Hugging Face's logo

stulcrad
/
CNEC2_0_Supertypes_xlm-roberta-large

Token Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
stulcrad commited on
Commit
c7afef9
•
1 Parent(s): f3a648d

Model save

Browse files
Files changed (1) hide show
  1. README.md +17 -17
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
25
  metrics:
26
  - name: Precision
27
  type: precision
28
- value: 0.8682730923694779
29
  - name: Recall
30
  type: recall
31
- value: 0.8930194134655102
32
  - name: F1
33
  type: f1
34
- value: 0.8804724088780289
35
  - name: Accuracy
36
  type: accuracy
37
- value: 0.9714037375054324
38
  ---
39
 
40
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
44
 
45
  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
46
  It achieves the following results on the evaluation set:
47
- - Loss: 0.2170
48
- - Precision: 0.8683
49
- - Recall: 0.8930
50
- - F1: 0.8805
51
- - Accuracy: 0.9714
52
 
53
  ## Model description
54
 
@@ -79,14 +79,14 @@ The following hyperparameters were used during training:
79
 
80
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
81
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
82
- | 0.2309 | 1.0 | 3597 | 0.1817 | 0.8109 | 0.8484 | 0.8292 | 0.9601 |
83
- | 0.1752 | 2.0 | 7194 | 0.1677 | 0.8299 | 0.8806 | 0.8545 | 0.9639 |
84
- | 0.1305 | 3.0 | 10791 | 0.2261 | 0.8123 | 0.8653 | 0.838 | 0.9623 |
85
- | 0.1021 | 4.0 | 14388 | 0.1835 | 0.8391 | 0.8790 | 0.8586 | 0.9682 |
86
- | 0.0831 | 5.0 | 17985 | 0.1986 | 0.8564 | 0.8843 | 0.8701 | 0.9704 |
87
- | 0.0517 | 6.0 | 21582 | 0.1997 | 0.8636 | 0.8893 | 0.8763 | 0.9705 |
88
- | 0.0531 | 7.0 | 25179 | 0.2066 | 0.8610 | 0.8901 | 0.8753 | 0.9706 |
89
- | 0.0363 | 8.0 | 28776 | 0.2170 | 0.8683 | 0.8930 | 0.8805 | 0.9714 |
90
 
91
 
92
  ### Framework versions
 
25
  metrics:
26
  - name: Precision
27
  type: precision
28
+ value: 0.8664521319388576
29
  - name: Recall
30
  type: recall
31
+ value: 0.8897149938042132
32
  - name: F1
33
  type: f1
34
+ value: 0.8779294884858366
35
  - name: Accuracy
36
  type: accuracy
37
+ value: 0.9714616833260901
38
  ---
39
 
40
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
44
 
45
  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
46
  It achieves the following results on the evaluation set:
47
+ - Loss: 0.2219
48
+ - Precision: 0.8665
49
+ - Recall: 0.8897
50
+ - F1: 0.8779
51
+ - Accuracy: 0.9715
52
 
53
  ## Model description
54
 
 
79
 
80
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
81
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
82
+ | 0.228 | 1.0 | 3597 | 0.1773 | 0.8036 | 0.8364 | 0.8197 | 0.9616 |
83
+ | 0.176 | 2.0 | 7194 | 0.1703 | 0.8002 | 0.8505 | 0.8246 | 0.9605 |
84
+ | 0.1245 | 3.0 | 10791 | 0.1698 | 0.8009 | 0.8377 | 0.8189 | 0.9643 |
85
+ | 0.0916 | 4.0 | 14388 | 0.1898 | 0.8246 | 0.8662 | 0.8449 | 0.9658 |
86
+ | 0.082 | 5.0 | 17985 | 0.2007 | 0.8369 | 0.8711 | 0.8537 | 0.9675 |
87
+ | 0.0608 | 6.0 | 21582 | 0.1945 | 0.8446 | 0.8802 | 0.8621 | 0.9698 |
88
+ | 0.05 | 7.0 | 25179 | 0.2043 | 0.8614 | 0.8885 | 0.8747 | 0.9714 |
89
+ | 0.0334 | 8.0 | 28776 | 0.2219 | 0.8665 | 0.8897 | 0.8779 | 0.9715 |
90
 
91
 
92
  ### Framework versions