End of training

README.md

@@ -1,4 +1,5 @@
 ---
+library_name: transformers
 base_model: ai-forever/ruBert-large
 tags:
 - generated_from_trainer
@@ -24,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.7868756986512067
+      value: 0.8307441967265208
     - name: Recall
       type: recall
-      value: 0.7638984758119328
+      value: 0.7502322735093846
     - name: F1
       type: f1
-      value: 0.772014566268761
+      value: 0.783084706036028
     - name: Accuracy
       type: accuracy
-      value: 0.8826334008229377
+      value: 0.868562326706389
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [ai-forever/ruBert-large](https://huggingface.co/ai-forever/ruBert-large) on the universal_dependencies dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8204
-- Precision: 0.7869
-- Recall: 0.7639
-- F1: 0.7720
-- Accuracy: 0.8826
+- Loss: 0.4344
+- Precision: 0.8307
+- Recall: 0.7502
+- F1: 0.7831
+- Accuracy: 0.8686
 
 ## Model description
 
@@ -67,20 +68,32 @@ 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   | 338  | 0.4759          | 0.7967    | 0.7249 | 0.7532 | 0.8557   |
+| No log        | 2.0   | 676  | 0.4344          | 0.8307    | 0.7502 | 0.7831 | 0.8686   |
+| No log        | 3.0   | 1014 | 0.6906          | 0.7842    | 0.7480 | 0.7563 | 0.8674   |
+| No log        | 4.0   | 1352 | 0.4757          | 0.8185    | 0.7578 | 0.7777 | 0.8816   |
+| No log        | 5.0   | 1690 | 0.6291          | 0.7791    | 0.7721 | 0.7670 | 0.8792   |
+| No log        | 6.0   | 2028 | 0.6466          | 0.7967    | 0.7677 | 0.7721 | 0.8863   |
+| No log        | 7.0   | 2366 | 0.7072          | 0.7751    | 0.7700 | 0.7704 | 0.8809   |
+| No log        | 8.0   | 2704 | 0.7623          | 0.7957    | 0.7678 | 0.7749 | 0.8838   |
+| No log        | 9.0   | 3042 | 0.7458          | 0.7922    | 0.7716 | 0.7773 | 0.8873   |
+| No log        | 10.0  | 3380 | 0.7560          | 0.7916    | 0.7709 | 0.7767 | 0.8869   |
 
 
 ### 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

pytorch_model.bin

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