Hugging Face's logo

sohamtiwari3120
/
deberta-v3-base-finetuned-ner

Token Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
1
sohamtiwari3120 commited on
Commit
e7deaf0
·
1 Parent(s): c804eb4

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +34 -21
README.md CHANGED
@@ -16,18 +16,18 @@ should probably proofread and complete it, then remove this comment. -->
16
 
17
  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
18
  It achieves the following results on the evaluation set:
19
- - Loss: 1.9895
20
- - Overall Precision: 0.5201
21
- - Overall Recall: 0.3319
22
- - Overall F1: 0.4052
23
- - Overall Accuracy: 0.9326
24
- - Datasetname F1: 0.4952
25
- - Hyperparametername F1: 0.48
26
- - Hyperparametervalue F1: 0.5
27
- - Methodname F1: 0.3933
28
- - Metricname F1: 0.2488
29
- - Metricvalue F1: 0.2456
30
- - Taskname F1: 0.6393
31
 
32
  ## Model description
33
 
@@ -46,8 +46,8 @@ More information needed
46
  ### Training hyperparameters
47
 
48
  The following hyperparameters were used during training:
49
- - learning_rate: 3e-05
50
- - train_batch_size: 4
51
  - eval_batch_size: 8
52
  - seed: 42
53
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@@ -58,13 +58,26 @@ The following hyperparameters were used during training:
58
 
59
  | 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 |
60
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
61
- | No log | 1.0 | 141 | 1.2556 | 0.2784 | 0.1520 | 0.1967 | 0.9212 | 0.0 | 0.3478 | 0.2581 | 0.3750 | 0.0 | 0.0 | 0.0556 |
62
- | No log | 2.0 | 282 | 0.8945 | 0.3020 | 0.5096 | 0.3793 | 0.9088 | 0.5 | 0.1538 | 0.2778 | 0.3540 | 0.4566 | 0.0896 | 0.3756 |
63
- | No log | 3.0 | 423 | 1.0233 | 0.3702 | 0.4518 | 0.4069 | 0.9268 | 0.4211 | 0.2647 | 0.3333 | 0.3529 | 0.4658 | 0.1613 | 0.5270 |
64
- | 0.6352 | 4.0 | 564 | 1.1734 | 0.4316 | 0.4390 | 0.4352 | 0.9310 | 0.4854 | 0.3462 | 0.3415 | 0.4352 | 0.4269 | 0.2295 | 0.5827 |
65
- | 0.6352 | 5.0 | 705 | 1.3147 | 0.4840 | 0.4540 | 0.4685 | 0.9390 | 0.5143 | 0.5 | 0.625 | 0.5739 | 0.3495 | 0.2333 | 0.5865 |
66
- | 0.6352 | 6.0 | 846 | 2.1441 | 0.5618 | 0.3405 | 0.4240 | 0.9373 | 0.5185 | 0.5581 | 0.6061 | 0.4898 | 0.2365 | 0.1071 | 0.6126 |
67
- | 0.6352 | 7.0 | 987 | 1.9895 | 0.5201 | 0.3319 | 0.4052 | 0.9326 | 0.4952 | 0.48 | 0.5 | 0.3933 | 0.2488 | 0.2456 | 0.6393 |
 
 
 
 
 
 
 
 
 
 
 
 
 
68
 
69
 
70
  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.7679
20
+ - Overall Precision: 0.4915
21
+ - Overall Recall: 0.6463
22
+ - Overall F1: 0.5584
23
+ - Overall Accuracy: 0.9555
24
+ - Datasetname F1: 0.3304
25
+ - Hyperparametername F1: 0.6341
26
+ - Hyperparametervalue F1: 0.7463
27
+ - Methodname F1: 0.6093
28
+ - Metricname F1: 0.7089
29
+ - Metricvalue F1: 0.7500
30
+ - Taskname F1: 0.4426
31
 
32
  ## Model description
33
 
 
46
  ### Training hyperparameters
47
 
48
  The following hyperparameters were used during training:
49
+ - learning_rate: 2e-05
50
+ - train_batch_size: 8
51
  - eval_batch_size: 8
52
  - seed: 42
53
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 
58
 
59
  | 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 |
60
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
61
+ | 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 |
62
+ | 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 |
63
+ | 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 |
64
+ | 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 |
65
+ | 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 |
66
+ | 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 |
67
+ | 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 |
68
+ | 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 |
69
+ | 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 |
70
+ | 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 |
71
+ | 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 |
72
+ | 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 |
73
+ | 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 |
74
+ | 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 |
75
+ | 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 |
76
+ | 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 |
77
+ | 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 |
78
+ | 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 |
79
+ | 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 |
80
+ | 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 |
81
 
82
 
83
  ### Framework versions