Update README.md

README.md

@@ -5,104 +5,78 @@ base_model:
 pipeline_tag: time-series-forecasting
 ---
 
-# Model Card for Model ID
+# Model Card for Chronos T5 Small Fine-Tuned Model
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Summary
 
-## Evaluation
+This model is fine-tuned for time-series forecasting tasks and serves as a tool for both practical predictions and research into time-series modeling. It is based on the `amazon/chronos-t5-small` architecture and has been adapted using a dataset with 15 million rows of proprietary time-series data. Due to confidentiality restrictions, dataset details cannot be shared.
+
+## Fine-Tuning Dataset
+
+The model was fine-tuned on a proprietary dataset containing 15 million rows of time-series data. While details about the dataset are confidential, the following general characteristics are provided:
+- The dataset consists of multi-dimensional time-series data.  
+- Features include historical values, contextual attributes, and external covariates relevant to forecasting.  
+- The data spans multiple domains, enabling generalization across a wide range of forecasting tasks.  
+
+This large-scale dataset ensures the model captures complex patterns and temporal dependencies necessary for accurate forecasting.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+## Evaluation
 
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The model was evaluated using several publicly available time-series datasets, including:
+- **electricity_15min**  
+- **monash_electricity_hourly**  
+- **monash_electricity_weekly**  
+- **monash_kdd_cup_2018**  
+- **monash_pedestrian_counts**  
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation was conducted across datasets representing various domains such as electricity usage, pedestrian counts, and competition data.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Two primary metrics were used for evaluation:
+- **MASE (Mean Absolute Scaled Error):** A normalized metric for assessing forecast accuracy.
+- **WQL (Weighted Quantile Loss):** Measures the quality of probabilistic predictions.
 
 ### Results
 
-[More Information Needed]
+| Dataset                     | Model                        | MASE   | WQL     |
+|-----------------------------|------------------------------|--------|---------|
+| electricity_15min           | amazon/chronos-t5-small     | 0.425  | 0.085   |
+| monash_electricity_hourly   | amazon/chronos-t5-small     | 1.537  | 0.110   |
+| monash_electricity_weekly   | amazon/chronos-t5-small     | 1.943  | 0.086   |
+| monash_kdd_cup_2018         | amazon/chronos-t5-small     | 0.693  | 0.309   |
+| monash_pedestrian_counts    | amazon/chronos-t5-small     | 0.308  | 0.247   |
 
 #### Summary
 
+The fine-tuned model performs well on short-term electricity datasets (e.g., **electricity_15min**) with low MASE and WQL values. Performance varies depending on the dataset's characteristics, particularly with longer-term or aggregated data.
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+The model is based on the `amazon/chronos-t5-small` architecture, fine-tuned specifically for time-series forecasting tasks. It leverages pre-trained capabilities for sequence-to-sequence modeling, adapted to handle multi-horizon forecasting scenarios.
 
-## More Information [optional]
+## Citation
 
-[More Information Needed]
+If you use this model in your research or applications, please cite it as:
 
-## Model Card Authors [optional]
+```bibtex
+@misc{Fevzi2024LLaMA-2-7B-NIEXCHE,
+  author = {Fevzi KILAS},
+  title = {LLaMA-2-7B-NIEXCHE: A Turkish Agriculture QA Model},
+  year = {2024},
+  howpublished = {https://huggingface.co/NIEXCHE/turkish_agriculture_QA_llama2_22.6k}
+}
+```
+## Contact:
 
-[More Information Needed]
+[NIEXCHE (Fevzi KILAS)](https://niexche.github.io/) 
 
-## Model Card Contact
 
-[More Information Needed]