sacculifer/dimbat_disaster_distilbert

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Tweets disaster detection 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: 0.1400
• —Train Accuracy: 0.9516
• —Validation Loss: 0.1995
• —Validation Accuracy: 0.9324
• —Epoch: 2

Model description

Labels <br> not disaster --- 0 <br> disaster --- 1

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/dimbatdisaster_distilbert")

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