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README.md
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---
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language: en
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tags:
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- text-classifciation
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license: apache-2.0
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datasets:
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- tweets
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widget:
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- text: "Vaccine is effective"
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---
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# Vaccinating COVID tweets
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- A part of MDLD for DS class at SNU
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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.
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# Vaccinating COVID tweets
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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[this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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between english and English.
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## Model description
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You can embed local or remote images using `![](...)`
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## Intended uses & limitations
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#### How to use
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```python
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# You can include sample code which will be formatted
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```
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#### Limitations and bias
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Provide examples of latent issues and potential remediations.
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## Training data
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Describe the data you used to train the model.
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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.
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## Training procedure
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Preprocessing, hardware used, hyperparameters...
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## Eval results
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{...,
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year={2020}
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}
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```
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------------------------
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## Intended uses & limitations
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
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fine-tuned versions on a task that interests you.
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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generation you should look at model like GPT2.
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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='ans/vaccinating-covid-tweets')
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>>> unmasker("Hello I'm a [MASK] model.")
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[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
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'score': 0.1073106899857521,
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'token': 4827,
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'token_str': 'fashion'},
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{'sequence': "[CLS] hello i'm a role model. [SEP]",
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'score': 0.08774490654468536,
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'token': 2535,
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'token_str': 'role'},
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{'sequence': "[CLS] hello i'm a new model. [SEP]",
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'score': 0.05338378623127937,
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'token': 2047,
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'token_str': 'new'},
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{'sequence': "[CLS] hello i'm a super model. [SEP]",
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'score': 0.04667217284440994,
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'token': 3565,
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'token_str': 'super'},
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{'sequence': "[CLS] hello i'm a fine model. [SEP]",
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'score': 0.027095865458250046,
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'token': 2986,
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'token_str': 'fine'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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model = BertModel.from_pretrained("bert-base-uncased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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### Limitations and bias
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Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
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This bias will also affect all fine-tuned versions of this model.
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## Training data
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The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
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unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
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headers).
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## Training procedure
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### Preprocessing
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The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
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then of the form:
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```
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[CLS] Sentence A [SEP] Sentence B [SEP]
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```
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With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
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the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
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consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
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"sentences" has a combined length of less than 512 tokens.
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The details of the masking procedure for each sentence are the following:
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- 15% of the tokens are masked.
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- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
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- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
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- In the 10% remaining cases, the masked tokens are left as is.
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### Pretraining
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The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
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of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
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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,
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learning rate warmup for 10,000 steps and linear decay of the learning rate after.
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## Evaluation results
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When fine-tuned on downstream tasks, this model achieves the following results:
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Glue test results:
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| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
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|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
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| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
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# Contributors
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- Ahn, Hyunju
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- An, Jiyong
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- An, Seungchan
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- Jeong, Seokho
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- Kim, Jungmin
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- Kim, Sangbeom
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- Advisor: Dr. Wen-Syan Li
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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.
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