
# marmolpen3/sla-obligations-rights

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

1. Fitting a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with the features of the fitted sentence transformer.

This model has been trained with SLA sentences from real providers that offer software as a service. The outputs will be of numerical value, 0, 1 or 2, these being obligation, right, none respectively.


## 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("marmolpen3/sla-obligations-rights")
# Run inference
preds = model(["NTTA's goal is to deliver SCD Content 100% of the time.", "You can request for a service credit by contacting Support."])
# Solution [0, 1] = ["Obligation", "Right"]
```

## BibTeX entry and citation info

We are developing a system, it is in process, but you can visit it and contribute [here](https://github.com/isa-group/iContracts)

SetFit is the framework we use to train the model:
```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}
}
```

The model used is [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2). If you need it you can cite their publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["setfit", "sentence-transformers", "text-classification"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
sentence-transformers pytorch mpnet setfit text-classification en arxiv:2209.11055 arxiv:1908.10084 license:apache-2.0 region:us
TAGS
#sentence-transformers #pytorch #mpnet #setfit #text-classification #en #arxiv-2209.11055 #arxiv-1908.10084 #license-apache-2.0 #region-us 


# marmolpen3/sla-obligations-rights

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

1. Fitting a Sentence Transformer with contrastive learning.
2. Training a classification head with the features of the fitted sentence transformer.

This model has been trained with SLA sentences from real providers that offer software as a service. The outputs will be of numerical value, 0, 1 or 2, these being obligation, right, none respectively.


## Usage

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



You can then run inference as follows:



## BibTeX entry and citation info

We are developing a system, it is in process, but you can visit it and contribute here

SetFit is the framework we use to train the model:


The model used is paraphrase-mpnet-base-v2. If you need it you can cite their publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

# marmolpen3/sla-obligations-rights

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

1. Fitting a Sentence Transformer with contrastive learning.
2. Training a classification head with the features of the fitted sentence transformer.

This model has been trained with SLA sentences from real providers that offer software as a service. The outputs will be of numerical value, 0, 1 or 2, these being obligation, right, none respectively. ## Usage

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



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

We are developing a system, it is in process, but you can visit it and contribute here

SetFit is the framework we use to train the model:


The model used is paraphrase-mpnet-base-v2. If you need it you can cite their publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
TAGS
#sentence-transformers #pytorch #mpnet #setfit #text-classification #en #arxiv-2209.11055 #arxiv-1908.10084 #license-apache-2.0 #region-us 
 # marmolpen3/sla-obligations-rights

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

1. Fitting a Sentence Transformer with contrastive learning.
2. Training a classification head with the features of the fitted sentence transformer.

This model has been trained with SLA sentences from real providers that offer software as a service. The outputs will be of numerical value, 0, 1 or 2, these being obligation, right, none respectively. ## Usage

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



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

We are developing a system, it is in process, but you can visit it and contribute here

SetFit is the framework we use to train the model:


The model used is paraphrase-mpnet-base-v2. If you need it you can cite their publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks: