Create README.md

README.md

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+---
+tags:
+
+model-index:
+- name: bertweet-covid--vaccine-tweets-finetuned
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
+
+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.
+It achieves the following results on the evaluation set (20% from the dataset randomly shuffled and selected to serve as a test set):
+- Validation Loss: 0.246620
+- Accuracy: 0.902417%
+
+To use the model, use the inference API.
+
+Alternatively, to run locally
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-pretraining-covid-vaccine-tweets-finetuned")
+
+model = AutoModelForSequenceClassification.from_pretrained("justinqbui/bertweet-pretraining-covid-vaccine-tweets-finetuned")
+
+```
+
+## Model description
+
+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.
+
+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.
+
+The tokenizer requires the emoji library to be installed.
+```
+!pip install nltk emoji
+```
+
+## Intended uses & limitations
+
+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.
+
+This model's dataset relies on the crowd-sourcing annotations being accurate.
+
+
+
+
+## Training and evaluation data
+
+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.
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-5
+- train_batch_size: 128
+- eval_batch_size: 128
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+- 
+
+### Training results
+
+| Training Loss | Epoch | Validation Loss | Accuracy |
+|:-------------:|:-----:|:---------------:|:--------:|
+| 0.435500      | 1.0   | 0.401900        | 0.906893 |
+| 0.309700      | 2.0   | 0.265500        | 0.907789 |
+| 0.266200      | 3.0   | 0.216500        | 0.911370 |
+
+
+### Framework versions
+
+- Transformers 4.13.0
+- Pytorch 1.10.0+cu111
+- Datasets 1.16.1
+- Tokenizers 0.10.3