--- base_model: jinaai/jina-embeddings-v2-base-code datasets: [] language: [] 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): \r\n\tif (r > n- r): \r\n\t\tr = n - r \r\n\tC = [0 for\ \ i in range(r + 1)] \r\n\tC[0] = 1 \r\n\tfor i in range(1, n + 1): \r\n\t\tfor\ \ j in range(min(i, r), 0, -1): \r\n\t\t\tC[j] = (C[j] + C[j-1]) % p \r\n\treturn\ \ C[r] " - "import cmath\r\ndef len_complex(a,b):\r\n cn=complex(a,b)\r\n length=abs(cn)\r\ \n return length" - "def specified_element(nums, N):\r\n result = [i[N] for i in nums]\r\n 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): \r\n arr = [0] * n; \r\n i = 0; \r\n odd = 1;\ \ \r\n while (odd <= n): \r\n arr[i] = odd; \r\n i += 1; \r\ \n odd += 2;\r\n even = 2; \r\n while (even <= n): \r\n arr[i]\ \ = even; \r\n i += 1;\r\n even += 2; \r\n return arr[k - 1]; " - "def sort_matrix(M):\r\n result = sorted(M, key=sum)\r\n return result" - "INT_BITS = 32\r\ndef left_Rotate(n,d): \r\n 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):\r\n res = [ele for ele in test_list if len(ele)\ \ != K]\r\n return (res) " - "def is_Sub_Array(A,B,n,m): \r\n i = 0; j = 0; \r\n while (i < n and j <\ \ m): \r\n if (A[i] == B[j]): \r\n i += 1; \r\n \ \ j += 1; \r\n if (j == m): \r\n return True; \r\n\ \ else: \r\n i = i - j + 1; \r\n j = 0; \r\n\ \ return False; " - "def remove_length(test_str, K):\r\n temp = test_str.split()\r\n res = [ele\ \ for ele in temp if len(ele) != K]\r\n res = ' '.join(res)\r\n 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):\r\n iSize = len(my_matrix[0])\r\n sum_list\ \ = []\r\n sum_list.extend([sum (lines) for lines in my_matrix]) \r\n \ \ for col in range(iSize):\r\n sum_list.append(sum(row[col] for row in\ \ my_matrix))\r\n result1 = 0\r\n for i in range(0,iSize):\r\n result1\ \ +=my_matrix[i][i]\r\n sum_list.append(result1) \r\n result2 = 0\r\ \n for i in range(iSize-1,-1,-1):\r\n result2 +=my_matrix[i][i]\r\n\ \ sum_list.append(result2)\r\n if len(set(sum_list))>1:\r\n return\ \ False\r\n return True" - "def count_occurance(s):\r\n count=0\r\n for i in range(len(s)):\r\n if (s[i]==\ \ 's' and s[i+1]=='t' and s[i+2]== 'd'):\r\n count = count + 1\r\n return\ \ count" - "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\ \n\telif b==1:\r\n\t\treturn a\r\n\telse:\r\n\t\treturn 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):\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" - "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" - "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)" 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 - type: dot_accuracy value: 0.3189056517809246 name: Dot Accuracy - type: manhattan_accuracy value: 0.49047258618028966 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.47951587657351136 name: Euclidean Accuracy - type: max_accuracy value: 0.49047258618028966 name: Max Accuracy --- # SentenceTransformer based on jinaai/jina-embeddings-v2-base-code This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v2-base-code](https://huggingface.co./jinaai/jina-embeddings-v2-base-code). 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. