Hugging Face's logo

emilys
/
twitter-roberta-base-CoNLL

Token Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
2
emilys commited on
Commit
464c67c
•
1 Parent(s): 82d2378

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +103 -36
README.md CHANGED
@@ -2,7 +2,7 @@
2
  tags:
3
  - generated_from_trainer
4
  datasets:
5
- - wnut_17
6
  metrics:
7
  - precision
8
  - recall
@@ -15,22 +15,22 @@ model-index:
15
  name: Token Classification
16
  type: token-classification
17
  dataset:
18
- name: wnut_17
19
- type: wnut_17
20
- args: wnut_17
21
  metrics:
22
  - name: Precision
23
  type: precision
24
- value: 0.7024901703800787
25
  - name: Recall
26
  type: recall
27
- value: 0.6411483253588517
28
  - name: F1
29
  type: f1
30
- value: 0.6704190118824266
31
  - name: Accuracy
32
  type: accuracy
33
- value: 0.9645967075573635
34
  ---
35
 
36
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -38,13 +38,13 @@ should probably proofread and complete it, then remove this comment. -->
38
 
39
  # twitter-roberta-base-CoNLL
40
 
41
- This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the wnut_17 dataset.
42
  It achieves the following results on the evaluation set:
43
- - Loss: 0.1880
44
- - Precision: 0.7025
45
- - Recall: 0.6411
46
- - F1: 0.6704
47
- - Accuracy: 0.9646
48
 
49
  ## Model description
50
 
@@ -63,7 +63,7 @@ More information needed
63
  ### Training hyperparameters
64
 
65
  The following hyperparameters were used during training:
66
- - learning_rate: 2e-05
67
  - train_batch_size: 64
68
  - eval_batch_size: 1024
69
  - seed: 42
@@ -75,27 +75,94 @@ The following hyperparameters were used during training:
75
 
76
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
77
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
78
- | No log | 0.46 | 25 | 0.3912 | 0.0 | 0.0 | 0.0 | 0.9205 |
79
- | No log | 0.93 | 50 | 0.2847 | 0.25 | 0.0024 | 0.0047 | 0.9209 |
80
- | No log | 1.39 | 75 | 0.2449 | 0.5451 | 0.3469 | 0.4240 | 0.9426 |
81
- | No log | 1.85 | 100 | 0.1946 | 0.6517 | 0.4856 | 0.5565 | 0.9492 |
82
- | No log | 2.31 | 125 | 0.1851 | 0.6921 | 0.5646 | 0.6219 | 0.9581 |
83
- | No log | 2.78 | 150 | 0.1672 | 0.6867 | 0.5873 | 0.6331 | 0.9594 |
84
- | No log | 3.24 | 175 | 0.1675 | 0.6787 | 0.5837 | 0.6277 | 0.9615 |
85
- | No log | 3.7 | 200 | 0.1644 | 0.6765 | 0.6328 | 0.6539 | 0.9638 |
86
- | No log | 4.17 | 225 | 0.1672 | 0.6997 | 0.6495 | 0.6737 | 0.9640 |
87
- | No log | 4.63 | 250 | 0.1652 | 0.6915 | 0.6435 | 0.6667 | 0.9649 |
88
- | No log | 5.09 | 275 | 0.1882 | 0.7067 | 0.6053 | 0.6521 | 0.9629 |
89
- | No log | 5.56 | 300 | 0.1783 | 0.7128 | 0.6352 | 0.6717 | 0.9645 |
90
- | No log | 6.02 | 325 | 0.1813 | 0.7011 | 0.6172 | 0.6565 | 0.9639 |
91
- | No log | 6.48 | 350 | 0.1804 | 0.7139 | 0.6447 | 0.6776 | 0.9647 |
92
- | No log | 6.94 | 375 | 0.1902 | 0.7218 | 0.6268 | 0.6709 | 0.9641 |
93
- | No log | 7.41 | 400 | 0.1883 | 0.7106 | 0.6316 | 0.6688 | 0.9641 |
94
- | No log | 7.87 | 425 | 0.1862 | 0.7067 | 0.6340 | 0.6683 | 0.9643 |
95
- | No log | 8.33 | 450 | 0.1882 | 0.7053 | 0.6328 | 0.6671 | 0.9639 |
96
- | No log | 8.8 | 475 | 0.1919 | 0.7055 | 0.6304 | 0.6658 | 0.9638 |
97
- | 0.1175 | 9.26 | 500 | 0.1938 | 0.7045 | 0.6304 | 0.6654 | 0.9640 |
98
- | 0.1175 | 9.72 | 525 | 0.1880 | 0.7025 | 0.6411 | 0.6704 | 0.9646 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
99
 
