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--- |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:317521 |
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- loss:TripletLoss |
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widget: |
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- source_sentence: Write a function to extract every specified element from a given |
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two dimensional list. |
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sentences: |
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- "def nCr_mod_p(n, r, p): \r\n\tif (r > n- r): \r\n\t\tr = n - r \r\n\tC = [0 for\ |
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\ i in range(r + 1)] \r\n\tC[0] = 1 \r\n\tfor i in range(1, n + 1): \r\n\t\tfor\ |
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\ j in range(min(i, r), 0, -1): \r\n\t\t\tC[j] = (C[j] + C[j-1]) % p \r\n\treturn\ |
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\ C[r] " |
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- "import cmath\r\ndef len_complex(a,b):\r\n cn=complex(a,b)\r\n length=abs(cn)\r\ |
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\n return length" |
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- "def specified_element(nums, N):\r\n result = [i[N] for i in nums]\r\n return\ |
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\ result" |
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- source_sentence: Write a python function to find the kth element in an array containing |
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odd elements first and then even elements. |
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sentences: |
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- "def get_Number(n, k): \r\n arr = [0] * n; \r\n i = 0; \r\n odd = 1;\ |
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\ \r\n while (odd <= n): \r\n arr[i] = odd; \r\n i += 1; \r\ |
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\n odd += 2;\r\n even = 2; \r\n while (even <= n): \r\n arr[i]\ |
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\ = even; \r\n i += 1;\r\n even += 2; \r\n return arr[k - 1]; " |
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- "def sort_matrix(M):\r\n result = sorted(M, key=sum)\r\n return result" |
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- "INT_BITS = 32\r\ndef left_Rotate(n,d): \r\n return (n << d)|(n >> (INT_BITS\ |
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\ - d)) " |
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- source_sentence: Write a function to remove all the words with k length in the given |
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string. |
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sentences: |
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- "def remove_tuples(test_list, K):\r\n res = [ele for ele in test_list if len(ele)\ |
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\ != K]\r\n return (res) " |
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- "def is_Sub_Array(A,B,n,m): \r\n i = 0; j = 0; \r\n while (i < n and j <\ |
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\ m): \r\n if (A[i] == B[j]): \r\n i += 1; \r\n \ |
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\ j += 1; \r\n if (j == m): \r\n return True; \r\n\ |
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\ else: \r\n i = i - j + 1; \r\n j = 0; \r\n\ |
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\ return False; " |
