Add new SentenceTransformer model.

a22076c verified 5 months ago

28.9 kB

	---
	base_model: srikarvar/fine_tuned_model_5
	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:560
	- loss:OnlineContrastiveLoss
	widget:
	- source_sentence: The `Garage` class has a `to_services` method which is used to
	transform tasks into a list of `ServiceRecord` objects that are scheduled.
	sentences:
	- The `to_services` method in the Garage class is used to convert Garage tasks to
	a list of scheduled `ServiceRecord` objects.
	- It returns a `Recipe` for the specified serving size.
	- The AI community is a group of individuals who collaborate on models, datasets,
	and tools to advance artificial intelligence research.
	- source_sentence: The main version of the guide contains the INSTALLATION page. Click
	the link to be directed there.
	sentences:
	- You can bake bread by following the Bake bread tutorial.
	- The base class for documents generated from a data stream is StreamBasedBuilder.
	- You can find the INSTALLATION page in the main version of the guide. Click on
	the provided link to redirect to the main version.
	- source_sentence: A major distinction between a ProductList and an InventoryList
	is that a ProductList allows for random access to the items, while an InventoryList
	updates gradually as it is navigated.
	sentences:
	- The how-to guides for the platform include Setup, Processing, Streaming, TensorFlow
	integration, PyTorch integration, Cache management, Cloud storage, Search index,
	Analytics, and Data Pipelines.
	- 'Yes, there is a tutorial for analyzing stock market data. You can find it at
	the link provided: /docs/stocks/v2.10.0/data_analysis.'
	- The main difference between a ProductList and an InventoryList is that a ProductList
	provides random access to the items, while an InventoryList updates progressively
	as you browse the list.
	- source_sentence: ImageFolder is a dataset builder that eliminates the need for coding
	to quickly load a dataset with thousands of image files. It will automatically
	incorporate any extra data such as resolution, format, or tags, provided that
	it is included in a metadata file (metadata.csv/metadata.jsonl).
	sentences:
	- The function `calc_and_sum` returns the calculated value and sum.
	- Some examples of supported network drives are Network File System (NFS), Server
	Message Block (SMB), and WebDAV.
	- ImageFolder is a dataset builder designed to quickly load an image dataset with
	several thousand image files without requiring you to write any code. It automatically
	loads any additional information about your dataset, such as image resolution,
	format, or image tags, as long as you include this information in a metadata file
	(metadata.csv/metadata.jsonl).
	- source_sentence: The `num_services` method gives the quantity of services in the
	garage.
	sentences:
	- A signature in the sales database is a unique identifier for a transaction that
	is updated every time a change is made. It is computed by combining the previous
	signature and a hash of the latest update applied.
	- The `num_services` method returns the number of services in the garage.
	- It returns the number of entries in the dataset.
	model-index:
	- name: SentenceTransformer based on srikarvar/fine_tuned_model_5
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: pair class dev
	type: pair-class-dev
	metrics:
	- type: cosine_accuracy
	value: 0.9821428571428571
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.9922685623168945
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.9909909909909909
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.9922685623168945
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 1.0
	name: Cosine Precision
	- type: cosine_recall
	value: 0.9821428571428571
	name: Cosine Recall
	- type: cosine_ap
	value: 1.0
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.9821428571428571
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 0.9922685623168945
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.9909909909909909
	name: Dot F1
	- type: dot_f1_threshold
	value: 0.9922685623168945
	name: Dot F1 Threshold
	- type: dot_precision
	value: 1.0
	name: Dot Precision
	- type: dot_recall
	value: 0.9821428571428571
	name: Dot Recall
	- type: dot_ap
	value: 1.0
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.9821428571428571
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 1.8805665969848633
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.9909909909909909
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 1.8805665969848633
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 1.0
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.9821428571428571
	name: Manhattan Recall
	- type: manhattan_ap
	value: 1.0
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.9821428571428571
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 0.12164457887411118
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.9909909909909909
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 0.12164457887411118
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 1.0
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.9821428571428571
	name: Euclidean Recall
	- type: euclidean_ap
	value: 1.0
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.9821428571428571
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 1.8805665969848633
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.9909909909909909
	name: Max F1
	- type: max_f1_threshold
	value: 1.8805665969848633
	name: Max F1 Threshold
	- type: max_precision
	value: 1.0
	name: Max Precision
	- type: max_recall
	value: 0.9821428571428571
	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: 0.9821428571428571
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.9922685623168945
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.9909909909909909
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.9922685623168945
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 1.0
	name: Cosine Precision
	- type: cosine_recall
	value: 0.9821428571428571
	name: Cosine Recall
	- type: cosine_ap
	value: 1.0
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.9821428571428571
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 0.9922685623168945
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.9909909909909909
	name: Dot F1
	- type: dot_f1_threshold
	value: 0.9922685623168945
	name: Dot F1 Threshold
	- type: dot_precision
	value: 1.0
	name: Dot Precision
	- type: dot_recall
	value: 0.9821428571428571
	name: Dot Recall
	- type: dot_ap
	value: 1.0
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.9821428571428571
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 1.8805665969848633
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.9909909909909909
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 1.8805665969848633
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 1.0
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.9821428571428571
	name: Manhattan Recall
	- type: manhattan_ap
	value: 1.0
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.9821428571428571
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 0.12164457887411118
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.9909909909909909
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 0.12164457887411118
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 1.0
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.9821428571428571
	name: Euclidean Recall
	- type: euclidean_ap
	value: 1.0
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.9821428571428571
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 1.8805665969848633
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.9909909909909909
	name: Max F1
	- type: max_f1_threshold
	value: 1.8805665969848633
	name: Max F1 Threshold
	- type: max_precision
	value: 1.0
	name: Max Precision
	- type: max_recall
	value: 0.9821428571428571
	name: Max Recall
	- type: max_ap
	value: 1.0
	name: Max Ap
	---

