Edit model card

Pre-trained BERT on Twitter US Political Election 2020

Pre-trained weights for Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.

We use the initialized weights from BERT-base (uncased) or bert-base-uncased.

Training Data

This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election.

Training Objective

This model is initialized with BERT-base and trained with normal MLM objective.

Usage

This pre-trained language model can be fine-tunned to any downstream task (e.g. classification).

Please see the official repository for more detail.

from transformers import BertTokenizer, BertForMaskedLM, pipeline
import torch

# Choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Select mode path here
pretrained_LM_path = "kornosk/bert-political-election2020-twitter-mlm"

# Load model
tokenizer = BertTokenizer.from_pretrained(pretrained_LM_path)
model = BertForMaskedLM.from_pretrained(pretrained_LM_path)

# Fill mask
example = "Trump is the [MASK] of USA"
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
# Use following line instead of the above one does not work.
# Huggingface have been updated, newer version accepts a string of model name instead.
fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer)

outputs = fill_mask(example)
print(outputs)

# See embeddings
inputs = tokenizer(example, return_tensors="pt")
outputs = model(**inputs)
print(outputs)

# OR you can use this model to train on your downstream task!
# Please consider citing our paper if you feel this is useful :)

Reference

Citation

@inproceedings{kawintiranon2021knowledge,
    title={Knowledge Enhanced Masked Language Model for Stance Detection},
    author={Kawintiranon, Kornraphop and Singh, Lisa},
    booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
    year={2021},
    publisher={Association for Computational Linguistics},
    url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
Downloads last month
18
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.