---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta_large-unbalanced_simple-ner-conll2003_0908_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: 0.9552732335537766
    - name: Recall
      type: recall
      value: 0.9718484419263456
    - name: F1
      type: f1
      value: 0.9634895559066174
    - name: Accuracy
      type: accuracy
      value: 0.989226995491912
---

<!-- 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-unbalanced_simple-ner-conll2003_0908_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: 0.0881
- Precision: 0.9553
- Recall: 0.9718
- F1: 0.9635
- Accuracy: 0.9892

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.07          | 1.0   | 878  | 0.0249          | 0.9616    | 0.9746 | 0.9681 | 0.9936   |
| 0.0176        | 2.0   | 1756 | 0.0241          | 0.9699    | 0.9818 | 0.9758 | 0.9948   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1