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- "def remove_length(test_str, K):\r\n temp = test_str.split()\r\n res = [ele\ |
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\ for ele in temp if len(ele) != K]\r\n res = ' '.join(res)\r\n return (res) " |
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- source_sentence: Write a function to find the occurence of characters 'std' in the |
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given string 1. list item 1. list item 1. list item 2. list item 2. list item |
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2. list item |
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sentences: |
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- "def magic_square_test(my_matrix):\r\n iSize = len(my_matrix[0])\r\n sum_list\ |
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\ = []\r\n sum_list.extend([sum (lines) for lines in my_matrix]) \r\n \ |
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\ for col in range(iSize):\r\n sum_list.append(sum(row[col] for row in\ |
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\ my_matrix))\r\n result1 = 0\r\n for i in range(0,iSize):\r\n result1\ |
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\ +=my_matrix[i][i]\r\n sum_list.append(result1) \r\n result2 = 0\r\ |
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\n for i in range(iSize-1,-1,-1):\r\n result2 +=my_matrix[i][i]\r\n\ |
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\ sum_list.append(result2)\r\n if len(set(sum_list))>1:\r\n return\ |
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\ False\r\n return True" |
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- "def count_occurance(s):\r\n count=0\r\n for i in range(len(s)):\r\n if (s[i]==\ |
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\ 's' and s[i+1]=='t' and s[i+2]== 'd'):\r\n count = count + 1\r\n return\ |
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\ count" |
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- "def power(a,b):\r\n\tif b==0:\r\n\t\treturn 1\r\n\telif a==0:\r\n\t\treturn 0\r\ |
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\n\telif b==1:\r\n\t\treturn a\r\n\telse:\r\n\t\treturn a*power(a,b-1)" |
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- source_sentence: Write a function to find sum and average of first n natural numbers. |
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sentences: |
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- "def long_words(n, str):\r\n word_len = []\r\n txt = str.split(\" \")\r\n\ |
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\ for x in txt:\r\n if len(x) > n:\r\n word_len.append(x)\r\ |
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\n return word_len\t" |
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- "def long_words(n, str):\r\n word_len = []\r\n txt = str.split(\" \")\r\n\ |
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\ for x in txt:\r\n if len(x) > n:\r\n word_len.append(x)\r\ |
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\n return word_len\t" |
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- "def sum_average(number):\r\n total = 0\r\n for value in range(1, number + 1):\r\ |
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\n total = total + value\r\n average = total / number\r\n return (total,average)" |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.997141408425864 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.0028145001873883936 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.99605382088609 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.997141408425864 |
