---
license: mit
base_model: romainlhardy/roberta-large-finetuned-ner
tags:
- generated_from_trainer
datasets:
- plod-cw
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-ner-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: plod-cw
      type: plod-cw
      config: PLOD-CW
      split: validation
      args: PLOD-CW
    metrics:
    - name: Precision
      type: precision
      value: 0.9597188892697978
    - name: Recall
      type: recall
      value: 0.9502715546503734
    - name: F1
      type: f1
      value: 0.9549718574108819
    - name: Accuracy
      type: accuracy
      value: 0.949480642115203
---

<!-- 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-large-finetuned-ner-finetuned-ner

This model is a fine-tuned version of [romainlhardy/roberta-large-finetuned-ner](https://huggingface.co/romainlhardy/roberta-large-finetuned-ner) on the plod-cw dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2327
- Precision: 0.9597
- Recall: 0.9503
- F1: 0.9550
- Accuracy: 0.9495

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results



### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2