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sacculifer/
dimbat_disaster_distilbert

Name: dimbat_disaster_distilbert
Availability: InStock

transformers

distilbert

text-classification

generated_from_keras_callback

autotrain_compatible

endpoints_compatible

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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
not disaster --- 0
disaster --- 1

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_distilbert")

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