Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/shrugging-grace/tweetclassifier/README.md
README.md
ADDED
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# shrugging-grace/tweetclassifier
|
2 |
+
|
3 |
+
## Model description
|
4 |
+
This model classifies tweets as either relating to the Covid-19 pandemic or not.
|
5 |
+
|
6 |
+
## Intended uses & limitations
|
7 |
+
It is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to weekly Prime Ministers' Questions.
|
8 |
+
|
9 |
+
#### How to use
|
10 |
+
``LABEL_0`` means that the tweet relates to Covid-19
|
11 |
+
|
12 |
+
``LABEL_1`` means that the tweet does not relate to Covid-19
|
13 |
+
|
14 |
+
## Training data
|
15 |
+
The model was trained on 1000 tweets (with the "#PMQs'), which were manually labeled by the author. The tweets were collected between May-July 2020.
|
16 |
+
|
17 |
+
### BibTeX entry and citation info
|
18 |
+
|
19 |
+
This was based on a pretrained version of BERT.
|
20 |
+
|
21 |
+
@article{devlin2018bert,
|
22 |
+
title={Bert: Pre-training of deep bidirectional transformers for language understanding},
|
23 |
+
author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
|
24 |
+
journal={arXiv preprint arXiv:1810.04805},
|
25 |
+
year={2018}
|
26 |
+
}
|