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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Climate-TwitterBERT-xmas
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. -->
# Climate-TwitterBERT-xmas
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3607
- Accuracy: 0.888
- Precision: 0.7544
- Recall: 0.7544
- F1-weighted: 0.888
- F1: 0.7544
## 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: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1-weighted | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----------:|:------:|
| 0.4175 | 3.64 | 50 | 0.2775 | 0.912 | 0.8070 | 0.8070 | 0.912 | 0.8070 |
| 0.1843 | 7.27 | 100 | 0.2945 | 0.908 | 0.8542 | 0.7193 | 0.9051 | 0.7810 |
| 0.0731 | 10.91 | 150 | 0.3607 | 0.888 | 0.7544 | 0.7544 | 0.888 | 0.7544 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
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