End of training

README.md

@@ -26,16 +26,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5261875761266748
+      value: 0.589041095890411
     - name: Recall
       type: recall
-      value: 0.40037071362372567
+      value: 0.31881371640407785
     - name: F1
       type: f1
-      value: 0.45473684210526316
+      value: 0.41371016235718583
     - name: Accuracy
       type: accuracy
-      value: 0.946346885554273
+      value: 0.9420717369928605
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2640
-- Precision: 0.5262
-- Recall: 0.4004
-- F1: 0.4547
-- Accuracy: 0.9463
+- Loss: 0.2718
+- Precision: 0.5890
+- Recall: 0.3188
+- F1: 0.4137
+- Accuracy: 0.9421
 
 ## Model description
 
@@ -80,8 +80,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 213  | 0.2505          | 0.5255    | 0.3920 | 0.4490 | 0.9457   |
-| No log        | 2.0   | 426  | 0.2640          | 0.5262    | 0.4004 | 0.4547 | 0.9463   |
+| No log        | 1.0   | 213  | 0.2808          | 0.5195    | 0.2475 | 0.3352 | 0.9384   |
+| No log        | 2.0   | 426  | 0.2718          | 0.5890    | 0.3188 | 0.4137 | 0.9421   |
 
 
 ### Framework versions

model.safetensors

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 size 265503852

runs/Jan18_18-46-12_a6a673b6a857/events.out.tfevents.1737225973.a6a673b6a857.2828.2

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