---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta_large-simple-chunk-conll2003_0819_v0
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: train
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.926378388345934
    - name: Recall
      type: recall
      value: 0.9125794732061762
    - name: F1
      type: f1
      value: 0.9194271595900438
    - name: Accuracy
      type: accuracy
      value: 0.9727360116679926
---

<!-- 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. -->

# roberta_large-simple-chunk-conll2003_0819_v0

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0950
- Precision: 0.9264
- Recall: 0.9126
- F1: 0.9194
- Accuracy: 0.9727

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1794        | 1.0   | 878  | 0.0934          | 0.9264    | 0.9217 | 0.9241 | 0.9725   |
| 0.0837        | 2.0   | 1756 | 0.0859          | 0.9351    | 0.9266 | 0.9308 | 0.9749   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1