---
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