Model: Nutanix/jina-embeddings-v2-base-code-mbpp
Author: Nutanix
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:317521, loss:TripletLoss, custom_code, arxiv:1908.10084, arxiv:1703.07737, base_model:jinaai/jina-embeddings-v2-base-code, base_model:finetune:jinaai/jina-embeddings-v2-base-code, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: jinaai/jina-embeddings-v2-base-code
Card metadata: base_model: jinaai/jina-embeddings-v2-base-code
library_name: sentence-transformers
metrics: cosine_accuracy, dot_accuracy, manhattan_accuracy, euclidean_accuracy, max_accuracy
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:317521, loss:TripletLoss
widget: source_sentence: Write a function to extract every specified element from a given two dimensional list.
sentences: def nCr_mod_p(n, r, p): 
	if (r > n- r): 
		r = n - r 
	C = [0 for i in range(r + 1)] 
	C[0] = 1 
	for i in range(1, n + 1): 
		for j in range(min(i, r), 0, -1): 
			C[j] = (C[j] + C[j-1]) % p 
	return C[r] , import cmath
def len_complex(a,b):
  cn=complex(a,b)
  length=abs(cn)
  return length, def specified_element(nums, N):
    result = [i[N] for i in nums]
    return result, source_sentence: Write a python function to find the kth element in an array containing odd elements first and then even elements.
sentences: def get_Number(n, k): 
    arr = [0] * n; 
    i = 0; 
    odd = 1; 
    while (odd <= n):   
        arr[i] = odd; 
        i += 1; 
        odd += 2;
    even = 2; 
    while (even <= n): 
        arr[i] = even; 
        i += 1;
        even += 2; 
    return arr[k - 1]; , def sort_matrix(M):
    result = sorted(M, key=sum)
    return result, INT_BITS = 32
def left_Rotate(n,d):   
    return (n << d)|(n >> (INT_BITS - d))  , source_sentence: Write a function to remove all the words with k length in the given string.
sentences: def remove_tuples(test_list, K):
  res = [ele for ele in test_list if len(ele) != K]
  return (res) , def is_Sub_Array(A,B,n,m): 
    i = 0; j = 0; 
    while (i < n and j < m):  
        if (A[i] == B[j]): 
            i += 1; 
            j += 1; 
            if (j == m): 
                return True;  
        else: 
            i = i - j + 1; 
            j = 0;       
    return False; , def remove_length(test_str, K):
  temp = test_str.split()
  res = [ele for ele in temp if len(ele) != K]
  res = ' '.join(res)
  return (res) , source_sentence: Write a function to find the occurence of characters 'std' in the given string 1. list item 1. list item 1. list item 2. list item 2. list item 2. list item
sentences: def magic_square_test(my_matrix):
    iSize = len(my_matrix[0])
    sum_list = []
    sum_list.extend([sum (lines) for lines in my_matrix])   
    for col in range(iSize):
        sum_list.append(sum(row[col] for row in my_matrix))
    result1 = 0
    for i in range(0,iSize):
        result1 +=my_matrix[i][i]
    sum_list.append(result1)      
    result2 = 0
    for i in range(iSize-1,-1,-1):
        result2 +=my_matrix[i][i]
    sum_list.append(result2)
    if len(set(sum_list))>1:
        return False
    return True, def count_occurance(s):
  count=0
  for i in range(len(s)):
    if (s[i]== 's' and s[i+1]=='t' and s[i+2]== 'd'):
      count = count + 1
  return count, def power(a,b):
	if b==0:
		return 1
	elif a==0:
		return 0
	elif b==1:
		return a
	else:
		return a*power(a,b-1), source_sentence: Write a function to find sum and average of first n natural numbers.
sentences: def long_words(n, str):
    word_len = []
    txt = str.split(" ")
    for x in txt:
        if len(x) > n:
            word_len.append(x)
    return word_len	, def long_words(n, str):
    word_len = []
    txt = str.split(" ")
    for x in txt:
        if len(x) > n:
            word_len.append(x)
    return word_len	, def sum_average(number):
 total = 0
 for value in range(1, number + 1):
    total = total + value
 average = total / number
 return (total,average)
model-index: name: SentenceTransformer based on jinaai/jina-embeddings-v2-base-code
results: task: type: triplet
name: Triplet
dataset: name: sts dev
type: sts-dev
metrics: type: cosine_accuracy
value: 0.4794644366223058
name: Cosine Accuracy
verified: no, type: dot_accuracy
value: 0.3189056517809246
name: Dot Accuracy
verified: no, type: manhattan_accuracy
value: 0.49047258618028966
name: Manhattan Accuracy
verified: no, type: euclidean_accuracy
value: 0.47951587657351136
name: Euclidean Accuracy
verified: no, type: max_accuracy
value: 0.49047258618028966
name: Max Accuracy
verified: no
Likes: 0 | All-time downloads: 128
Created: 2024-08-16 15:35:55