metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DeBERTa-finetuned-ner-S800
results: []
DeBERTa-finetuned-ner-S800
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.6730
- Recall: 0.7899
- F1: 0.7268
- Accuracy: 0.9783
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 55 | 0.0744 | 0.5840 | 0.6527 | 0.6164 | 0.9703 |
| No log | 2.0 | 110 | 0.0639 | 0.6332 | 0.7689 | 0.6945 | 0.9764 |
| No log | 3.0 | 165 | 0.0585 | 0.6424 | 0.7801 | 0.7046 | 0.9766 |
| No log | 4.0 | 220 | 0.0581 | 0.6754 | 0.7955 | 0.7305 | 0.9785 |
| No log | 5.0 | 275 | 0.0606 | 0.6730 | 0.7899 | 0.7268 | 0.9783 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3