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---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deBert-finetuned-ner-v1
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. -->
# deBert-finetuned-ner-v1
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Precision: 0.9674
- Recall: 0.9784
- F1: 0.9728
- Accuracy: 0.9997
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.037 | 1.0 | 950 | 0.0017 | 0.9350 | 0.9664 | 0.9505 | 0.9995 |
| 0.0013 | 2.0 | 1900 | 0.0011 | 0.9644 | 0.9758 | 0.9701 | 0.9996 |
| 0.0006 | 3.0 | 2850 | 0.0010 | 0.9674 | 0.9784 | 0.9728 | 0.9997 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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