100
 
101
  ### Framework versions
 
2
  tags:
3
  - generated_from_trainer
4
  datasets:
5
+ - conll2003
6
  metrics:
7
  - precision
8
  - recall
 
15
  name: Token Classification
16
  type: token-classification
17
  dataset:
18
+ name: conll2003
19
+ type: conll2003
20
+ args: conll2003
21
  metrics:
22
  - name: Precision
23
  type: precision
24
+ value: 0.952492082013669
25
  - name: Recall
26
  type: recall
27
+ value: 0.9616290811174689
28
  - name: F1
29
  type: f1
30
+ value: 0.9570387739720292
31
  - name: Accuracy
32
  type: accuracy
33
+ value: 0.9925236556208871
34
  ---
35
 
36
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
38
 
39
  # twitter-roberta-base-CoNLL
40
 
41
+ This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the conll2003 dataset.
42
  It achieves the following results on the evaluation set:
43
+ - Loss: 0.0422
44
+ - Precision: 0.9525
45
+ - Recall: 0.9616
46
+ - F1: 0.9570
47
+ - Accuracy: 0.9925
48
 
49
  ## Model description
50
 
 
63
  ### Training hyperparameters
64
 
65
  The following hyperparameters were used during training:
66
+ - learning_rate: 6e-05
67
  - train_batch_size: 64
68
  - eval_batch_size: 1024
69
  - seed: 42
 
75
 
76
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
77
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
78
+ | No log | 0.11 | 25 | 0.2063 | 0.6517 | 0.6659 | 0.6587 | 0.9386 |
79
+ | No log | 0.23 | 50 | 0.0810 | 0.8373 | 0.8766 | 0.8565 | 0.9771 |
80
+ | No log | 0.34 | 75 | 0.0651 | 0.8937 | 0.9058 | 0.8997 | 0.9827 |
81
+ | No log | 0.45 | 100 | 0.0537 | 0.9014 | 0.9135 | 0.9074 | 0.9849 |
82
+ | No log | 0.57 | 125 | 0.0464 | 0.9097 | 0.9244 | 0.9170 | 0.9867 |
83
+ | No log | 0.68 | 150 | 0.0423 | 0.9243 | 0.9350 | 0.9296 | 0.9885 |
84
+ | No log | 0.8 | 175 | 0.0381 | 0.9250 | 0.9438 | 0.9343 | 0.9900 |
85
+ | No log | 0.91 | 200 | 0.0388 | 0.9264 | 0.9446 | 0.9354 | 0.9896 |
86
+ | No log | 1.02 | 225 | 0.0394 | 0.9328 | 0.9441 | 0.9384 | 0.9898 |
87
+ | No log | 1.14 | 250 | 0.0423 | 0.9348 | 0.9458 | 0.9403 | 0.9896 |
88
+ | No log | 1.25 | 275 | 0.0432 | 0.9304 | 0.9406 | 0.9355 | 0.9892 |
89
+ | No log | 1.36 | 300 | 0.0382 | 0.9393 | 0.9473 | 0.9433 | 0.9901 |
90
+ | No log | 1.48 | 325 | 0.0381 | 0.9326 | 0.9504 | 0.9414 | 0.9901 |
91
+ | No log | 1.59 | 350 | 0.0387 | 0.9337 | 0.9524 | 0.9429 | 0.9902 |
92
+ | No log | 1.7 | 375 | 0.0365 | 0.9404 | 0.9475 | 0.9439 | 0.9901 |
93
+ | No log | 1.82 | 400 | 0.0382 | 0.9431 | 0.9517 | 0.9474 | 0.9905 |
94
+ | No log | 1.93 | 425 | 0.0373 | 0.9399 | 0.9524 | 0.9461 | 0.9903 |
95
+ | No log | 2.05 | 450 | 0.0367 | 0.9440 | 0.9556 | 0.9497 | 0.9910 |
96
+ | No log | 2.16 | 475 | 0.0396 | 0.9400 | 0.9551 | 0.9475 | 0.9907 |
97
+ | 0.0771 | 2.27 | 500 | 0.0353 | 0.9442 | 0.9574 | 0.9508 | 0.9912 |
98
+ | 0.0771 | 2.39 | 525 | 0.0394 | 0.9401 | 0.9507 | 0.9454 | 0.9906 |
99
+ | 0.0771 | 2.5 | 550 | 0.0370 | 0.9447 | 0.9522 | 0.9485 | 0.9910 |
100
+ | 0.0771 | 2.61 | 575 | 0.0352 | 0.9404 | 0.9541 | 0.9472 | 0.9908 |
101
+ | 0.0771 | 2.73 | 600 | 0.0386 | 0.9345 | 0.9554 | 0.9448 | 0.9908 |
102
+ | 0.0771 | 2.84 | 625 | 0.0366 | 0.9428 | 0.9576 | 0.9502 | 0.9916 |
103
+ | 0.0771 | 2.95 | 650 | 0.0353 | 0.9427 | 0.9546 | 0.9486 | 0.9913 |
104
+ | 0.0771 | 3.07 | 675 | 0.0359 | 0.9412 | 0.9544 | 0.9478 | 0.9911 |
105
+ | 0.0771 | 3.18 | 700 | 0.0356 | 0.9476 | 0.9593 | 0.9534 | 0.9920 |
106
+ | 0.0771 | 3.3 | 725 | 0.0345 | 0.9484 | 0.9586 | 0.9535 | 0.9918 |
107
+ | 0.0771 | 3.41 | 750 | 0.0345 | 0.9427 | 0.9557 | 0.9492 | 0.9916 |
108
+ | 0.0771 | 3.52 | 775 | 0.0364 | 0.9389 | 0.9569 | 0.9478 | 0.9914 |
109
+ | 0.0771 | 3.64 | 800 | 0.0360 | 0.9430 | 0.9584 | 0.9507 | 0.9915 |
110
+ | 0.0771 | 3.75 | 825 | 0.0387 | 0.9458 | 0.9552 | 0.9505 | 0.9915 |
111
+ | 0.0771 | 3.86 | 850 | 0.0347 | 0.9468 | 0.9576 | 0.9521 | 0.9917 |
112
+ | 0.0771 | 3.98 | 875 | 0.0357 | 0.9445 | 0.9574 | 0.9509 | 0.9915 |
113
+ | 0.0771 | 4.09 | 900 | 0.0382 | 0.9464 | 0.9578 | 0.9521 | 0.9918 |
114
+ | 0.0771 | 4.2 | 925 | 0.0391 | 0.9475 | 0.9562 | 0.9518 | 0.9918 |
115
+ | 0.0771 | 4.32 | 950 | 0.0428 | 0.9466 | 0.9547 | 0.9506 | 0.9912 |
116
+ | 0.0771 | 4.43 | 975 | 0.0404 | 0.9459 | 0.9554 | 0.9506 | 0.9913 |
117
+ | 0.0118 | 4.55 | 1000 | 0.0403 | 0.9375 | 0.9549 | 0.9461 | 0.9909 |
118
+ | 0.0118 | 4.66 | 1025 | 0.0369 | 0.9482 | 0.9586 | 0.9534 | 0.9919 |
119
+ | 0.0118 | 4.77 | 1050 | 0.0374 | 0.9457 | 0.9584 | 0.9520 | 0.9918 |
120
+ | 0.0118 | 4.89 | 1075 | 0.0359 | 0.9507 | 0.9571 | 0.9539 | 0.9923 |
121
+ | 0.0118 | 5.0 | 1100 | 0.0373 | 0.9453 | 0.9594 | 0.9523 | 0.9919 |