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name: Euclidean Accuracy |
|
- type: max_accuracy |
|
value: 0.997141408425864 |
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name: Max Accuracy |
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--- |
|
|
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# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
|
``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Nutanix/bge-base-mbpp") |
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# Run inference |
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sentences = [ |
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'Write a function to find sum and average of first n natural numbers.', |
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'def sum_average(number):\r\n total = 0\r\n for value in range(1, number + 1):\r\n total = total + value\r\n average = total / number\r\n return (total,average)', |
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'def long_words(n, str):\r\n word_len = []\r\n txt = str.split(" ")\r\n for x in txt:\r\n if len(x) > n:\r\n word_len.append(x)\r\n return word_len\t', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
|
</details> |
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--> |
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|
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<!-- |
|
### Downstream Usage (Sentence Transformers) |
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|
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Triplet |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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|
|
| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.9971 | |
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| dot_accuracy | 0.0028 | |
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| manhattan_accuracy | 0.9961 | |
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| euclidean_accuracy | 0.9971 | |
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| **max_accuracy** | **0.9971** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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|
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
|
### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 1 |
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- `bf16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
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- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
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- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | sts-dev_max_accuracy | |
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|:------:|:-----:|:-------------:|:--------------------:| |
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| 0.0050 | 100 | 4.3364 | - | |
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| 0.0101 | 200 | 4.122 | - | |
|
| 0.0151 | 300 | 4.0825 | - | |
|
| 0.0202 | 400 | 4.0381 | - | |
|
| 0.0252 | 500 | 4.015 | - | |
|
| 0.0302 | 600 | 3.9996 | - | |
|
| 0.0353 | 700 | 3.9567 | - | |
|
| 0.0403 | 800 | 3.9593 | - | |
|
| 0.0453 | 900 | 3.9456 | - | |
|
| 0.0504 | 1000 | 3.938 | - | |
|
| 0.0554 | 1100 | 3.933 | - | |
|
| 0.0605 | 1200 | 3.905 | - | |
|
| 0.0655 | 1300 | 3.906 | - | |
|
| 0.0705 | 1400 | 3.9073 | - | |
|
