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--- |
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license: mit |
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datasets: |
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- WhereIsAI/github-issue-similarity |
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language: |
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- en |
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library_name: sentence-transformers |
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pipeline_tag: feature-extraction |
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--- |
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# WhereIsAI/UAE-Code-Large-V1 |
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📢 `WhereIsAI/UAE-Code-Large-V1` **is licensed under MIT. Feel free to use it in any scenario.** |
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If you use it for academic papers, we would greatly appreciate it if you could cite us. 👉 [citation info](#citation). |
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This model is trained on the [GIS: Github Issue Similarity](https://huggingface.co/datasets/WhereIsAI/github-issue-similarity) dataset using [AnglE](https://github.com/SeanLee97/AnglE) loss (https://arxiv.org/abs/2309.12871). |
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It can be used to measure **code/issue similarity**. |
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Results (test set): |
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- Spearman correlation: 71.19 |
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- Accuracy: 84.37 |
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## Usage |
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### 1. angle-emb |
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You can use it via `angle-emb` as follows: |
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install: |
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``` |
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python -m pip install -U angle-emb |
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``` |
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example: |
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```python |
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from scipy import spatial |
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from angle_emb import AnglE |
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model = AnglE.from_pretrained('WhereIsAI/UAE-Code-Large-V1').cuda() |
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quick_sort = '''# Approach 2: Quicksort using list comprehension |
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def quicksort(arr): |
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if len(arr) <= 1: |
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return arr |
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else: |
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pivot = arr[0] |
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left = [x for x in arr[1:] if x < pivot] |
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right = [x for x in arr[1:] if x >= pivot] |
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return quicksort(left) + [pivot] + quicksort(right) |
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# Example usage |
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arr = [1, 7, 4, 1, 10, 9, -2] |
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sorted_arr = quicksort(arr) |
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print("Sorted Array in Ascending Order:") |
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print(sorted_arr)''' |
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bubble_sort = '''def bubblesort(elements): |
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# Looping from size of array from last index[-1] to index [0] |
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for n in range(len(elements)-1, 0, -1): |
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swapped = False |
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for i in range(n): |
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if elements[i] > elements[i + 1]: |
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swapped = True |
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# swapping data if the element is less than next element in the array |
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elements[i], elements[i + 1] = elements[i + 1], elements[i] |
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if not swapped: |
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# exiting the function if we didn't make a single swap |
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# meaning that the array is already sorted. |
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return |
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elements = [39, 12, 18, 85, 72, 10, 2, 18] |
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print("Unsorted list is,") |
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print(elements) |
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bubblesort(elements) |
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print("Sorted Array is, ") |
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print(elements)''' |
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vecs = model.encode([ |
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'def echo(): print("hello world")', |
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quick_sort, |
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bubble_sort |
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]) |
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print('cos sim (0, 1):', 1 - spatial.distance.cosine(vecs[0], vecs[1])) |
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print('cos sim (0, 2)', 1 - spatial.distance.cosine(vecs[0], vecs[2])) |
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print('cos sim (1, 2):', 1 - spatial.distance.cosine(vecs[1], vecs[2])) |
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``` |
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output: |
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``` |
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cos sim (0, 1): 0.34329649806022644 |
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cos sim (0, 2) 0.3627094626426697 |
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cos sim (1, 2): 0.6972219347953796 |
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``` |
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## sentence-transformers |
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You can also use it via `sentence-transformers` |
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```python |
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from scipy import spatial |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer('WhereIsAI/UAE-Code-Large-V1').cuda() |
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quick_sort = '''# Approach 2: Quicksort using list comprehension |
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def quicksort(arr): |
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if len(arr) <= 1: |
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return arr |
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else: |
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pivot = arr[0] |
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left = [x for x in arr[1:] if x < pivot] |
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right = [x for x in arr[1:] if x >= pivot] |
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return quicksort(left) + [pivot] + quicksort(right) |
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# Example usage |
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arr = [1, 7, 4, 1, 10, 9, -2] |
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sorted_arr = quicksort(arr) |
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print("Sorted Array in Ascending Order:") |
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print(sorted_arr)''' |
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bubble_sort = '''def bubblesort(elements): |
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# Looping from size of array from last index[-1] to index [0] |
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for n in range(len(elements)-1, 0, -1): |
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swapped = False |
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for i in range(n): |
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if elements[i] > elements[i + 1]: |
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swapped = True |
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# swapping data if the element is less than next element in the array |
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elements[i], elements[i + 1] = elements[i + 1], elements[i] |
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if not swapped: |
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# exiting the function if we didn't make a single swap |
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# meaning that the array is already sorted. |
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return |
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elements = [39, 12, 18, 85, 72, 10, 2, 18] |
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print("Unsorted list is,") |
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print(elements) |
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bubblesort(elements) |
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print("Sorted Array is, ") |
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print(elements)''' |
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vecs = model.encode([ |
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'def echo(): print("hello world")', |
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quick_sort, |
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bubble_sort |
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]) |
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print('cos sim (0, 1):', 1 - spatial.distance.cosine(vecs[0], vecs[1])) |
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print('cos sim (0, 2)', 1 - spatial.distance.cosine(vecs[0], vecs[2])) |
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print('cos sim (1, 2):', 1 - spatial.distance.cosine(vecs[1], vecs[2])) |
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``` |
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output: |
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``` |
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cos sim (0, 1): 0.34329649806022644 |
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cos sim (0, 2) 0.3627094626426697 |
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cos sim (1, 2): 0.6972219347953796 |
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``` |
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# Citation |
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```bibtex |
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@article{li2023angle, |
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title={AnglE-optimized Text Embeddings}, |
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author={Li, Xianming and Li, Jing}, |
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journal={arXiv preprint arXiv:2309.12871}, |
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year={2023} |
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} |
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``` |
