Training complete

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: bert-finetuned-ner
+  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: 0.537984496124031
+    - name: Recall
+      type: recall
+      value: 0.3215940685820204
+    - name: F1
+      type: f1
+      value: 0.4025522041763341
+    - name: Accuracy
+      type: accuracy
+      value: 0.9367336683417086
+---
+
+<!-- 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-finetuned-ner
+
+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: 0.4341
+- Precision: 0.5380
+- Recall: 0.3216
+- F1: 0.4026
+- Accuracy: 0.9367
+
+## 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: 8
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 425  | 0.4089          | 0.4827    | 0.2586 | 0.3368 | 0.9314   |
+| 0.2032        | 2.0   | 850  | 0.4356          | 0.5435    | 0.2836 | 0.3727 | 0.9337   |
+| 0.0815        | 3.0   | 1275 | 0.4341          | 0.5380    | 0.3216 | 0.4026 | 0.9367   |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.0.0+cu117
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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