| 0.0756 | 1500 | 3.9193 | - | |
|
| 0.0806 | 1600 | 3.9016 | - | |
|
| 0.0857 | 1700 | 3.8899 | - | |
|
| 0.0907 | 1800 | 3.9 | - | |
|
| 0.0957 | 1900 | 3.8983 | - | |
|
| 0.1008 | 2000 | 3.876 | - | |
|
| 0.1058 | 2100 | 3.9001 | - | |
|
| 0.1109 | 2200 | 3.8818 | - | |
|
| 0.1159 | 2300 | 3.8788 | - | |
|
| 0.1209 | 2400 | 3.8815 | - | |
|
| 0.1260 | 2500 | 3.8664 | - | |
|
| 0.1310 | 2600 | 3.854 | - | |
|
| 0.1360 | 2700 | 3.8674 | - | |
|
| 0.1411 | 2800 | 3.8525 | - | |
|
| 0.1461 | 2900 | 3.8733 | - | |
|
| 0.1512 | 3000 | 3.8538 | - | |
|
| 0.1562 | 3100 | 3.8348 | - | |
|
| 0.1612 | 3200 | 3.8378 | - | |
|
| 0.1663 | 3300 | 3.8504 | - | |
|
| 0.1713 | 3400 | 3.8409 | - | |
|
| 0.1764 | 3500 | 3.8436 | - | |
|
| 0.1814 | 3600 | 3.8422 | - | |
|
| 0.1864 | 3700 | 3.8629 | - | |
|
| 0.1915 | 3800 | 3.8589 | - | |
|
| 0.1965 | 3900 | 3.8572 | - | |
|
| 0.2016 | 4000 | 3.8309 | - | |
|
| 0.2066 | 4100 | 3.8465 | - | |
|
| 0.2116 | 4200 | 3.8311 | - | |
|
| 0.2167 | 4300 | 3.8124 | - | |
|
| 0.2217 | 4400 | 3.8412 | - | |
|
| 0.2267 | 4500 | 3.8228 | - | |
|
| 0.2318 | 4600 | 3.8012 | - | |
|
| 0.2368 | 4700 | 3.8185 | - | |
|
| 0.2419 | 4800 | 3.8242 | - | |
|
| 0.2469 | 4900 | 3.7917 | - | |
|
| 0.2519 | 5000 | 3.8022 | - | |
|
| 0.2570 | 5100 | 3.7991 | - | |
|
| 0.2620 | 5200 | 3.7943 | - | |
|
| 0.2671 | 5300 | 3.7874 | - | |
|
| 0.2721 | 5400 | 3.7987 | - | |
|
| 0.2771 | 5500 | 3.7982 | - | |
|
| 0.2822 | 5600 | 3.7789 | - | |
|
| 0.2872 | 5700 | 3.7837 | - | |
|
| 0.2923 | 5800 | 3.7762 | - | |
|
| 0.2973 | 5900 | 3.7854 | - | |
|
| 0.3023 | 6000 | 3.7719 | - | |
|
| 0.3074 | 6100 | 3.7925 | - | |
|
| 0.3124 | 6200 | 3.7795 | - | |
|
| 0.3174 | 6300 | 3.7725 | - | |
|
| 0.3225 | 6400 | 3.7897 | - | |
|
| 0.3275 | 6500 | 3.773 | - | |
|
| 0.3326 | 6600 | 3.7803 | - | |
|
| 0.3376 | 6700 | 3.7476 | - | |
|
| 0.3426 | 6800 | 3.7585 | - | |
|
| 0.3477 | 6900 | 3.7426 | - | |
|
| 0.3527 | 7000 | 3.7529 | - | |
|
| 0.3578 | 7100 | 3.7745 | - | |
|
| 0.3628 | 7200 | 3.7771 | - | |
|
| 0.3678 | 7300 | 3.7598 | - | |
|
| 0.3729 | 7400 | 3.7428 | - | |
|
| 0.3779 | 7500 | 3.7409 | - | |
|
| 0.3829 | 7600 | 3.7569 | - | |
|
| 0.3880 | 7700 | 3.7517 | - | |
|
| 0.3930 | 7800 | 3.7484 | - | |
|
| 0.3981 | 7900 | 3.7415 | - | |
|
| 0.4031 | 8000 | 3.7228 | - | |
|
| 0.4081 | 8100 | 3.7569 | - | |
|
| 0.4132 | 8200 | 3.7421 | - | |
|
| 0.4182 | 8300 | 3.7233 | - | |
|
| 0.4233 | 8400 | 3.72 | - | |
|
| 0.4283 | 8500 | 3.7431 | - | |
|
| 0.4333 | 8600 | 3.7258 | - | |
|
| 0.4384 | 8700 | 3.73 | - | |
|
| 0.4434 | 8800 | 3.7286 | - | |
|
| 0.4485 | 8900 | 3.7487 | - | |
|
| 0.4535 | 9000 | 3.7359 | - | |
|
| 0.4585 | 9100 | 3.7387 | - | |
|
| 0.4636 | 9200 | 3.7135 | - | |
|
| 0.4686 | 9300 | 3.7219 | - | |
|
| 0.4736 | 9400 | 3.7189 | - | |
|
| 0.4787 | 9500 | 3.7234 | - | |
|
| 0.4837 | 9600 | 3.7333 | - | |
|
| 0.4888 | 9700 | 3.7027 | - | |
|
| 0.4938 | 9800 | 3.7358 | - | |
|
| 0.4988 | 9900 | 3.6959 | - | |
|
| 0.5039 | 10000 | 3.7051 | - | |
|
| 0.5089 | 10100 | 3.7205 | - | |
|
| 0.5140 | 10200 | 3.711 | - | |
|
| 0.5190 | 10300 | 3.6898 | - | |
|
| 0.5240 | 10400 | 3.7103 | - | |
|
| 0.5291 | 10500 | 3.695 | - | |
|
| 0.5341 | 10600 | 3.7108 | - | |
|
| 0.5392 | 10700 | 3.7226 | - | |
|
| 0.5442 | 10800 | 3.7004 | - | |
|
| 0.5492 | 10900 | 3.736 | - | |
|
| 0.5543 | 11000 | 3.7135 | - | |
|
| 0.5593 | 11100 | 3.7148 | - | |
|
| 0.5643 | 11200 | 3.7285 | - | |
|
| 0.5694 | 11300 | 3.694 | - | |
|
| 0.5744 | 11400 | 3.6913 | - | |
|
| 0.5795 | 11500 | 3.69 | - | |
