
# fathyshalab/reklambox2-8-21-xlm

This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Usage

To use this model for inference, first install the SetFit library:

```bash
python -m pip install setfit
```

You can then run inference as follows:

```python
from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/reklambox2-8-21-xlm")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```

## BibTeX entry and citation info

```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}
}
```

{"license": "apache-2.0", "tags": ["setfit", "sentence-transformers", "text-classification"], "pipeline_tag": "text-classification"}
sentence-transformers pytorch xlm-roberta setfit text-classification arxiv:2209.11055 license:apache-2.0 region:us
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us 


# fathyshalab/reklambox2-8-21-xlm

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:



## BibTeX entry and citation info



# fathyshalab/reklambox2-8-21-xlm

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us 
 # fathyshalab/reklambox2-8-21-xlm

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us 
# fathyshalab/reklambox2-8-21-xlm

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:## BibTeX entry and citation info