update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- ner-tr
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-turkish-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ner-tr
+      type: ner-tr
+      config: NERTR
+      split: train
+      args: NERTR
+    metrics:
+    - name: Precision
+      type: precision
+      value: 1.0
+    - name: Recall
+      type: recall
+      value: 1.0
+    - name: F1
+      type: f1
+      value: 1.0
+    - name: Accuracy
+      type: accuracy
+      value: 1.0
+---
+
+<!-- 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. -->
+
+# distilbert-turkish-ner
+
+This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the ner-tr dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0013
+- Precision: 1.0
+- Recall: 1.0
+- F1: 1.0
+- Accuracy: 1.0
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
+| 0.5744        | 1.0   | 529  | 0.0058          | 1.0       | 1.0    | 1.0 | 1.0      |
+| 0.0094        | 2.0   | 1058 | 0.0017          | 1.0       | 1.0    | 1.0 | 1.0      |
+| 0.0047        | 3.0   | 1587 | 0.0013          | 1.0       | 1.0    | 1.0 | 1.0      |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1