---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- bert-ner-address-2
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: ner_results
  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. -->

# ner_results

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Precision: 0.9999
- Recall: 0.9999
- F1: 0.9999

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|
| 0.0003        | 1.0   | 35645  | 0.0010          | 0.9997    | 0.9998 | 0.9997 |
| 0.0           | 2.0   | 71290  | 0.0008          | 0.9998    | 0.9998 | 0.9998 |
| 0.0           | 3.0   | 106935 | 0.0003          | 0.9999    | 0.9999 | 0.9999 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3