ans
/

vaccinating-covid-tweets

@@ -5,10 +5,10 @@ license: apache-2.0
 datasets:
 - tweets
 widget:
-- text: "COVID-19 vaccine is effective to prevent from infection."
 ---
-# Disclaimer: This page is in maintenance. DO NOT ...
 # Vaccinating COVID tweets
 - A part of MDLD for DS class at SNU
@@ -49,164 +49,6 @@ Preprocessing, hardware used, hyperparameters...
   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
@@ -214,9 +56,4 @@ Glue test results:
 - 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.

 datasets:
 - tweets
 widget:
+- text: "COVID-19 vaccine is ineffective to prevent from infection."
 ---
+# Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
 # Vaccinating COVID tweets
 - A part of MDLD for DS class at SNU
   year={2020}
 }
 ```
 # Contributors
 - Ahn, Hyunju
 - An, Jiyong
 - Jeong, Seokho
 - Kim, Jungmin
 - Kim, Sangbeom