Update README.md

README.md

@@ -14,7 +14,7 @@ library_name: fasttext
 [Dataset](https://huggingface.co/datasets/kenhktsui/math-classifiers-data)
 This is part of my [fasttext classifier collection](https://huggingface.co/collections/kenhktsui/fasttext-model-for-pretraining-data-curation-67220374c8acb97a1839553c) for curating pretraining dataset.
 This classifier classifies a text into Maths or Others.   
-The model is trained over 1.6M records, which is a 50:50 mix of maths and non maths in website and achieved a test F1 score of 0.97. It is an intended upsampling of maths data.
+The model is trained over 1.6M records, which is a 50:50 mix of maths and non maths in website and achieved a test F1 score of 0.99 (too good to be true?). It is an intended upsampling of maths data.
 The classifier can be used for LLM pretraining data curation, to enhance capability in mathematics.
 It is ultra fast ⚡ with a throughtput of ~2000 doc/s with CPU.
 
@@ -47,21 +47,24 @@ def predict(text_list: List[str]) -> List[dict]:
 predict([
   """This is a lightning fast model, which can classify at throughtput of 2000 doc/s with CPU""",
   """Differential geometry is a mathematical discipline that studies the geometry of smooth shapes and smooth spaces, otherwise known as smooth manifolds. It uses the techniques of single variable calculus, vector calculus, linear algebra and multilinear algebra.""",
+  """Given $p$: $|4x-3|\leqslant 1$ and $q$: $x^{2}-(2a+1)x+a^{2}+a\leqslant 0$, find the range of values for $a$ if $p$ is a necessary but not sufficient condition for $q$."""
 ])
-# [{'label': 'Others', 'score': 0.99998367},
-# {'label': 'Maths', 'score': 0.99995637},
+# [{'label': 'Others', 'score': 1.00000834},
+# {'label': 'Maths', 'score': 0.99995351},
+# {'label': 'Maths', 'score': 0.99801832}]
 ```
 ## 📊Evaluation
 full version
 ```
               precision    recall  f1-score   support
 
-       Maths       0.98      0.98      0.98    200000
-      Others       0.98      0.98      0.98    200000
+       Maths       0.99      0.98      0.99    200000
+      Others       0.98      0.99      0.99    200000
+
+    accuracy                           0.99    400000
+   macro avg       0.99      0.99      0.99    400000
+weighted avg       0.99      0.99      0.99    400000
 
-    accuracy                           0.98    400000
-   macro avg       0.98      0.98      0.98    400000
-weighted avg       0.98      0.98      0.98    400000
 ```