---
model_creators: 
- Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan 
license: mit
tags:
- generated_from_trainer
datasets:
- plo_dfiltered_config
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: plo_dfiltered_config
      type: plo_dfiltered_config
      args: PLODfiltered
    metrics:
    - name: Precision
      type: precision
      value: 0.9644756447594547
    - name: Recall
      type: recall
      value: 0.9583209148378798
    - name: F1
      type: f1
      value: 0.9613884293804785
    - name: Accuracy
      type: accuracy
      value: 0.9575894768204436
---

<!-- 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-base-finetuned-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the plo_dfiltered_config dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1148
- Precision: 0.9645
- Recall: 0.9583
- F1: 0.9614
- Accuracy: 0.9576

## Model description

RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. 

More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.

This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.

## 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: 32
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1179        | 1.99  | 7000  | 0.1130          | 0.9602    | 0.9517 | 0.9559 | 0.9522   |
| 0.0878        | 3.98  | 14000 | 0.1106          | 0.9647    | 0.9564 | 0.9606 | 0.9567   |
| 0.0724        | 5.96  | 21000 | 0.1149          | 0.9646    | 0.9582 | 0.9614 | 0.9576   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1