Add new SentenceTransformer model.

a8c401c verified 3 months ago

25.2 kB

	---
	base_model: intfloat/multilingual-e5-small
	datasets: []
	language: []
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- dot_accuracy
	- dot_accuracy_threshold
	- dot_f1
	- dot_f1_threshold
	- dot_precision
	- dot_recall
	- dot_ap
	- manhattan_accuracy
	- manhattan_accuracy_threshold
	- manhattan_f1
	- manhattan_f1_threshold
	- manhattan_precision
	- manhattan_recall
	- manhattan_ap
	- euclidean_accuracy
	- euclidean_accuracy_threshold
	- euclidean_f1
	- euclidean_f1_threshold
	- euclidean_precision
	- euclidean_recall
	- euclidean_ap
	- max_accuracy
	- max_accuracy_threshold
	- max_f1
	- max_f1_threshold
	- max_precision
	- max_recall
	- max_ap
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:333
	- loss:ContrastiveLoss
	widget:
	- source_sentence: What is the capital of Canada?
	sentences:
	- Main ingredient in guacamole
	- Prime Minister of the United Kingdom
	- What is the capital of Australia?
	- source_sentence: What is the freezing point of water?
	sentences:
	- Paracetamol side effects
	- Temperature at which water freezes
	- Who discovered electricity?
	- source_sentence: Who invented the telephone?
	sentences:
	- Positive effects of exercise
	- Current population of Japan
	- Who created the telephone?
	- source_sentence: Who discovered gravity?
	sentences:
	- Steps to cook pasta
	- Who found out about gravity?
	- How to reset a password
	- source_sentence: What is the capital of Italy?
	sentences:
	- What is water's chemical formula?
	- Italy's capital city
	- I need help with my homework
	model-index:
	- name: SentenceTransformer based on intfloat/multilingual-e5-small
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: pair class dev
	type: pair-class-dev
	metrics:
	- type: cosine_accuracy
	value: 1.0
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.8237255811691284
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 1.0
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.8237255811691284
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 1.0
	name: Cosine Precision
	- type: cosine_recall
	value: 1.0
	name: Cosine Recall
	- type: cosine_ap
	value: 1.0
	name: Cosine Ap
	- type: dot_accuracy
	value: 1.0
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 0.8237255215644836
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 1.0
	name: Dot F1
	- type: dot_f1_threshold
	value: 0.8237255215644836
	name: Dot F1 Threshold
	- type: dot_precision
	value: 1.0
	name: Dot Precision
	- type: dot_recall
	value: 1.0
	name: Dot Recall
	- type: dot_ap
	value: 1.0
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.972972972972973
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 7.9234113693237305
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.9795918367346939
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 9.902971267700195
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.96
	name: Manhattan Precision
	- type: manhattan_recall
	value: 1.0
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.9983333333333333
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 1.0
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 0.5937579870223999
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 1.0
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 0.5937579870223999
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 1.0
	name: Euclidean Precision
	- type: euclidean_recall
	value: 1.0
	name: Euclidean Recall
	- type: euclidean_ap
	value: 1.0
	name: Euclidean Ap
	- type: max_accuracy
	value: 1.0
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 7.9234113693237305
	name: Max Accuracy Threshold
	- type: max_f1
	value: 1.0
	name: Max F1
	- type: max_f1_threshold
	value: 9.902971267700195
	name: Max F1 Threshold
	- type: max_precision
	value: 1.0
	name: Max Precision
	- type: max_recall
	value: 1.0
	name: Max Recall
	- type: max_ap
	value: 1.0
	name: Max Ap
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: pair class test
	type: pair-class-test
	metrics:
	- type: cosine_accuracy
	value: 1.0
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.8052735328674316
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 1.0
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.8052735328674316
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 1.0
	name: Cosine Precision
	- type: cosine_recall
	value: 1.0
	name: Cosine Recall
	- type: cosine_ap
	value: 1.0
	name: Cosine Ap
	- type: dot_accuracy
	value: 1.0
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 0.8052735328674316
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 1.0
	name: Dot F1
	- type: dot_f1_threshold
	value: 0.8052735328674316
	name: Dot F1 Threshold
	- type: dot_precision
	value: 1.0
	name: Dot Precision
	- type: dot_recall
	value: 1.0
	name: Dot Recall
	- type: dot_ap
	value: 1.0
	name: Dot Ap
	- type: manhattan_accuracy
	value: 1.0
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 9.779541969299316
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 1.0
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 9.779541969299316
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 1.0
	name: Manhattan Precision
	- type: manhattan_recall
	value: 1.0
	name: Manhattan Recall
	- type: manhattan_ap
	value: 1.0
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 1.0
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 0.6235698461532593
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 1.0
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 0.6235698461532593
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 1.0
	name: Euclidean Precision
	- type: euclidean_recall
	value: 1.0
	name: Euclidean Recall
	- type: euclidean_ap
	value: 1.0
	name: Euclidean Ap
	- type: max_accuracy
	value: 1.0
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 9.779541969299316
	name: Max Accuracy Threshold
	- type: max_f1
	value: 1.0
	name: Max F1
	- type: max_f1_threshold
	value: 9.779541969299316
	name: Max F1 Threshold
	- type: max_precision
	value: 1.0
	name: Max Precision
	- type: max_recall
	value: 1.0
	name: Max Recall
	- type: max_ap
	value: 1.0
	name: Max Ap
	---

