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---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- surrey-nlp/PLOD-unfiltered
metrics:
- precision
- recall
- f1
- accuracy
model_creators:
- Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan
widget:
- text: Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons.
- text: RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory
cortex in Figure 1.
- text: Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar
imaging (EPI).
base_model: roberta-large
model-index:
- name: roberta-large-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: surrey-nlp/PLOD-unfiltered
type: token-classification
args: PLODunfiltered
metrics:
- type: precision
value: 0.9662545190541101
name: Precision
- type: recall
value: 0.9627013733169376
name: Recall
- type: f1
value: 0.9644746737300262
name: F1
- type: accuracy
value: 0.9607518572002093
name: Accuracy
---
<!-- 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
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [PLOD-unfiltered](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1393
- Precision: 0.9663
- Recall: 0.9627
- F1: 0.9645
- Accuracy: 0.9608
## 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
The model is fine-tuned using [PLOD-Unfiltered](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) dataset.
This dataset is used for training and evaluating the model. The PLOD Dataset is published at LREC 2022. The dataset can help build sequence labeling models for the task of Abbreviation Detection.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1281 | 1.0 | 14233 | 0.1300 | 0.9557 | 0.9436 | 0.9496 | 0.9457 |
| 0.1056 | 2.0 | 28466 | 0.1076 | 0.9620 | 0.9552 | 0.9586 | 0.9545 |
| 0.0904 | 3.0 | 42699 | 0.1054 | 0.9655 | 0.9585 | 0.9620 | 0.9583 |
| 0.0743 | 4.0 | 56932 | 0.1145 | 0.9658 | 0.9602 | 0.9630 | 0.9593 |
| 0.0523 | 5.0 | 71165 | 0.1206 | 0.9664 | 0.9619 | 0.9641 | 0.9604 |
| 0.044 | 6.0 | 85398 | 0.1393 | 0.9663 | 0.9627 | 0.9645 | 0.9608 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|