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--- |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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: La app de BBVA está caída, pero se pide paciencia para los depósitos de mañana. |
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- text: Tengo un problema con un cajero automático que no me dio el dinero pero sí |
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lo cargó. |
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- text: El chip de mi tarjeta de Banorte no funciona, hice una transferencia a mi |
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tarjeta de BBVA y el cajero se quedó con ella, ¿cómo va su sábado? |
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- text: Evo Banco reporta un asombroso incremento del 700% en sus depósitos en un |
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año y ahora ofrece la posibilidad de contratar servicios a través de WhatsApp. |
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- text: Los nuevos jubilados que acrediten su pensión en BBVA recibirán un regalo |
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de bienvenida de $130.000. |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8028571428571428 |
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name: Accuracy |
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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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 128 tokens |
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- **Number of Classes:** 2 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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### 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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### Model Labels |
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| Label | Examples | |
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|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| discard | <ul><li>'Marcos informa que se puede realizar el pago de productos de BBVA a través de la Línea BBVA, cajeros automáticos, practicajas, ventanilla de sucursal o diversos comercios.'</li><li>'Se ha celebrado una reunión de alto nivel en 2024 para concretar proyectos de inversión, incluyendo la cooperación con BBVA para la construcción de un portadrones y en el ámbito turístico.'</li><li>'Diversificar es clave para alcanzar nuestros objetivos en inversiones y en la vida, descubre cómo tus decisiones financieras pueden impactar tu vida personal en este artículo.'</li></ul> | |
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| relevant | <ul><li>'La persona recibió un correo idéntico al que le explicaron que es una técnica de estafa que simula enviarlo desde su propia cuenta.'</li><li>'La cancelación de la cuenta se ha demorado un mes y al solicitar 200 euros para un viaje, me han cobrado 9 euros de comisión.'</li><li>'El Santander logró récords en beneficios y comisiones a los desfavorecidos bajo el ministerio del consagrado en Consumo, mientras se obsesionan con la apariencia y carecen de dignidad y principios.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.8029 | |
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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("saraestevez/setfit-minilm-bank-tweets-processed-400") |
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# Run inference |
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preds = model("La app de BBVA está caída, pero se pide paciencia para los depósitos de mañana.") |
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``` |
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### Out-of-Scope Use |
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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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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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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 | 21.6612 | 44 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| discard | 400 | |
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| relevant | 400 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- 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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| 0.0005 | 1 | 0.3197 | - | |
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| 0.025 | 50 | 0.2199 | - | |
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| 0.05 | 100 | 0.2876 | - | |
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| 0.075 | 150 | 0.2568 | - | |
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| 0.1 | 200 | 0.196 | - | |
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| 0.125 | 250 | 0.15 | - | |
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| 0.15 | 300 | 0.1475 | - | |
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| 0.175 | 350 | 0.081 | - | |
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| 0.2 | 400 | 0.0441 | - | |
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| 0.225 | 450 | 0.0228 | - | |
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| 0.25 | 500 | 0.0017 | - | |
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| 0.275 | 550 | 0.0083 | - | |
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| 0.3 | 600 | 0.002 | - | |
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| 0.325 | 650 | 0.0013 | - | |
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| 0.35 | 700 | 0.0011 | - | |
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| 0.375 | 750 | 0.0014 | - | |
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| 0.4 | 800 | 0.0004 | - | |
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| 0.425 | 850 | 0.0001 | - | |
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| 0.45 | 900 | 0.0118 | - | |
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| 0.475 | 950 | 0.0002 | - | |
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| 0.5 | 1000 | 0.0012 | - | |
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| 0.525 | 1050 | 0.0003 | - | |
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| 0.55 | 1100 | 0.0001 | - | |
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| 0.575 | 1150 | 0.0003 | - | |
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| 0.6 | 1200 | 0.0001 | - | |
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| 0.625 | 1250 | 0.0001 | - | |
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| 0.65 | 1300 | 0.0001 | - | |
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| 0.675 | 1350 | 0.0002 | - | |
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| 0.7 | 1400 | 0.0197 | - | |
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| 0.725 | 1450 | 0.0002 | - | |
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| 0.75 | 1500 | 0.0002 | - | |
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| 0.775 | 1550 | 0.0001 | - | |
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| 0.8 | 1600 | 0.0004 | - | |
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| 0.825 | 1650 | 0.0001 | - | |
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| 0.85 | 1700 | 0.0001 | - | |
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| 0.875 | 1750 | 0.0001 | - | |
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| 0.9 | 1800 | 0.0001 | - | |
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| 0.925 | 1850 | 0.0001 | - | |
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| 0.95 | 1900 | 0.0158 | - | |
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| 0.975 | 1950 | 0.0001 | - | |
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| 1.0 | 2000 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.11.0rc1 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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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}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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