Add new SentenceTransformer model.

373834e verified 3 months ago

25.4 kB

	---
	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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