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---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-TCR-NER_data-univner_half
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-TCR-NER_data-univner_half
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: 0.1170
- Precision: 0.8494
- Recall: 0.8655
- F1: 0.8574
- 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1168 | 0.58 | 500 | 0.0625 | 0.8182 | 0.8512 | 0.8344 | 0.9825 |
| 0.0433 | 1.17 | 1000 | 0.0594 | 0.8396 | 0.8632 | 0.8512 | 0.9843 |
| 0.0305 | 1.75 | 1500 | 0.0677 | 0.8296 | 0.8703 | 0.8495 | 0.9836 |
| 0.0213 | 2.33 | 2000 | 0.0761 | 0.8253 | 0.8833 | 0.8533 | 0.9839 |
| 0.0185 | 2.91 | 2500 | 0.0738 | 0.8600 | 0.8612 | 0.8606 | 0.9850 |
| 0.012 | 3.5 | 3000 | 0.0784 | 0.8374 | 0.8572 | 0.8471 | 0.9835 |
| 0.0124 | 4.08 | 3500 | 0.0832 | 0.8363 | 0.8704 | 0.8530 | 0.9843 |
| 0.0095 | 4.66 | 4000 | 0.0806 | 0.8423 | 0.8713 | 0.8565 | 0.9845 |
| 0.008 | 5.24 | 4500 | 0.1049 | 0.8218 | 0.8625 | 0.8417 | 0.9823 |
| 0.0071 | 5.83 | 5000 | 0.0879 | 0.8420 | 0.8632 | 0.8525 | 0.9842 |
| 0.0068 | 6.41 | 5500 | 0.0918 | 0.8507 | 0.8733 | 0.8619 | 0.9846 |
| 0.0058 | 6.99 | 6000 | 0.0951 | 0.8488 | 0.8667 | 0.8577 | 0.9845 |
| 0.0047 | 7.58 | 6500 | 0.0991 | 0.8467 | 0.8651 | 0.8558 | 0.9842 |
| 0.0047 | 8.16 | 7000 | 0.1025 | 0.8603 | 0.8573 | 0.8588 | 0.9845 |
| 0.0043 | 8.74 | 7500 | 0.1020 | 0.8473 | 0.8678 | 0.8574 | 0.9845 |
| 0.0031 | 9.32 | 8000 | 0.1085 | 0.8437 | 0.8582 | 0.8509 | 0.9842 |
| 0.0038 | 9.91 | 8500 | 0.1082 | 0.8602 | 0.8440 | 0.8520 | 0.9839 |
| 0.0024 | 10.49 | 9000 | 0.1163 | 0.8533 | 0.8544 | 0.8539 | 0.9838 |
| 0.0038 | 11.07 | 9500 | 0.1139 | 0.8528 | 0.8567 | 0.8548 | 0.9843 |
| 0.0024 | 11.66 | 10000 | 0.1130 | 0.8619 | 0.8476 | 0.8547 | 0.9841 |
| 0.0024 | 12.24 | 10500 | 0.1170 | 0.8494 | 0.8655 | 0.8574 | 0.9842 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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