	# SentenceTransformer based on intfloat/multilingual-e5-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-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: [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 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': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, '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})
	(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("srikarvar/multilingual-e5-small-cogcache-contrastive")
	# Run inference
	sentences = [
	'What is the capital of Italy?',
	"Italy's capital city",
	'I need help with my homework',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# 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

	#### Binary Classification
	* Dataset: `pair-class-dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:--------\|
	\| cosine_accuracy \| 1.0 \|
	\| cosine_accuracy_threshold \| 0.8237 \|
	\| cosine_f1 \| 1.0 \|
	\| cosine_f1_threshold \| 0.8237 \|
	\| cosine_precision \| 1.0 \|
	\| cosine_recall \| 1.0 \|
	\| cosine_ap \| 1.0 \|
	\| dot_accuracy \| 1.0 \|
	\| dot_accuracy_threshold \| 0.8237 \|
	\| dot_f1 \| 1.0 \|
	\| dot_f1_threshold \| 0.8237 \|
	\| dot_precision \| 1.0 \|
	\| dot_recall \| 1.0 \|
	\| dot_ap \| 1.0 \|
	\| manhattan_accuracy \| 0.973 \|
	\| manhattan_accuracy_threshold \| 7.9234 \|
	\| manhattan_f1 \| 0.9796 \|
	\| manhattan_f1_threshold \| 9.903 \|
	\| manhattan_precision \| 0.96 \|
	\| manhattan_recall \| 1.0 \|
	\| manhattan_ap \| 0.9983 \|
	\| euclidean_accuracy \| 1.0 \|
	\| euclidean_accuracy_threshold \| 0.5938 \|
	\| euclidean_f1 \| 1.0 \|
	\| euclidean_f1_threshold \| 0.5938 \|
	\| euclidean_precision \| 1.0 \|
	\| euclidean_recall \| 1.0 \|
	\| euclidean_ap \| 1.0 \|
	\| max_accuracy \| 1.0 \|
	\| max_accuracy_threshold \| 7.9234 \|
	\| max_f1 \| 1.0 \|
	\| max_f1_threshold \| 9.903 \|
	\| max_precision \| 1.0 \|
	\| max_recall \| 1.0 \|
	\| max_ap \| 1.0 \|

	#### Binary Classification
	* Dataset: `pair-class-test`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:--------\|
	\| cosine_accuracy \| 1.0 \|
	\| cosine_accuracy_threshold \| 0.8053 \|
	\| cosine_f1 \| 1.0 \|
	\| cosine_f1_threshold \| 0.8053 \|
	\| cosine_precision \| 1.0 \|
	\| cosine_recall \| 1.0 \|
	\| cosine_ap \| 1.0 \|
	\| dot_accuracy \| 1.0 \|
	\| dot_accuracy_threshold \| 0.8053 \|
	\| dot_f1 \| 1.0 \|
	\| dot_f1_threshold \| 0.8053 \|
	\| dot_precision \| 1.0 \|
	\| dot_recall \| 1.0 \|
	\| dot_ap \| 1.0 \|
	\| manhattan_accuracy \| 1.0 \|
	\| manhattan_accuracy_threshold \| 9.7795 \|
	\| manhattan_f1 \| 1.0 \|
	\| manhattan_f1_threshold \| 9.7795 \|
	\| manhattan_precision \| 1.0 \|
	\| manhattan_recall \| 1.0 \|
	\| manhattan_ap \| 1.0 \|
	\| euclidean_accuracy \| 1.0 \|
	\| euclidean_accuracy_threshold \| 0.6236 \|
	\| euclidean_f1 \| 1.0 \|
	\| euclidean_f1_threshold \| 0.6236 \|
	\| euclidean_precision \| 1.0 \|
	\| euclidean_recall \| 1.0 \|
	\| euclidean_ap \| 1.0 \|
	\| max_accuracy \| 1.0 \|
	\| max_accuracy_threshold \| 9.7795 \|
	\| max_f1 \| 1.0 \|
	\| max_f1_threshold \| 9.7795 \|
	\| max_precision \| 1.0 \|
	\| max_recall \| 1.0 \|
	\| max_ap \| 1.0 \|

