sacculifer/dimbat_disaster_distilbert

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")