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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: Strengthen macro-fiscal resilience through risk-informed public investment |
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planning, including scenario-based budgeting and contingent financing arrangements. |
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- text: 'finding environmentally sustainable energy solutions is central to the document. |
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it seeks to facilitate cultural, institutional and technological change in a way |
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that supports ''''aggressive'''' advances in energy efficiency and conservation, |
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minimises greenhouse emissions and ultimately provides green growth. these energy |
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efficiency and conservation goals are seen as ''''no regrets'''' mitigation actions |
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that can have positive impacts on society and the economy, principally by reducing |
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costs and dependency on fossil fuel imports. overall the policy propose to reduce |
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the percentage of petroleum in the country''''s energy supply mix from the current |
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95 percent (does not state to what level) and increase the percentage of renewables |
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in the energy mix with proposed targets of 11 percent by 2012, 12.5 percent by |
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2015 and 20 percent by 2030. six sub-policies exist to support the national energy |
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policy, namely: - a carbon emissions trading policy developed to address jamaica''''s |
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participation in the clean development mechanism - energy-from-waste policy - |
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national renewable energy policy 2010-2030 - national energy from waste policy |
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2010-2030 - energy conservation and efficiency policy - biofuels policy' |
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- text: 'objetivos: 1. promover la garantía del derecho a la alimentación para la |
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población general y en especial para las personas y grupos de mayor vulnerabilidad. |
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2. respetar la identidad cultural, las necesidades nutricionales según el ciclo |
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de vida y la diversidad de formas de producción, de consumo y comercialización |
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agropecuaria, fortaleciendo los mercados locales, sin contraponerse al comercio |
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agroalimentario internacional, favoreciéndose la producción nacional en granos |
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básicos, frutas y vegetales. 3. promover la igualdad entre hombres y mujeres, |
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dando las mismas posibilidades de acceso a recursos productivos, servicios y oportunidades |
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para asumir responsabilidades y roles en la seguridad alimentaria y nutricional. |
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4.transformar el enfoque de las políticas públicas y sociales, para que pasen |
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las personas de ser clientela pasiva y vulnerable que requiere de asistencia, |
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a personas sujetos de derechos.' |
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- text: Regulatory arrangements will be reformed to accelerate innovation in agriculture, |
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including pilot programs, regulatory sandboxes for new inputs and services, clear |
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intellectual property protection, and predictable approval timelines for agrochemical |
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and digital solutions that meet safety and environmental criteria. |
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- text: Climate-smart strategies will protect livelihoods by diversifying income sources, |
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expanding agroforestry and drought-resistant crops, and implementing risk-transfer |
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mechanisms that shield poor households from shocks, thereby contributing to sustained |
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declines in poverty levels. |
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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-L12-70prop-data-augmented") |
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# Run inference |
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preds = model("Strengthen macro-fiscal resilience through risk-informed public investment planning, including scenario-based budgeting and contingent financing arrangements.") |
