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---
license: mit
base_model: digitalepidemiologylab/covid-twitter-bert-v2
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: FakeNewsDetection_Cross-Sean
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. -->
# FakeNewsDetection_Cross-Sean
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0824
- F1: 0.9882
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0786 | 1.0 | 1100 | 0.0654 | 0.9845 |
| 0.0386 | 2.0 | 2200 | 0.0574 | 0.9852 |
| 0.0222 | 3.0 | 3300 | 0.0689 | 0.9864 |
| 0.0098 | 4.0 | 4400 | 0.0924 | 0.9848 |
| 0.0059 | 5.0 | 5500 | 0.0824 | 0.9882 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
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