Upload DebertaV2ForTokenClassification

README.md

@@ -1,22 +1,25 @@
 ---
-library_name: transformers
-license: mit
 base_model: microsoft/mdeberta-v3-base
-tags:
-- generated_from_trainer
 datasets:
 - eriktks/conll2003
+language:
+- en
+library_name: transformers
+license: mit
 metrics:
 - precision
 - recall
 - f1
 - accuracy
+pipeline_tag: token-classification
+tags:
+- generated_from_trainer
 model-index:
 - name: mdeberta
   results:
   - task:
-      name: Token Classification
       type: token-classification
+      name: Token Classification
     dataset:
       name: eriktks/conll2003
       type: eriktks/conll2003
@@ -24,21 +27,18 @@ model-index:
       split: validation
       args: conll2003
     metrics:
-    - name: Precision
-      type: precision
+    - type: precision
       value: 0.9566232899566233
-    - name: Recall
-      type: recall
+      name: Precision
+    - type: recall
       value: 0.9649949511948839
-    - name: F1
-      type: f1
+      name: Recall
+    - type: f1
       value: 0.9607908847184986
-    - name: Accuracy
-      type: accuracy
+      name: F1
+    - type: accuracy
       value: 0.9929130485572991
-language:
-- en
-pipeline_tag: token-classification
+      name: Accuracy
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You

config.json

@@ -1,7 +1,7 @@
 {
   "_name_or_path": "/content/drive/MyDrive/Colab Notebooks/mdeberta",
   "architectures": [
-    "DebertaV2Model"
+    "DebertaV2ForTokenClassification"
   ],
   "attention_probs_dropout_prob": 0.1,
   "finetuning_task": "ner",

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 1112897768
+oid sha256:c12d8fc3d0895363dbbd4102e8da04ef3f298051ab14d22e5f5a9042f218f4bd
+size 1112927188