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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 | 69.0403 | 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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| 0.0001 | 1 | 0.2247 | - | |
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| 0.0065 | 50 | 0.2105 | - | |
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| 0.0130 | 100 | 0.1984 | - | |
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| 0.0195 | 150 | 0.1899 | - | |
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| 0.0260 | 200 | 0.1916 | - | |
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| 0.0325 | 250 | 0.1769 | - | |
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| 0.0390 | 300 | 0.1679 | - | |
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| 0.0455 | 350 | 0.1677 | - | |
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| 0.0520 | 400 | 0.1591 | - | |
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| 0.0585 | 450 | 0.1521 | - | |
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| 0.0650 | 500 | 0.1522 | - | |
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| 0.0715 | 550 | 0.1497 | - | |
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| 0.0780 | 600 | 0.1494 | - | |
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| 0.0845 | 650 | 0.1457 | - | |
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| 0.0910 | 700 | 0.1503 | - | |
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| 0.0975 | 750 | 0.1328 | - | |
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| 0.1040 | 800 | 0.1251 | - | |
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| 0.1105 | 850 | 0.1395 | - | |
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| 0.1170 | 900 | 0.1298 | - | |
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| 0.1235 | 950 | 0.1221 | - | |
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| 0.1300 | 1000 | 0.1313 | - | |
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| 0.1365 | 1050 | 0.1267 | - | |
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| 0.1429 | 1100 | 0.1367 | - | |
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| 0.1494 | 1150 | 0.1324 | - | |
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| 0.1559 | 1200 | 0.1201 | - | |
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| 0.1624 | 1250 | 0.1244 | - | |
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| 0.1689 | 1300 | 0.1231 | - | |
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| 0.1754 | 1350 | 0.1214 | - | |
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| 0.1819 | 1400 | 0.1098 | - | |
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| 0.1884 | 1450 | 0.1152 | - | |
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| 0.1949 | 1500 | 0.1149 | - | |
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| 0.2014 | 1550 | 0.1185 | - | |
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| 0.2079 | 1600 | 0.1123 | - | |
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| 0.2144 | 1650 | 0.1092 | - | |
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| 0.2209 | 1700 | 0.1097 | - | |
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| 0.2274 | 1750 | 0.1159 | - | |
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| 0.2339 | 1800 | 0.1076 | - | |
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| 0.2404 | 1850 | 0.114 | - | |
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| 0.2469 | 1900 | 0.1055 | - | |
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| 0.2534 | 1950 | 0.1033 | - | |
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| 0.2599 | 2000 | 0.1016 | - | |
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| 0.2664 | 2050 | 0.1004 | - | |
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| 0.2729 | 2100 | 0.0973 | - | |
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| 0.2794 | 2150 | 0.1051 | - | |
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| 0.2859 | 2200 | 0.0954 | - | |
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| 0.2924 | 2250 | 0.0998 | - | |
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| 0.2989 | 2300 | 0.0984 | - | |
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| 0.3054 | 2350 | 0.0906 | - | |
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| 0.3119 | 2400 | 0.0939 | - | |
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| 0.3184 | 2450 | 0.1023 | - | |
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| 0.3249 | 2500 | 0.0983 | - | |
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| 0.3314 | 2550 | 0.0952 | - | |
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| 0.3379 | 2600 | 0.099 | - | |
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| 0.3444 | 2650 | 0.0994 | - | |
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| 0.3509 | 2700 | 0.0975 | - | |
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| 0.3574 | 2750 | 0.0871 | - | |
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| 0.3639 | 2800 | 0.0969 | - | |
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| 0.3704 | 2850 | 0.0845 | - | |
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| 0.3769 | 2900 | 0.1007 | - | |
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| 0.3834 | 2950 | 0.0887 | - | |
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| 0.3899 | 3000 | 0.0807 | - | |
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| 0.3964 | 3050 | 0.0859 | - | |
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| 0.4029 | 3100 | 0.0826 | - | |
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| 0.4094 | 3150 | 0.0784 | - | |
