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--- |
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datasets: justinqbui/covid_fact_checked_google_api |
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model-index: |
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- name: bertweet-covid--vaccine-tweets-finetuned |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets |
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This model is a fine-tuned version of [justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets](https://huggingface.co./justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets) which was finetuned by using [this google fact check](https://huggingface.co./datasets/justinqbui/covid_fact_checked_google_api) ~3k dataset size and webscraped data from [polifact covid info](https://huggingface.co./datasets/justinqbui/covid_fact_checked_polifact) ~ 1200 dataset size and ~1200 tweets pulled from the CDC with tweets containing the words covid or vaccine. |
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It achieves the following results on the evaluation set (20% from the dataset randomly shuffled and selected to serve as a test set): |
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- Validation Loss: 0.267367 |
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- Accuracy: 91.1370% |
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To use the model, use the inference API. |
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Alternatively, to run locally |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-covid-vaccine-tweets-finetuned") |
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model = AutoModelForSequenceClassification.from_pretrained("justinqbui/bertweet-covid-vaccine-tweets-finetuned") |
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``` |
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## Model description |
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This model is a fine-tuned version of pretrained version [justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets](https://huggingface.co./justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets). Click on [this](https://huggingface.co./justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets) to see how the pre-training was done. |
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This model was fine-tuned with a dataset of ~5500. A web scraper was used to scrape polifact and a script was used to pull from the google fact check API. Because ~80% of both these datasets were either false or misleading, I pulled about ~1200 tweets from the CDC related to covid and labelled them as true. ~30% of this dataset is considered true and the rest false or misleading. Please see the published datasets above for more detailed information. |
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The tokenizer requires the emoji library to be installed. |
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``` |
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!pip install nltk emoji |
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``` |
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## Intended uses & limitations |
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The intended use of this model is to detect if the contents of a covid tweet is potentially false or misleading. This model is not an end all be all. It has many limitations. For example, if someone makes a post containing an image, but has attached a satirical image, this model would not be able to distinguish this. If a user links a website, the tokenizer allocates a special token for links, meaning the contents of the linked website is completely lost. If someone tweets a reply, this model can't look at the parent tweets, and will lack context. |
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This model's dataset relies on the crowd-sourcing annotations being accurate. This data is only accurate of up until early December 2021. For example, it probably wouldn't do very ell with tweets regarded the new omicron variant. |
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Example true inputs: |
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``` |
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Covid vaccines are safe and effective. -> 97% true |
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Vaccinations are safe and help prevent covid. -> 97% true |
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``` |
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Example false inputs: |
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``` |
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Covid vaccines will kill you. -> 97% false |
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covid vaccines make you infertile. -> 97% false |
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``` |
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## Training and evaluation data |
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This model was finetuned by using [this google fact check](https://huggingface.co./datasets/justinqbui/covid_fact_checked_google_api) ~3k dataset size and webscraped data from [polifact covid info](https://huggingface.co./datasets/justinqbui/covid_fact_checked_polifact) ~ 1200 dataset size and ~1200 tweets pulled from the CDC with tweets containing the words covid or vaccine. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-5 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:---------------:|:--------:| |
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| 0.435500 | 1.0 | 0.401900 | 0.906893 | |
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| 0.309700 | 2.0 | 0.265500 | 0.907789 | |
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| 0.266200 | 3.0 | 0.216500 | 0.911370 | |
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### Framework versions |
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- Transformers 4.13.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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