Training complete

README.md

@@ -6,9 +6,36 @@ tags:
 - generated_from_trainer
 datasets:
 - conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
 - name: distil-bert-finetuned-ner
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9052718286655683
+    - name: Recall
+      type: recall
+      value: 0.9247728037697744
+    - name: F1
+      type: f1
+      value: 0.914918414918415
+    - name: Accuracy
+      type: accuracy
+      value: 0.9830174591322859
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -17,6 +44,12 @@ should probably proofread and complete it, then remove this comment. -->
 # distil-bert-finetuned-ner
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0601
+- Precision: 0.9053
+- Recall: 0.9248
+- F1: 0.9149
+- Accuracy: 0.9830
 
 ## Model description
 
@@ -43,6 +76,13 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 1
 
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0765        | 1.0   | 1756 | 0.0601          | 0.9053    | 0.9248 | 0.9149 | 0.9830   |
+
+
 ### Framework versions
 
 - Transformers 4.44.2

runs/Sep26_12-42-15_adee95e190b2/events.out.tfevents.1727354539.adee95e190b2.2471.0

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