| --- |
| widget: |
| - text: "They're able to charge women more for the same exact procedure a man gets." |
| example_title: "Example: Yes" |
| - text: "There's no way they would give it up." |
| example_title: "Example: No" |
| --- |
| |
| # ba-claim/bert |
| ## Model Details |
| Fine-tuned BERT Model for Claim Relevance Identification |
|
|
| Based on this model: https://huggingface.co/textattack/bert-base-uncased-yelp-polarity |
|
|
| ### Model Description |
| This Hugging Face model is a fine-tuned BERT model specifically developed for identifying relevant claims in the context of combating fake news. |
| The model was trained as part of a bachelor thesis project aimed at automating the fact-checking process by automatically identifying claims of interest. |
|
|
| The project participated in the CheckThat!2023 competition, focusing on task 1B, organized by the Conference and Labs of the Evaluation Forum (CLEF). |
| The CheckThat! lab provided relevant training data for predicting the checkworthiness of claims. |
| The data was analyzed, and various transformer models, including DistilBERT and ELECTRA, were experimented with to identify the most effective architecture. |
|
|
| Overall, this fine-tuned BERT model serves as a valuable tool in automating the identification of relevant claims, reducing the need for manual fact-checking, and contributing to efforts to combat the challenges posed by the widespread dissemination of fake news. |
|
|
|
|
| #### Examples |
|
|
| 37440 There's no way they would give it up. No |
|
|
| 37463 They're able to charge women more for the same exact procedure a man gets. Yes |
|
|
|
|
| ## Training Details |
|
|
| |Hyperparameters|| |
| |----|----| |
| | Learning Rate|9.459e-05| |
| | Weight Decay|2.737e-04| |
| | Batch Size|64| |
| | Number of Epochs|4| |
|
|
|
|
|
|
|
|