|
| 0.5845 | 11600 | 3.7249 | - | |
|
| 0.5895 | 11700 | 3.6907 | - | |
|
| 0.5946 | 11800 | 3.7135 | - | |
|
| 0.5996 | 11900 | 3.7172 | - | |
|
| 0.6047 | 12000 | 3.7087 | - | |
|
| 0.6097 | 12100 | 3.7045 | - | |
|
| 0.6147 | 12200 | 3.7043 | - | |
|
| 0.6198 | 12300 | 3.693 | - | |
|
| 0.6248 | 12400 | 3.6982 | - | |
|
| 0.6298 | 12500 | 3.6922 | - | |
|
| 0.6349 | 12600 | 3.6857 | - | |
|
| 0.6399 | 12700 | 3.6834 | - | |
|
| 0.6450 | 12800 | 3.7052 | - | |
|
| 0.6500 | 12900 | 3.6935 | - | |
|
| 0.6550 | 13000 | 3.6736 | - | |
|
| 0.6601 | 13100 | 3.7026 | - | |
|
| 0.6651 | 13200 | 3.6846 | - | |
|
| 0.6702 | 13300 | 3.704 | - | |
|
| 0.6752 | 13400 | 3.6818 | - | |
|
| 0.6802 | 13500 | 3.7075 | - | |
|
| 0.6853 | 13600 | 3.6688 | - | |
|
| 0.6903 | 13700 | 3.6933 | - | |
|
| 0.6954 | 13800 | 3.6971 | - | |
|
| 0.7004 | 13900 | 3.6785 | - | |
|
| 0.7054 | 14000 | 3.7088 | - | |
|
| 0.7105 | 14100 | 3.7127 | - | |
|
| 0.7155 | 14200 | 3.6996 | - | |
|
| 0.7205 | 14300 | 3.6901 | - | |
|
| 0.7256 | 14400 | 3.6914 | - | |
|
| 0.7306 | 14500 | 3.6659 | - | |
|
| 0.7357 | 14600 | 3.6859 | - | |
|
| 0.7407 | 14700 | 3.68 | - | |
|
| 0.7457 | 14800 | 3.6874 | - | |
|
| 0.7508 | 14900 | 3.6854 | - | |
|
| 0.7558 | 15000 | 3.671 | - | |
|
| 0.7609 | 15100 | 3.6909 | - | |
|
| 0.7659 | 15200 | 3.7014 | - | |
|
| 0.7709 | 15300 | 3.6828 | - | |
|
| 0.7760 | 15400 | 3.6773 | - | |
|
| 0.7810 | 15500 | 3.6863 | - | |
|
| 0.7861 | 15600 | 3.6892 | - | |
|
| 0.7911 | 15700 | 3.6864 | - | |
|
| 0.7961 | 15800 | 3.6586 | - | |
|
| 0.8012 | 15900 | 3.6639 | - | |
|
| 0.8062 | 16000 | 3.6843 | - | |
|
| 0.8112 | 16100 | 3.6865 | - | |
|
| 0.8163 | 16200 | 3.678 | - | |
|
| 0.8213 | 16300 | 3.6825 | - | |
|
| 0.8264 | 16400 | 3.7068 | - | |
|
| 0.8314 | 16500 | 3.6886 | - | |
|
| 0.8364 | 16600 | 3.6905 | - | |
|
| 0.8415 | 16700 | 3.6905 | - | |
|
| 0.8465 | 16800 | 3.6677 | - | |
|
| 0.8516 | 16900 | 3.684 | - | |
|
| 0.8566 | 17000 | 3.6872 | - | |
|
| 0.8616 | 17100 | 3.6849 | - | |
|
| 0.8667 | 17200 | 3.662 | - | |
|
| 0.8717 | 17300 | 3.6887 | - | |
|
| 0.8768 | 17400 | 3.6999 | - | |
|
| 0.8818 | 17500 | 3.6916 | - | |
|
| 0.8868 | 17600 | 3.6853 | - | |
|
| 0.8919 | 17700 | 3.6971 | - | |
|
| 0.8969 | 17800 | 3.6846 | - | |
|
| 0.9019 | 17900 | 3.6701 | - | |
|
| 0.9070 | 18000 | 3.6911 | - | |
|
| 0.9120 | 18100 | 3.7021 | - | |
|
| 0.9171 | 18200 | 3.6851 | - | |
|
| 0.9221 | 18300 | 3.6924 | - | |
|
| 0.9271 | 18400 | 3.6644 | - | |
|
| 0.9322 | 18500 | 3.6674 | - | |
|
| 0.9372 | 18600 | 3.6962 | - | |
|
| 0.9423 | 18700 | 3.6759 | - | |
|
| 0.9473 | 18800 | 3.6839 | - | |
|
| 0.9523 | 18900 | 3.6822 | - | |
|
| 0.9574 | 19000 | 3.6947 | - | |
|
| 0.9624 | 19100 | 3.6589 | - | |
|
| 0.9674 | 19200 | 3.6817 | - | |
|
| 0.9725 | 19300 | 3.6754 | - | |
|
| 0.9775 | 19400 | 3.6947 | - | |
|
| 0.9826 | 19500 | 3.6785 | - | |
|
| 0.9876 | 19600 | 3.6776 | - | |
|
| 0.9926 | 19700 | 3.6791 | - | |
|
| 0.9977 | 19800 | 3.6795 | - | |
|
| 1.0 | 19846 | - | 0.9971 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.40.0 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.33.0 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### TripletLoss |
|
```bibtex |
|
@misc{hermans2017defense, |
|
title={In Defense of the Triplet Loss for Person Re-Identification}, |
|
author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
|
year={2017}, |
|
eprint={1703.07737}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
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