Instructions to use sarkerlab/SocBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarkerlab/SocBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sarkerlab/SocBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sarkerlab/SocBERT-base") model = AutoModelForMaskedLM.from_pretrained("sarkerlab/SocBERT-base") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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# SocBERT model
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Pretrained model on 20GB English tweets and 72GB Reddit comments using a masked language modeling (MLM) objective.
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The model was trained from scratch following the model architecture of RoBERTa-base.
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We benchmarked SocBERT, on 40 text classification tasks with social media data.
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The experiment results can be found in our paper:
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```
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@inproceedings{socbert:2023,
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title = {{SocBERT: A Pretrained Model for Social Media Text}},
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author = {Yuting Guo and Abeed Sarker},
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booktitle = {Proceedings of the Fourth Workshop on Insights from Negative Results in NLP},
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year = {2023}
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}
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```
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