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bert-finetuned-ner

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bert-finetuned-ner
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---
license: apache-2.0
base_model: bert-base-cased
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
      config: tweetner7
      split: validation_2021
      args: tweetner7
    metrics:
    - name: Precision
      type: precision
      value: 0.7025612778848802
    - name: Recall
      type: recall
      value: 0.6474619289340101
    - name: F1
      type: f1
      value: 0.6738872011623299
    - name: Accuracy
      type: accuracy
      value: 0.8775995608952857
---

<!-- 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: 0.4089
- Precision: 0.7026
- Recall: 0.6475
- F1: 0.6739
- Accuracy: 0.8776

## 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  | 0.4428          | 0.7259    | 0.5860 | 0.6485 | 0.8705   |
| 0.5414        | 2.0   | 624  | 0.4090          | 0.7146    | 0.6297 | 0.6695 | 0.8775   |
| 0.5414        | 3.0   | 936  | 0.4089          | 0.7026    | 0.6475 | 0.6739 | 0.8776   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3