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---
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-scr-ner-full-mdeberta_data-univner_en55
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. -->
# scenario-non-kd-scr-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: 0.3347
- Precision: 0.5325
- Recall: 0.4752
- F1: 0.5022
- Accuracy: 0.9624
## 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: 32
- eval_batch_size: 32
- seed: 55
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1623 | 1.2755 | 500 | 0.1875 | 0.4252 | 0.3002 | 0.3519 | 0.9534 |
| 0.0693 | 2.5510 | 1000 | 0.1568 | 0.4472 | 0.4689 | 0.4578 | 0.9583 |
| 0.0358 | 3.8265 | 1500 | 0.1803 | 0.5255 | 0.4472 | 0.4832 | 0.9611 |
| 0.0189 | 5.1020 | 2000 | 0.2008 | 0.5077 | 0.4793 | 0.4931 | 0.9608 |
| 0.0098 | 6.3776 | 2500 | 0.2203 | 0.4759 | 0.5207 | 0.4973 | 0.9599 |
| 0.0072 | 7.6531 | 3000 | 0.2339 | 0.5105 | 0.4783 | 0.4939 | 0.9613 |
| 0.0046 | 8.9286 | 3500 | 0.2526 | 0.5327 | 0.4638 | 0.4958 | 0.9612 |
| 0.003 | 10.2041 | 4000 | 0.2658 | 0.5150 | 0.4793 | 0.4965 | 0.9604 |
| 0.0021 | 11.4796 | 4500 | 0.2805 | 0.5541 | 0.4669 | 0.5067 | 0.9616 |
| 0.0015 | 12.7551 | 5000 | 0.2782 | 0.5156 | 0.4959 | 0.5055 | 0.9621 |
| 0.0018 | 14.0306 | 5500 | 0.3001 | 0.5448 | 0.4720 | 0.5058 | 0.9614 |
| 0.0014 | 15.3061 | 6000 | 0.2938 | 0.5354 | 0.5083 | 0.5215 | 0.9629 |
| 0.0012 | 16.5816 | 6500 | 0.3020 | 0.5554 | 0.5135 | 0.5336 | 0.9633 |
| 0.0008 | 17.8571 | 7000 | 0.3080 | 0.5345 | 0.4886 | 0.5105 | 0.9627 |
| 0.0007 | 19.1327 | 7500 | 0.3184 | 0.5346 | 0.4638 | 0.4967 | 0.9626 |
| 0.0004 | 20.4082 | 8000 | 0.3198 | 0.5233 | 0.4762 | 0.4986 | 0.9612 |
| 0.0005 | 21.6837 | 8500 | 0.3303 | 0.5583 | 0.4658 | 0.5079 | 0.9617 |
| 0.0004 | 22.9592 | 9000 | 0.3305 | 0.5575 | 0.4969 | 0.5255 | 0.9629 |
| 0.0003 | 24.2347 | 9500 | 0.3357 | 0.5440 | 0.4669 | 0.5025 | 0.9617 |
| 0.0002 | 25.5102 | 10000 | 0.3258 | 0.5404 | 0.5124 | 0.5260 | 0.9624 |
| 0.0002 | 26.7857 | 10500 | 0.3360 | 0.5587 | 0.4928 | 0.5237 | 0.9628 |
| 0.0001 | 28.0612 | 11000 | 0.3318 | 0.5208 | 0.4803 | 0.4997 | 0.9618 |
| 0.0001 | 29.3367 | 11500 | 0.3347 | 0.5325 | 0.4752 | 0.5022 | 0.9624 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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