Training complete

README.md

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+---
+license: mit
+base_model: cointegrated/rubert-tiny2
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: rubert-tiny-two-example
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6808952126871202
+    - name: Recall
+      type: recall
+      value: 0.7731403567822283
+    - name: F1
+      type: f1
+      value: 0.7240917329970842
+    - name: Accuracy
+      type: accuracy
+      value: 0.948779898526214
+---
+
+<!-- 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. -->
+
+# rubert-tiny-two-example
+
+This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1719
+- Precision: 0.6809
+- Recall: 0.7731
+- F1: 0.7241
+- Accuracy: 0.9488
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2925        | 1.0   | 1756 | 0.2403          | 0.5587    | 0.6641 | 0.6068 | 0.9273   |
+| 0.1975        | 2.0   | 3512 | 0.1833          | 0.6607    | 0.7526 | 0.7036 | 0.9457   |
+| 0.1726        | 3.0   | 5268 | 0.1719          | 0.6809    | 0.7731 | 0.7241 | 0.9488   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cpu
+- Datasets 2.19.2
+- Tokenizers 0.19.1

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