| --- |
| base_model: BAAI/bge-base-en-v1.5 |
| 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 BAAI/bge-base-en-v1.5 |
| results: |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9642051425254077 |
| name: Cosine Accuracy |
| - type: dot_accuracy |
| value: 0.03578016034567647 |
| name: Dot Accuracy |
| - type: manhattan_accuracy |
| value: 0.9623165614597189 |
| name: Manhattan Accuracy |
| - type: euclidean_accuracy |
| value: 0.9642051425254077 |
| name: Euclidean Accuracy |
| - type: max_accuracy |
| value: 0.9642051425254077 |
| name: Max Accuracy |
| --- |
| |
| # SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
| 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. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel |
| (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}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## 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/bge-base-mbpp-processed") |
| # 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Triplet |
| * Dataset: `sts-dev` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
|
|
| | Metric | Value | |
| |:-------------------|:-----------| |
| | cosine_accuracy | 0.9642 | |
| | dot_accuracy | 0.0358 | |
| | manhattan_accuracy | 0.9623 | |
| | euclidean_accuracy | 0.9642 | |
| | **max_accuracy** | **0.9642** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 1 |
| - `bf16`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `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 |
| - `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`: True |
| - `fp16`: False |
| - `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 |
| |
| </details> |
| |
| ### Training Logs |
| <details><summary>Click to expand</summary> |
| |
| | Epoch | Step | Training Loss | sts-dev_max_accuracy | |
| |:------:|:-----:|:-------------:|:--------------------:| |
| | 0.0050 | 100 | 4.2871 | - | |
| | 0.0101 | 200 | 4.1063 | - | |
| | 0.0151 | 300 | 4.0535 | - | |
| | 0.0202 | 400 | 4.0162 | - | |
| | 0.0252 | 500 | 3.9802 | - | |
| | 0.0302 | 600 | 3.982 | - | |
| | 0.0353 | 700 | 3.9349 | - | |
| | 0.0403 | 800 | 3.9372 | - | |
| | 0.0453 | 900 | 3.9175 | - | |
| | 0.0504 | 1000 | 3.9201 | - | |
| | 0.0554 | 1100 | 3.9036 | - | |
| | 0.0605 | 1200 | 3.8985 | - | |
| | 0.0655 | 1300 | 3.8994 | - | |
| | 0.0705 | 1400 | 3.8979 | - | |
| | 0.0756 | 1500 | 3.8846 | - | |
| | 0.0806 | 1600 | 3.897 | - | |
| | 0.0857 | 1700 | 3.8779 | - | |
| | 0.0907 | 1800 | 3.895 | - | |
| | 0.0957 | 1900 | 3.8793 | - | |
| | 0.1008 | 2000 | 3.8677 | - | |
| | 0.1058 | 2100 | 3.8672 | - | |
| | 0.1109 | 2200 | 3.8744 | - | |
| | 0.1159 | 2300 | 3.8721 | - | |
| | 0.1209 | 2400 | 3.8554 | - | |
| | 0.1260 | 2500 | 3.8379 | - | |
| | 0.1310 | 2600 | 3.8477 | - | |
| | 0.1360 | 2700 | 3.8625 | - | |
| | 0.1411 | 2800 | 3.8426 | - | |
| | 0.1461 | 2900 | 3.8475 | - | |
| | 0.1512 | 3000 | 3.8462 | - | |
| | 0.1562 | 3100 | 3.8314 | - | |
| | 0.1612 | 3200 | 3.8227 | - | |
| | 0.1663 | 3300 | 3.8311 | - | |