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| 0.4159 | 3200 | 0.0851 | - | |
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| 0.4224 | 3250 | 0.0834 | - | |
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| 0.4288 | 3300 | 0.0922 | - | |
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| 0.4353 | 3350 | 0.0862 | - | |
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| 0.4418 | 3400 | 0.0856 | - | |
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| 0.4483 | 3450 | 0.0848 | - | |
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| 0.4548 | 3500 | 0.0735 | - | |
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| 0.4613 | 3550 | 0.0752 | - | |
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| 0.4678 | 3600 | 0.0881 | - | |
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| 0.4743 | 3650 | 0.0836 | - | |
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| 0.4808 | 3700 | 0.0808 | - | |
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| 0.4873 | 3750 | 0.0963 | - | |
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| 0.4938 | 3800 | 0.0816 | - | |
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| 0.5003 | 3850 | 0.0809 | - | |
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| 0.5068 | 3900 | 0.0833 | - | |
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| 0.5133 | 3950 | 0.0852 | - | |
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| 0.5198 | 4000 | 0.0788 | - | |
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| 0.5263 | 4050 | 0.0742 | - | |
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| 0.5328 | 4100 | 0.0693 | - | |
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| 0.5393 | 4150 | 0.0856 | - | |
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| 0.5458 | 4200 | 0.072 | - | |
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| 0.5523 | 4250 | 0.0805 | - | |
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| 0.5588 | 4300 | 0.0741 | - | |
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| 0.5653 | 4350 | 0.0845 | - | |
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| 0.5718 | 4400 | 0.0753 | - | |
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| 0.5783 | 4450 | 0.0814 | - | |
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| 0.5848 | 4500 | 0.0691 | - | |
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| 0.5913 | 4550 | 0.0823 | - | |
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| 0.5978 | 4600 | 0.0847 | - | |
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| 0.6043 | 4650 | 0.0714 | - | |
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| 0.6108 | 4700 | 0.0879 | - | |
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| 0.6173 | 4750 | 0.0711 | - | |
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| 0.6238 | 4800 | 0.0697 | - | |
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| 0.6303 | 4850 | 0.0741 | - | |
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| 0.6368 | 4900 | 0.0771 | - | |
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| 0.6433 | 4950 | 0.0837 | - | |
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| 0.6498 | 5000 | 0.0743 | - | |
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| 0.6563 | 5050 | 0.0755 | - | |
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| 0.6628 | 5100 | 0.0739 | - | |
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| 0.6693 | 5150 | 0.0816 | - | |
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| 0.6758 | 5200 | 0.0782 | - | |
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| 0.6823 | 5250 | 0.0755 | - | |
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| 0.6888 | 5300 | 0.0712 | - | |
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| 0.6953 | 5350 | 0.0639 | - | |
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| 0.7018 | 5400 | 0.0694 | - | |
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| 0.7083 | 5450 | 0.0806 | - | |
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| 0.7147 | 5500 | 0.071 | - | |
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| 0.7212 | 5550 | 0.0707 | - | |
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| 0.7277 | 5600 | 0.0751 | - | |
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| 0.7342 | 5650 | 0.0724 | - | |
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| 0.7407 | 5700 | 0.0688 | - | |
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| 0.7472 | 5750 | 0.067 | - | |
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| 0.7537 | 5800 | 0.0718 | - | |
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| 0.7602 | 5850 | 0.0681 | - | |
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| 0.7667 | 5900 | 0.0694 | - | |
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| 0.7732 | 5950 | 0.0693 | - | |
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| 0.7797 | 6000 | 0.0731 | - | |
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| 0.7862 | 6050 | 0.0626 | - | |
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| 0.7927 | 6100 | 0.0691 | - | |
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| 0.7992 | 6150 | 0.0711 | - | |
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| 0.8057 | 6200 | 0.0627 | - | |
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| 0.8122 | 6250 | 0.0726 | - | |
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| 0.8187 | 6300 | 0.068 | - | |
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| 0.8252 | 6350 | 0.0766 | - | |
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| 0.8317 | 6400 | 0.0617 | - | |
