---
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-pre-ner-full-mdeberta_data-univner_en55
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-kd-pre-ner-full-mdeberta_data-univner_en55

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 62.9050
- Precision: 0.7596
- Recall: 0.7360
- F1: 0.7476
- Accuracy: 0.9801

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 55
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 148.0892      | 1.2755 | 500  | 103.3325        | 0.3059    | 0.2578 | 0.2798 | 0.9556   |
| 87.0932       | 2.5510 | 1000 | 80.2722         | 0.6704    | 0.6739 | 0.6722 | 0.9755   |
| 72.3221       | 3.8265 | 1500 | 72.3381         | 0.7265    | 0.7039 | 0.7150 | 0.9775   |
| 65.7687       | 5.1020 | 2000 | 68.3339         | 0.7549    | 0.7174 | 0.7357 | 0.9783   |
| 61.9669       | 6.3776 | 2500 | 65.6428         | 0.7442    | 0.7319 | 0.7380 | 0.9789   |
| 59.6427       | 7.6531 | 3000 | 64.0535         | 0.7581    | 0.7267 | 0.7421 | 0.9798   |
| 58.1252       | 8.9286 | 3500 | 62.9050         | 0.7596    | 0.7360 | 0.7476 | 0.9801   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1