| | 0.1713 | 3400 | 3.8305 | - | |
| | 0.1764 | 3500 | 3.8249 | - | |
| | 0.1814 | 3600 | 3.842 | - | |
| | 0.1864 | 3700 | 3.8259 | - | |
| | 0.1915 | 3800 | 3.8295 | - | |
| | 0.1965 | 3900 | 3.8217 | - | |
| | 0.2016 | 4000 | 3.8362 | - | |
| | 0.2066 | 4100 | 3.8354 | - | |
| | 0.2116 | 4200 | 3.8175 | - | |
| | 0.2167 | 4300 | 3.8108 | - | |
| | 0.2217 | 4400 | 3.84 | - | |
| | 0.2267 | 4500 | 3.8206 | - | |
| | 0.2318 | 4600 | 3.8065 | - | |
| | 0.2368 | 4700 | 3.8161 | - | |
| | 0.2419 | 4800 | 3.8234 | - | |
| | 0.2469 | 4900 | 3.8112 | - | |
| | 0.2519 | 5000 | 3.8006 | - | |
| | 0.2570 | 5100 | 3.7996 | - | |
| | 0.2620 | 5200 | 3.8104 | - | |
| | 0.2671 | 5300 | 3.7975 | - | |
| | 0.2721 | 5400 | 3.7944 | - | |
| | 0.2771 | 5500 | 3.8037 | - | |
| | 0.2822 | 5600 | 3.7996 | - | |
| | 0.2872 | 5700 | 3.7883 | - | |
| | 0.2923 | 5800 | 3.7978 | - | |
| | 0.2973 | 5900 | 3.7889 | - | |
| | 0.3023 | 6000 | 3.7794 | - | |
| | 0.3074 | 6100 | 3.7669 | - | |
| | 0.3124 | 6200 | 3.7844 | - | |
| | 0.3174 | 6300 | 3.7687 | - | |
| | 0.3225 | 6400 | 3.7915 | - | |
| | 0.3275 | 6500 | 3.7505 | - | |
| | 0.3326 | 6600 | 3.7979 | - | |
| | 0.3376 | 6700 | 3.7681 | - | |
| | 0.3426 | 6800 | 3.7738 | - | |
| | 0.3477 | 6900 | 3.7702 | - | |
| | 0.3527 | 7000 | 3.7679 | - | |
| | 0.3578 | 7100 | 3.7862 | - | |
| | 0.3628 | 7200 | 3.7718 | - | |
| | 0.3678 | 7300 | 3.7898 | - | |
| | 0.3729 | 7400 | 3.7832 | - | |
| | 0.3779 | 7500 | 3.7701 | - | |
| | 0.3829 | 7600 | 3.7805 | - | |
| | 0.3880 | 7700 | 3.7725 | - | |
| | 0.3930 | 7800 | 3.7476 | - | |
| | 0.3981 | 7900 | 3.7612 | - | |
| | 0.4031 | 8000 | 3.7555 | - | |
| | 0.4081 | 8100 | 3.7489 | - | |
| | 0.4132 | 8200 | 3.7507 | - | |
| | 0.4182 | 8300 | 3.741 | - | |
| | 0.4233 | 8400 | 3.7465 | - | |
| | 0.4283 | 8500 | 3.7445 | - | |
| | 0.4333 | 8600 | 3.7536 | - | |
| | 0.4384 | 8700 | 3.7279 | - | |
| | 0.4434 | 8800 | 3.745 | - | |
| | 0.4485 | 8900 | 3.7712 | - | |
| | 0.4535 | 9000 | 3.7429 | - | |
| | 0.4585 | 9100 | 3.7386 | - | |
| | 0.4636 | 9200 | 3.7328 | - | |
| | 0.4686 | 9300 | 3.735 | - | |
| | 0.4736 | 9400 | 3.7451 | - | |
| | 0.4787 | 9500 | 3.7292 | - | |
| | 0.4837 | 9600 | 3.7381 | - | |
| | 0.4888 | 9700 | 3.7346 | - | |
| | 0.4938 | 9800 | 3.7396 | - | |
| | 0.4988 | 9900 | 3.7122 | - | |
| | 0.5039 | 10000 | 3.7295 | - | |
| | 0.5089 | 10100 | 3.738 | - | |
| | 0.5140 | 10200 | 3.7272 | - | |
| | 0.5190 | 10300 | 3.7233 | - | |
| | 0.5240 | 10400 | 3.7066 | - | |
| | 0.5291 | 10500 | 3.713 | - | |
| | 0.5341 | 10600 | 3.7185 | - | |
| | 0.5392 | 10700 | 3.7236 | - | |
| | 0.5442 | 10800 | 3.7086 | - | |
| | 0.5492 | 10900 | 3.718 | - | |
| | 0.5543 | 11000 | 3.7296 | - | |
| | 0.5593 | 11100 | 3.7276 | - | |
| | 0.5643 | 11200 | 3.7164 | - | |
| | 0.5694 | 11300 | 3.7119 | - | |
| | 0.5744 | 11400 | 3.6979 | - | |
| | 0.5795 | 11500 | 3.7095 | - | |
| | 0.5845 | 11600 | 3.7067 | - | |
| | 0.5895 | 11700 | 3.7018 | - | |
| | 0.5946 | 11800 | 3.727 | - | |
