Create README.md

README.md

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+# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)
+
+Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://2021.naacl.org/program/accepted/), NAACL 2021.
+
+# Training Data
+
+This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Joe Biden.
+
+# Training Objective
+
+This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection.
+
+# Usage
+
+This pre-trained language model is fine-tuned to the stance detection task.
+
+Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+# choose GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# select mode path here
+pretrained_LM_path = "kornosk/bert-election2020-twitter-stance"
+
+# load model
+tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
+model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
+
+id2label = {
+    0: "AGAINST",
+    1: "FAVOR",
+    2: "NONE"
+}
+
+##### Prediction Neutral #####
+sentence = "Hello World."
+inputs = tokenizer(sentence.lower(), return_tensors="pt")
+outputs = model(**inputs)
+predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
+
+print("Sentence:", sentence)
+print("Prediction:", id2label[np.argmax(predicted_probability)])
+print("Against:", predicted_probability[0])
+print("Favor:", predicted_probability[1])
+print("Neutral:", predicted_probability[2])
+
+##### Prediction Favor #####
+sentence = "Go Go Biden!!!"
+inputs = tokenizer(sentence.lower(), return_tensors="pt")
+outputs = model(**inputs)
+predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
+
+print("Sentence:", sentence)
+print("Prediction:", id2label[np.argmax(predicted_probability)])
+print("Against:", predicted_probability[0])
+print("Favor:", predicted_probability[1])
+print("Neutral:", predicted_probability[2])
+
+##### Prediction Against #####
+sentence = "Biden is the worst."
+inputs = tokenizer(sentence.lower(), return_tensors="pt")
+outputs = model(**inputs)
+predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
+
+print("Sentence:", sentence)
+print("Prediction:", id2label[np.argmax(predicted_probability)])
+print("Against:", predicted_probability[0])
+print("Favor:", predicted_probability[1])
+print("Neutral:", predicted_probability[2])
+
+# please consider citing our paper if you feel this is useful :)
+```
+
+# Reference
+
+- [Knowledge Enhance Masked Language Model for Stance Detection](https://2021.naacl.org/program/accepted/), NAACL 2021.
+
+# Citation
+```bibtex
+@inproceedings{kawintiranon2021knowledge,
+    title={Knowledge Enhanced Masked Language Model for Stance Detection},
+    author={Kawintiranon, Kornraphop and Singh, Lisa},
+    booktitle={Proceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
+    year={2021},
+    url={#}
+}
+```