Model save

README.md

@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [castorini/afriteva_v2_base](https://huggingface.co/castorini/afriteva_v2_base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.2679
-- Accuracy: {'accuracy': 0.1489841986455982}
+- Loss: 1.3508
+- Accuracy: {'accuracy': 0.1460214446952596}
 
 ## Model description
 
@@ -44,33 +44,24 @@ 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: 50
+- num_epochs: 30
 
 ### Training results
 
 | Training Loss | Epoch   | Step | Validation Loss | Accuracy                          |
 |:-------------:|:-------:|:----:|:---------------:|:---------------------------------:|
-| 5.906         | 2.3810  | 100  | 3.0052          | {'accuracy': 0.09734762979683972} |
-| 2.8177        | 4.7619  | 200  | 1.8325          | {'accuracy': 0.13346501128668173} |
-| 2.1137        | 7.1429  | 300  | 1.5931          | {'accuracy': 0.1426354401805869}  |
-| 1.8929        | 9.5238  | 400  | 1.5164          | {'accuracy': 0.143058690744921}   |
-| 1.742         | 11.9048 | 500  | 1.4720          | {'accuracy': 0.1429176072234763}  |
-| 1.5919        | 14.2857 | 600  | 1.3984          | {'accuracy': 0.1457392776523702}  |
-| 1.5163        | 16.6667 | 700  | 1.3713          | {'accuracy': 0.14672686230248308} |
-| 1.4357        | 19.0476 | 800  | 1.3317          | {'accuracy': 0.14771444695259595} |
-| 1.3989        | 21.4286 | 900  | 1.3123          | {'accuracy': 0.14799661399548533} |
-| 1.3938        | 23.8095 | 1000 | 1.2860          | {'accuracy': 0.14827878103837472} |
-| 1.3455        | 26.1905 | 1100 | 1.2969          | {'accuracy': 0.1489841986455982}  |
-| 1.3374        | 28.5714 | 1200 | 1.2839          | {'accuracy': 0.1487020316027088}  |
-| 1.3164        | 30.9524 | 1300 | 1.2789          | {'accuracy': 0.1487020316027088}  |
-| 1.3068        | 33.3333 | 1400 | 1.2856          | {'accuracy': 0.1489841986455982}  |
-| 1.3059        | 35.7143 | 1500 | 1.2734          | {'accuracy': 0.14912528216704288} |
-| 1.2776        | 38.0952 | 1600 | 1.2733          | {'accuracy': 0.1494074492099323}  |
-| 1.2822        | 40.4762 | 1700 | 1.2664          | {'accuracy': 0.1488431151241535}  |
-| 1.2718        | 42.8571 | 1800 | 1.2709          | {'accuracy': 0.14785553047404063} |
-| 1.2624        | 45.2381 | 1900 | 1.2653          | {'accuracy': 0.14926636568848758} |
-| 1.2559        | 47.6190 | 2000 | 1.2688          | {'accuracy': 0.14926636568848758} |
-| 1.2601        | 50.0    | 2100 | 1.2679          | {'accuracy': 0.1489841986455982}  |
+| 4.8624        | 2.3810  | 100  | 2.5503          | {'accuracy': 0.11554740406320542} |
+| 2.5725        | 4.7619  | 200  | 1.7106          | {'accuracy': 0.13755643340857787} |
+| 1.936         | 7.1429  | 300  | 1.5616          | {'accuracy': 0.14094243792325056} |
+| 1.804         | 9.5238  | 400  | 1.5510          | {'accuracy': 0.14122460496613995} |
+| 1.755         | 11.9048 | 500  | 1.5             | {'accuracy': 0.14094243792325056} |
+| 1.6713        | 14.2857 | 600  | 1.4747          | {'accuracy': 0.14051918735891647} |
+| 1.624         | 16.6667 | 700  | 1.4347          | {'accuracy': 0.14193002257336343} |
+| 1.5757        | 19.0476 | 800  | 1.4028          | {'accuracy': 0.14432844243792325} |
+| 1.5407        | 21.4286 | 900  | 1.3813          | {'accuracy': 0.14475169300225735} |
+| 1.5199        | 23.8095 | 1000 | 1.3686          | {'accuracy': 0.1451749435665914}  |
+| 1.4855        | 26.1905 | 1100 | 1.3569          | {'accuracy': 0.14531602708803612} |
+| 1.4744        | 28.5714 | 1200 | 1.3508          | {'accuracy': 0.1460214446952596}  |
 
 
 ### Framework versions