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@irfan.ullah1992:

Irfan Ullah
Irfan Ullah
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Region: PK
Tuesday 27 August 2024 15:43:14 GMT
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zubairmalang27
Mano :
🥰🥰🥰
2024-08-28 05:47:58
1
naeem.ullah7948
Naeem Ullah :
🥰🥰🥰
2024-08-29 03:07:28
1
sameengul35
S. love 💕 you A :
🥰🥰🥰
2024-08-27 15:55:20
0
arifkhan_9
✪Aʀɪf✓khᴀn✪ :
🥰🥰🥰
2024-08-27 16:10:54
0
sajid.muhmand1
👑 Sajid Danish 👑 :
😂😂😂
2024-08-27 16:34:51
0
naeem.ullah7948
Naeem Ullah :
❤️❤️❤️
2024-08-29 03:07:31
0
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Other Videos

Panci listrik bisa buat masak nasi dan lauk #fyppppppppppppppppppppppp #fypage #pancilistrik
Panci listrik bisa buat masak nasi dan lauk #fyppppppppppppppppppppppp #fypage #pancilistrik
Lâu chưa quậy#xh
Lâu chưa quậy#xh
How do language models turn words into numbers they can actually work with? 🧠 That's where text embeddings come in. Here's the intuition: 🔹 Language models split text into tokens and represent each token as a vector — a list of numbers. 🔹 Similar words tend to have similar vectors, so they end up close together in the embedding space. 🔹 Embeddings can capture more than similarity — differences between vectors can also represent relationships between words. 🔹 For example, in classic word embeddings, the relationship between “man” and “woman” is roughly reflected in the relationship between “king” and “queen.” 🔹 Real embeddings can have hundreds or thousands of dimensions, even though we often visualize them in just 2D or 3D. But there's an important distinction: 🔹 Classic word embeddings give a word a single learned vector. 🔹 Modern LLMs use contextual representations, so the representation of a token can change depending on the words around it — allowing “bank” to represent a river bank in one context and a financial institution in another. The big picture: A language model turns tokens into vectors, and those vectors encode patterns and relationships learned from text. #TextEmbeddings #NLP #LLMs #MachineLearning #AI
How do language models turn words into numbers they can actually work with? 🧠 That's where text embeddings come in. Here's the intuition: 🔹 Language models split text into tokens and represent each token as a vector — a list of numbers. 🔹 Similar words tend to have similar vectors, so they end up close together in the embedding space. 🔹 Embeddings can capture more than similarity — differences between vectors can also represent relationships between words. 🔹 For example, in classic word embeddings, the relationship between “man” and “woman” is roughly reflected in the relationship between “king” and “queen.” 🔹 Real embeddings can have hundreds or thousands of dimensions, even though we often visualize them in just 2D or 3D. But there's an important distinction: 🔹 Classic word embeddings give a word a single learned vector. 🔹 Modern LLMs use contextual representations, so the representation of a token can change depending on the words around it — allowing “bank” to represent a river bank in one context and a financial institution in another. The big picture: A language model turns tokens into vectors, and those vectors encode patterns and relationships learned from text. #TextEmbeddings #NLP #LLMs #MachineLearning #AI
#ئەکتیڤبن🥀🖤ـہہـ٨ــہ ئەو دوو خوشک🥴
#ئەکتیڤبن🥀🖤ـہہـ٨ــہ ئەو دوو خوشک🥴
যদি কখনও একাকিত্ম অনুভব করো তাহলে আমাকে মনে কইরো, আমি আবার সঙ্গ দিবো, সঙ্গী হওয়ার সাধ্য নেই আমার! #foryou #viral #trending #sadvideos😭😭 #ফরইউতে_দেখতে_চাই @TikTok @TikTokBangladesh™ @পিছুটান🖤
যদি কখনও একাকিত্ম অনুভব করো তাহলে আমাকে মনে কইরো, আমি আবার সঙ্গ দিবো, সঙ্গী হওয়ার সাধ্য নেই আমার! #foryou #viral #trending #sadvideos😭😭 #ফরইউতে_দেখতে_চাই @TikTok @TikTokBangladesh™ @পিছুটান🖤
#fyppppppppppppppppppppppp #rending_video_viral_tiktok #foryoupage❤️❤️ #viral?videotiktok😇😇 #statusvideo
#fyppppppppppppppppppppppp #rending_video_viral_tiktok #foryoupage❤️❤️ #viral?videotiktok😇😇 #statusvideo

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