hks1444
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MARBERT

Token Classification

Transformers

Safetensors

bert

Generated from Trainer

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hks1444 commited on Jan 21

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End of training

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README.md +14 -14
model.safetensors +1 -1

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@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [UBC-NLP/MARBERT](https://huggingface.co/UBC-NLP/MARBERT) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0663
-- Precision: 0.4706
 - Recall: 0.6957
-- F1: 0.5614
-- Accuracy: 0.7165
 ## Model description
@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 5    | 0.8651          | 0.0       | 0.0    | 0.0    | 0.625    |
-| No log        | 2.0   | 10   | 0.8085          | 0.0       | 0.0    | 0.0    | 0.6528   |
-| No log        | 3.0   | 15   | 0.7283          | 0.0       | 0.0    | 0.0    | 0.75     |
-| No log        | 4.0   | 20   | 0.6817          | 0.1429    | 0.1429 | 0.1429 | 0.7222   |
-| No log        | 5.0   | 25   | 0.6265          | 0.0       | 0.0    | 0.0    | 0.7361   |
-| No log        | 6.0   | 30   | 0.6199          | 0.2857    | 0.2857 | 0.2857 | 0.7778   |
-| No log        | 7.0   | 35   | 0.5375          | 0.0       | 0.0    | 0.0    | 0.7778   |
-| No log        | 8.0   | 40   | 0.5271          | 0.1667    | 0.1429 | 0.1538 | 0.8056   |
-| No log        | 9.0   | 45   | 0.5728          | 0.2857    | 0.2857 | 0.2857 | 0.7778   |
-| No log        | 10.0  | 50   | 0.5742          | 0.1429    | 0.1429 | 0.1429 | 0.7639   |
 ### Framework versions

 This model is a fine-tuned version of [UBC-NLP/MARBERT](https://huggingface.co/UBC-NLP/MARBERT) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.0270
+- Precision: 0.4848
 - Recall: 0.6957
+- F1: 0.5714
+- Accuracy: 0.6772
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 5    | 0.8519          | 0.0       | 0.0    | 0.0    | 0.625    |
+| No log        | 2.0   | 10   | 0.8108          | 0.0       | 0.0    | 0.0    | 0.6806   |
+| No log        | 3.0   | 15   | 0.7524          | 0.0       | 0.0    | 0.0    | 0.7361   |
+| No log        | 4.0   | 20   | 0.6927          | 0.2222    | 0.2857 | 0.25   | 0.7083   |
+| No log        | 5.0   | 25   | 0.6995          | 0.0       | 0.0    | 0.0    | 0.7222   |
+| No log        | 6.0   | 30   | 0.8241          | 0.1818    | 0.2857 | 0.2222 | 0.625    |
+| No log        | 7.0   | 35   | 0.6811          | 0.2222    | 0.2857 | 0.25   | 0.75     |
+| No log        | 8.0   | 40   | 0.6957          | 0.25      | 0.2857 | 0.2667 | 0.7639   |
+| No log        | 9.0   | 45   | 0.7392          | 0.2222    | 0.2857 | 0.25   | 0.6944   |
+| No log        | 10.0  | 50   | 0.7612          | 0.2222    | 0.2857 | 0.25   | 0.6944   |
 ### Framework versions

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