---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweetner7
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: tweetner7
      type: tweetner7
      args: tweetner7
    metrics:
    - name: Precision
      type: precision
      value: 0.6747522170057382
    - name: Recall
      type: recall
      value: 0.6565989847715736
    - name: F1
      type: f1
      value: 0.6655518394648829
    - name: Accuracy
      type: accuracy
      value: 0.8729035799231567
---

<!-- 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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweetner7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4113
- Precision: 0.6748
- Recall: 0.6566
- F1: 0.6656
- Accuracy: 0.8729

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 312  | 0.4366          | 0.7320    | 0.5947 | 0.6562 | 0.8730   |
| 0.5402        | 2.0   | 624  | 0.4120          | 0.7271    | 0.6127 | 0.6650 | 0.8763   |
| 0.5402        | 3.0   | 936  | 0.4113          | 0.6748    | 0.6566 | 0.6656 | 0.8729   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.12.1