End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: g-bert-NER
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: test
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8925347222222222
+    - name: Recall
+      type: recall
+      value: 0.9102337110481586
+    - name: F1
+      type: f1
+      value: 0.901297335203366
+    - name: Accuracy
+      type: accuracy
+      value: 0.9799720038763864
+---
+
+<!-- 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. -->
+
+# g-bert-NER
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1387
+- Precision: 0.8925
+- Recall: 0.9102
+- F1: 0.9013
+- Accuracy: 0.9800
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1773        | 1.0   | 878  | 0.1028          | 0.8910    | 0.8947 | 0.8928 | 0.9781   |
+| 0.036         | 2.0   | 1756 | 0.1125          | 0.8901    | 0.9132 | 0.9015 | 0.9793   |
+| 0.0194        | 3.0   | 2634 | 0.1202          | 0.8948    | 0.9093 | 0.9020 | 0.9800   |
+| 0.0112        | 4.0   | 3512 | 0.1346          | 0.8889    | 0.9136 | 0.9011 | 0.9794   |
+| 0.0081        | 5.0   | 4390 | 0.1387          | 0.8925    | 0.9102 | 0.9013 | 0.9800   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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