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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
<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")
{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp_isorz6_", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us 




# 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")
# 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")
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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")
passage: TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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")