القائمة
Encode text prompts into fixed-length dense vectors using SentenceTransformer for cosine-similarity evaluation in image-to-text retrieval tasks
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | cv-sentence-transformer-target-encoding |
| description | Encode text prompts into fixed-length dense vectors using SentenceTransformer for cosine-similarity evaluation in image-to-text retrieval tasks |
In image-to-prompt competitions, the target is not raw text but its SentenceTransformer embedding. Models predict embedding vectors and are scored by cosine similarity against the ground-truth prompt embeddings. Understanding this encoding step is essential: predictions must live in the same embedding space as the evaluation targets.
from sentence_transformers import SentenceTransformer
import numpy as np
st_model = SentenceTransformer("all-MiniLM-L6-v2")
prompts = ["a painting of a sunset over mountains", "cyberpunk city at night"]
embeddings = st_model.encode(prompts, normalize_embeddings=True)
# embeddings.shape: (2, 384)
# Cosine similarity between two prompt embeddings
similarity = np.dot(embeddings[0], embeddings[1])
np.dot(pred, target) / (norm(pred) * norm(target))all-MiniLM-L6-v2 (384-dim) is common; check competition descriptionnormalize_embeddings=True to simplify cosine similarity to dot productbatch_size=32 for large datasets to avoid OOM