finetuned-distilbert-ner
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2859
- Precision: 0.7770
- Recall: 0.8073
- F1: 0.7918
- Accuracy: 0.9184
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: 2e-05
- 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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.6383 | 1.0 | 958 | 0.3064 | 0.7433 | 0.7959 | 0.7687 | 0.9097 |
0.2487 | 2.0 | 1916 | 0.2855 | 0.7646 | 0.8041 | 0.7838 | 0.9154 |
0.2021 | 3.0 | 2874 | 0.2859 | 0.7770 | 0.8073 | 0.7918 | 0.9184 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Kankanaghosh/finetuned-distilbert-ner
Base model
distilbert/distilbert-base-uncased