---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- lltala/edgar_all_4-simple-no-valid-roberta-base
model-index:
- name: ner__edgar_all_4-simple-no-valid-roberta-base__roberta-base
  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__edgar_all_4-simple-no-valid-roberta-base__roberta-base

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lltala/edgar_all_4-simple-no-valid-roberta-base dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0045
- Loc Precision: 0.8614
- Loc Recall: 0.9355
- Loc F1: 0.8969
- Loc Number: 93
- Org Precision: 0.9807
- Org Recall: 0.9880
- Org F1: 0.9844
- Org Number: 669
- Per Precision: 0.9432
- Per Recall: 0.9881
- Per F1: 0.9651
- Per Number: 84
- Overall Precision: 0.9629
- Overall Recall: 0.9823
- Overall F1: 0.9725
- Overall Accuracy: 0.9987

## 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: 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: 3.0

### Training results



### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1