	# SentenceTransformer based on srikarvar/fine_tuned_model_5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [srikarvar/fine_tuned_model_5](https://huggingface.co/srikarvar/fine_tuned_model_5) on the json dataset. 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: [srikarvar/fine_tuned_model_5](https://huggingface.co/srikarvar/fine_tuned_model_5) <!-- at revision 4e4dc22ad09f760a0a35c55d14d2f89ebe2d2ff2 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- json
	<!-- - 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/fine_tuned_model_12")
	# Run inference
	sentences = [
	'The `num_services` method gives the quantity of services in the garage.',
	'The `num_services` method returns the number of services in the garage.',
	'It returns the number of entries in the dataset.',
	]
	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 \| 0.9821 \|
	\| cosine_accuracy_threshold \| 0.9923 \|
	\| cosine_f1 \| 0.991 \|
	\| cosine_f1_threshold \| 0.9923 \|
	\| cosine_precision \| 1.0 \|
	\| cosine_recall \| 0.9821 \|
	\| cosine_ap \| 1.0 \|
	\| dot_accuracy \| 0.9821 \|
	\| dot_accuracy_threshold \| 0.9923 \|
	\| dot_f1 \| 0.991 \|
	\| dot_f1_threshold \| 0.9923 \|
	\| dot_precision \| 1.0 \|
	\| dot_recall \| 0.9821 \|
	\| dot_ap \| 1.0 \|
	\| manhattan_accuracy \| 0.9821 \|
	\| manhattan_accuracy_threshold \| 1.8806 \|
	\| manhattan_f1 \| 0.991 \|
	\| manhattan_f1_threshold \| 1.8806 \|
	\| manhattan_precision \| 1.0 \|
	\| manhattan_recall \| 0.9821 \|
	\| manhattan_ap \| 1.0 \|
	\| euclidean_accuracy \| 0.9821 \|
	\| euclidean_accuracy_threshold \| 0.1216 \|
	\| euclidean_f1 \| 0.991 \|
	\| euclidean_f1_threshold \| 0.1216 \|
	\| euclidean_precision \| 1.0 \|
	\| euclidean_recall \| 0.9821 \|
	\| euclidean_ap \| 1.0 \|
	\| max_accuracy \| 0.9821 \|
	\| max_accuracy_threshold \| 1.8806 \|
	\| max_f1 \| 0.991 \|
	\| max_f1_threshold \| 1.8806 \|
	\| max_precision \| 1.0 \|
	\| max_recall \| 0.9821 \|
	\| 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 \| 0.9821 \|
	\| cosine_accuracy_threshold \| 0.9923 \|
	\| cosine_f1 \| 0.991 \|
	\| cosine_f1_threshold \| 0.9923 \|
	\| cosine_precision \| 1.0 \|
	\| cosine_recall \| 0.9821 \|
	\| cosine_ap \| 1.0 \|
	\| dot_accuracy \| 0.9821 \|
	\| dot_accuracy_threshold \| 0.9923 \|
	\| dot_f1 \| 0.991 \|
	\| dot_f1_threshold \| 0.9923 \|
	\| dot_precision \| 1.0 \|
	\| dot_recall \| 0.9821 \|
	\| dot_ap \| 1.0 \|
	\| manhattan_accuracy \| 0.9821 \|
	\| manhattan_accuracy_threshold \| 1.8806 \|
	\| manhattan_f1 \| 0.991 \|
	\| manhattan_f1_threshold \| 1.8806 \|
	\| manhattan_precision \| 1.0 \|
	\| manhattan_recall \| 0.9821 \|
	\| manhattan_ap \| 1.0 \|
	\| euclidean_accuracy \| 0.9821 \|
	\| euclidean_accuracy_threshold \| 0.1216 \|
	\| euclidean_f1 \| 0.991 \|
	\| euclidean_f1_threshold \| 0.1216 \|
	\| euclidean_precision \| 1.0 \|
	\| euclidean_recall \| 0.9821 \|
	\| euclidean_ap \| 1.0 \|
	\| max_accuracy \| 0.9821 \|
	\| max_accuracy_threshold \| 1.8806 \|
	\| max_f1 \| 0.991 \|
	\| max_f1_threshold \| 1.8806 \|
	\| max_precision \| 1.0 \|
	\| max_recall \| 0.9821 \|
	\| 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.
	-->

