| | --- |
| | license: mit |
| | base_model: microsoft/deberta-v3-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: test-ner |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/fredjaoko123-optistock-co-ke/huggingface/runs/cqpagizt) |
| | # test-ner |
| |
|
| | This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1608 |
| | - Precision: 0.8335 |
| | - Recall: 0.8535 |
| | - F1: 0.8434 |
| | - Accuracy: 0.9650 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.1261 | 1.0 | 1274 | 0.1797 | 0.8049 | 0.8044 | 0.8047 | 0.9571 | |
| | | 0.069 | 2.0 | 2548 | 0.1500 | 0.8278 | 0.8303 | 0.8290 | 0.9646 | |
| | | 0.0465 | 3.0 | 3822 | 0.1608 | 0.8335 | 0.8535 | 0.8434 | 0.9650 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.42.3 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| |
|
