danyaljj

from transformers import pipeline

# Qwen/Qwen2.5-0.5B
# EleutherAI/pythia-410m
# HuggingFaceTB/SmolLM2-360M
# Qwen/Qwen2-1.5B
# Qwen/Qwen2.5-1.5B
gen = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B")

prompt = "The best university in the world is" 

danyaljj

from transformers import pipeline

# Qwen/Qwen2.5-0.5B
# EleutherAI/pythia-410m
# HuggingFaceTB/SmolLM2-360M
# Qwen/Qwen2-1.5B
# Qwen/Qwen2.5-1.5B
gen = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B")

prompt = "The best university in the world is" 

danyaljj

"""
Split messages JSON into one file per conversation (turn).

Usage:
    python analysis/1.split_conversations.py messages_202606221221.json [output_dir] [--since 2026-06-01]

Each file is named:  YYYY-MM-DDTHH-MM_<slug>.json
The trace payloads are fully deserialized — no double-JSON-encoded strings.
"""

danyaljj

"""
Split messages JSON into one file per conversation (turn).

Usage:
    python analysis/1.split_conversations.py messages_202606221221.json [output_dir] [--since 2026-06-01]

Each file is named:  YYYY-MM-DDTHH-MM_<slug>.json
The trace payloads are fully deserialized — no double-JSON-encoded strings.
"""

danyaljj

Option Explicit

Sub AddElements()
Dim shp As Shape

Dim i As Integer, n As Integer
n = ActivePresentation.Slides.Count
For i = 1 To n
    Dim s As Slide
    Set s = ActivePresentation.Slides(i)

danyaljj

import requests

PAPER_ID = "0a2c5dfa8b273c0130a0b59be7ce5dba575303be"
BASE_URL = "https://api.semanticscholar.org/graph/v1"
S2_API_KEY = "..."

HEADERS = {"x-api-key": S2_API_KEY}
PAPER_FIELDS = "title,abstract,year,authors,citationCount,referenceCount,externalIds,venue,publicationDate"
REF_FIELDS = "title,authors,year,citationCount,externalIds"

danyaljj

{
  "Health & Clinical Sciences": {
    "Dentistry": {
      "General Dentistry": [
        "Dental Research and COVID-19"
      ],
      "Oral Surgery": [
        "Dental Anxiety and Anesthesia Techniques",
        "Dental Implant Techniques and Outcomes",
        "Dental Radiography and Imaging",

danyaljj

Dataset / Platform	Scope (what it includes)	Approx. size	Bulk access	License / reuse for products & demos
Semantic Scholar Open Research Corpus (S2ORC)	Papers (metadata, abstracts, references); full text only for OA subset	~80M papers; ~8–12M full text	Static snapshots (≤2020) + AP

danyaljj

from itertools import permutations
import numpy as np
from collections import defaultdict
from scipy.stats import kendalltau

# Define the input rankings: each ranking is a list from most to least preferred
rankings = [
    ['A', 'B', 'C'],
    ['B', 'C', 'A'],
    ['C', 'A', 'B']

danyaljj

import numpy as np
import matplotlib.pyplot as plt

# Set seed for reproducibility
np.random.seed(123)

# Parameters
T = 10000       # number of time steps
s = 0.01        # step size
dim = 2         # 2D space