Create README.md

README.md

@@ -1,57 +1,106 @@
 ---
 license: mit
-tags:
-- generated_from_trainer
-base_model: WhereIsAI/UAE-Large-V1
-model-index:
-- name: GIS-Large-V1
-  results: []
+datasets:
+- WhereIsAI/github-issue-similarity
+language:
+- en
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# SeanLee97/GIS-Large-V1
 
-# GIS-Large-V1
 
-This model is a fine-tuned version of [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) on the None dataset.
+This model is trained on the [github-issue-similarity](https://huggingface.co/datasets/WhereIsAI/github-issue-similarity) dataset using [AnglE](https://github.com/SeanLee97/AnglE) and is used for measuring code similarity.
 
-## Model description
+Results:
 
-More information needed
+- Spearman correlation: 71.19
+- Accuracy: 84.37
 
-## Intended uses & limitations
 
-More information needed
+## Usage
 
-## Training and evaluation data
+### 1 Install
 
-More information needed
+```
+python -m pip install -U angle-emb
+```
 
-## Training procedure
+### 2 Example
 
-### Training hyperparameters
+```python
+from scipy import spatial
+from angle_emb import AnglE
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-06
-- train_batch_size: 4
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 3
-- total_train_batch_size: 12
-- total_eval_batch_size: 24
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 200
-- num_epochs: 1
+model = AnglE.from_pretrained('SeanLee97/UAE-GIS-Large-V1').cuda()
 
-### Training results
+quick_sort = '''# Approach 2: Quicksort using list comprehension
 
+def quicksort(arr):
+    if len(arr) <= 1:
+        return arr
+    else:
+        pivot = arr[0]
+        left = [x for x in arr[1:] if x < pivot]
+        right = [x for x in arr[1:] if x >= pivot]
+        return quicksort(left) + [pivot] + quicksort(right)
+ 
+# Example usage
+arr = [1, 7, 4, 1, 10, 9, -2]
+sorted_arr = quicksort(arr)
+print("Sorted Array in Ascending Order:")
+print(sorted_arr)'''
 
 
-### Framework versions
+bubble_sort = '''def bubblesort(elements):
+    # Looping from size of array from last index[-1] to index [0]
+    for n in range(len(elements)-1, 0, -1):
+        swapped = False
+        for i in range(n):
+            if elements[i] > elements[i + 1]:
+                swapped = True
+                # swapping data if the element is less than next element in the array
+                elements[i], elements[i + 1] = elements[i + 1], elements[i]
+        if not swapped:
+            # exiting the function if we didn't make a single swap
+            # meaning that the array is already sorted.
+            return
 
-- Transformers 4.39.3
-- Pytorch 2.0.1
-- Datasets 2.16.0
-- Tokenizers 0.15.0
+elements = [39, 12, 18, 85, 72, 10, 2, 18]
+
+print("Unsorted list is,")
+print(elements)
+bubblesort(elements)
+print("Sorted Array is, ")
+print(elements)'''
+
+vecs = model.encode([
+    'def echo(): hello world',
+    quick_sort,
+    bubble_sort
+])
+
+
+print('cos sim (0, 1):', 1 - spatial.distance.cosine(vecs[0], vecs[1]))
+print('cos sim (0, 2)', 1 - spatial.distance.cosine(vecs[0], vecs[2]))
+print('cos sim (1, 2):', 1 - spatial.distance.cosine(vecs[1], vecs[2]))
+
+```
+
+output:
+
+```
+cos sim (0, 1): 0.3169282078742981
+cos sim (0, 2) 0.3370905816555023
+cos sim (1, 2): 0.6972219347953796
+```
+
+# Citation
+
+```bibtex
+@article{li2023angle,
+  title={AnglE-optimized Text Embeddings},
+  author={Li, Xianming and Li, Jing},
+  journal={arXiv preprint arXiv:2309.12871},
+  year={2023}
+}
+```