metadata

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

roberta-base-finetuned-ner

This model is a fine-tuned version of 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