File size: 6,925 Bytes
572c34f e7deaf0 572c34f e7deaf0 572c34f 64559b3 572c34f e7deaf0 572c34f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: deberta-v3-base-finetuned-ner
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. -->
# deberta-v3-base-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7679
- Overall Precision: 0.4915
- Overall Recall: 0.6463
- Overall F1: 0.5584
- Overall Accuracy: 0.9555
- Datasetname F1: 0.3304
- Hyperparametername F1: 0.6341
- Hyperparametervalue F1: 0.7463
- Methodname F1: 0.6093
- Metricname F1: 0.7089
- Metricvalue F1: 0.7500
- Taskname F1: 0.4426
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
| No log | 1.0 | 132 | 0.5046 | 0.2771 | 0.5041 | 0.3576 | 0.9356 | 0.2405 | 0.1988 | 0.4545 | 0.4638 | 0.4539 | 0.6486 | 0.2793 |
| No log | 2.0 | 264 | 0.3928 | 0.3344 | 0.6463 | 0.4407 | 0.9376 | 0.2449 | 0.3968 | 0.6292 | 0.5641 | 0.5373 | 0.4583 | 0.3359 |
| No log | 3.0 | 396 | 0.4714 | 0.4419 | 0.6179 | 0.5153 | 0.9533 | 0.3822 | 0.5310 | 0.7536 | 0.6262 | 0.6328 | 0.6857 | 0.3291 |
| 0.5663 | 4.0 | 528 | 0.3741 | 0.4493 | 0.7114 | 0.5507 | 0.9509 | 0.4717 | 0.7241 | 0.6353 | 0.5918 | 0.5714 | 0.6275 | 0.4372 |
| 0.5663 | 5.0 | 660 | 0.4202 | 0.3930 | 0.6870 | 0.5 | 0.9458 | 0.2759 | 0.6525 | 0.65 | 0.5596 | 0.7097 | 0.7368 | 0.3573 |
| 0.5663 | 6.0 | 792 | 0.4676 | 0.4244 | 0.6850 | 0.5241 | 0.9473 | 0.3333 | 0.5949 | 0.7397 | 0.5653 | 0.6988 | 0.7568 | 0.3652 |
| 0.5663 | 7.0 | 924 | 0.5744 | 0.4328 | 0.5955 | 0.5013 | 0.9517 | 0.2585 | 0.6167 | 0.5915 | 0.5825 | 0.6386 | 0.7500 | 0.3824 |
| 0.1503 | 8.0 | 1056 | 0.5340 | 0.4309 | 0.6585 | 0.5209 | 0.9499 | 0.2976 | 0.6299 | 0.7105 | 0.6140 | 0.6708 | 0.7568 | 0.3544 |
| 0.1503 | 9.0 | 1188 | 0.5229 | 0.4628 | 0.6829 | 0.5517 | 0.9531 | 0.4630 | 0.5103 | 0.6087 | 0.625 | 0.6541 | 0.7778 | 0.4493 |
| 0.1503 | 10.0 | 1320 | 0.6287 | 0.4978 | 0.6748 | 0.5729 | 0.9563 | 0.4314 | 0.6500 | 0.7463 | 0.6413 | 0.7432 | 0.7568 | 0.4108 |
| 0.1503 | 11.0 | 1452 | 0.5163 | 0.4571 | 0.7033 | 0.5540 | 0.9519 | 0.3925 | 0.5256 | 0.6024 | 0.6828 | 0.6626 | 0.7368 | 0.4466 |
| 0.0735 | 12.0 | 1584 | 0.6737 | 0.5046 | 0.6687 | 0.5752 | 0.9555 | 0.3883 | 0.6615 | 0.6757 | 0.6074 | 0.7051 | 0.7778 | 0.4577 |
| 0.0735 | 13.0 | 1716 | 0.5849 | 0.44 | 0.6931 | 0.5383 | 0.9480 | 0.3770 | 0.6555 | 0.6479 | 0.5922 | 0.6957 | 0.6512 | 0.4071 |
| 0.0735 | 14.0 | 1848 | 0.8314 | 0.5018 | 0.5793 | 0.5377 | 0.9539 | 0.3 | 0.6549 | 0.6667 | 0.5613 | 0.7361 | 0.7368 | 0.4294 |
| 0.0735 | 15.0 | 1980 | 0.5986 | 0.4549 | 0.6768 | 0.5441 | 0.9506 | 0.3793 | 0.6000 | 0.6667 | 0.6181 | 0.7089 | 0.6829 | 0.3978 |
| 0.0408 | 16.0 | 2112 | 0.7579 | 0.4900 | 0.6443 | 0.5566 | 0.9541 | 0.4103 | 0.6032 | 0.6765 | 0.6238 | 0.7123 | 0.6667 | 0.4217 |
| 0.0408 | 17.0 | 2244 | 0.9175 | 0.5285 | 0.6037 | 0.5636 | 0.9565 | 0.4 | 0.6789 | 0.7692 | 0.5949 | 0.7101 | 0.6857 | 0.4122 |
| 0.0408 | 18.0 | 2376 | 0.7771 | 0.5041 | 0.6179 | 0.5553 | 0.9562 | 0.3684 | 0.6207 | 0.7246 | 0.5842 | 0.7383 | 0.6667 | 0.4353 |
| 0.0226 | 19.0 | 2508 | 0.7992 | 0.5213 | 0.6463 | 0.5771 | 0.9569 | 0.32 | 0.6724 | 0.7353 | 0.6485 | 0.7114 | 0.7179 | 0.4510 |
| 0.0226 | 20.0 | 2640 | 0.7679 | 0.4915 | 0.6463 | 0.5584 | 0.9555 | 0.3304 | 0.6341 | 0.7463 | 0.6093 | 0.7089 | 0.7500 | 0.4426 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|