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# Tweets disaster type classification 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: 0.0875
- Train Accuracy: 0.8783
- Validation Loss: 0.2980
- Validation Accuracy: 0.8133
- Epoch: 5


## Model description

Labels
<br>
disease --- 1
<br>
earthquake --- 2
<br>
flood --- 3
<br>
hurricane & tornado --- 4
<br>
wildfire --- 5
<br>
industrial accident --- 6
<br>
societal crime --- 7
<br>
transportation accident --- 8
<br>
meteor crash --- 9
<br>
haze --- 0

## Intended uses & limitation

This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset

### 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_type_distilbert")

model = TFAutoModelForSequenceClassification.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")
{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmpzujlpono", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 




# Tweets disaster type classification 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: 0.0875
- Train Accuracy: 0.8783
- Validation Loss: 0.2980
- Validation Accuracy: 0.8133
- Epoch: 5


## Model description

Labels
<br>
disease --- 1
<br>
earthquake --- 2
<br>
flood --- 3
<br>
hurricane & tornado --- 4
<br>
wildfire --- 5
<br>
industrial accident --- 6
<br>
societal crime --- 7
<br>
transportation accident --- 8
<br>
meteor crash --- 9
<br>
haze --- 0

## Intended uses & limitation

This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset

### 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_type_distilbert")

model = TFAutoModelForSequenceClassification.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")
# Tweets disaster type classification 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: 0.0875
- Train Accuracy: 0.8783
- Validation Loss: 0.2980
- Validation Accuracy: 0.8133
- Epoch: 5 ## Model description

Labels
<br>
disease --- 1
<br>
earthquake --- 2
<br>
flood --- 3
<br>
hurricane & tornado --- 4
<br>
wildfire --- 5
<br>
industrial accident --- 6
<br>
societal crime --- 7
<br>
transportation accident --- 8
<br>
meteor crash --- 9
<br>
haze --- 0 ## Intended uses & limitation

This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset ### 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_type_distilbert")

model = TFAutoModelForSequenceClassification.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
 # Tweets disaster type classification 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: 0.0875
- Train Accuracy: 0.8783
- Validation Loss: 0.2980
- Validation Accuracy: 0.8133
- Epoch: 5 ## Model description

Labels
<br>
disease --- 1
<br>
earthquake --- 2
<br>
flood --- 3
<br>
hurricane & tornado --- 4
<br>
wildfire --- 5
<br>
industrial accident --- 6
<br>
societal crime --- 7
<br>
transportation accident --- 8
<br>
meteor crash --- 9
<br>
haze --- 0 ## Intended uses & limitation

This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset ### 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_type_distilbert")

model = TFAutoModelForSequenceClassification.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")
passage: TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
# Tweets disaster type classification 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: 0.0875
- Train Accuracy: 0.8783
- Validation Loss: 0.2980
- Validation Accuracy: 0.8133
- Epoch: 5## Model description

Labels
<br>
disease --- 1
<br>
earthquake --- 2
<br>
flood --- 3
<br>
hurricane & tornado --- 4
<br>
wildfire --- 5
<br>
industrial accident --- 6
<br>
societal crime --- 7
<br>
transportation accident --- 8
<br>
meteor crash --- 9
<br>
haze --- 0## Intended uses & limitation

This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset### 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