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
+  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
+
+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: 0.3522
+- Precision: 0.6297
+- Recall: 0.6417
+- F1: 0.6356
+- Accuracy: 0.9185
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 191  | 0.4601          | 0.4861    | 0.4578 | 0.4715 | 0.8853   |
+| No log        | 2.0   | 382  | 0.3989          | 0.5806    | 0.5243 | 0.5510 | 0.8996   |
+| 0.5081        | 3.0   | 573  | 0.3547          | 0.5723    | 0.6017 | 0.5866 | 0.9059   |
+| 0.5081        | 4.0   | 764  | 0.3507          | 0.6161    | 0.6115 | 0.6138 | 0.9135   |
+| 0.5081        | 5.0   | 955  | 0.3412          | 0.6299    | 0.6252 | 0.6276 | 0.9161   |
+| 0.2299        | 6.0   | 1146 | 0.3418          | 0.6162    | 0.6465 | 0.6310 | 0.9175   |
+| 0.2299        | 7.0   | 1337 | 0.3497          | 0.6288    | 0.6287 | 0.6287 | 0.9175   |
+| 0.1618        | 8.0   | 1528 | 0.3474          | 0.6340    | 0.6397 | 0.6368 | 0.9189   |
+| 0.1618        | 9.0   | 1719 | 0.3501          | 0.6262    | 0.6432 | 0.6346 | 0.9179   |
+| 0.1618        | 10.0  | 1910 | 0.3522          | 0.6297    | 0.6417 | 0.6356 | 0.9185   |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1

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