readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: tmp_isorz6_
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # 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>
    batch_size = 16
    <br>
    num_epochs = 5
    <br>
    batches_per_epoch = len(tokenized_tweet["train"])//batch_size
    <br>
    total_train_steps = int(batches_per_epoch * num_epochs)
    <br>
    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")