End of training

README.md

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+---
+license: mit
+base_model: cointegrated/rubert-tiny2
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: rubert-tiny2-odonata-extended-ner
+  results: []
+---
+
+<!-- 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-tiny2-odonata-extended-ner
+
+This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0226
+- Precision: 0.5313
+- Recall: 0.551
+- F1: 0.5410
+- Accuracy: 0.9930
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 32   | 0.1425          | 0.0       | 0.0    | 0.0    | 0.9910   |
+| No log        | 2.0   | 64   | 0.0643          | 0.0       | 0.0    | 0.0    | 0.9910   |
+| No log        | 3.0   | 96   | 0.0604          | 0.0       | 0.0    | 0.0    | 0.9910   |
+| No log        | 4.0   | 128  | 0.0575          | 0.0       | 0.0    | 0.0    | 0.9910   |
+| No log        | 5.0   | 160  | 0.0523          | 0.0       | 0.0    | 0.0    | 0.9910   |
+| No log        | 6.0   | 192  | 0.0427          | 0.0       | 0.0    | 0.0    | 0.9910   |
+| No log        | 7.0   | 224  | 0.0330          | 0.7753    | 0.069  | 0.1267 | 0.9913   |
+| No log        | 8.0   | 256  | 0.0288          | 0.6309    | 0.376  | 0.4712 | 0.9921   |
+| No log        | 9.0   | 288  | 0.0264          | 0.6145    | 0.373  | 0.4642 | 0.9924   |
+| No log        | 10.0  | 320  | 0.0251          | 0.5489    | 0.432  | 0.4835 | 0.9926   |
+| No log        | 11.0  | 352  | 0.0242          | 0.5354    | 0.484  | 0.5084 | 0.9927   |
+| No log        | 12.0  | 384  | 0.0235          | 0.5393    | 0.515  | 0.5269 | 0.9928   |
+| No log        | 13.0  | 416  | 0.0230          | 0.5310    | 0.54   | 0.5354 | 0.9929   |
+| No log        | 14.0  | 448  | 0.0228          | 0.5287    | 0.553  | 0.5406 | 0.9930   |
+| No log        | 15.0  | 480  | 0.0226          | 0.5313    | 0.551  | 0.5410 | 0.9930   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cpu
+- Datasets 2.19.2
+- Tokenizers 0.19.1