sacculifer/dimbat_disaster_type_distilbert

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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 <br> disease --- 1 <br> earthquake --- 2 <br> flood --- 3 <br> hurricane & tornado --- 4 <br> wildfire --- 5 <br> industrial accident --- 6 <br> societal crime --- 7 <br> transportation accident --- 8 <br> meteor crash --- 9 <br> 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: <br> batchsize = 16 <br> numepochs = 5 <br> batchesperepoch = len(tokenizedtweet["train"])//batchsize <br> totaltrainsteps = int(batchesperepoch * numepochs) <br> optimizer, schedule = createoptimizer(initlr=2e-5, numwarmupsteps=0, numtrainsteps=totaltrain_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.frompretrained("sacculifer/dimbatdisastertypedistilbert")

model = TFAutoModelForSequenceClassification.frompretrained("sacculifer/dimbatdisastertypedistilbert")