End of training

README.md

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+---
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: my_awesome_wnut_model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: test
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.55
+    - name: Recall
+      type: recall
+      value: 0.37720111214087115
+    - name: F1
+      type: f1
+      value: 0.44749862561847165
+    - name: Accuracy
+      type: accuracy
+      value: 0.9481063520560827
+---
+
+<!-- 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. -->
+
+# my_awesome_wnut_model
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3958
+- Precision: 0.55
+- Recall: 0.3772
+- F1: 0.4475
+- Accuracy: 0.9481
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2562          | 0.5704    | 0.2929 | 0.3870 | 0.9417   |
+| No log        | 2.0   | 426  | 0.2776          | 0.5462    | 0.3179 | 0.4019 | 0.9436   |
+| 0.1469        | 3.0   | 639  | 0.2834          | 0.5453    | 0.3624 | 0.4354 | 0.9475   |
+| 0.1469        | 4.0   | 852  | 0.3004          | 0.5669    | 0.3652 | 0.4442 | 0.9480   |
+| 0.0325        | 5.0   | 1065 | 0.3360          | 0.5858    | 0.3735 | 0.4561 | 0.9482   |
+| 0.0325        | 6.0   | 1278 | 0.3471          | 0.5149    | 0.3855 | 0.4409 | 0.9474   |
+| 0.0325        | 7.0   | 1491 | 0.3883          | 0.5552    | 0.3633 | 0.4392 | 0.9474   |
+| 0.0117        | 8.0   | 1704 | 0.3881          | 0.5602    | 0.3707 | 0.4462 | 0.9477   |
+| 0.0117        | 9.0   | 1917 | 0.4008          | 0.5582    | 0.3689 | 0.4442 | 0.9478   |
+| 0.0051        | 10.0  | 2130 | 0.3958          | 0.55      | 0.3772 | 0.4475 | 0.9481   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0