---
license: mit
base_model: surrey-nlp/roberta-large-finetuned-abbr
tags:
- generated_from_trainer
datasets:
- plod-filtered
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-abbr-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: plod-filtered
      type: plod-filtered
      config: PLODfiltered
      split: validation
      args: PLODfiltered
    metrics:
    - name: Precision
      type: precision
      value: 0.9800350338833268
    - name: Recall
      type: recall
      value: 0.9766733969309696
    - name: F1
      type: f1
      value: 0.9783513277508114
    - name: Accuracy
      type: accuracy
      value: 0.9761728475392376
---

<!-- 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-abbr-finetuned-ner

This model is a fine-tuned version of [surrey-nlp/roberta-large-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) on the plod-filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0913
- Precision: 0.9800
- Recall: 0.9767
- F1: 0.9784
- Accuracy: 0.9762

## 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: 16
- 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

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0805        | 0.99  | 7000  | 0.0761          | 0.9762    | 0.9722 | 0.9742 | 0.9720   |
| 0.0655        | 1.99  | 14000 | 0.0682          | 0.9769    | 0.9748 | 0.9759 | 0.9735   |
| 0.0469        | 2.98  | 21000 | 0.0718          | 0.9787    | 0.9746 | 0.9767 | 0.9744   |
| 0.0336        | 3.98  | 28000 | 0.0851          | 0.9800    | 0.9753 | 0.9776 | 0.9753   |
| 0.0259        | 4.97  | 35000 | 0.0913          | 0.9800    | 0.9767 | 0.9784 | 0.9762   |
| 0.0197        | 5.97  | 42000 | 0.0948          | 0.9801    | 0.9774 | 0.9787 | 0.9766   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0