update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- mit_restaurant
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-finetuned-mit-restaurant-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: mit_restaurant
+      type: mit_restaurant
+      config: mit_restaurant
+      split: validation
+      args: mit_restaurant
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.776800439802089
+    - name: Recall
+      type: recall
+      value: 0.7983050847457627
+    - name: F1
+      type: f1
+      value: 0.7874059626636947
+    - name: Accuracy
+      type: accuracy
+      value: 0.9116093286947559
+---
+
+<!-- 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-finetuned-mit-restaurant-ner
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3210
+- Precision: 0.7768
+- Recall: 0.7983
+- F1: 0.7874
+- Accuracy: 0.9116
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.6991        | 1.0   | 863  | 0.3478          | 0.7113    | 0.7684 | 0.7387 | 0.8994   |
+| 0.2773        | 2.0   | 1726 | 0.3264          | 0.7533    | 0.7989 | 0.7754 | 0.9063   |
+| 0.2164        | 3.0   | 2589 | 0.3137          | 0.7644    | 0.8045 | 0.7839 | 0.9121   |
+| 0.1789        | 4.0   | 3452 | 0.3163          | 0.7755    | 0.7983 | 0.7867 | 0.9115   |
+| 0.1573        | 5.0   | 4315 | 0.3210          | 0.7768    | 0.7983 | 0.7874 | 0.9116   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.9.0
+- Tokenizers 0.13.2