update model card README.md

README.md

@@ -1,6 +1,4 @@
 ---
-license: mit
-base_model: xlm-roberta-base
 tags:
 - generated_from_trainer
 datasets:
@@ -16,13 +14,11 @@ model-index:
     dataset:
       name: xtreme
       type: xtreme
-      config: PAN-X.ar
-      split: validation
       args: PAN-X.ar
     metrics:
     - name: F1
       type: f1
-      value: 0.8937845609378455
+      value: 0.8894684900606231
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +26,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # xlm-roberta-base-finetuned-panx-ar
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
+This model is a fine-tuned version of [tner/xlm-roberta-base-panx-dataset-ar](https://huggingface.co/tner/xlm-roberta-base-panx-dataset-ar) on the xtreme dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2109
-- F1: 0.8938
+- Loss: 0.2237
+- F1: 0.8895
 
 ## Model description
 
@@ -58,22 +54,20 @@ 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: 5
+- num_epochs: 3
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
-| 0.3671        | 1.0   | 834  | 0.2355          | 0.8350 |
-| 0.2041        | 2.0   | 1668 | 0.2085          | 0.8616 |
-| 0.1405        | 3.0   | 2502 | 0.1882          | 0.8800 |
-| 0.0964        | 4.0   | 3336 | 0.2028          | 0.8819 |
-| 0.065         | 5.0   | 4170 | 0.2109          | 0.8938 |
+| 0.234         | 1.0   | 525  | 0.2382          | 0.8587 |
+| 0.1244        | 2.0   | 1050 | 0.2153          | 0.8844 |
+| 0.0738        | 3.0   | 1575 | 0.2237          | 0.8895 |
 
 
 ### Framework versions
 
-- Transformers 4.31.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.4
-- Tokenizers 0.13.3
+- Transformers 4.16.2
+- Pytorch 2.1.0+cu118
+- Datasets 1.16.1
+- Tokenizers 0.15.0