Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+metrics:
+- bertscore
 ---
+TheStats Model Card
+Model Information
+Model Name: TheStats
+Model Type: Sequence Classification
+Model Version: 2.0
+Tokenizer: AutoTokenizer from "circulartext/thestats2"
+Model: AutoModelForSequenceClassification from "circulartext/thestats2"
+Description
+TheStats is a sequence classification model designed to predict the importance of phrases in the context of statistical sports data. The model assigns scores ranging from 1 to 10, with lower scores indicating less importance and higher scores indicating greater importance of the phrases.
+
+Intended Use
+TheStats is intended to assist in analyzing and summarizing statistical sports data by identifying the significance of different phrases within the data. It can help prioritize and highlight essential information for data interpretation and decision-making.
+
+Model Performance
+The performance of TheStats in predicting the importance of phrases has been evaluated on a diverse range of sports data. The model is designed to provide meaningful importance scores to assist users in understanding and making decisions based on the data.
+
+Limitations
+The model's predictions are based on the training data it has been exposed to and may not generalize well to domains or topics outside of statistical sports data.
+The quality of predictions may depend on the quality and relevance of the input data.
+Ethical Considerations
+The following ethical considerations are important when using TheStats:
+
+The model's predictions should be used as a supplementary tool in data analysis, and human judgment should always be exercised.
+Avoid using the model for any discriminatory, harmful, or biased purposes.
+Usage Guidelines
+Use TheStats for analyzing and prioritizing phrases within statistical sports data.
+Do not solely rely on the model's predictions; always consider them in the context of your domain knowledge.
+Be aware of the limitations of the model and the potential for incorrect or biased predictions.
+Follow ethical guidelines and use the model responsibly.