End of training

README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: codebert-base-finetuned-code-ner-15e
+  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. -->
+
+# codebert-base-finetuned-code-ner-15e
+
+This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3831
+- Precision: 0.6363
+- Recall: 0.6494
+- F1: 0.6428
+- Accuracy: 0.9197
+
+## 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: 64
+- eval_batch_size: 64
+- 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   | 191  | 0.4566          | 0.5021    | 0.4220 | 0.4585 | 0.8827   |
+| No log        | 2.0   | 382  | 0.3756          | 0.5699    | 0.5764 | 0.5731 | 0.9043   |
+| 0.5133        | 3.0   | 573  | 0.3605          | 0.6001    | 0.5767 | 0.5882 | 0.9093   |
+| 0.5133        | 4.0   | 764  | 0.3500          | 0.6130    | 0.6130 | 0.6130 | 0.9153   |
+| 0.5133        | 5.0   | 955  | 0.3501          | 0.6337    | 0.6172 | 0.6254 | 0.9178   |
+| 0.2203        | 6.0   | 1146 | 0.3645          | 0.6250    | 0.6352 | 0.6300 | 0.9163   |
+| 0.2203        | 7.0   | 1337 | 0.3488          | 0.6263    | 0.6422 | 0.6341 | 0.9189   |
+| 0.1457        | 8.0   | 1528 | 0.3575          | 0.6372    | 0.6397 | 0.6384 | 0.9194   |
+| 0.1457        | 9.0   | 1719 | 0.3662          | 0.6406    | 0.6343 | 0.6375 | 0.9189   |
+| 0.1457        | 10.0  | 1910 | 0.3613          | 0.6374    | 0.6473 | 0.6423 | 0.9201   |
+| 0.107         | 11.0  | 2101 | 0.3716          | 0.6329    | 0.6544 | 0.6435 | 0.9197   |
+| 0.107         | 12.0  | 2292 | 0.3754          | 0.6328    | 0.6487 | 0.6406 | 0.9193   |
+| 0.107         | 13.0  | 2483 | 0.3826          | 0.6395    | 0.6490 | 0.6443 | 0.9204   |
+| 0.0863        | 14.0  | 2674 | 0.3821          | 0.6368    | 0.6535 | 0.6451 | 0.9200   |
+| 0.0863        | 15.0  | 2865 | 0.3831          | 0.6363    | 0.6494 | 0.6428 | 0.9197   |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1

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