	## Training Details

	### Training Dataset

	#### json

	* Dataset: json
	* Size: 560 training samples
	* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
	* Approximate statistics based on the first 560 samples:
	\| \| label \| sentence2 \| sentence1 \|
	\|:--------\|:-----------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| int \| string \| string \|
	\| details \| <ul><li>1: 100.00%</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 30.18 tokens</li><li>max: 98 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 30.0 tokens</li><li>max: 98 tokens</li></ul> \|
	* Samples:
	\| label \| sentence2 \| sentence1 \|
	\|:---------------\|:---------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|
	\| <code>1</code> \| <code>It is not available in v2.10.0.</code> \| <code>No, it doesn't exist in v2.10.0.</code> \|
	\| <code>1</code> \| <code>You can become a member of the research forum and pose questions to the AI community.</code> \| <code>You can join and ask questions in the AI research forum.</code> \|
	\| <code>1</code> \| <code>No information regarding initializing a project for PyTorch is included in the guide.</code> \| <code>The guide does not provide information on how to initialize a project for PyTorch.</code> \|
	* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

	### Evaluation Dataset

	#### json

	* Dataset: json
	* Size: 560 evaluation samples
	* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
	* Approximate statistics based on the first 560 samples:
	\| \| label \| sentence2 \| sentence1 \|
	\|:--------\|:-----------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| int \| string \| string \|
	\| details \| <ul><li>1: 100.00%</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 32.29 tokens</li><li>max: 82 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 31.96 tokens</li><li>max: 82 tokens</li></ul> \|
	* Samples:
	\| label \| sentence2 \| sentence1 \|
	\|:---------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>1</code> \| <code>The how-to guides for the platform include instructions for Setup, Processing, Streaming, TensorFlow integration, PyTorch integration, Caching, Cloud storage, Indexing, Analytics, and Data Pipelines.</code> \| <code>The how-to guides for the platform include Setup, Processing, Streaming, TensorFlow integration, PyTorch integration, Cache management, Cloud storage, Search index, Analytics, and Data Pipelines.</code> \|
	\| <code>1</code> \| <code>In the absence of a model script, all files in the supported formats will be loaded. However, if a model script is present, it will be downloaded and executed in order to download and prepare the model.</code> \| <code>If there’s no model script, all the files in the supported formats are loaded. If there’s a model script, it is downloaded and executed to download and prepare the model.</code> \|
	\| <code>1</code> \| <code>React, Angular, and Vue are compatible with the Plugin library.</code> \| <code>The Plugin library can be used with React, Angular, and Vue.</code> \|
	* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `gradient_accumulation_steps`: 2
	- `num_train_epochs`: 4
	- `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`: 32
	- `per_device_eval_batch_size`: 32
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 2
	- `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`: 4
	- `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
	- `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 \| - \| - \| 1.0 \| - \|
	\| 1.0 \| 8 \| - \| 0.0028 \| 1.0 \| - \|
	\| 1.25 \| 10 \| 0.1425 \| - \| - \| - \|
	\| 2.0 \| 16 \| - \| 0.0003 \| 1.0 \| - \|
	\| 2.5 \| 20 \| 0.002 \| - \| - \| - \|
	\| 3.0 \| 24 \| - \| 0.0001 \| 1.0 \| - \|
	\| 3.75 \| 30 \| 0.0008 \| - \| - \| - \|
	\| 4.0 \| 32 \| - \| 0.0001 \| 1.0 \| 1.0 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.1.0
	- Transformers: 4.41.2
	- PyTorch: 2.1.2+cu121
	- Accelerate: 0.34.2
	- 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",
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->