muhammadravi251001
/

fine-tuned-NLI-multilingual-with-xlm-roberta-large

@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5146
-- Accuracy: 0.8579
-- F1: 0.8583
 ## Model description
@@ -39,10 +39,10 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
-- train_batch_size: 8
-- eval_batch_size: 8
 - seed: 42
-- gradient_accumulation_steps: 16
 - total_train_batch_size: 128
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -50,24 +50,21 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
-| 0.4787        | 0.5   | 1574  | 0.4285          | 0.8364   | 0.8358 |
-| 0.4418        | 1.0   | 3148  | 0.4040          | 0.8494   | 0.8496 |
-| 0.3942        | 1.5   | 4722  | 0.3971          | 0.8514   | 0.8505 |
-| 0.3722        | 2.0   | 6296  | 0.3835          | 0.8579   | 0.8581 |
-| 0.3206        | 2.5   | 7870  | 0.4139          | 0.8587   | 0.8586 |
-| 0.3229        | 3.0   | 9444  | 0.4033          | 0.8600   | 0.8602 |
-| 0.2616        | 3.5   | 11018 | 0.4457          | 0.8585   | 0.8591 |
-| 0.2862        | 4.0   | 12592 | 0.4319          | 0.8619   | 0.8617 |
-| 0.2261        | 4.5   | 14166 | 0.4859          | 0.8562   | 0.8570 |
-| 0.2215        | 5.0   | 15740 | 0.4728          | 0.8592   | 0.8599 |
-| 0.1874        | 5.5   | 17314 | 0.5146          | 0.8579   | 0.8583 |
 ### Framework versions
 - Transformers 4.26.1
-- Pytorch 1.13.1+cu117
 - Datasets 2.2.0
-- Tokenizers 0.13.2

 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.4158
+- Accuracy: 0.8600
+- F1: 0.8612
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
 - seed: 42
+- gradient_accumulation_steps: 8
 - total_train_batch_size: 128
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 ### Training results
+| Training Loss | Epoch | Step  | Accuracy | F1     | Validation Loss |
+|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|
+| 0.4647        | 0.5   | 1613  | 0.8396   | 0.8403 | 0.4262          |
+| 0.4437        | 1.0   | 3226  | 0.8511   | 0.8522 | 0.4042          |
+| 0.3956        | 1.5   | 4839  | 0.3783   | 0.8604 | 0.8602          |
+| 0.3639        | 2.0   | 6452  | 0.3913   | 0.8592 | 0.8600          |
+| 0.323         | 2.5   | 8065  | 0.3783   | 0.8657 | 0.8659          |
+| 0.3186        | 3.0   | 9678  | 0.3850   | 0.8626 | 0.8625          |
+| 0.2485        | 3.5   | 11291 | 0.4326   | 0.8597 | 0.8592          |
+| 0.2509        | 4.0   | 12904 | 0.4158   | 0.8600 | 0.8612          |
 ### Framework versions
 - Transformers 4.26.1
+- Pytorch 2.0.1+cu117
 - Datasets 2.2.0
+- Tokenizers 0.13.3