---
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