fix ML articles

qdrant-landing/content/articles/bm42.md

@@ -67,11 +67,11 @@ The last component of the formula can be intuitively interpreted as **term impor
 This might look a bit complicated, so let's break it down.
 
 $$
-\text{Term importance in document }(q_i) =  \color{red}\frac{f(q_i, D)\color{black} \cdot \color{blue}(k_1 + 1) \color{black} }{\color{red}f(q_i, D)\color{black} + \color{blue}k_1\color{black} \cdot \left(1 - \color{blue}b\color{black} + \color{blue}b\color{black} \cdot \frac{|D|}{\text{avgdl}}\right)}
+\text{Term importance in document }(q_i) =  \color{red}\frac{f(q_i, D)\color{gray} \cdot \color{blue}(k_1 + 1) \color{gray} }{\color{red}f(q_i, D)\color{gray} + \color{blue}k_1\color{gray} \cdot \left(1 - \color{blue}b\color{gray} + \color{blue}b\color{gray} \cdot \frac{|D|}{\text{avgdl}}\right)}
 $$
 
-- The $\color{red}f(q_i, D)\color{black}$ - is the frequency of the term $q_i$ in the document $D$. Or in other words, the number of times the term $q_i$ appears in the document $D$.
-- The $\color{blue}k_1\color{black}$ and $\color{blue}b\color{black}$ are the hyperparameters of the BM25 formula. In most implementations, they are constants set to $k_1=1.5$ and $b=0.75$. Those constants define relative implications of the term frequency and the document length in the formula.
+- The $\color{red}f(q_i, D)\color{gray}$ - is the frequency of the term $q_i$ in the document $D$. Or in other words, the number of times the term $q_i$ appears in the document $D$.
+- The $\color{blue}k_1\color{gray}$ and $\color{blue}b\color{gray}$ are the hyperparameters of the BM25 formula. In most implementations, they are constants set to $k_1=1.5$ and $b=0.75$. Those constants define relative implications of the term frequency and the document length in the formula.
 - The $\frac{|D|}{\text{avgdl}}$ - is the relative length of the document $D$ compared to the average document length in the corpora. The intuition befind this part is following: if the token is found in the smaller document, it is more likely that this token is important for this document.
 
 #### Will BM25 term importance in the document work for RAG?
@@ -168,7 +168,7 @@ weights = torch.mean(attentions[-1][0,:,0], axis=0)
 #                │               │  │   └─── [CLS] token is the first one
 #                │               │  └─────── First item of the batch         
 #                │               └────────── Last transformer layer       
-#                └────────────────────────── Averate all 6 attention heads
+#                └────────────────────────── Average all 6 attention heads
 
 for weight, token in zip(weights, tokens):
     print(f"{token}: {weight}")