Update README.md

README.md

@@ -2,12 +2,12 @@
 
 language: en
 tags:
-- bertweet
+- text-classifciation
 license: apache-2.0
 datasets:
 - tweets
-- fake new information
-
+widget:
+- text: "Vaccine is effective"
 ---
 
 # Vaccinating COVID tweets
@@ -15,19 +15,7 @@ datasets:
 
 Fine-tuned model on English language using a masked language modeling (MLM) objective from BERTweet in [this repository](https://github.com/VinAIResearch/BERTweet) for the classification task for false/misleading information about COVID-19 vaccines.
 
-# Contributors
-- Ahn, Hyunju
-- An, Jiyong
-- An, Seungchan
-- Jeong, Seokho
-- Kim, Jungmin
-- Kim, Sangbeom
-- Advisor: Dr. Wen-Syan Li
-
-Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-
-# BERT base model (uncased)
+# Vaccinating COVID tweets
 
 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
 
@@ -37,13 +25,41 @@ Pretrained model on English language using a masked language modeling (MLM) obje
 
 between english and English.
 
+## Model description
 
+You can embed local or remote images using `![](...)`
 
-## Model description
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# You can include sample code which will be formatted
+```
+
+#### Limitations and bias
 
-BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
+Provide examples of latent issues and potential remediations.
 
+## Training data
 
+Describe the data you used to train the model.
+If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
+
+## Training procedure
+
+Preprocessing, hardware used, hyperparameters...
+
+## Eval results
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{...,
+  year={2020}
+}
+```
+------------------------
 
 ## Intended uses & limitations
 
@@ -185,7 +201,7 @@ The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total)
 
 of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
 
-used is Adam with a learning rate of 1e-4, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,
+used is Adam with a learning rate of 1e-4, \\\\\\\\(\\\\beta_{1} = 0.9\\\\\\\\) and \\\\\\\\(\\\\beta_{2} = 0.999\\\\\\\\), a weight decay of 0.01,
 
 learning rate warmup for 10,000 steps and linear decay of the learning rate after.
 
@@ -201,42 +217,16 @@ Glue test results:
 
 |      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |
 
-### BibTeX entry and citation info
-
-```bibtex
-
-@article{DBLP:journals/corr/abs-1810-04805,
-
-  author    = {Jacob Devlin and
-
-               Ming{-}Wei Chang and
-
-               Kenton Lee and
-
-               Kristina Toutanova},
-
-  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
-
-               Understanding},
-
-  journal   = {CoRR},
-
-  volume    = {abs/1810.04805},
-
-  year      = {2018},
-
-  url       = {http://arxiv.org/abs/1810.04805},
-
-  archivePrefix = {arXiv},
-
-  eprint    = {1810.04805},
-
-  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
+# Contributors
+- Ahn, Hyunju
+- An, Jiyong
+- An, Seungchan
+- Jeong, Seokho
+- Kim, Jungmin
+- Kim, Sangbeom
+- Advisor: Dr. Wen-Syan Li
 
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
+Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.
 
-  bibsource = {dblp computer science bibliography, https://dblp.org}
 
-}
 
-```