Instructions to use iTroned/weight_test_early_fusion_sentiment_False_hate_speech_False_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_False_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_False_extra_layer_True", device_map="auto") - Notebooks
- Google Colab
- Kaggle
weight_test_early_fusion_sentiment_False_hate_speech_False_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: 0.7058
- Accuracy Offensive: 0.7980
- F1 Offensive: 0.7770
- Accuracy Targeted: 0.7727
- F1 Targeted: 0.5053
- Accuracy Stance: 0.7247
- F1 Stance: 0.3955
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.8406 | 1.0 | 1324 | 0.7136 | 0.7938 | 0.7702 | 0.7659 | 0.4982 | 0.6952 | 0.2286 |
| 0.6996 | 2.0 | 2648 | 0.7058 | 0.7980 | 0.7770 | 0.7727 | 0.5053 | 0.7247 | 0.3955 |
| 0.6313 | 3.0 | 3972 | 0.8419 | 0.7874 | 0.7714 | 0.7632 | 0.5038 | 0.6798 | 0.3803 |
| 0.5086 | 4.0 | 5296 | 1.2924 | 0.7949 | 0.7662 | 0.7704 | 0.5001 | 0.7356 | 0.3709 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
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