---
license: mit
base_model: Jean-Baptiste/roberta-large-ner-english
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta
  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

This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggingface.co/Jean-Baptiste/roberta-large-ner-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3908
- Precision: 0.5990
- Recall: 0.5581
- F1: 0.5778
- Accuracy: 0.9470

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 151  | 0.2078          | 0.1899    | 0.2388 | 0.2115 | 0.9246   |
| No log        | 2.0   | 302  | 0.1499          | 0.4322    | 0.5535 | 0.4854 | 0.9393   |
| No log        | 3.0   | 453  | 0.1916          | 0.5204    | 0.4946 | 0.5072 | 0.9418   |
| 0.1542        | 4.0   | 604  | 0.1671          | 0.4615    | 0.5109 | 0.4849 | 0.9426   |
| 0.1542        | 5.0   | 755  | 0.1940          | 0.4841    | 0.4829 | 0.4835 | 0.9439   |
| 0.1542        | 6.0   | 906  | 0.2462          | 0.5066    | 0.5651 | 0.5343 | 0.9428   |
| 0.0616        | 7.0   | 1057 | 0.2106          | 0.5041    | 0.5271 | 0.5153 | 0.9437   |
| 0.0616        | 8.0   | 1208 | 0.2621          | 0.5620    | 0.5202 | 0.5403 | 0.9474   |
| 0.0616        | 9.0   | 1359 | 0.2903          | 0.5242    | 0.5550 | 0.5392 | 0.9440   |
| 0.0326        | 10.0  | 1510 | 0.3083          | 0.5883    | 0.5628 | 0.5753 | 0.9483   |
| 0.0326        | 11.0  | 1661 | 0.3125          | 0.5451    | 0.5853 | 0.5645 | 0.9444   |
| 0.0326        | 12.0  | 1812 | 0.3616          | 0.5503    | 0.5388 | 0.5445 | 0.9427   |
| 0.0326        | 13.0  | 1963 | 0.3398          | 0.5978    | 0.5023 | 0.5459 | 0.9447   |
| 0.0155        | 14.0  | 2114 | 0.2942          | 0.5701    | 0.5550 | 0.5625 | 0.9467   |
| 0.0155        | 15.0  | 2265 | 0.3723          | 0.5771    | 0.5597 | 0.5683 | 0.9462   |
| 0.0155        | 16.0  | 2416 | 0.3651          | 0.5751    | 0.5760 | 0.5755 | 0.9439   |
| 0.0062        | 17.0  | 2567 | 0.3674          | 0.5667    | 0.5891 | 0.5777 | 0.9455   |
| 0.0062        | 18.0  | 2718 | 0.3866          | 0.5897    | 0.5403 | 0.5639 | 0.9463   |
| 0.0062        | 19.0  | 2869 | 0.3908          | 0.5990    | 0.5581 | 0.5778 | 0.9470   |
| 0.0033        | 20.0  | 3020 | 0.4036          | 0.5914    | 0.5620 | 0.5763 | 0.9467   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3