Update README.md

README.md

@@ -6,6 +6,17 @@ tags:
 model-index:
 - name: distilbert-base-german-cased_finetuned_ai4privacy_v2
   results: []
+datasets:
+- ai4privacy/pii-masking-200k
+- Isotonic/pii-masking-200k
+language:
+- de
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,81 +24,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilbert-base-german-cased_finetuned_ai4privacy_v2
 
-This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the None dataset.
+This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the German subset of [pii-masking-200k](https://huggingface.co/ai4privacy/pii-masking-200k) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0821
 - Overall Precision: 0.9086
 - Overall Recall: 0.9379
 - Overall F1: 0.9230
 - Overall Accuracy: 0.9679
-- Accountname F1: 0.9985
-- Accountnumber F1: 0.9849
-- Age F1: 0.9792
-- Amount F1: 0.9387
-- Bic F1: 0.9641
-- Bitcoinaddress F1: 0.9637
-- Buildingnumber F1: 0.9011
-- City F1: 0.9260
-- Companyname F1: 0.9782
-- County F1: 0.9778
-- Creditcardcvv F1: 0.9543
-- Creditcardissuer F1: 1.0
-- Creditcardnumber F1: 0.8796
-- Currency F1: 0.7027
-- Currencycode F1: 0.8328
-- Currencyname F1: 0.3466
-- Currencysymbol F1: 0.9420
-- Date F1: 0.8156
-- Dob F1: 0.6575
-- Email F1: 0.9971
-- Ethereumaddress F1: 0.9947
-- Eyecolor F1: 0.9833
-- Firstname F1: 0.9614
-- Gender F1: 0.9881
-- Height F1: 0.9842
-- Iban F1: 0.9819
-- Ip F1: 0.2023
-- Ipv4 F1: 0.6631
-- Ipv6 F1: 0.7243
-- Jobarea F1: 0.9722
-- Jobtitle F1: 0.9904
-- Jobtype F1: 0.9725
-- Lastname F1: 0.9185
-- Litecoinaddress F1: 0.8545
-- Mac F1: 0.9780
-- Maskednumber F1: 0.8365
-- Middlename F1: 0.9156
-- Nearbygpscoordinate F1: 1.0
-- Ordinaldirection F1: 0.9853
-- Password F1: 0.9782
-- Phoneimei F1: 0.9947
-- Phonenumber F1: 0.9883
-- Pin F1: 0.9189
-- Prefix F1: 0.9594
-- Secondaryaddress F1: 0.9831
-- Sex F1: 0.9993
-- Ssn F1: 0.9898
-- State F1: 0.9739
-- Street F1: 0.9355
-- Time F1: 0.9764
-- Url F1: 0.9984
-- Useragent F1: 0.9885
-- Username F1: 0.9798
-- Vehiclevin F1: 0.9614
-- Vehiclevrm F1: 1.0
-- Zipcode F1: 0.9100
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
 
 ## Training procedure