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stance_class_l
This model is a fine-tuned version of vinai/bertweet-base on the dataset of 804 labeled tweets on the cancer risk controversy of Roundup Weedkiller . It classified the stance of an individual's tweet toward Bayer, Monsanto, or other relevant organizations in the crisis. Two stances are classified: (0) Aggressive, (1) Non-Aggressive (neutral and accommodative). It achieves the following results on the evaluation set:
- Loss: 0.6084
- Accuracy: 0.8447
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: 2.924e-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3566 | 1.0 | 17 | 0.4855 | 0.7578 |
| 0.2532 | 2.0 | 34 | 0.3632 | 0.8509 |
| 0.2351 | 3.0 | 51 | 0.3773 | 0.8509 |
| 0.043 | 4.0 | 68 | 0.3553 | 0.8571 |
| 0.08 | 5.0 | 85 | 0.4682 | 0.8447 |
| 0.3089 | 6.0 | 102 | 0.4686 | 0.8509 |
| 0.035 | 7.0 | 119 | 0.5876 | 0.8323 |
| 0.0188 | 8.0 | 136 | 0.5469 | 0.8571 |
| 0.021 | 9.0 | 153 | 0.5022 | 0.8447 |
| 0.0533 | 10.0 | 170 | 0.5240 | 0.8385 |
| 0.0175 | 11.0 | 187 | 0.6352 | 0.8447 |
| 0.0106 | 12.0 | 204 | 0.5856 | 0.8447 |
| 1.9534 | 13.0 | 221 | 0.5938 | 0.8509 |
| 0.0143 | 14.0 | 238 | 0.6074 | 0.8447 |
| 0.0079 | 15.0 | 255 | 0.6084 | 0.8447 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2
