Update README.md

README.md

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-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
 # distilbert-base-fire-class
 
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4997
 
 ## Model description
 
-More information needed
+The distilbert-base-fire-class model is a distilled version of BERT, specifically fine-tuned for classification tasks related to the dataset on tweets about fire incidents. This model can be used for analyzing and classifying tweets that mention fire-related events or discussions.
 
 ## Intended uses & limitations
 
-More information needed
+### Intended Uses
+The distilbert-base-fire-class model is specifically designed for distinguishing whether a text is discussing fire-related incidents or not. It can be applied in various contexts such as:
+- Monitoring social media: Detecting and analyzing tweets or posts about fire incidents to help in early detection and response.
+- News filtering: Identifying news articles or reports that focus on fire events for more efficient information processing.
+- Emergency response systems: Assisting in categorizing incoming reports or alerts based on their relevance to fire-related topics.
+- Research and analysis: Supporting studies on public reactions and discussions about fire incidents by classifying relevant texts.
+
+### Limitations
+While the model is effective in its intended use, there are several limitations to consider:
+- Dataset Specificity: The model is trained on a specific dataset of tweets about fire, which may limit its accuracy when applied to other types of texts or platforms.
+- Language and Context: The model may not perform as well on texts that include slang, regional dialects, or highly context-specific language that was not well-represented in the training data.
+- Generalization: This model is focused on fire-related text classification and may not generalize well to other topics without further fine-tuning or additional training data.
 
 ## Training and evaluation data
 
-More information needed
+The model was trained using the [tweets_about_fire](https://huggingface.co/datasets/JudeChaer/tweets_about_fire) dataset. This dataset consists of tweets specifically mentioning fire incidents, providing a focused context for training the classification model.
 
 ## Training procedure
 
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 | 0.2904        | 2.0   | 736  | 0.6664          |
 | 0.2408        | 3.0   | 1104 | 0.5989          |
 
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 ### Framework versions
 
 - Transformers 4.38.2
 - Pytorch 2.2.1+cu121
 - Datasets 2.18.0
 - Tokenizers 0.15.2
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