---
language:
- en
license: mit
base_model: roberta-base
tags:
- pytorch
- RobertaForTokenClassification
- named-entity-recognition
- roberta-base
- generated_from_trainer
metrics:
- recall
- precision
- f1
- accuracy
model-index:
- name: roberta-base-ontonotes
  results: []
---

<!-- 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-base-ontonotes

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tner/ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0695
- Recall: 0.9227
- Precision: 0.9013
- F1: 0.9118
- Accuracy: 0.9820

## 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: 8e-05
- train_batch_size: 32
- eval_batch_size: 160
- seed: 75241309
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 6000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.1305        | 0.31  | 600  | 0.1169          | 0.8550 | 0.8139    | 0.8340 | 0.9681   |
| 0.118         | 0.63  | 1200 | 0.0925          | 0.8769 | 0.8592    | 0.8680 | 0.9750   |
| 0.0937        | 0.94  | 1800 | 0.0874          | 0.8939 | 0.8609    | 0.8771 | 0.9764   |
| 0.0698        | 1.25  | 2400 | 0.0821          | 0.9066 | 0.8775    | 0.8918 | 0.9784   |
| 0.0663        | 1.56  | 3000 | 0.0827          | 0.9124 | 0.8764    | 0.8940 | 0.9789   |
| 0.0624        | 1.88  | 3600 | 0.0732          | 0.9179 | 0.8868    | 0.9021 | 0.9804   |
| 0.0364        | 2.19  | 4200 | 0.0750          | 0.9204 | 0.8968    | 0.9085 | 0.9816   |
| 0.0429        | 2.5   | 4800 | 0.0699          | 0.9198 | 0.9031    | 0.9114 | 0.9818   |
| 0.0323        | 2.82  | 5400 | 0.0697          | 0.9227 | 0.9008    | 0.9116 | 0.9819   |
| 0.0334        | 3.13  | 6000 | 0.0695          | 0.9227 | 0.9013    | 0.9118 | 0.9820   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0