smanjil commited on
Commit
933eee6
1 Parent(s): f96227d

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +10 -10
README.md CHANGED
@@ -11,7 +11,7 @@ tags:
11
 
12
  # German Medical BERT
13
 
14
- This is a fine-tuned model on Medical domain for German language and based on German BERT. This model has only been trained to improve on target task (Masked Language Model). It can later be used to perform a downstream task of your needs, while I performed it for NTS-ICD-10 text classification task.
15
 
16
  ## Overview
17
  **Language model:** bert-base-german-cased
@@ -22,26 +22,26 @@ This is a fine-tuned model on Medical domain for German language and based on Ge
22
 
23
  **Eval data:** NTS-ICD-10 dataset (Classification)
24
 
25
- **Infrastructure:** Gogle Colab
26
 
27
 
28
  ## Details
29
  - We fine-tuned using Pytorch with Huggingface library on Colab GPU.
30
- - With standard parameter settings for fine-tuning as mentioned in original BERT's paper.
31
- - Although had to train for upto 25 epochs for classification.
32
 
33
  ## Performance (Micro precision, recall and f1 score for multilabel code classification)
34
 
35
- |Models\\\\t\\\\t\\\\t|P\\\\t|R\\\\t|F1\\\\t|
36
- |:--------------\\\\t|:------|:------|:------|
37
- |German BERT\\\\t\\\\t|86.04\\\\t|75.82\\\\t|80.60\\\\t|
38
- |German MedBERT-256\\\\t|87.41\\\\t|77.97\\\\t|82.42\\\\t|
39
- |German MedBERT-512\\\\t|87.75\\\\t|78.26\\\\t|82.73\\\\t|
40
 
41
  ## Author
42
  Manjil Shrestha: `shresthamanjil21 [at] gmail.com`
43
 
44
- ## Related Paper
45
  [Report](https://opus4.kobv.de/opus4-rhein-waal/frontdoor/index/index/searchtype/collection/id/16225/start/0/rows/10/doctypefq/masterthesis/docId/740)
46
 
47
  Get in touch:
 
11
 
12
  # German Medical BERT
13
 
14
+ This is a fine-tuned model on the Medical domain for the German language and based on German BERT. This model has only been trained to improve on-target task (Masked Language Model). It can later be used to perform a downstream task of your needs, while I performed it for the NTS-ICD-10 text classification task.
15
 
16
  ## Overview
17
  **Language model:** bert-base-german-cased
 
22
 
23
  **Eval data:** NTS-ICD-10 dataset (Classification)
24
 
25
+ **Infrastructure:** Google Colab
26
 
27
 
28
  ## Details
29
  - We fine-tuned using Pytorch with Huggingface library on Colab GPU.
30
+ - With standard parameter settings for fine-tuning as mentioned in the original BERT paper.
31
+ - Although had to train for up to 25 epochs for classification.
32
 
33
  ## Performance (Micro precision, recall and f1 score for multilabel code classification)
34
 
35
+ |Models\\\\\\\\t\\\\\\\\t\\\\\\\\t|P\\\\\\\\t|R\\\\\\\\t|F1\\\\\\\\t|
36
+ |:--------------\\\\\\\\t|:------|:------|:------|
37
+ |German BERT\\\\\\\\t\\\\\\\\t|86.04\\\\\\\\t|75.82\\\\\\\\t|80.60\\\\\\\\t|
38
+ |German MedBERT-256\\\\\\\\t|87.41\\\\\\\\t|77.97\\\\\\\\t|82.42\\\\\\\\t|
39
+ |German MedBERT-512\\\\\\\\t|87.75\\\\\\\\t|78.26\\\\\\\\t|82.73\\\\\\\\t|
40
 
41
  ## Author
42
  Manjil Shrestha: `shresthamanjil21 [at] gmail.com`
43
 
44
+ ## Related Paper:
45
  [Report](https://opus4.kobv.de/opus4-rhein-waal/frontdoor/index/index/searchtype/collection/id/16225/start/0/rows/10/doctypefq/masterthesis/docId/740)
46
 
47
  Get in touch: