metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DeBERTa-finetuned-ner-copious
results: []
DeBERTa-finetuned-ner-copious
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: 0.0499
- Precision: 0.7867
- Recall: 0.8333
- F1: 0.8094
- Accuracy: 0.9842
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 | 63 | 0.0632 | 0.6793 | 0.7383 | 0.7076 | 0.9789 |
| No log | 2.0 | 126 | 0.0507 | 0.7559 | 0.8320 | 0.7921 | 0.9837 |
| No log | 3.0 | 189 | 0.0517 | 0.7771 | 0.8306 | 0.8029 | 0.9840 |
| No log | 4.0 | 252 | 0.0517 | 0.7822 | 0.8457 | 0.8127 | 0.9839 |
| No log | 5.0 | 315 | 0.0499 | 0.7867 | 0.8333 | 0.8094 | 0.9842 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3