update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- collection3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: sberbank-rubert-base-collection3
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: collection3
+      type: collection3
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.938019472809309
+    - name: Recall
+      type: recall
+      value: 0.9594364828758805
+    - name: F1
+      type: f1
+      value: 0.9486071085494716
+    - name: Accuracy
+      type: accuracy
+      value: 0.9860420020488805
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# sberbank-rubert-base-collection3
+
+This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on the collection3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0772
+- Precision: 0.9380
+- Recall: 0.9594
+- F1: 0.9486
+- Accuracy: 0.9860
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0899        | 1.0   | 2326  | 0.0760          | 0.9040    | 0.9330 | 0.9182 | 0.9787   |
+| 0.0522        | 2.0   | 4652  | 0.0680          | 0.9330    | 0.9339 | 0.9335 | 0.9821   |
+| 0.0259        | 3.0   | 6978  | 0.0745          | 0.9308    | 0.9512 | 0.9409 | 0.9838   |
+| 0.0114        | 4.0   | 9304  | 0.0731          | 0.9372    | 0.9573 | 0.9471 | 0.9857   |
+| 0.0027        | 5.0   | 11630 | 0.0772          | 0.9380    | 0.9594 | 0.9486 | 0.9860   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.7.0
+- Datasets 2.10.1
+- Tokenizers 0.13.2