# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("sarkerlab/SocBERT-base")
model = AutoModelForMaskedLM.from_pretrained("sarkerlab/SocBERT-base")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
SocBERT model
Pretrained model on 20GB English tweets and 72GB Reddit comments using a masked language modeling (MLM) objective. The tweets are from Archive and collected from Twitter Streaming API. The Reddit comments are ramdonly sampled from all subreddits from 2015-2019. SocBERT-base was pretrained on 819M sequence blocks for 100K steps. SocBERT-final was pretrained on 929M (819M+110M) sequence blocks for 112K (100K+12K) steps. We benchmarked SocBERT, on 40 text classification tasks with social media data.
The experiment results can be found in our paper:
@inproceedings{socbert:2023,
title = {{SocBERT: A Pretrained Model for Social Media Text}},
author = {Yuting Guo and Abeed Sarker},
booktitle = {Proceedings of the Fourth Workshop on Insights from Negative Results in NLP},
year = {2023}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sarkerlab/SocBERT-base")