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---
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
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