Hugging Face's logo

kanak8278
/
xlnet-large-cased-ner-food-combined-v2

Token Classification
Transformers
PyTorch
TensorBoard
xlnet
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
kanak8278 commited on
Commit
be210b1
•
1 Parent(s): 0347b0f

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +14 -29
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
19
 
20
  This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
21
  It achieves the following results on the evaluation set:
22
- - Loss: 0.1884
23
- - Precision: 0.8153
24
- - Recall: 0.8947
25
- - F1: 0.8531
26
- - Accuracy: 0.9729
27
 
28
  ## Model description
29
 
@@ -43,8 +43,8 @@ More information needed
43
 
44
  The following hyperparameters were used during training:
45
  - learning_rate: 5e-06
46
- - train_batch_size: 8
47
- - eval_batch_size: 8
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
@@ -54,28 +54,13 @@ The following hyperparameters were used during training:
54
 
55
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
- | No log | 0.45 | 400 | 0.1389 | 0.7251 | 0.8609 | 0.7872 | 0.9622 |
58
- | 0.2073 | 0.9 | 800 | 0.1628 | 0.8309 | 0.8797 | 0.8546 | 0.9747 |
59
- | 0.157 | 1.35 | 1200 | 0.1346 | 0.7899 | 0.8888 | 0.8364 | 0.9710 |
60
- | 0.1362 | 1.8 | 1600 | 0.1191 | 0.7340 | 0.8880 | 0.8037 | 0.9633 |
61
- | 0.1356 | 2.25 | 2000 | 0.1253 | 0.6966 | 0.8888 | 0.7810 | 0.9570 |
62
- | 0.1356 | 2.7 | 2400 | 0.1194 | 0.7556 | 0.8855 | 0.8154 | 0.9659 |
63
- | 0.1175 | 3.15 | 2800 | 0.1546 | 0.8378 | 0.8880 | 0.8622 | 0.9754 |
64
- | 0.1064 | 3.6 | 3200 | 0.1342 | 0.7955 | 0.8909 | 0.8405 | 0.9711 |
65
- | 0.1116 | 4.04 | 3600 | 0.1314 | 0.7981 | 0.8984 | 0.8453 | 0.9713 |
66
- | 0.0981 | 4.49 | 4000 | 0.1433 | 0.8059 | 0.8834 | 0.8429 | 0.9717 |
67
- | 0.0981 | 4.94 | 4400 | 0.1439 | 0.8051 | 0.9026 | 0.8510 | 0.9719 |
68
- | 0.0936 | 5.39 | 4800 | 0.1661 | 0.8180 | 0.8943 | 0.8544 | 0.9735 |
69
- | 0.082 | 5.84 | 5200 | 0.1558 | 0.8179 | 0.8843 | 0.8498 | 0.9727 |
70
- | 0.084 | 6.29 | 5600 | 0.1553 | 0.7918 | 0.8930 | 0.8394 | 0.9699 |
71
- | 0.0782 | 6.74 | 6000 | 0.1457 | 0.7817 | 0.8943 | 0.8342 | 0.9684 |
72
- | 0.0782 | 7.19 | 6400 | 0.1793 | 0.8134 | 0.8913 | 0.8506 | 0.9726 |
73
- | 0.0694 | 7.64 | 6800 | 0.1638 | 0.7974 | 0.8930 | 0.8425 | 0.9707 |
74
- | 0.0757 | 8.09 | 7200 | 0.1690 | 0.8042 | 0.8976 | 0.8483 | 0.9714 |
75
- | 0.0665 | 8.54 | 7600 | 0.1813 | 0.8110 | 0.8951 | 0.8510 | 0.9724 |
76
- | 0.0607 | 8.99 | 8000 | 0.1907 | 0.8226 | 0.8938 | 0.8567 | 0.9738 |
77
- | 0.0607 | 9.44 | 8400 | 0.1848 | 0.8062 | 0.8938 | 0.8478 | 0.9719 |
78
- | 0.0649 | 9.89 | 8800 | 0.1884 | 0.8153 | 0.8947 | 0.8531 | 0.9729 |
79
 
80
 
81
  ### Framework versions
 
19
 
20
  This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
21
  It achieves the following results on the evaluation set:
22
+ - Loss: 0.0681
23
+ - Precision: 0.8554
24
+ - Recall: 0.8743
25
+ - F1: 0.8647
26
+ - Accuracy: 0.9769
27
 
28
  ## Model description
29
 
 
43
 
44
  The following hyperparameters were used during training:
45
  - learning_rate: 5e-06
46
+ - train_batch_size: 16
47
+ - eval_batch_size: 24
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
 
54
 
55
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
+ | 0.2606 | 1.12 | 500 | 0.0822 | 0.7976 | 0.8664 | 0.8306 | 0.9712 |
58
+ | 0.0837 | 2.25 | 1000 | 0.0955 | 0.7657 | 0.8764 | 0.8173 | 0.9683 |
59
+ | 0.0706 | 3.37 | 1500 | 0.0732 | 0.8322 | 0.8714 | 0.8513 | 0.9750 |
60
+ | 0.0631 | 4.49 | 2000 | 0.0681 | 0.8554 | 0.8743 | 0.8647 | 0.9769 |
61
+ | 0.0549 | 5.62 | 2500 | 0.0713 | 0.8356 | 0.8868 | 0.8604 | 0.9754 |
62
+ | 0.0521 | 6.74 | 3000 | 0.0700 | 0.8425 | 0.8863 | 0.8639 | 0.9759 |
63
+ | 0.0493 | 7.87 | 3500 | 0.0721 | 0.8444 | 0.8859 | 0.8647 | 0.9763 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64
 
65
 
66
  ### Framework versions