Instructions to use iTroned/weight_test_early_fusion_sentiment_False_hate_speech_True_extra_layer_True with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/weight_test_early_fusion_sentiment_False_hate_speech_True_extra_layer_True with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/weight_test_early_fusion_sentiment_False_hate_speech_True_extra_layer_True", device_map="auto") - Notebooks
- Google Colab
- Kaggle
weight_test_early_fusion_sentiment_False_hate_speech_True_extra_layer_True
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7273
- Accuracy Offensive: 0.7912
- F1 Offensive: 0.7681
- Accuracy Targeted: 0.7659
- F1 Targeted: 0.4991
- Accuracy Stance: 0.7402
- F1 Stance: 0.4097
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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Offensive | F1 Offensive | Accuracy Targeted | F1 Targeted | Accuracy Stance | F1 Stance |
|---|---|---|---|---|---|---|---|---|---|
| 0.8206 | 1.0 | 1324 | 0.7313 | 0.7779 | 0.7573 | 0.7602 | 0.4944 | 0.7489 | 0.3436 |
| 0.6967 | 2.0 | 2648 | 0.7273 | 0.7912 | 0.7681 | 0.7659 | 0.4991 | 0.7402 | 0.4097 |
| 0.6145 | 3.0 | 3972 | 0.8465 | 0.7696 | 0.7556 | 0.7398 | 0.4900 | 0.6952 | 0.4176 |
| 0.5232 | 4.0 | 5296 | 1.2311 | 0.7915 | 0.7668 | 0.7647 | 0.4990 | 0.7281 | 0.4282 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
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