metadata

language: en
tags:
  - text-classifciation
license: apache-2.0
datasets:
  - tweets
widget:
  - text: Vaccine is effective

Vaccinating COVID tweets

A part of MDLD for DS class at SNU

Fine-tuned model on English language using a masked language modeling (MLM) objective from BERTweet in this repository for the classification task for false/misleading information about COVID-19 vaccines.

Vaccinating COVID tweets

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in

this paper and first released in

this repository. This model is uncased: it does not make a difference

between english and English.

Model description

You can embed local or remote images using ![](...)

Intended uses & limitations

How to use

# You can include sample code which will be formatted

Limitations and bias

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

@inproceedings{...,
  year={2020}
}

Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to

be fine-tuned on a downstream task. See the model hub to look for

fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)

to make decisions, such as sequence classification, token classification or question answering. For tasks such as text

generation you should look at model like GPT2.

How to use

You can use this model directly with a pipeline for masked language modeling:


>>> from transformers import pipeline

>>> unmasker = pipeline('fill-mask', model='ans/vaccinating-covid-tweets')

>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",

  'score': 0.1073106899857521,

  'token': 4827,

  'token_str': 'fashion'},

 {'sequence': "[CLS] hello i'm a role model. [SEP]",

  'score': 0.08774490654468536,

  'token': 2535,

  'token_str': 'role'},

 {'sequence': "[CLS] hello i'm a new model. [SEP]",

  'score': 0.05338378623127937,

  'token': 2047,

  'token_str': 'new'},

 {'sequence': "[CLS] hello i'm a super model. [SEP]",

  'score': 0.04667217284440994,

  'token': 3565,

  'token_str': 'super'},

 {'sequence': "[CLS] hello i'm a fine model. [SEP]",

  'score': 0.027095865458250046,

  'token': 2986,

  'token_str': 'fine'}]

Here is how to use this model to get the features of a given text in PyTorch:


from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

model = BertModel.from_pretrained("bert-base-uncased")

text = "Replace me by any text you'd like."

encoded_input = tokenizer(text, return_tensors='pt')

output = model(**encoded_input)

Limitations and bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased

This bias will also affect all fine-tuned versions of this model.

Training data

The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038

unpublished books and English Wikipedia (excluding lists, tables and

headers).

Training procedure

Preprocessing

The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are

then of the form:


[CLS] Sentence A [SEP] Sentence B [SEP]

With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in

the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a

consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two

"sentences" has a combined length of less than 512 tokens.

The details of the masking procedure for each sentence are the following:

15% of the tokens are masked.
In 80% of the cases, the masked tokens are replaced by [MASK].
In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
In the 10% remaining cases, the masked tokens are left as is.

Pretraining

The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size

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,

learning rate warmup for 10,000 steps and linear decay of the learning rate after.

Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|

| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |

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.