End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: wiki_hu_ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      config: hu
+      split: validation
+      args: hu
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8669236159775753
+    - name: Recall
+      type: recall
+      value: 0.8782479057219935
+    - name: F1
+      type: f1
+      value: 0.872549019607843
+    - name: Accuracy
+      type: accuracy
+      value: 0.9632061446977205
+---
+
+<!-- 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. -->
+
+# wiki_hu_ner
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1585
+- Precision: 0.8669
+- Recall: 0.8782
+- F1: 0.8725
+- Accuracy: 0.9632
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2429        | 1.0   | 1250 | 0.1849          | 0.8047    | 0.8153 | 0.8100 | 0.9448   |
+| 0.1371        | 2.0   | 2500 | 0.1505          | 0.8455    | 0.8577 | 0.8516 | 0.9576   |
+| 0.0986        | 3.0   | 3750 | 0.1516          | 0.8520    | 0.8708 | 0.8613 | 0.9603   |
+| 0.0695        | 4.0   | 5000 | 0.1500          | 0.8656    | 0.8745 | 0.8700 | 0.9624   |
+| 0.0489        | 5.0   | 6250 | 0.1585          | 0.8669    | 0.8782 | 0.8725 | 0.9632   |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

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