Update README.md

README.md

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+---
+
+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](https://github.com/VinAIResearch/BERTweet) 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](https://arxiv.org/abs/1810.04805) and first released in
+
+[this repository](https://github.com/google-research/bert). 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
+
+```python
+# 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
+
+```bibtex
+@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](https://huggingface.co/models?filter=bert) 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:
+
+```python
+
+>>> 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:
+
+```python
+
+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](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
+
+unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/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:
+
+| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
+
+|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
+
+|      | 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.
+
+
+