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [jinaai/jina-embeddings-v2-base-code](https://huggingface.co./jinaai/jina-embeddings-v2-base-code) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Nutanix/jina-embeddings-v2-base-code-mbpp") # Run inference sentences = [ 'Write a function to find sum and average of first n natural numbers.', '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)', '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', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `sts-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.4795 | | dot_accuracy | 0.3189 | | manhattan_accuracy | 0.4905 | | euclidean_accuracy | 0.4795 | | **max_accuracy** | **0.4905** | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `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 - `optim_target_modules`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | sts-dev_max_accuracy | |:------:|:-----:|:-------------:|:--------------------:| | 0 | 0 | - | 0.5027 | | 0.0050 | 100 | 5.0 | - | | 0.0101 | 200 | 5.0 | - | | 0.0151 | 300 | 4.9999 | - | | 0.0202 | 400 | 5.0001 | - | | 0.0252 | 500 | 5.0 | - | | 0.0302 | 600 | 5.0 | - | | 0.0353 | 700 | 4.9999 | - | | 0.0403 | 800 | 5.0001 | - | | 0.0453 | 900 | 5.0 | - | | 0.0504 | 1000 | 5.0001 | - | | 0.0554 | 1100 | 4.9999 | - | | 0.0605 | 1200 | 5.0 | - | | 0.0655 | 1300 | 5.0 | - | | 0.0705 | 1400 | 4.9999 | - | | 0.0756 | 1500 | 5.0 | - | | 0.0806 | 1600 | 4.9999 | - | | 0.0857 | 1700 | 5.0001 | - | | 0.0907 | 1800 | 5.0001 | - | | 0.0957 | 1900 | 5.0 | - | | 0.1008 | 2000 | 5.0001 | - | | 0.1058 | 2100 | 5.0 | - | | 0.1109 | 2200 | 4.9999 | - | | 0.1159 | 2300 | 4.9999 | - | | 0.1209 | 2400 | 5.0 | - | | 0.1260 | 2500 | 5.0 | - | | 0.1310 | 2600 | 5.0001 | - | | 0.1360 | 2700 | 4.9999 | - | | 0.1411 | 2800 | 5.0001 | - | | 0.1461 | 2900 | 5.0001 | - | | 0.1512 | 3000 | 5.0 | - | | 0.1562 | 3100 | 5.0001 | - | | 0.1612 | 3200 | 4.9999 | - | | 0.1663 | 3300 | 5.0001 | - | | 0.1713 | 3400 | 4.9999 | - | | 0.1764 | 3500 | 4.9999 | - | | 0.1814 | 3600 | 4.9999 | - | | 0.1864 | 3700 | 5.0 | - | | 0.1915 | 3800 | 4.9999 | - | | 0.1965 | 3900 | 5.0 | - | | 0.2016 | 4000 | 5.0 | - | | 0.2066 | 4100 | 5.0 | - | | 0.2116 | 4200 | 5.0002 | - | | 0.2167 | 4300 | 5.0002 | - | | 0.2217 | 4400 | 5.0 | - | | 0.2267 | 4500 | 5.0001 | - | | 0.2318 | 4600 | 5.0001 | - | | 0.2368 | 4700 | 5.0001 | - | | 0.2419 | 4800 | 4.9998 | - | | 0.2469 | 4900 | 5.0 | - | | 0.2519 | 5000 | 4.9999 | - | | 0.2570 | 5100 | 4.9999 | - | | 0.2620 | 5200 | 5.0001 | - | | 0.2671 | 5300 | 5.0001 | - | | 0.2721 | 5400 | 4.9999 | - | | 0.2771 | 5500 | 5.0 | - | | 0.2822 | 5600 | 5.0002 | - | | 0.2872 | 5700 | 5.0002 | - | | 0.2923 | 5800 | 4.9999 | - | | 0.2973 | 5900 | 5.0 | - | | 0.3023 | 6000 | 5.0001 | - | | 0.3074 | 6100 | 4.9999 | - | | 0.3124 | 6200 | 4.9997 | - | | 0.3174 | 6300 | 4.9999 | - | | 0.3225 | 6400 | 5.0 | - | | 0.3275 | 6500 | 4.9998 | - | | 0.3326 | 6600 | 5.0 | - | | 0.3376 | 6700 | 4.9998 | - | | 0.3426 | 6800 | 5.0001 | - | | 0.3477 | 6900 | 5.0002 | - | | 0.3527 | 7000 | 5.0 | - | | 0.3578 | 7100 | 4.9998 | - | | 0.3628 | 7200 | 5.0003 | - | | 0.3678 | 7300 | 5.0 | - | | 0.3729 | 7400 | 5.0002 | - | | 0.3779 | 7500 | 5.0 | - | | 0.3829 | 7600 | 5.0001 | - | | 0.3880 | 7700 | 5.0002 | - | | 0.3930 | 7800 | 5.0001 | - | | 0.3981 | 7900 | 5.0001 | - | | 0.4031 | 8000 | 5.0 | - | | 0.4081 | 8100 | 4.9998 | - | | 0.4132 | 8200 | 4.9999 | - | | 0.4182 | 8300 | 5.0001 | - | | 0.4233 | 8400 | 5.0001 | - | | 0.4283 | 8500 | 5.0 | - | | 0.4333 | 8600 | 5.0002 | - | | 0.4384 | 8700 | 5.0001 | - | | 0.4434 | 8800 | 5.0 | - | | 0.4485 | 8900 | 4.9996 | - | | 0.4535 | 9000 | 4.9999 | - | | 0.4585 | 9100 | 5.0 | - | | 0.4636 | 9200 | 4.9999 | - | | 0.4686 | 9300 | 4.9999 | - | | 0.4736 | 9400 | 4.9998 | - | | 0.4787 | 9500 | 5.0001 | - | | 0.4837 | 9600 | 4.9998 | - | | 0.4888 | 9700 | 4.9999 | - | | 0.4938 | 9800 | 5.0 | - | | 0.4988 | 9900 | 4.9998 | - | | 0.5039 | 10000 | 5.0 | - | | 0.5089 | 10100 | 5.0002 | - | | 0.5140 | 10200 | 5.0003 | - | | 0.5190 | 10300 | 4.9998 | - | | 0.5240 | 10400 | 4.9999 | - | | 0.5291 | 10500 | 5.0 | - | | 0.5341 | 10600 | 4.9999 | - | | 0.5392 | 10700 | 5.0 | - | | 0.5442 | 10800 | 5.0001 | - | | 0.5492 | 10900 | 4.9999 | - | | 0.5543 | 11000 | 5.0 | - | | 0.5593 | 11100 | 4.9999 | - | | 0.5643 | 11200 | 5.0 | - | | 0.5694 | 11300 | 4.9999 | - | | 0.5744 | 11400 | 4.9997 | - | | 0.5795 | 11500 | 5.0002 | - | | 0.5845 | 11600 | 4.9999 | - | | 0.5895 | 11700 | 5.0001 | - | | 0.5946 | 11800 | 5.0001 | - | | 0.5996 | 11900 | 5.0004 | - | | 0.6047 | 12000 | 4.9998 | - | | 0.6097 | 12100 | 5.0002 | - | | 0.6147 | 12200 | 4.9998 | - | | 0.6198 | 12300 | 5.0001 | - | | 0.6248 | 12400 | 5.0001 | - | | 0.6298 | 12500 | 5.0001 | - | | 0.6349 | 12600 | 4.9999 | - | | 0.6399 | 12700 | 5.0001 | - | | 0.6450 | 12800 | 4.9999 | - | | 0.6500 | 12900 | 5.0001 | - | | 0.6550 | 13000 | 4.9999 | - | | 0.6601 | 13100 | 5.0002 | - | | 0.6651 | 13200 | 5.0001 | - | | 0.6702 | 13300 | 5.0002 | - | | 0.6752 | 13400 | 4.9997 | - | | 0.6802 | 13500 | 5.0001 | - | | 0.6853 | 13600 | 4.9996 | - | | 0.6903 | 13700 | 4.9999 | - | | 0.6954 | 13800 | 5.0002 | - | | 0.7004 | 13900 | 4.9997 | - | | 0.7054 | 14000 | 5.0 | - | | 0.7105 | 14100 | 5.0001 | - | | 0.7155 | 14200 | 5.0001 | - | | 0.7205 | 14300 | 4.9999 | - | | 0.7256 | 14400 | 4.9999 | - | | 0.7306 | 14500 | 4.9998 | - | | 0.7357 | 14600 | 5.0 | - | | 0.7407 | 14700 | 5.0002 | - | | 0.7457 | 14800 | 5.0001 | - | | 0.7508 | 14900 | 4.9998 | - | | 0.7558 | 15000 | 5.0002 | - | | 0.7609 | 15100 | 5.0002 | - | | 0.7659 | 15200 | 5.0 | - | | 0.7709 | 15300 | 5.0002 | - | | 0.7760 | 15400 | 5.0 | - | | 0.7810 | 15500 | 5.0001 | - | | 0.7861 | 15600 | 5.0 | - | | 0.7911 | 15700 | 5.0004 | - | | 0.7961 | 15800 | 5.0 | - | | 0.8012 | 15900 | 5.0001 | - | | 0.8062 | 16000 | 5.0003 | - | | 0.8112 | 16100 | 4.9999 | - | | 0.8163 | 16200 | 5.0 | - | | 0.8213 | 16300 | 4.9999 | - | | 0.8264 | 16400 | 5.0 | - | | 0.8314 | 16500 | 4.9999 | - | | 0.8364 | 16600 | 4.9998 | - | | 0.8415 | 16700 | 4.9998 | - | | 0.8465 | 16800 | 5.0002 | - | | 0.8516 | 16900 | 4.9999 | - | | 0.8566 | 17000 | 4.9999 | - | | 0.8616 | 17100 | 4.9997 | - | | 0.8667 | 17200 | 5.0001 | - | | 0.8717 | 17300 | 4.9999 | - | | 0.8768 | 17400 | 5.0001 | - | | 0.8818 | 17500 | 4.9999 | - | | 0.8868 | 17600 | 5.0001 | - | | 0.8919 | 17700 | 5.0001 | - | | 0.8969 | 17800 | 5.0001 | - | | 0.9019 | 17900 | 4.9996 | - | | 0.9070 | 18000 | 5.0001 | - | | 0.9120 | 18100 | 4.9997 | - | | 0.9171 | 18200 | 5.0001 | - | | 0.9221 | 18300 | 4.9998 | - | | 0.9271 | 18400 | 4.9997 | - | | 0.9322 | 18500 | 4.9999 | - | | 0.9372 | 18600 | 5.0001 | - | | 0.9423 | 18700 | 5.0004 | - | | 0.9473 | 18800 | 4.9997 | - | | 0.9523 | 18900 | 4.9999 | - | | 0.9574 | 19000 | 5.0001 | - | | 0.9624 | 19100 | 4.9999 | - | | 0.9674 | 19200 | 5.0 | - | | 0.9725 | 19300 | 4.9999 | - | | 0.9775 | 19400 | 4.9999 | - | | 0.9826 | 19500 | 4.9999 | - | | 0.9876 | 19600 | 4.9998 | - | | 0.9926 | 19700 | 5.0 | - | | 0.9977 | 19800 | 4.9999 | - | | 1.0 | 19846 | - | 0.4905 |
### 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} } ```