.0555 | - | |
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| 1.5611 | 9000 | 0.0523 | - | |
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| 1.5698 | 9050 | 0.0563 | - | |
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| 1.5785 | 9100 | 0.0473 | - | |
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| 1.5872 | 9150 | 0.0455 | - | |
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| 1.5958 | 9200 | 0.0469 | - | |
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| 1.6045 | 9250 | 0.0456 | - | |
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| 1.6132 | 9300 | 0.048 | - | |
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| 1.6219 | 9350 | 0.0498 | - | |
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| 1.6305 | 9400 | 0.0568 | - | |
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| 1.6392 | 9450 | 0.0501 | - | |
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| 1.6479 | 9500 | 0.0509 | - | |
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| 1.6565 | 9550 | 0.0482 | - | |
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| 1.6652 | 9600 | 0.0479 | - | |
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| 1.6739 | 9650 | 0.0442 | - | |
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| 1.6826 | 9700 | 0.0528 | - | |
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| 1.6912 | 9750 | 0.0453 | - | |
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| 1.6999 | 9800 | 0.041 | - | |
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| 1.7086 | 9850 | 0.0507 | - | |
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| 1.7173 | 9900 | 0.0495 | - | |
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| 1.7259 | 9950 | 0.0517 | - | |
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| 1.7346 | 10000 | 0.052 | - | |
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| 1.7433 | 10050 | 0.047 | - | |
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| 1.7520 | 10100 | 0.052 | - | |
|
|
| 1.7606 | 10150 | 0.0565 | - | |
|
|
| 1.7693 | 10200 | 0.0458 | - | |
|
|
| 1.7780 | 10250 | 0.0409 | - | |
|
|
| 1.7866 | 10300 | 0.0487 | - | |
|
|
| 1.7953 | 10350 | 0.0516 | - | |
|
|
| 1.8040 | 10400 | 0.049 | - | |
|
|
| 1.8127 | 10450 | 0.0511 | - | |
|
|
| 1.8213 | 10500 | 0.0498 | - | |
|
|
| 1.8300 | 10550 | 0.0449 | - | |
|
|
| 1.8387 | 10600 | 0.047 | - | |
|
|
| 1.8474 | 10650 | 0.0463 | - | |
|
|
| 1.8560 | 10700 | 0.0457 | - | |
|
|
| 1.8647 | 10750 | 0.0495 | - | |
|
|
| 1.8734 | 10800 | 0.0454 | - | |
|
|
| 1.8820 | 10850 | 0.0486 | - | |
|
|
| 1.8907 | 10900 | 0.049 | - | |
|
|
| 1.8994 | 10950 | 0.0502 | - | |
|
|
| 1.9081 | 11000 | 0.0454 | - | |
|
|
| 1.9167 | 11050 | 0.0478 | - | |
|
|
| 1.9254 | 11100 | 0.0509 | - | |
|
|
| 1.9341 | 11150 | 0.0518 | - | |
|
|
| 1.9428 | 11200 | 0.0445 | - | |
|
|
| 1.9514 | 11250 | 0.043 | - | |
|
|
| 1.9601 | 11300 | 0.0414 | - | |
|
|
| 1.9688 | 11350 | 0.0452 | - | |
|
|
| 1.9775 | 11400 | 0.0468 | - | |
|
|
| 1.9861 | 11450 | 0.0426 | - | |
|
|
| 1.9948 | 11500 | 0.0457 | - | |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.12 |
|
|
- SetFit: 1.1.3 |
|
|
- Sentence Transformers: 5.1.1 |
|
|
- Transformers: 4.57.1 |
|
|
- PyTorch: 2.8.0+cu126 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.22.1 |
|
|
|
|
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## Citation |
|
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### BibTeX |
|
|
```bibtex |
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|
@article{https://doi.org/10.48550/arxiv.2209.11055, |
|
|
doi = {10.48550/ARXIV.2209.11055}, |
|
|
url = {https://arxiv.org/abs/2209.11055}, |
|
|
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
|
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
|
|
title = {Efficient Few-Shot Learning Without Prompts}, |
|
|
publisher = {arXiv}, |
|
|
year = {2022}, |
|
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
|
|
``` |
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