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| 0.8382 | 6450 | 0.0671 | - | |
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| 0.8447 | 6500 | 0.0645 | - | |
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| 0.8512 | 6550 | 0.0722 | - | |
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| 0.8577 | 6600 | 0.0751 | - | |
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| 0.8642 | 6650 | 0.0591 | - | |
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| 0.8707 | 6700 | 0.0664 | - | |
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| 0.8772 | 6750 | 0.0735 | - | |
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| 0.8837 | 6800 | 0.0709 | - | |
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| 0.8902 | 6850 | 0.0632 | - | |
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| 0.8967 | 6900 | 0.0679 | - | |
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| 0.9032 | 6950 | 0.0596 | - | |
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| 0.9097 | 7000 | 0.0676 | - | |
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| 0.9162 | 7050 | 0.066 | - | |
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| 0.9227 | 7100 | 0.069 | - | |
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| 0.9292 | 7150 | 0.0615 | - | |
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| 0.9357 | 7200 | 0.0579 | - | |
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| 0.9422 | 7250 | 0.0576 | - | |
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| 0.9487 | 7300 | 0.0558 | - | |
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| 0.9552 | 7350 | 0.0556 | - | |
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| 0.9617 | 7400 | 0.0637 | - | |
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| 0.9682 | 7450 | 0.0615 | - | |
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| 0.9747 | 7500 | 0.0677 | - | |
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| 0.9812 | 7550 | 0.0584 | - | |
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| 0.9877 | 7600 | 0.0661 | - | |
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| 0.9942 | 7650 | 0.0583 | - | |
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| 1.0006 | 7700 | 0.0639 | - | |
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| 1.0071 | 7750 | 0.0598 | - | |
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| 1.0136 | 7800 | 0.0586 | - | |
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| 1.0201 | 7850 | 0.055 | - | |
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| 1.0266 | 7900 | 0.0636 | - | |
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| 1.0331 | 7950 | 0.0623 | - | |
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| 1.0396 | 8000 | 0.0661 | - | |
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| 1.0461 | 8050 | 0.0633 | - | |
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| 1.0526 | 8100 | 0.056 | - | |
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| 1.0591 | 8150 | 0.0555 | - | |
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| 1.0656 | 8200 | 0.0608 | - | |
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| 1.0721 | 8250 | 0.0491 | - | |
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| 1.0786 | 8300 | 0.0592 | - | |
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| 1.0851 | 8350 | 0.0645 | - | |
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| 1.0916 | 8400 | 0.0553 | - | |
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| 1.0981 | 8450 | 0.0547 | - | |
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| 1.1046 | 8500 | 0.0494 | - | |
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| 1.1111 | 8550 | 0.0594 | - | |
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| 1.1176 | 8600 | 0.058 | - | |
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| 1.1241 | 8650 | 0.0589 | - | |
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| 1.1306 | 8700 | 0.0552 | - | |
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| 1.1371 | 8750 | 0.0554 | - | |
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| 1.1436 | 8800 | 0.0566 | - | |
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| 1.1501 | 8850 | 0.0558 | - | |
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| 1.1566 | 8900 | 0.0596 | - | |
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| 1.1631 | 8950 | 0.0551 | - | |
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| 1.1696 | 9000 | 0.061 | - | |
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| 1.1761 | 9050 | 0.0689 | - | |
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| 1.1826 | 9100 | 0.0565 | - | |
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| 1.1891 | 9150 | 0.0581 | - | |
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| 1.1956 | 9200 | 0.0606 | - | |
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| 1.2021 | 9250 | 0.057 | - | |
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| 1.2086 | 9300 | 0.0577 | - | |
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| 1.2151 | 9350 | 0.0629 | - | |
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| 1.2216 | 9400 | 0.0592 | - | |
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| 1.2281 | 9450 | 0.0547 | - | |
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| 1.2346 | 9500 | 0.0606 | - | |
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| 1.2411 | 9550 | 0.0588 | - | |
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| 1.2476 | 9600 | 0.0581 | - | |
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| 1.2541 | 9650 | 0.0624 | - | |
