
# SciBERT NLP4SG

SciBERT NLP4SG is a SciBERT model fine-tuned to detect NLP4SG papers based on their title and abstract.

We present the details in the paper:

The training corpus is a combination of the [NLP4SGPapers training set](https://huggingface.co/datasets/feradauto/NLP4SGPapers) which is manually annotated, and some papers identified by keywords.

For more details about the training data and the model, visit the original repo [here](https://github.com/feradauto/nlp4sg).

Please cite the following paper:
```
@misc{gonzalez2023good,
      title={Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good}, 
      author={Fernando Gonzalez and Zhijing Jin and Jad Beydoun and Bernhard Schölkopf and Tom Hope and Mrinmaya Sachan and Rada Mihalcea},
      year={2023},
      eprint={2305.05471},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["en"], "license": "apache-2.0", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "On Unifying Misinformation Detection. In this paper, we introduce UNIFIEDM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news and verifying rumors. By grouping these tasks together, UNIFIEDM2 learns a richer representation of misinformation, which leads to stateof-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UNIFIEDM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and model's generalizability to unseen events.", "example_title": "Misinformation Detection"}]}
transformers pytorch bert text-classification en arxiv:2305.05471 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2305.05471 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# SciBERT NLP4SG

SciBERT NLP4SG is a SciBERT model fine-tuned to detect NLP4SG papers based on their title and abstract.

We present the details in the paper:

The training corpus is a combination of the NLP4SGPapers training set which is manually annotated, and some papers identified by keywords.

For more details about the training data and the model, visit the original repo here.

Please cite the following paper:

# SciBERT NLP4SG

SciBERT NLP4SG is a SciBERT model fine-tuned to detect NLP4SG papers based on their title and abstract.

We present the details in the paper:

The training corpus is a combination of the NLP4SGPapers training set which is manually annotated, and some papers identified by keywords.

For more details about the training data and the model, visit the original repo here.

Please cite the following paper:
TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2305.05471 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # SciBERT NLP4SG

SciBERT NLP4SG is a SciBERT model fine-tuned to detect NLP4SG papers based on their title and abstract.

We present the details in the paper:

The training corpus is a combination of the NLP4SGPapers training set which is manually annotated, and some papers identified by keywords.

For more details about the training data and the model, visit the original repo here.

Please cite the following paper: