Hugging Face's logo

gijs
/
aces-roberta-13

Token Classification
Transformers
PyTorch
roberta
Generated from Trainer
Model card Files
xet
 Community
2
gijs commited on
Commit
b62e036
·
1 Parent(s): 9444293

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +82 -0
README.md ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - precision
7
+ - recall
8
+ - f1
9
+ - accuracy
10
+ model-index:
11
+ - name: aces-roberta-13
12
+ results: []
13
+ ---
14
+
15
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
16
+ should probably proofread and complete it, then remove this comment. -->
17
+
18
+ # aces-roberta-13
19
+
20
+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 0.4600
23
+ - Precision: 0.8364
24
+ - Recall: 0.8452
25
+ - F1: 0.8383
26
+ - Accuracy: 0.8452
27
+ - F1 Who: 0.9189
28
+ - F1 What: 0.8621
29
+ - F1 Where: 0.9231
30
+ - F1 How: 0.9141
31
+
32
+ ## Model description
33
+
34
+ More information needed
35
+
36
+ ## Intended uses & limitations
37
+
38
+ More information needed
39
+
40
+ ## Training and evaluation data
41
+
42
+ More information needed
43
+
44
+ ## Training procedure
45
+
46
+ ### Training hyperparameters
47
+
48
+ The following hyperparameters were used during training:
49
+ - learning_rate: 1e-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
54
+ - lr_scheduler_type: linear
55
+ - num_epochs: 5
56
+
57
+ ### Training results
58
+
59
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | F1 Who | F1 What | F1 Where | F1 How |
60
+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
61
+ | 1.9849 | 0.35 | 20 | 1.4123 | 0.5426 | 0.6351 | 0.5494 | 0.6351 | 0.1026 | 0.6222 | 0.3232 | 0.7857 |
62
+ | 1.2159 | 0.7 | 40 | 0.9450 | 0.6559 | 0.7188 | 0.6592 | 0.7188 | 0.6780 | 0.7539 | 0.7071 | 0.7882 |
63
+ | 0.8634 | 1.05 | 60 | 0.6885 | 0.7652 | 0.7994 | 0.7725 | 0.7994 | 0.9067 | 0.8152 | 0.8070 | 0.8940 |
64
+ | 0.6777 | 1.4 | 80 | 0.6144 | 0.7650 | 0.7946 | 0.7711 | 0.7946 | 0.9189 | 0.7876 | 0.8039 | 0.9085 |
65
+ | 0.6051 | 1.75 | 100 | 0.5485 | 0.8126 | 0.8278 | 0.8150 | 0.8278 | 0.9315 | 0.8362 | 0.8148 | 0.9241 |
66
+ | 0.5511 | 2.11 | 120 | 0.5264 | 0.8113 | 0.8167 | 0.8036 | 0.8167 | 0.9315 | 0.8444 | 0.8257 | 0.9199 |
67
+ | 0.486 | 2.46 | 140 | 0.4867 | 0.8230 | 0.8357 | 0.8248 | 0.8357 | 0.9315 | 0.8539 | 0.9091 | 0.9048 |
68
+ | 0.4813 | 2.81 | 160 | 0.4767 | 0.8285 | 0.8278 | 0.8213 | 0.8278 | 0.9189 | 0.8701 | 0.9076 | 0.9135 |
69
+ | 0.4494 | 3.16 | 180 | 0.5042 | 0.8152 | 0.8199 | 0.8126 | 0.8199 | 0.9315 | 0.8427 | 0.8333 | 0.8956 |
70
+ | 0.4018 | 3.51 | 200 | 0.4802 | 0.8248 | 0.8357 | 0.8249 | 0.8357 | 0.9189 | 0.8736 | 0.8780 | 0.9357 |
71
+ | 0.4205 | 3.86 | 220 | 0.4723 | 0.8340 | 0.8389 | 0.8346 | 0.8389 | 0.9189 | 0.8636 | 0.9138 | 0.8986 |
72
+ | 0.3535 | 4.21 | 240 | 0.4669 | 0.8324 | 0.8452 | 0.8364 | 0.8452 | 0.9189 | 0.8571 | 0.9138 | 0.9167 |
73
+ | 0.3808 | 4.56 | 260 | 0.4585 | 0.8349 | 0.8452 | 0.8383 | 0.8452 | 0.9189 | 0.8621 | 0.9231 | 0.9141 |
74
+ | 0.3491 | 4.91 | 280 | 0.4600 | 0.8364 | 0.8452 | 0.8383 | 0.8452 | 0.9189 | 0.8621 | 0.9231 | 0.9141 |
75
+
76
+
77
+ ### Framework versions
78
+
79
+ - Transformers 4.26.0
80
+ - Pytorch 1.13.1+cu117
81
+ - Datasets 2.8.0
82
+ - Tokenizers 0.13.2