End of training

README.md

@@ -4,7 +4,7 @@ base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
 datasets:
-- indian_names
+- ner
 metrics:
 - precision
 - recall
@@ -17,24 +17,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: indian_names
-      type: indian_names
+      name: ner
+      type: ner
       config: indian_names
       split: train
       args: indian_names
     metrics:
     - name: Precision
       type: precision
-      value: 0.9939821779886587
+      value: 0.9269461077844311
     - name: Recall
       type: recall
-      value: 0.9958260869565217
+      value: 0.9381818181818182
     - name: F1
       type: f1
-      value: 0.9949032781188464
+      value: 0.9325301204819277
     - name: Accuracy
       type: accuracy
-      value: 0.999003984063745
+      value: 0.9986404599129894
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_wnut_model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indian_names dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0050
-- Precision: 0.9940
-- Recall: 0.9958
-- F1: 0.9949
-- Accuracy: 0.9990
+- Loss: 0.0067
+- Precision: 0.9269
+- Recall: 0.9382
+- F1: 0.9325
+- Accuracy: 0.9986
 
 ## Model description
 
@@ -79,11 +79,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 66   | 0.0440          | 0.9579    | 0.9650 | 0.9614 | 0.9906   |
-| No log        | 2.0   | 132  | 0.0191          | 0.9870    | 0.9821 | 0.9845 | 0.9959   |
-| No log        | 3.0   | 198  | 0.0098          | 0.9919    | 0.9899 | 0.9909 | 0.9980   |
-| No log        | 4.0   | 264  | 0.0061          | 0.9927    | 0.9935 | 0.9931 | 0.9987   |
-| No log        | 5.0   | 330  | 0.0050          | 0.9940    | 0.9958 | 0.9949 | 0.9990   |
+| No log        | 1.0   | 63   | 0.0500          | 0.8048    | 0.4097 | 0.5430 | 0.9883   |
+| No log        | 2.0   | 126  | 0.0305          | 0.8104    | 0.7564 | 0.7824 | 0.9936   |
+| No log        | 3.0   | 189  | 0.0136          | 0.8643    | 0.8412 | 0.8526 | 0.9965   |
+| No log        | 4.0   | 252  | 0.0089          | 0.8571    | 0.9164 | 0.8858 | 0.9976   |
+| No log        | 5.0   | 315  | 0.0067          | 0.9269    | 0.9382 | 0.9325 | 0.9986   |
 
 
 ### Framework versions