---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBerta-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9502164502164502
    - name: Recall
      type: recall
      value: 0.9604510265903736
    - name: F1
      type: f1
      value: 0.9553063274188148
    - name: Accuracy
      type: accuracy
      value: 0.9898284802552852
---

<!-- 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-finetuned-ner

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0605
- Precision: 0.9502
- Recall: 0.9605
- F1: 0.9553
- Accuracy: 0.9898

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0248        | 1.0   | 1756 | 0.0636          | 0.9474    | 0.9547 | 0.9510 | 0.9885   |
| 0.014         | 2.0   | 3512 | 0.0734          | 0.9483    | 0.9578 | 0.9530 | 0.9886   |
| 0.0124        | 3.0   | 5268 | 0.0605          | 0.9502    | 0.9605 | 0.9553 | 0.9898   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1