Training complete

README.md

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+---
+license: mit
+base_model: dslim/bert-large-NER
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-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. -->
+
+# bert-NER
+
+This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3706
+- Precision: 0.5564
+- Recall: 0.6510
+- F1: 0.6
+- Accuracy: 0.9117
+
+## 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: 4
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4407        | 1.0   | 746  | 0.3447          | 0.4835    | 0.5572 | 0.5177 | 0.8881   |
+| 0.245         | 2.0   | 1492 | 0.3399          | 0.5439    | 0.5630 | 0.5533 | 0.9014   |
+| 0.0865        | 3.0   | 2238 | 0.3706          | 0.5564    | 0.6510 | 0.6    | 0.9117   |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1