Hugging Face's logo

cartesinus
/
xlm-r-base-amazon-massive-slot

Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
xlm-roberta
Generated from Trainer
nlu
slot-tagging
Eval Results
Inference Endpoints
Model card Files Metrics Community
2
cartesinus commited on
Commit
0bd1713
•
1 Parent(s): 5b43243

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +84 -0
README.md ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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: xlm-r-base-amazon-massive-slot
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
+ # xlm-r-base-amazon-massive-slot
19
+
20
+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 0.5006
23
+ - Precision: 0.8144
24
+ - Recall: 0.8683
25
+ - F1: 0.8405
26
+ - Accuracy: 0.9333
27
+
28
+ ## Model description
29
+
30
+ More information needed
31
+
32
+ ## Intended uses & limitations
33
+
34
+ More information needed
35
+
36
+ ## Training and evaluation data
37
+
38
+ More information needed
39
+
40
+ ## Training procedure
41
+
42
+ ### Training hyperparameters
43
+
44
+ The following hyperparameters were used during training:
45
+ - learning_rate: 2e-05
46
+ - train_batch_size: 16
47
+ - eval_batch_size: 16
48
+ - seed: 42
49
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
+ - lr_scheduler_type: linear
51
+ - num_epochs: 20
52
+
53
+ ### Training results
54
+
55
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
+ | 1.1445 | 1.0 | 720 | 0.5446 | 0.6681 | 0.6770 | 0.6725 | 0.8842 |
58
+ | 0.5908 | 2.0 | 1440 | 0.3869 | 0.7331 | 0.7706 | 0.7514 | 0.9083 |
59
+ | 0.3228 | 3.0 | 2160 | 0.3285 | 0.7658 | 0.8288 | 0.7961 | 0.9219 |
60
+ | 0.2561 | 4.0 | 2880 | 0.3063 | 0.7819 | 0.8402 | 0.8100 | 0.9257 |
61
+ | 0.1808 | 5.0 | 3600 | 0.3000 | 0.8011 | 0.8429 | 0.8214 | 0.9305 |
62
+ | 0.1487 | 6.0 | 4320 | 0.2982 | 0.8201 | 0.8492 | 0.8344 | 0.9361 |
63
+ | 0.1156 | 7.0 | 5040 | 0.3252 | 0.8009 | 0.8569 | 0.8280 | 0.9313 |
64
+ | 0.094 | 8.0 | 5760 | 0.3481 | 0.8127 | 0.8502 | 0.8310 | 0.9333 |
65
+ | 0.0843 | 9.0 | 6480 | 0.3764 | 0.7990 | 0.8613 | 0.8290 | 0.9304 |
66
+ | 0.0641 | 10.0 | 7200 | 0.3822 | 0.7930 | 0.8609 | 0.8256 | 0.9280 |
67
+ | 0.0547 | 11.0 | 7920 | 0.3889 | 0.8223 | 0.8649 | 0.8431 | 0.9354 |
68
+ | 0.04 | 12.0 | 8640 | 0.4416 | 0.8019 | 0.8633 | 0.8314 | 0.9288 |
69
+ | 0.0368 | 13.0 | 9360 | 0.4339 | 0.8117 | 0.8606 | 0.8354 | 0.9328 |
70
+ | 0.0297 | 14.0 | 10080 | 0.4698 | 0.8062 | 0.8623 | 0.8333 | 0.9314 |
71
+ | 0.0227 | 15.0 | 10800 | 0.4763 | 0.8058 | 0.8656 | 0.8346 | 0.9327 |
72
+ | 0.0185 | 16.0 | 11520 | 0.4793 | 0.8124 | 0.8613 | 0.8361 | 0.9326 |
73
+ | 0.0182 | 17.0 | 12240 | 0.4835 | 0.8191 | 0.8629 | 0.8404 | 0.9341 |
74
+ | 0.0147 | 18.0 | 12960 | 0.4981 | 0.8140 | 0.8693 | 0.8407 | 0.9336 |
75
+ | 0.0111 | 19.0 | 13680 | 0.5002 | 0.8099 | 0.8719 | 0.8398 | 0.9340 |
76
+ | 0.0128 | 20.0 | 14400 | 0.5006 | 0.8144 | 0.8683 | 0.8405 | 0.9333 |
77
+
78
+
79
+ ### Framework versions
80
+
81
+ - Transformers 4.22.2
82
+ - Pytorch 1.12.1+cu113
83
+ - Datasets 2.5.1
84
+ - Tokenizers 0.12.1