Hugging Face's logo

jarvisx17
/
medicine-ner

Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
2
jarvisx17 commited on
Commit
c9b8b32
·
1 Parent(s): e2560c8

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +108 -0
README.md ADDED
@@ -0,0 +1,108 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - jxner
7
+ metrics:
8
+ - precision
9
+ - recall
10
+ - f1
11
+ - accuracy
12
+ model-index:
13
+ - name: medicine-ner
14
+ results:
15
+ - task:
16
+ name: Token Classification
17
+ type: token-classification
18
+ dataset:
19
+ name: jxner
20
+ type: jxner
21
+ config: wnut_17
22
+ split: test
23
+ args: wnut_17
24
+ metrics:
25
+ - name: Precision
26
+ type: precision
27
+ value: 0.0
28
+ - name: Recall
29
+ type: recall
30
+ value: 0.0
31
+ - name: F1
32
+ type: f1
33
+ value: 0.0
34
+ - name: Accuracy
35
+ type: accuracy
36
+ value: 0.9
37
+ ---
38
+
39
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
40
+ should probably proofread and complete it, then remove this comment. -->
41
+
42
+ # medicine-ner
43
+
44
+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the jxner dataset.
45
+ It achieves the following results on the evaluation set:
46
+ - Loss: 0.5562
47
+ - Precision: 0.0
48
+ - Recall: 0.0
49
+ - F1: 0.0
50
+ - Accuracy: 0.9
51
+
52
+ ## Model description
53
+
54
+ More information needed
55
+
56
+ ## Intended uses & limitations
57
+
58
+ More information needed
59
+
60
+ ## Training and evaluation data
61
+
62
+ More information needed
63
+
64
+ ## Training procedure
65
+
66
+ ### Training hyperparameters
67
+
68
+ The following hyperparameters were used during training:
69
+ - learning_rate: 2e-05
70
+ - train_batch_size: 16
71
+ - eval_batch_size: 16
72
+ - seed: 42
73
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
74
+ - lr_scheduler_type: linear
75
+ - num_epochs: 20
76
+
77
+ ### Training results
78
+
79
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
80
+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
81
+ | No log | 1.0 | 1 | 1.7398 | 0.0370 | 0.125 | 0.0571 | 0.65 |
82
+ | No log | 2.0 | 2 | 1.5750 | 0.0 | 0.0 | 0.0 | 0.86 |
83
+ | No log | 3.0 | 3 | 1.4146 | 0.0 | 0.0 | 0.0 | 0.88 |
84
+ | No log | 4.0 | 4 | 1.2611 | 0.0 | 0.0 | 0.0 | 0.9 |
85
+ | No log | 5.0 | 5 | 1.1173 | 0.0 | 0.0 | 0.0 | 0.9 |
86
+ | No log | 6.0 | 6 | 0.9869 | 0.0 | 0.0 | 0.0 | 0.9 |
87
+ | No log | 7.0 | 7 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.9 |
88
+ | No log | 8.0 | 8 | 0.7804 | 0.0 | 0.0 | 0.0 | 0.9 |
89
+ | No log | 9.0 | 9 | 0.7074 | 0.0 | 0.0 | 0.0 | 0.9 |
90
+ | No log | 10.0 | 10 | 0.6545 | 0.0 | 0.0 | 0.0 | 0.9 |
91
+ | No log | 11.0 | 11 | 0.6181 | 0.0 | 0.0 | 0.0 | 0.9 |
92
+ | No log | 12.0 | 12 | 0.5938 | 0.0 | 0.0 | 0.0 | 0.9 |
93
+ | No log | 13.0 | 13 | 0.5780 | 0.0 | 0.0 | 0.0 | 0.9 |
94
+ | No log | 14.0 | 14 | 0.5682 | 0.0 | 0.0 | 0.0 | 0.9 |
95
+ | No log | 15.0 | 15 | 0.5623 | 0.0 | 0.0 | 0.0 | 0.9 |
96
+ | No log | 16.0 | 16 | 0.5589 | 0.0 | 0.0 | 0.0 | 0.9 |
97
+ | No log | 17.0 | 17 | 0.5571 | 0.0 | 0.0 | 0.0 | 0.9 |
98
+ | No log | 18.0 | 18 | 0.5563 | 0.0 | 0.0 | 0.0 | 0.9 |
99
+ | No log | 19.0 | 19 | 0.5562 | 0.0 | 0.0 | 0.0 | 0.9 |
100
+ | No log | 20.0 | 20 | 0.5562 | 0.0 | 0.0 | 0.0 | 0.9 |
101
+
102
+
103
+ ### Framework versions
104
+
105
+ - Transformers 4.27.3
106
+ - Pytorch 1.13.1+cu116
107
+ - Datasets 2.10.1
108
+ - Tokenizers 0.13.2