Hugging Face's logo

liuyanchen1015
/
roberta-base-mnli_IndE

Text Classification
Transformers
PyTorch
roberta
generated_from_trainer
Inference Endpoints
Model card Files Community
1
liuyanchen1015 commited on
Commit
dee7adb
•
1 Parent(s): ecadf54

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +34 -22
README.md CHANGED
@@ -3,19 +3,19 @@ license: mit
3
  tags:
4
  - generated_from_trainer
5
  model-index:
6
- - name: mnli_IndE
7
  results: []
8
  ---
9
 
10
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
  should probably proofread and complete it, then remove this comment. -->
12
 
13
- # mnli_IndE
14
 
15
  This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
16
  It achieves the following results on the evaluation set:
17
- - Loss: 0.5406
18
- - Acc: 0.8536
19
 
20
  ## Model description
21
 
@@ -40,29 +40,41 @@ The following hyperparameters were used during training:
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
- - num_epochs: 3
44
 
45
  ### Training results
46
 
47
  | Training Loss | Epoch | Step | Validation Loss | Acc |
48
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
49
- | 0.3901 | 0.17 | 2000 | 0.4456 | 0.8354 |
50
- | 0.3758 | 0.33 | 4000 | 0.4508 | 0.8356 |
51
- | 0.3668 | 0.5 | 6000 | 0.4372 | 0.8425 |
52
- | 0.3653 | 0.67 | 8000 | 0.4357 | 0.8400 |
53
- | 0.3543 | 0.83 | 10000 | 0.4030 | 0.8517 |
54
- | 0.3559 | 1.0 | 12000 | 0.4242 | 0.8472 |
55
- | 0.2523 | 1.17 | 14000 | 0.4746 | 0.8464 |
56
- | 0.2521 | 1.33 | 16000 | 0.4780 | 0.8470 |
57
- | 0.2525 | 1.5 | 18000 | 0.4664 | 0.8507 |
58
- | 0.2464 | 1.67 | 20000 | 0.4806 | 0.8484 |
59
- | 0.2495 | 1.83 | 22000 | 0.4868 | 0.8464 |
60
- | 0.2451 | 2.0 | 24000 | 0.4794 | 0.8508 |
61
- | 0.1737 | 2.17 | 26000 | 0.5492 | 0.8491 |
62
- | 0.1727 | 2.33 | 28000 | 0.5552 | 0.8531 |
63
- | 0.1736 | 2.5 | 30000 | 0.5418 | 0.8515 |
64
- | 0.1746 | 2.67 | 32000 | 0.5511 | 0.8516 |
65
- | 0.1717 | 2.83 | 34000 | 0.5406 | 0.8536 |
 
 
 
 
 
 
 
 
 
 
 
 
66
 
67
 
68
  ### Framework versions
 
3
  tags:
4
  - generated_from_trainer
5
  model-index:
6
+ - name: roberta-base-mnli_IndE
7
  results: []
8
  ---
9
 
10
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
  should probably proofread and complete it, then remove this comment. -->
12
 
13
+ # roberta-base-mnli_IndE
14
 
15
  This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
16
  It achieves the following results on the evaluation set:
17
+ - Loss: 0.7633
18
+ - Acc: 0.8517
19
 
20
  ## Model description
21
 
 
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
+ - num_epochs: 5
44
 
45
  ### Training results
46
 
47
  | Training Loss | Epoch | Step | Validation Loss | Acc |
48
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
49
+ | 0.3903 | 0.17 | 2000 | 0.4502 | 0.8359 |
50
+ | 0.3776 | 0.33 | 4000 | 0.4488 | 0.8378 |
51
+ | 0.3694 | 0.5 | 6000 | 0.4400 | 0.8408 |
52
+ | 0.3679 | 0.67 | 8000 | 0.4412 | 0.8395 |
53
+ | 0.3584 | 0.83 | 10000 | 0.4079 | 0.8514 |
54
+ | 0.3618 | 1.0 | 12000 | 0.4326 | 0.8433 |
55
+ | 0.2582 | 1.17 | 14000 | 0.4738 | 0.8459 |
56
+ | 0.2603 | 1.33 | 16000 | 0.4921 | 0.8468 |
57
+ | 0.2608 | 1.5 | 18000 | 0.4542 | 0.8498 |
58
+ | 0.2591 | 1.67 | 20000 | 0.4709 | 0.8483 |
59
+ | 0.263 | 1.83 | 22000 | 0.4955 | 0.8466 |
60
+ | 0.2611 | 2.0 | 24000 | 0.4829 | 0.8513 |
61
+ | 0.1802 | 2.17 | 26000 | 0.5470 | 0.8493 |
62
+ | 0.1819 | 2.33 | 28000 | 0.5523 | 0.8503 |
63
+ | 0.1847 | 2.5 | 30000 | 0.5160 | 0.8519 |
64
+ | 0.1886 | 2.67 | 32000 | 0.5229 | 0.8521 |
65
+ | 0.1877 | 2.83 | 34000 | 0.5024 | 0.8528 |
66
+ | 0.1839 | 3.0 | 36000 | 0.5456 | 0.8536 |
67
+ | 0.1322 | 3.17 | 38000 | 0.6997 | 0.8492 |
68
+ | 0.1385 | 3.33 | 40000 | 0.6212 | 0.8534 |
69
+ | 0.1326 | 3.5 | 42000 | 0.6629 | 0.8529 |
70
+ | 0.1355 | 3.67 | 44000 | 0.6448 | 0.8516 |
71
+ | 0.1332 | 3.83 | 46000 | 0.6411 | 0.8544 |
72
+ | 0.1372 | 4.0 | 48000 | 0.6574 | 0.8526 |
73
+ | 0.1056 | 4.17 | 50000 | 0.7427 | 0.8529 |
74
+ | 0.1053 | 4.33 | 52000 | 0.7466 | 0.8518 |
75
+ | 0.1062 | 4.5 | 54000 | 0.7734 | 0.8536 |
76
+ | 0.1056 | 4.67 | 56000 | 0.7623 | 0.8518 |
77
+ | 0.1072 | 4.83 | 58000 | 0.7633 | 0.8517 |
78
 
79
 
80
  ### Framework versions