End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- szeged_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: hun_wnut_modell
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: szeged_ner
+      type: szeged_ner
+      config: business
+      split: test
+      args: business
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8590342679127726
+    - name: Recall
+      type: recall
+      value: 0.9004081632653061
+    - name: F1
+      type: f1
+      value: 0.8792347548824233
+    - name: Accuracy
+      type: accuracy
+      value: 0.9881996563884619
+---
+
+<!-- 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. -->
+
+# hun_wnut_modell
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the szeged_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0419
+- Precision: 0.8590
+- Recall: 0.9004
+- F1: 0.8792
+- Accuracy: 0.9882
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2035        | 1.0   | 511  | 0.0665          | 0.8124    | 0.8343 | 0.8232 | 0.9813   |
+| 0.075         | 2.0   | 1022 | 0.0501          | 0.8280    | 0.8841 | 0.8551 | 0.9847   |
+| 0.0498        | 3.0   | 1533 | 0.0444          | 0.8452    | 0.8914 | 0.8677 | 0.9866   |
+| 0.0354        | 4.0   | 2044 | 0.0417          | 0.8661    | 0.8980 | 0.8818 | 0.9885   |
+| 0.0275        | 5.0   | 2555 | 0.0419          | 0.8590    | 0.9004 | 0.8792 | 0.9882   |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

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