---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- nerd
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: ner_nerd_fine
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: nerd
      type: nerd
      args: nerd
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9058961278375514
---

<!-- 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. -->

# ner_nerd_fine

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5332
- Precision: 0.6337
- Recall: 0.6731
- F1: 0.6528
- Accuracy: 0.9059

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6337        | 1.0   | 8235  | 0.3391          | 0.5974    | 0.6567 | 0.6256 | 0.9010   |
| 0.3086        | 2.0   | 16470 | 0.3188          | 0.6276    | 0.6607 | 0.6437 | 0.9061   |
| 0.2394        | 3.0   | 24705 | 0.3304          | 0.6284    | 0.6740 | 0.6504 | 0.9064   |
| 0.1841        | 4.0   | 32940 | 0.3451          | 0.6286    | 0.6749 | 0.6509 | 0.9065   |
| 0.1392        | 5.0   | 41175 | 0.3837          | 0.6251    | 0.6745 | 0.6489 | 0.9056   |
| 0.1056        | 6.0   | 49410 | 0.4185          | 0.6307    | 0.6751 | 0.6521 | 0.9057   |
| 0.0812        | 7.0   | 57645 | 0.4615          | 0.6288    | 0.6774 | 0.6522 | 0.9052   |
| 0.0629        | 8.0   | 65880 | 0.4933          | 0.6332    | 0.6755 | 0.6537 | 0.9065   |
| 0.0492        | 9.0   | 74115 | 0.5266          | 0.6360    | 0.6752 | 0.6550 | 0.9067   |
| 0.0401        | 10.0  | 82350 | 0.5452          | 0.6340    | 0.6760 | 0.6543 | 0.9065   |


### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.2