Instructions to use iTroned/weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_False 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_False 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_False", device_map="auto") - Notebooks
- Google Colab
- Kaggle
weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_False
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6833
- Accuracy Offensive: 0.7923
- F1 Offensive: 0.7774
- Accuracy Targeted: 0.7670
- F1 Targeted: 0.5052
- Accuracy Stance: 0.7205
- F1 Stance: 0.4072
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.7599 | 1.0 | 1324 | 0.7011 | 0.7893 | 0.7722 | 0.7719 | 0.5050 | 0.7508 | 0.3467 |
| 0.6508 | 2.0 | 2648 | 0.6833 | 0.7923 | 0.7774 | 0.7670 | 0.5052 | 0.7205 | 0.4072 |
| 0.5456 | 3.0 | 3972 | 0.9016 | 0.7829 | 0.7664 | 0.7587 | 0.5006 | 0.7281 | 0.4437 |
| 0.3634 | 4.0 | 5296 | 1.3758 | 0.7783 | 0.7598 | 0.7564 | 0.5525 | 0.7364 | 0.4550 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
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