ner_model
This model is a fine-tuned version of distilbert-base-multilingual-cased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2729
- Precision: 0.6122
- Recall: 0.4306
- F1: 0.5056
- Accuracy: 0.9499
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Evaluation results
- Precision on wnut_17validation set self-reported0.612
- Recall on wnut_17validation set self-reported0.431
- F1 on wnut_17validation set self-reported0.506
- Accuracy on wnut_17validation set self-reported0.950