---
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")