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xlm-roberta-base-finetuned-partypredictor

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threite commited on Feb 13, 2023

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update model card README.md

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@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.7088
-- Accuracy: 0.1529
 ## Model description
@@ -42,7 +42,7 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 8
 ### Training results
@@ -53,10 +53,20 @@ The following hyperparameters were used during training:
 | 1.7543        | 2.28  | 15000 | 0.1031   | 1.8126          |
 | 1.7273        | 3.05  | 20000 | 0.1331   | 1.8048          |
 | 1.7435        | 3.81  | 25000 | 0.2675   | 1.7892          |
-| 1.7606        | 4.99  | 30000 | 1.7848   | 0.3121          |
-| 1.7546        | 5.82  | 35000 | 1.7737   | 0.3121          |
-| 1.7417        | 6.65  | 40000 | 1.7699   | 0.3121          |
-| 1.7007        | 7.48  | 45000 | 1.7088   | 0.1529          |
 ### Framework versions

 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.6783
+- Accuracy: 0.2495
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 16
 ### Training results
 | 1.7543        | 2.28  | 15000 | 0.1031   | 1.8126          |
 | 1.7273        | 3.05  | 20000 | 0.1331   | 1.8048          |
 | 1.7435        | 3.81  | 25000 | 0.2675   | 1.7892          |
+| 1.7606        | 4.99  | 30000 | 0.3121   | 1.7848          |
+| 1.7546        | 5.82  | 35000 | 0.3121   | 1.7737          |
+| 1.7417        | 6.65  | 40000 | 0.3121   | 1.7699          |
+| 1.7007        | 7.48  | 45000 | 0.1529   | 1.7088          |
+| 1.7542        | 7.87  | 50000 | 0.1331   | 1.8058          |
+| 1.75          | 8.66  | 55000 | 0.1331   | 1.8347          |
+| 1.7505        | 10.05 | 60000 | 1.8079   | 0.1231          |
+| 1.7545        | 10.88 | 65000 | 1.7756   | 0.3121          |
+| 1.7322        | 11.72 | 70000 | 1.7371   | 0.2707          |
+| 1.7082        | 12.56 | 75000 | 1.6886   | 0.2419          |
+| 1.7035        | 13.4  | 80000 | 1.6844   | 0.2638          |
+| 1.6889        | 14.23 | 85000 | 1.6728   | 0.2525          |
+| 1.6779        | 15.07 | 90000 | 1.6737   | 0.2490          |
+| 1.6821        | 15.91 | 95000 | 1.6783   | 0.2495          |
 ### Framework versions