End of training

README.md

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+---
+license: mit
+base_model: dslim/bert-large-NER
+tags:
+- generated_from_trainer
+datasets:
+- job-titles
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: my_awesome_wnut_model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: job-titles
+      type: job-titles
+      config: job-titles
+      split: test
+      args: job-titles
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9992003198720512
+    - name: Recall
+      type: recall
+      value: 0.9996
+    - name: F1
+      type: f1
+      value: 0.9994001199760049
+    - name: Accuracy
+      type: accuracy
+      value: 0.6346958244661334
+---
+
+<!-- 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 [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on the job-titles dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6603
+- Precision: 0.9992
+- Recall: 0.9996
+- F1: 0.9994
+- Accuracy: 0.6347
+
+## 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: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.6666        | 1.0   | 4587 | 0.6615          | 1.0       | 1.0    | 1.0    | 0.6331   |
+| 0.6617        | 2.0   | 9174 | 0.6603          | 0.9992    | 0.9996 | 0.9994 | 0.6347   |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1