metadata
license: mit
base_model: microsoft/deberta-v3-small
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-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0636
- Precision: 0.6312
- Recall: 0.7311
- F1: 0.6775
- Accuracy: 0.9769
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 | 0.0843 | 0.4846 | 0.5294 | 0.5060 | 0.9683 |
| No log | 2.0 | 110 | 0.0697 | 0.5695 | 0.7115 | 0.6326 | 0.9729 |
| No log | 3.0 | 165 | 0.0652 | 0.6099 | 0.7423 | 0.6696 | 0.9754 |
| No log | 4.0 | 220 | 0.0636 | 0.6445 | 0.7185 | 0.6795 | 0.9772 |
| No log | 5.0 | 275 | 0.0636 | 0.6312 | 0.7311 | 0.6775 | 0.9769 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3