---
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_full44
  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-kd-pre-ner-full-mdeberta_data-univner_full44

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: 0.2650
- Precision: 0.8107
- Recall: 0.8117
- F1: 0.8112
- Accuracy: 0.9806

## 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: 44
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3559        | 0.2911 | 500   | 0.7891          | 0.4246    | 0.3525 | 0.3852 | 0.9433   |
| 0.7017        | 0.5822 | 1000  | 0.5422          | 0.6316    | 0.6298 | 0.6307 | 0.9646   |
| 0.528         | 0.8732 | 1500  | 0.4693          | 0.6856    | 0.6830 | 0.6843 | 0.9692   |
| 0.4354        | 1.1643 | 2000  | 0.4211          | 0.7101    | 0.7376 | 0.7236 | 0.9724   |
| 0.385         | 1.4554 | 2500  | 0.3893          | 0.7482    | 0.7374 | 0.7428 | 0.9747   |
| 0.3575        | 1.7465 | 3000  | 0.3713          | 0.7678    | 0.7331 | 0.7500 | 0.9752   |
| 0.3298        | 2.0375 | 3500  | 0.3550          | 0.7497    | 0.7800 | 0.7645 | 0.9761   |
| 0.2879        | 2.3286 | 4000  | 0.3492          | 0.7964    | 0.7367 | 0.7654 | 0.9763   |
| 0.2748        | 2.6197 | 4500  | 0.3272          | 0.7660    | 0.7924 | 0.7790 | 0.9782   |
| 0.2644        | 2.9108 | 5000  | 0.3192          | 0.7817    | 0.7811 | 0.7814 | 0.9779   |
| 0.2416        | 3.2019 | 5500  | 0.3239          | 0.8004    | 0.7681 | 0.7839 | 0.9782   |
| 0.2303        | 3.4929 | 6000  | 0.3085          | 0.7846    | 0.7966 | 0.7905 | 0.9787   |
| 0.2252        | 3.7840 | 6500  | 0.3051          | 0.7973    | 0.7883 | 0.7928 | 0.9787   |
| 0.2159        | 4.0751 | 7000  | 0.3045          | 0.7987    | 0.7908 | 0.7948 | 0.9790   |
| 0.2067        | 4.3662 | 7500  | 0.2979          | 0.7969    | 0.7943 | 0.7956 | 0.9793   |
| 0.2028        | 4.6573 | 8000  | 0.2924          | 0.7855    | 0.8132 | 0.7991 | 0.9792   |
| 0.1985        | 4.9483 | 8500  | 0.2904          | 0.8008    | 0.7986 | 0.7997 | 0.9791   |
| 0.1867        | 5.2394 | 9000  | 0.2884          | 0.8       | 0.8033 | 0.8017 | 0.9797   |
| 0.1838        | 5.5305 | 9500  | 0.2841          | 0.7997    | 0.8220 | 0.8107 | 0.9800   |
| 0.1838        | 5.8216 | 10000 | 0.2810          | 0.7895    | 0.8165 | 0.8028 | 0.9798   |
| 0.1786        | 6.1126 | 10500 | 0.2767          | 0.8065    | 0.8150 | 0.8108 | 0.9802   |
| 0.1719        | 6.4037 | 11000 | 0.2790          | 0.8133    | 0.8057 | 0.8095 | 0.9803   |
| 0.1706        | 6.6948 | 11500 | 0.2795          | 0.8140    | 0.7983 | 0.8061 | 0.9802   |
| 0.1695        | 6.9859 | 12000 | 0.2723          | 0.8124    | 0.8121 | 0.8123 | 0.9807   |
| 0.1638        | 7.2770 | 12500 | 0.2726          | 0.8070    | 0.8078 | 0.8074 | 0.9803   |
| 0.162         | 7.5680 | 13000 | 0.2724          | 0.8118    | 0.8173 | 0.8146 | 0.9807   |
| 0.1619        | 7.8591 | 13500 | 0.2678          | 0.8018    | 0.8235 | 0.8125 | 0.9805   |
| 0.1594        | 8.1502 | 14000 | 0.2719          | 0.8103    | 0.8068 | 0.8086 | 0.9800   |
| 0.1571        | 8.4413 | 14500 | 0.2688          | 0.8097    | 0.8127 | 0.8112 | 0.9805   |
| 0.1585        | 8.7324 | 15000 | 0.2673          | 0.8126    | 0.8150 | 0.8138 | 0.9806   |
| 0.1546        | 9.0234 | 15500 | 0.2658          | 0.8105    | 0.8120 | 0.8112 | 0.9805   |
| 0.1534        | 9.3145 | 16000 | 0.2652          | 0.8101    | 0.8198 | 0.8149 | 0.9807   |
| 0.1535        | 9.6056 | 16500 | 0.2646          | 0.8097    | 0.8140 | 0.8119 | 0.9807   |
| 0.1531        | 9.8967 | 17000 | 0.2650          | 0.8107    | 0.8117 | 0.8112 | 0.9806   |


### Framework versions

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