update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- id_nergrit_corpus
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: mobilebert-uncased-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: id_nergrit_corpus
+      type: id_nergrit_corpus
+      config: ner
+      split: validation
+      args: ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6699979179679367
+    - name: Recall
+      type: recall
+      value: 0.6136244458216141
+    - name: F1
+      type: f1
+      value: 0.6405732911990843
+    - name: Accuracy
+      type: accuracy
+      value: 0.8974442203210374
+---
+
+<!-- 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. -->
+
+# mobilebert-uncased-finetuned-ner
+
+This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the id_nergrit_corpus dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3800
+- Precision: 0.6700
+- Recall: 0.6136
+- F1: 0.6406
+- Accuracy: 0.8974
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.6239        | 1.0   | 1567 | 0.4989          | 0.5842    | 0.4877 | 0.5316 | 0.8688   |
+| 0.5356        | 2.0   | 3134 | 0.4003          | 0.6368    | 0.5879 | 0.6113 | 0.8905   |
+| 0.4035        | 3.0   | 4701 | 0.3800          | 0.6700    | 0.6136 | 0.6406 | 0.8974   |
+
+
+### Framework versions
+
+- Transformers 4.29.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3