End of training

README.md

@@ -1,4 +1,5 @@
 ---
+library_name: transformers
 license: cc-by-4.0
 base_model: allegro/herbert-large-cased
 tags:
@@ -25,16 +26,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9876397732043457
+      value: 0.91656329817706
     - name: Recall
       type: recall
-      value: 0.991338336393956
+      value: 0.8825519391481612
     - name: F1
       type: f1
-      value: 0.9894469851843459
+      value: 0.892780213659273
     - name: Accuracy
       type: accuracy
-      value: 0.9953262628078375
+      value: 0.9827837758972863
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allegro/herbert-large-cased](https://huggingface.co/allegro/herbert-large-cased) on the universal_dependencies dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0398
-- Precision: 0.9876
-- Recall: 0.9913
-- F1: 0.9894
-- Accuracy: 0.9953
+- Loss: 0.0611
+- Precision: 0.9166
+- Recall: 0.8826
+- F1: 0.8928
+- Accuracy: 0.9828
 
 ## Model description
 
@@ -68,20 +69,32 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 32
+- train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 20
+- num_epochs: 10
 
 ### Training results
 
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 438  | 0.2798          | 0.8362    | 0.8222 | 0.8271 | 0.8779   |
+| No log        | 2.0   | 876  | 0.1613          | 0.9287    | 0.8511 | 0.8677 | 0.9240   |
+| No log        | 3.0   | 1314 | 0.0967          | 0.8845    | 0.8530 | 0.8562 | 0.9539   |
+| No log        | 4.0   | 1752 | 0.0917          | 0.9103    | 0.8461 | 0.8657 | 0.9629   |
+| No log        | 5.0   | 2190 | 0.0782          | 0.8965    | 0.8704 | 0.8764 | 0.9666   |
+| No log        | 6.0   | 2628 | 0.0766          | 0.8973    | 0.8704 | 0.8767 | 0.9691   |
+| No log        | 7.0   | 3066 | 0.0634          | 0.9171    | 0.8811 | 0.8923 | 0.9790   |
+| No log        | 8.0   | 3504 | 0.0626          | 0.9139    | 0.8909 | 0.8989 | 0.9796   |
+| No log        | 9.0   | 3942 | 0.0675          | 0.9131    | 0.8792 | 0.8893 | 0.9803   |
+| No log        | 10.0  | 4380 | 0.0611          | 0.9166    | 0.8826 | 0.8928 | 0.9828   |
 
 
 ### Framework versions
 
-- Transformers 4.39.3
-- Pytorch 1.11.0a0+17540c5
-- Datasets 2.21.0
-- Tokenizers 0.15.2
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.19.1