---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-base-cased_conll2003-sm-first-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - type: precision
      value: 0.944354983002326
      name: Precision
    - type: recall
      value: 0.9470662120940248
      name: Recall
    - type: f1
      value: 0.9457086543630173
      name: F1
    - type: accuracy
      value: 0.9860775887443339
      name: Accuracy
---

<!-- 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-base-cased_conll2003-sm-first-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0783
- Precision: 0.9444
- Recall: 0.9471
- F1: 0.9457
- Accuracy: 0.9861

## 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: 2
- eval_batch_size: 2
- 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0912        | 1.0   | 7021  | 0.0962          | 0.9191    | 0.9106 | 0.9148 | 0.9789   |
| 0.0302        | 2.0   | 14042 | 0.0748          | 0.9406    | 0.9413 | 0.9409 | 0.9847   |
| 0.0221        | 3.0   | 21063 | 0.0783          | 0.9444    | 0.9471 | 0.9457 | 0.9861   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1