122
+ | 0.0118 | 5.11 | 1125 | 0.0370 | 0.9499 | 0.9594 | 0.9546 | 0.9924 |
123
+ | 0.0118 | 5.23 | 1150 | 0.0388 | 0.9510 | 0.9601 | 0.9555 | 0.9922 |
124
+ | 0.0118 | 5.34 | 1175 | 0.0395 | 0.9486 | 0.9559 | 0.9522 | 0.9920 |
125
+ | 0.0118 | 5.45 | 1200 | 0.0391 | 0.9495 | 0.9591 | 0.9543 | 0.9924 |
126
+ | 0.0118 | 5.57 | 1225 | 0.0378 | 0.9517 | 0.9588 | 0.9552 | 0.9923 |
127
+ | 0.0118 | 5.68 | 1250 | 0.0388 | 0.9515 | 0.9615 | 0.9565 | 0.9924 |
128
+ | 0.0118 | 5.8 | 1275 | 0.0384 | 0.9512 | 0.9610 | 0.9560 | 0.9924 |
129
+ | 0.0118 | 5.91 | 1300 | 0.0395 | 0.9530 | 0.9613 | 0.9571 | 0.9924 |
130
+ | 0.0118 | 6.02 | 1325 | 0.0408 | 0.9499 | 0.9569 | 0.9534 | 0.9919 |
131
+ | 0.0118 | 6.14 | 1350 | 0.0412 | 0.9481 | 0.9616 | 0.9548 | 0.9922 |
132
+ | 0.0118 | 6.25 | 1375 | 0.0413 | 0.9521 | 0.9591 | 0.9556 | 0.9924 |
133
+ | 0.0118 | 6.36 | 1400 | 0.0412 | 0.9466 | 0.9584 | 0.9525 | 0.9917 |
134
+ | 0.0118 | 6.48 | 1425 | 0.0405 | 0.9504 | 0.9608 | 0.9556 | 0.9921 |
135
+ | 0.0118 | 6.59 | 1450 | 0.0400 | 0.9517 | 0.9615 | 0.9566 | 0.9925 |
136
+ | 0.0118 | 6.7 | 1475 | 0.0398 | 0.9510 | 0.9594 | 0.9552 | 0.9923 |
137
+ | 0.0049 | 6.82 | 1500 | 0.0395 | 0.9523 | 0.9615 | 0.9569 | 0.9925 |
138
+ | 0.0049 | 6.93 | 1525 | 0.0392 | 0.9520 | 0.9623 | 0.9571 | 0.9927 |
139
+ | 0.0049 | 7.05 | 1550 | 0.0390 | 0.9511 | 0.9593 | 0.9552 | 0.9923 |
140
+ | 0.0049 | 7.16 | 1575 | 0.0393 | 0.9520 | 0.9611 | 0.9565 | 0.9925 |
141
+ | 0.0049 | 7.27 | 1600 | 0.0389 | 0.9512 | 0.9613 | 0.9562 | 0.9925 |
142
+ | 0.0049 | 7.39 | 1625 | 0.0405 | 0.9518 | 0.9613 | 0.9565 | 0.9924 |
143
+ | 0.0049 | 7.5 | 1650 | 0.0410 | 0.9512 | 0.9606 | 0.9559 | 0.9925 |
144
+ | 0.0049 | 7.61 | 1675 | 0.0408 | 0.9526 | 0.9613 | 0.9569 | 0.9925 |
145
+ | 0.0049 | 7.73 | 1700 | 0.0436 | 0.9482 | 0.9610 | 0.9545 | 0.9922 |
146
+ | 0.0049 | 7.84 | 1725 | 0.0419 | 0.9495 | 0.9625 | 0.9560 | 0.9924 |
147
+ | 0.0049 | 7.95 | 1750 | 0.0429 | 0.9525 | 0.9618 | 0.9571 | 0.9926 |
148
+ | 0.0049 | 8.07 | 1775 | 0.0419 | 0.9509 | 0.9615 | 0.9562 | 0.9924 |
149
+ | 0.0049 | 8.18 | 1800 | 0.0422 | 0.9510 | 0.9601 | 0.9555 | 0.9923 |
150
+ | 0.0049 | 8.3 | 1825 | 0.0417 | 0.9521 | 0.9603 | 0.9562 | 0.9924 |
151
+ | 0.0049 | 8.41 | 1850 | 0.0415 | 0.9529 | 0.9611 | 0.9570 | 0.9925 |
152
+ | 0.0049 | 8.52 | 1875 | 0.0416 | 0.9523 | 0.9611 | 0.9567 | 0.9924 |
153
+ | 0.0049 | 8.64 | 1900 | 0.0419 | 0.9504 | 0.9608 | 0.9556 | 0.9922 |
154
+ | 0.0049 | 8.75 | 1925 | 0.0417 | 0.9520 | 0.9610 | 0.9564 | 0.9924 |
155
+ | 0.0049 | 8.86 | 1950 | 0.0419 | 0.9535 | 0.9621 | 0.9578 | 0.9926 |
156
+ | 0.0049 | 8.98 | 1975 | 0.0422 | 0.9531 | 0.9620 | 0.9575 | 0.9927 |
157
+ | 0.0022 | 9.09 | 2000 | 0.0423 | 0.9531 | 0.9613 | 0.9572 | 0.9926 |
158
+ | 0.0022 | 9.2 | 2025 | 0.0426 | 0.9520 | 0.9615 | 0.9567 | 0.9925 |
159
+ | 0.0022 | 9.32 | 2050 | 0.0425 | 0.9515 | 0.9606 | 0.9560 | 0.9925 |
160
+ | 0.0022 | 9.43 | 2075 | 0.0422 | 0.9517 | 0.9613 | 0.9565 | 0.9925 |
161
+ | 0.0022 | 9.55 | 2100 | 0.0423 | 0.9513 | 0.9606 | 0.9560 | 0.9925 |
162
+ | 0.0022 | 9.66 | 2125 | 0.0424 | 0.9513 | 0.9605 | 0.9559 | 0.9925 |
163
+ | 0.0022 | 9.77 | 2150 | 0.0423 | 0.9522 | 0.9611 | 0.9566 | 0.9925 |
164
+ | 0.0022 | 9.89 | 2175 | 0.0423 | 0.9522 | 0.9613 | 0.9567 | 0.9925 |
165
+ | 0.0022 | 10.0 | 2200 | 0.0422 | 0.9525 | 0.9616 | 0.9570 | 0.9925 |
166
 
167
 
168
  ### Framework versions