End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: copilot_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: 0.5803921568627451
+    - name: Recall
+      type: recall
+      value: 0.4114921223354958
+    - name: F1
+      type: f1
+      value: 0.48156182212581344
+    - name: Accuracy
+      type: accuracy
+      value: 0.9483562053781369
+---
+
+<!-- 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. -->
+
+# copilot_wnut_model
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3448
+- Precision: 0.5804
+- Recall: 0.4115
+- F1: 0.4816
+- Accuracy: 0.9484
+
+## 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  | 0.2743          | 0.6364    | 0.2725 | 0.3816 | 0.9401   |
+| No log        | 2.0   | 426  | 0.2598          | 0.5977    | 0.3346 | 0.4290 | 0.9445   |
+| 0.1759        | 3.0   | 639  | 0.3063          | 0.6741    | 0.3086 | 0.4234 | 0.9445   |
+| 0.1759        | 4.0   | 852  | 0.3097          | 0.5930    | 0.3605 | 0.4484 | 0.9463   |
+| 0.0477        | 5.0   | 1065 | 0.2962          | 0.5558    | 0.4106 | 0.4723 | 0.9474   |
+| 0.0477        | 6.0   | 1278 | 0.3218          | 0.5792    | 0.3967 | 0.4708 | 0.9474   |
+| 0.0477        | 7.0   | 1491 | 0.3199          | 0.5595    | 0.4096 | 0.4730 | 0.9477   |
+| 0.022         | 8.0   | 1704 | 0.3385          | 0.5938    | 0.4106 | 0.4855 | 0.9481   |
+| 0.022         | 9.0   | 1917 | 0.3311          | 0.5687    | 0.4217 | 0.4843 | 0.9478   |
+| 0.0123        | 10.0  | 2130 | 0.3448          | 0.5804    | 0.4115 | 0.4816 | 0.9484   |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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