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| 1.2606 | 9700 | 0.0589 | - | |
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| 1.2671 | 9750 | 0.0646 | - | |
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| 1.2736 | 9800 | 0.0559 | - | |
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| 1.2801 | 9850 | 0.0594 | - | |
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| 1.2865 | 9900 | 0.0586 | - | |
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| 1.2930 | 9950 | 0.0552 | - | |
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| 1.2995 | 10000 | 0.0513 | - | |
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| 1.3060 | 10050 | 0.0565 | - | |
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| 1.3125 | 10100 | 0.0626 | - | |
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| 1.3190 | 10150 | 0.0483 | - | |
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| 1.3255 | 10200 | 0.0643 | - | |
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| 1.3320 | 10250 | 0.0524 | - | |
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| 1.3385 | 10300 | 0.0559 | - | |
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| 1.3450 | 10350 | 0.0589 | - | |
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| 1.3515 | 10400 | 0.0562 | - | |
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| 1.3580 | 10450 | 0.0592 | - | |
|
|
| 1.3645 | 10500 | 0.047 | - | |
|
|
| 1.3710 | 10550 | 0.0531 | - | |
|
|
| 1.3775 | 10600 | 0.0506 | - | |
|
|
| 1.3840 | 10650 | 0.0579 | - | |
|
|
| 1.3905 | 10700 | 0.0569 | - | |
|
|
| 1.3970 | 10750 | 0.0579 | - | |
|
|
| 1.4035 | 10800 | 0.0504 | - | |
|
|
| 1.4100 | 10850 | 0.0547 | - | |
|
|
| 1.4165 | 10900 | 0.0497 | - | |
|
|
| 1.4230 | 10950 | 0.0533 | - | |
|
|
| 1.4295 | 11000 | 0.0488 | - | |
|
|
| 1.4360 | 11050 | 0.0537 | - | |
|
|
| 1.4425 | 11100 | 0.0544 | - | |
|
|
| 1.4490 | 11150 | 0.0548 | - | |
|
|
| 1.4555 | 11200 | 0.0475 | - | |
|
|
| 1.4620 | 11250 | 0.0519 | - | |
|
|
| 1.4685 | 11300 | 0.0568 | - | |
|
|
| 1.4750 | 11350 | 0.0567 | - | |
|
|
| 1.4815 | 11400 | 0.0473 | - | |
|
|
| 1.4880 | 11450 | 0.0535 | - | |
|
|
| 1.4945 | 11500 | 0.0531 | - | |
|
|
| 1.5010 | 11550 | 0.0567 | - | |
|
|
| 1.5075 | 11600 | 0.0529 | - | |
|
|
| 1.5140 | 11650 | 0.0544 | - | |
|
|
| 1.5205 | 11700 | 0.0612 | - | |
|
|
| 1.5270 | 11750 | 0.055 | - | |
|
|
| 1.5335 | 11800 | 0.0474 | - | |
|
|
| 1.5400 | 11850 | 0.0572 | - | |
|
|
| 1.5465 | 11900 | 0.0484 | - | |
|
|
| 1.5530 | 11950 | 0.0553 | - | |
|
|
| 1.5595 | 12000 | 0.0519 | - | |
|
|
| 1.5660 | 12050 | 0.0565 | - | |
|
|
| 1.5724 | 12100 | 0.0466 | - | |
|
|
| 1.5789 | 12150 | 0.0502 | - | |
|
|
| 1.5854 | 12200 | 0.0525 | - | |
|
|
| 1.5919 | 12250 | 0.054 | - | |
|
|
| 1.5984 | 12300 | 0.0556 | - | |
|
|
| 1.6049 | 12350 | 0.0515 | - | |
|
|
| 1.6114 | 12400 | 0.0476 | - | |
|
|
| 1.6179 | 12450 | 0.0579 | - | |
|
|
| 1.6244 | 12500 | 0.0567 | - | |
|
|
| 1.6309 | 12550 | 0.0551 | - | |
|
|
| 1.6374 | 12600 | 0.0518 | - | |
|
|
| 1.6439 | 12650 | 0.0508 | - | |
|
|
| 1.6504 | 12700 | 0.0503 | - | |
|
|
| 1.6569 | 12750 | 0.0484 | - | |
|
|
| 1.6634 | 12800 | 0.0531 | - | |
|
|
| 1.6699 | 12850 | 0.0553 | - | |
|
|
| 1.6764 | 12900 | 0.0588 | - | |
|
|
| 1.6829 | 12950 | 0.0547 | - | |
|
|
| 1.6894 | 13000 | 0.0587 | - | |
|
|
| 1.6959 | 13050 | 0.0562 | - | |
|
|
| 1.7024 | 13100 | 0.0558 | - | |
|
|
| 1.7089 | 13150 | 0.0559 | - | |
|
|
| 1.7154 | 13200 | 0.0547 | - | |
|
|
| 1.7219 | 13250 | 0.059 | - | |
|
|
| 1.7284 | 13300 | 0.053 | - | |
|
|
| 1.7349 | 13350 | 0.0532 | - | |
|
|
| 1.7414 | 13400 | 0.0552 | - | |
|
|
| 1.7479 | 13450 | 0.0443 | - | |
|
|
| 1.7544 | 13500 | 0.058 | - | |
|
|
| 1.7609 | 13550 | 0.0503 | - | |
|
|
| 1.7674 | 13600 | 0.0499 | - | |
|
|
| 1.7739 | 13650 | 0.0478 | - | |
|
|
| 1.7804 | 13700 | 0.0569 | - | |
|
|
| 1.7869 | 13750 | 0.052 | - | |
|
|
| 1.7934 | 13800 | 0.0458 | - | |
|
|
| 1.7999 | 13850 | 0.0551 | - | |
|
|
| 1.8064 | 13900 | 0.0567 | - | |
|
|
| 1.8129 | 13950 | 0.0511 | - | |
|
|
| 1.8194 | 14000 | 0.0546 | - | |
|
|
| 1.8259 | 14050 | 0.058 | - | |
|
|
| 1.8324 | 14100 | 0.0539 | - | |
|
|
| 1.8389 | 14150 | 0.0544 | - | |
|
|
| 1.8454 | 14200 | 0.061 | - | |
|
|
| 1.8519 | 14250 | 0.0521 | - | |
|
|
| 1.8583 | 14300 | 0.046 | - | |
|
|
| 1.8648 | 14350 | 0.0494 | - | |
|
|
| 1.8713 | 14400 | 0.0604 | - | |
|
|
| 1.8778 | 14450 | 0.0543 | - | |
|
|
| 1.8843 | 14500 | 0.0522 | - | |
|
|
| 1.8908 | 14550 | 0.0533 | - | |
|
|
| 1.8973 | 14600 | 0.0469 | - | |
|
|
| 1.9038 | 14650 | 0.0525 | - | |
|
|
| 1.9103 | 14700 | 0.0516 | - | |
|
|
| 1.9168 | 14750 | 0.0485 | - | |
|
|
| 1.9233 | 14800 | 0.0601 | - | |
|
|
| 1.9298 | 14850 | 0.0487 | - | |
|
|
| 1.9363 | 14900 | 0.0496 | - | |
|
|
| 1.9428 | 14950 | 0.0529 | - | |
|
|
| 1.9493 | 15000 | 0.054 | - | |
|
|
| 1.9558 | 15050 | 0.0431 | - | |
|
|
| 1.9623 | 15100 | 0.0449 | - | |
|
|
| 1.9688 | 15150 | 0.0602 | - | |
|
|
| 1.9753 | 15200 | 0.0447 | - | |
|
|
| 1.9818 | 15250 | 0.0506 | - | |
|
|
| 1.9883 | 15300 | 0.0503 | - | |
|
|
| 1.9948 | 15350 | 0.0515 | - | |
|
|
|
|
|
### 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 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
```bibtex |
|
|
@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}, |
|
|
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}, |
|
|
copyright = {Creative Commons Attribution 4.0 International} |
|
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} |
|
|
``` |
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