End of training

README.md

@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5661
-- Precision: 0.5387
-- Recall: 0.5925
-- F1: 0.5644
-- Accuracy: 0.8318
+- Loss: 0.5321
+- Precision: 0.5840
+- Recall: 0.6268
+- F1: 0.6047
+- Accuracy: 0.8423
 
 ## Model description
 
@@ -54,8 +54,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.6022        | 1.9970 | 1332 | 0.5910          | 0.5313    | 0.5817 | 0.5553 | 0.8284   |
-| 0.4725        | 3.9940 | 2664 | 0.5661          | 0.5387    | 0.5925 | 0.5644 | 0.8318   |
+| 0.4577        | 1.9981 | 2088 | 0.5131          | 0.5846    | 0.6146 | 0.5992 | 0.8432   |
+| 0.3523        | 3.9962 | 4176 | 0.5321          | 0.5840    | 0.6268 | 0.6047 | 0.8423   |
 
 
 ### Framework versions

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "lilyyellow/my_awesome_ner-token_classification_v1.0",
+  "_name_or_path": "NlpHUST/ner-vietnamese-electra-base",
   "architectures": [
     "ElectraForTokenClassification"
   ],
@@ -30,73 +30,65 @@
     "16": "I-URL",
     "17": "I-ACTION",
     "18": "B-PHONE",
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-    "21": "O",
-    "22": "I-PRODUCT",
-    "23": "B-EVENT",
-    "24": "B-ACTION",
-    "25": "I-ORGANIZATION",
-    "26": "B-PERSON",
+    "19": "I-AGE",
+    "20": "O",
+    "21": "I-PRODUCT",
+    "22": "B-EVENT",
+    "23": "B-ACTION",
+    "24": "I-ORGANIZATION",
+    "25": "B-PERSON",
+    "26": "B-AGE",
     "27": "I-TRANSPORTATION",
     "28": "I-EMAIL",
-    "29": "B-AGE",
-    "30": "B-EMAIL",
-    "31": "I-MISCELLANEOUS",
-    "32": "I-LOCATION",
-    "33": "I-DATETIME",
-    "34": "I-QUANTITY",
-    "35": "B-LOCATION",
-    "36": "B-MONEY",
-    "37": "I-MONEY",
-    "38": "B-MISCELLANEOUS",
-    "39": "I-PHONE",
-    "40": "B-LAW"
+    "29": "B-EMAIL",
+    "30": "B-QUANTITY-NUMBER",
+    "31": "I-LOCATION",
+    "32": "I-DATETIME",
+    "33": "B-LOCATION",
+    "34": "I-QUANTITY-NUMBER",
+    "35": "I-PHONE",
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   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
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+    "I-ORGANIZATION": 24,
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     "I-ROLE": 9,
     "I-TRANSPORTATION": 27,
     "I-URL": 16,
-    "O": 21
+    "O": 20
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,

model.safetensors

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training_args.bin

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