---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-ner
  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-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1963
- Precision: 0.3814
- Recall: 0.4134
- F1: 0.3968
- Accuracy: 0.9525

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 60   | 0.2553          | 0.1878    | 0.1075 | 0.1368 | 0.9435   |
| No log        | 2.0   | 120  | 0.2114          | 0.3456    | 0.2235 | 0.2714 | 0.9492   |
| No log        | 3.0   | 180  | 0.2007          | 0.3372    | 0.3673 | 0.3516 | 0.9494   |
| No log        | 4.0   | 240  | 0.1976          | 0.3618    | 0.3911 | 0.3758 | 0.9517   |
| No log        | 5.0   | 300  | 0.1963          | 0.3814    | 0.4134 | 0.3968 | 0.9525   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1