Update README.md

README.md

@@ -18,5 +18,60 @@ metrics:
 - f1
 - precision
 - recall
+pipeline_tag: image-classification
+base_model: efficientnet_b0
 ---
-# Model card for chart-recognizer
+# Chart Recognizer
+
+chart-recognizer is a finetuned model for classifying images. It uses efficientnet as its base model, making it a fast and small model.
+This model is trained on my own dataset of financial charts posted on Twitter, which can be found here [StephanAkkerman/fintwit-charts](https://huggingface.co/datasets/StephanAkkerman/fintwit-charts).
+
+## Intended Uses
+
+chart-recognizer is intended for classifying images, mainly images posted on social media.
+
+## Dataset
+
+chart-recognizer has been trained on my own dataset. So far I have not been able to find another image dataset about financial charts.
+- [StephanAkkerman/fintwit-charts](https://huggingface.co/datasets/StephanAkkerman/fintwit-charts): 1,978 images.
+
+## More Information
+
+For a comprehensive overview, including the training setup and analysis of the model, visit the [chart-recognizer GitHub repository](https://github.com/StephanAkkerman/chart-recognizer).
+
+## Usage
+
+Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model can be converted into a pipeline for image classification.
+
+```python
+from transformers import pipeline
+
+# Create a sentiment analysis pipeline
+pipe = pipeline(
+    "image-classification",
+    model="StephanAkkerman/chart-recognizer",
+)
+
+# Get the predicted sentiment
+print(pipe(image))
+```
+
+## Citing & Authors
+
+If you use chart-recognizer in your research, please cite me as follows:
+
+```
+@misc{chart-recognizer,
+  author = {Stephan Akkerman},
+  title = {chart-recognizer: A Specialized Image Model for Financial Charts},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/StephanAkkerman/chart-recognizer}}
+}
+```
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](https://choosealicense.com/licenses/mit/) file for details.
+