Tweets disaster type classification model

This model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tweets, Wiegmann,M. et al, 2020) dataset It achieves the following results on the evaluation set:

Train Loss: 0.0875
Train Accuracy: 0.8783
Validation Loss: 0.2980
Validation Accuracy: 0.8133
Epoch: 5

Model description

Labels
disease --- 1
earthquake --- 2
flood --- 3
hurricane & tornado --- 4
wildfire --- 5
industrial accident --- 6
societal crime --- 7
transportation accident --- 8
meteor crash --- 9
haze --- 0

Intended uses & limitation

This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset

Training hyperparameters

The following hyperparameters were used during training:

optimizer:
batch_size = 16
num_epochs = 5
batches_per_epoch = len(tokenized_tweet["train"])//batch_size
total_train_steps = int(batches_per_epoch * num_epochs)
optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
training_precision: float32

Framework versions

Transformers 4.16.2
TensorFlow 2.9.2
Datasets 2.4.0
Tokenizers 0.12.1

How to use it

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")

model = TFAutoModelForSequenceClassification.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")