	<!--
	## 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.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 333 training samples
	* Columns: <code>sentence1</code>, <code>label</code>, and <code>sentence2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| label \| sentence2 \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| int \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 10.25 tokens</li><li>max: 20 tokens</li></ul> \| <ul><li>0: ~51.65%</li><li>1: ~48.35%</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.42 tokens</li><li>max: 22 tokens</li></ul> \|
	* Samples:
	\| sentence1 \| label \| sentence2 \|
	\|:------------------------------------------------\|:---------------\|:---------------------------------------------------\|
	\| <code>How to improve my credit score?</code> \| <code>1</code> \| <code>Improving my credit score tips</code> \|
	\| <code>How does photosynthesis work?</code> \| <code>0</code> \| <code>What are the steps of photosynthesis?</code> \|
	\| <code>What is the population of Germany?</code> \| <code>0</code> \| <code>How many people live in Berlin?</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 37 evaluation samples
	* Columns: <code>sentence1</code>, <code>label</code>, and <code>sentence2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| label \| sentence2 \|
	\|:--------\|:---------------------------------------------------------------------------------\|:------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| int \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 10.0 tokens</li><li>max: 13 tokens</li></ul> \| <ul><li>0: ~35.14%</li><li>1: ~64.86%</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 8.68 tokens</li><li>max: 12 tokens</li></ul> \|
	* Samples:
	\| sentence1 \| label \| sentence2 \|
	\|:------------------------------------------------------------\|:---------------\|:------------------------------------------------\|
	\| <code>What is the price of Bitcoin?</code> \| <code>1</code> \| <code>Bitcoin's current value</code> \|
	\| <code>Who discovered gravity?</code> \| <code>1</code> \| <code>Who found out about gravity?</code> \|
	\| <code>What is the most spoken language in the world?</code> \| <code>1</code> \| <code>Language spoken by the most people</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `gradient_accumulation_steps`: 2
	- `learning_rate`: 3e-05
	- `weight_decay`: 0.01
	- `num_train_epochs`: 5
	- `lr_scheduler_type`: reduce_lr_on_plateau
	- `warmup_ratio`: 0.1
	- `load_best_model_at_end`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `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`: 2
	- `eval_accumulation_steps`: None
	- `learning_rate`: 3e-05
	- `weight_decay`: 0.01
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 5
	- `max_steps`: -1
	- `lr_scheduler_type`: reduce_lr_on_plateau
	- `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
	- `restore_callback_states_from_checkpoint`: 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`: 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`: True
	- `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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch_fused
	- `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_eval_metrics`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| pair-class-dev_max_ap \| pair-class-test_max_ap \|
	\|:----------:\|:------:\|:-------------:\|:---------:\|:---------------------:\|:----------------------:\|
	\| 0 \| 0 \| - \| - \| 0.8544 \| - \|
	\| 0.9524 \| 10 \| 0.0318 \| 0.0106 \| 0.9935 \| - \|
	\| 1.9048 \| 20 \| 0.0126 \| - \| - \| - \|
	\| 2.0 \| 21 \| - \| 0.0043 \| 1.0 \| - \|
	\| 2.8571 \| 30 \| 0.008 \| - \| - \| - \|
	\| 2.9524 \| 31 \| - \| 0.004 \| 1.0 \| - \|
	\| 3.8095 \| 40 \| 0.0056 \| - \| - \| - \|
	\| 4.0 \| 42 \| - \| 0.0040 \| 1.0 \| - \|
	\| 4.7619 \| 50 \| 0.0039 \| 0.0045 \| 1.0 \| 1.0 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.1.2+cu121
	- Accelerate: 0.32.1
	- Datasets: 2.19.1
	- 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",
	}
	```

	#### ContrastiveLoss
	```bibtex
	@inproceedings{hadsell2006dimensionality,
	author={Hadsell, R. and Chopra, S. and LeCun, Y.},
	booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
	title={Dimensionality Reduction by Learning an Invariant Mapping},
	year={2006},
	volume={2},
	number={},
	pages={1735-1742},
	doi={10.1109/CVPR.2006.100}
	}
	```

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