roberta-base-multinerd

Model Card for Model ID

roBERTa-base model was fine-tuned on 50% training English only split of MultiNERD dataset and later evaluated on full test split of the same.
The finetuning script can be fetched from fintuning.py.

Various other model were tested on the same selection of dataset and the best checkpoint was uploaded. The detailed configuration summary can be found in Appendix section of report.

Model Details

Model Description

Head over to github repo for all the scripts used to finetune and evalute token-classification model. The model is ready to use on Kaggle too!

• Developed by: Jayant Yadav

Uses

Token-classification of the following entities are possible:

Bias, Risks, and Limitations

Only trained on English split of MultiNERD dataset. Therefore will not perform well on other languages.

How to Get Started with the Model

Use the code below to get started with the model:

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("jayant-yadav/roberta-base-multinerd")
model = AutoModelForTokenClassification.from_pretrained("jayant-yadav/roberta-base-multinerd")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)

Training Details

Training Data

50% of train split of MultiNERD dataset was used to finetune the model.

Training Procedure

Preprocessing

English dataset was filterd out : train_dataset = train_dataset.filter(lambda x: x['lang'] == 'en')

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Evaluation

Evaluation was perfored on 50% of evaluation split of MultiNERD dataset.

Testing Data & Metrics

Testing Data

Tested on Full test split of MultiNERD dataset.

Metrics

Model versions and checkpoint were evaluated using F1, Precision and Recall.
For this seqeval metric was used: metric = load_metric("seqeval").

Results

Technical Specifications

Model Architecture and Objective

Follows the same as RoBERTa-BASE

Compute Infrastructure

Hardware

Kaggle - GPU T4x2
Google Colab - GPU T4x1

Software

pandas==1.5.3
numpy==1.23.5
seqeval==1.2.2
datasets==2.15.0
huggingface_hub==0.19.4
transformers[torch]==4.35.2
evaluate==0.4.1
matplotlib==3.7.1
collections
torch==2.0.0

Model Card Contact

jayant-yadav