| | 0.5996 | 11900 | 3.7136 | - | |
| | 0.6047 | 12000 | 3.7233 | - | |
| | 0.6097 | 12100 | 3.7076 | - | |
| | 0.6147 | 12200 | 3.7243 | - | |
| | 0.6198 | 12300 | 3.6966 | - | |
| | 0.6248 | 12400 | 3.7058 | - | |
| | 0.6298 | 12500 | 3.698 | - | |
| | 0.6349 | 12600 | 3.6934 | - | |
| | 0.6399 | 12700 | 3.7046 | - | |
| | 0.6450 | 12800 | 3.6986 | - | |
| | 0.6500 | 12900 | 3.7134 | - | |
| | 0.6550 | 13000 | 3.7019 | - | |
| | 0.6601 | 13100 | 3.7154 | - | |
| | 0.6651 | 13200 | 3.7056 | - | |
| | 0.6702 | 13300 | 3.6948 | - | |
| | 0.6752 | 13400 | 3.697 | - | |
| | 0.6802 | 13500 | 3.6998 | - | |
| | 0.6853 | 13600 | 3.6881 | - | |
| | 0.6903 | 13700 | 3.6982 | - | |
| | 0.6954 | 13800 | 3.6861 | - | |
| | 0.7004 | 13900 | 3.6816 | - | |
| | 0.7054 | 14000 | 3.7004 | - | |
| | 0.7105 | 14100 | 3.6895 | - | |
| | 0.7155 | 14200 | 3.7045 | - | |
| | 0.7205 | 14300 | 3.7078 | - | |
| | 0.7256 | 14400 | 3.6884 | - | |
| | 0.7306 | 14500 | 3.6839 | - | |
| | 0.7357 | 14600 | 3.6891 | - | |
| | 0.7407 | 14700 | 3.6864 | - | |
| | 0.7457 | 14800 | 3.7069 | - | |
| | 0.7508 | 14900 | 3.6879 | - | |
| | 0.7558 | 15000 | 3.7049 | - | |
| | 0.7609 | 15100 | 3.7099 | - | |
| | 0.7659 | 15200 | 3.6908 | - | |
| | 0.7709 | 15300 | 3.6973 | - | |
| | 0.7760 | 15400 | 3.6775 | - | |
| | 0.7810 | 15500 | 3.6776 | - | |
| | 0.7861 | 15600 | 3.706 | - | |
| | 0.7911 | 15700 | 3.6941 | - | |
| | 0.7961 | 15800 | 3.6974 | - | |
| | 0.8012 | 15900 | 3.6706 | - | |
| | 0.8062 | 16000 | 3.6922 | - | |
| | 0.8112 | 16100 | 3.6898 | - | |
| | 0.8163 | 16200 | 3.7005 | - | |
| | 0.8213 | 16300 | 3.691 | - | |
| | 0.8264 | 16400 | 3.7066 | - | |
| | 0.8314 | 16500 | 3.6959 | - | |
| | 0.8364 | 16600 | 3.6944 | - | |
| | 0.8415 | 16700 | 3.6724 | - | |
| | 0.8465 | 16800 | 3.6783 | - | |
| | 0.8516 | 16900 | 3.683 | - | |
| | 0.8566 | 17000 | 3.6929 | - | |
| | 0.8616 | 17100 | 3.6823 | - | |
| | 0.8667 | 17200 | 3.6737 | - | |
| | 0.8717 | 17300 | 3.6847 | - | |
| | 0.8768 | 17400 | 3.6786 | - | |
| | 0.8818 | 17500 | 3.7018 | - | |
| | 0.8868 | 17600 | 3.6944 | - | |
| | 0.8919 | 17700 | 3.687 | - | |
| | 0.8969 | 17800 | 3.6841 | - | |
| | 0.9019 | 17900 | 3.6764 | - | |
| | 0.9070 | 18000 | 3.6779 | - | |
| | 0.9120 | 18100 | 3.689 | - | |
| | 0.9171 | 18200 | 3.6837 | - | |
| | 0.9221 | 18300 | 3.7034 | - | |
| | 0.9271 | 18400 | 3.669 | - | |
| | 0.9322 | 18500 | 3.67 | - | |
| | 0.9372 | 18600 | 3.6868 | - | |
| | 0.9423 | 18700 | 3.6916 | - | |
| | 0.9473 | 18800 | 3.6751 | - | |
| | 0.9523 | 18900 | 3.6935 | - | |
| | 0.9574 | 19000 | 3.702 | - | |
| | 0.9624 | 19100 | 3.6761 | - | |
| | 0.9674 | 19200 | 3.6798 | - | |
| | 0.9725 | 19300 | 3.6844 | - | |
| | 0.9775 | 19400 | 3.6775 | - | |
| | 0.9826 | 19500 | 3.6679 | - | |
| | 0.9876 | 19600 | 3.6793 | - | |
| | 0.9926 | 19700 | 3.6833 | - | |
| | 0.9977 | 19800 | 3.6717 | - | |
| | 1.0 | 19846 | - | 0.9642 | |
| |
| </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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