harshavsk
/

ind_roberta

@@ -13,12 +13,12 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3372
-- Accuracy@en: 0.89
-- F1@en: 0.8859
-- Precision@en: 0.8864
-- Recall@en: 0.8854
-- Loss@en: 0.3372
 ## Model description
@@ -43,14 +43,27 @@ 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: 2
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en  | Precision@en | Recall@en | Loss@en |
 |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------------:|:---------:|:-------:|
-| 0.389         | 1.0   | 375  | 0.3372          | 0.89        | 0.8859 | 0.8864       | 0.8854    | 0.3372  |
-| 0.2437        | 2.0   | 750  | 0.3435          | 0.9         | 0.8967 | 0.8957       | 0.8977    | 0.3435  |
 ### Framework versions

 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.3951
+- Accuracy@en: 0.9367
+- F1@en: 0.9341
+- Precision@en: 0.9360
+- Recall@en: 0.9324
+- Loss@en: 0.3951
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 15
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en  | Precision@en | Recall@en | Loss@en |
 |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------------:|:---------:|:-------:|
+| 0.1854        | 1.0   | 375  | 0.4027          | 0.9033      | 0.8994 | 0.9012       | 0.8979    | 0.4027  |
+| 0.203         | 2.0   | 750  | 0.4013          | 0.89        | 0.8877 | 0.8845       | 0.8944    | 0.4013  |
+| 0.1282        | 3.0   | 1125 | 0.6106          | 0.89        | 0.8883 | 0.8858       | 0.8983    | 0.6106  |
+| 0.0811        | 4.0   | 1500 | 0.3951          | 0.9367      | 0.9341 | 0.9360       | 0.9324    | 0.3951  |
+| 0.0425        | 5.0   | 1875 | 0.4764          | 0.93        | 0.9282 | 0.9250       | 0.9333    | 0.4764  |
+| 0.005         | 6.0   | 2250 | 0.5299          | 0.9367      | 0.9343 | 0.9349       | 0.9337    | 0.5299  |
+| 0.0147        | 7.0   | 2625 | 0.5200          | 0.93        | 0.9285 | 0.9249       | 0.9359    | 0.5200  |
+| 0.0182        | 8.0   | 3000 | 0.5532          | 0.9267      | 0.9242 | 0.9232       | 0.9253    | 0.5532  |
+| 0.0125        | 9.0   | 3375 | 0.5398          | 0.9367      | 0.9346 | 0.9331       | 0.9363    | 0.5398  |
+| 0.0171        | 10.0  | 3750 | 0.5157          | 0.9367      | 0.9349 | 0.9321       | 0.9389    | 0.5157  |
+| 0.0109        | 11.0  | 4125 | 0.6538          | 0.92        | 0.9184 | 0.9149       | 0.9261    | 0.6538  |
+| 0.0054        | 12.0  | 4500 | 0.5676          | 0.93        | 0.9281 | 0.9253       | 0.9320    | 0.5676  |
+| 0.0047        | 13.0  | 4875 | 0.6763          | 0.9167      | 0.9146 | 0.9114       | 0.9195    | 0.6763  |
+| 0.0076        | 14.0  | 5250 | 0.6970          | 0.9133      | 0.9109 | 0.9084       | 0.9141    | 0.6970  |
+| 0.0066        | 15.0  | 5625 | 0.6947          | 0.9167      | 0.9146 | 0.9114       | 0.9195    | 0.6947  |
 ### Framework versions