Instructions to use ba-claim/electra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ba-claim/electra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ba-claim/electra")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ba-claim/electra") model = AutoModelForSequenceClassification.from_pretrained("ba-claim/electra", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ba-claim/electra: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
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https://huggingface.co/ba-claim/electra/resolve/main/README.md
- Command line
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hf download hf://ba-claim/electra/README.md
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curl -L -o README.md https://huggingface.co/ba-claim/electra/resolve/main/README.md
1.71 kB
| 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/electra | |
| ## Model Details | |
| Fine-tuned ELECTRA Model for Claim Relevance Identification | |
| Based on this model: https://huggingface.co/bhadresh-savani/electra-base-emotion | |
| ### Model Description | |
| This Hugging Face model is a fine-tuned ELECTRA 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 that aimed to automate the process of fact-checking 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 BERT and ELECTRA, were experimented with to identify the most effective architecture. | |
| Overall, this fine-tuned ELECTRA model serves as a valuable tool in automating the identification of relevant claims, reducing the need | |
| for manual fact-checking and contributing to the efforts in mitigating 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|2.650e-05| | |
| | Weight Decay|91.942e-04| | |
| | Batch Size|32| | |
| | Number of Epochs|5| | |