Update README.md

README.md

@@ -7,4 +7,53 @@ library_name: sklearn
 pipeline_tag: text-classification
 tags:
 - clim
----
+---
+
+
+# bert-model-disaster-tweets-classification
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Natural-Language-Processing-with-Disaster-Tweets dataset.
+It achieves the following results on the evaluation set:
+- Accuracy: 0.82
+- F1 Score: 0.82
+
+## Model description
+
+Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top, using an optimizer : incorporates weight decay, which is a regularization technique that helps prevent overfitting during training.
+
+## Intended uses & limitations
+
+Use to classify if a tweet represents a disaster or not.
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with epsilon = 1e-8.
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Epoch | Average training loss | Training epoch  | Accuracy | F1   |
+|:-----:|:---------------------:|:---------------:|:--------:|:----:|
+| 1.0   | 0.47                  | 0:00:49         | 0.82     | 0.82 |
+| 2.0   | 0.36                  | 0:00:36         | 0.82     | 0.82 |
+| 3.0   | 0.29                  | 0:00:51         | 0.82     | 0.82 |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3