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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:2806
	- loss:TripletLoss
	base_model: intfloat/e5-large-v2
	widget:
	- source_sentence: What kind of interests do Joanna and Nate share?
	sentences:
	- '[4:21 pm on 16 July, 2023] John: John scored 40 points in a game last week, his
	highest ever.'
	- '[2:01 pm on 23 January, 2022] Nate: Thanks! The turtles might be small, but both
	sure have big personalities. I really reccomend having something like these little
	guys for times of stress.'
	- '[11:54 am on 2 May, 2022] Joanna: Joanna has been working on some projects and
	testing dairy-free dessert recipes for friends and family.'
	- source_sentence: What is Dave's favorite activity?
	sentences:
	- '[11:53 am on 23 March, 2023] Calvin: I''m so excited to learn about Japanese
	culture and get a chance to expand.'
	- '[2:55 pm on 31 August, 2023] Calvin: Yeah, Dave! It''s like every mark and strum
	holds a story. Take a look.'
	- '[3:13 pm on 8 October, 2023] Dave: Restoring things can be tough for Dave, but
	the feeling of accomplishment it gives him is great.'
	- source_sentence: What health issue did Sam face that motivated him to change his
	lifestyle?
	sentences:
	- '[12:17 am on 10 January, 2024] Sam: Sam planned to make an appointment with the
	doctor to get advice on a balanced diet plan and low-impact exercises.'
	- '[1:32 pm on 6 January, 2024] Evan: Thanks, Sam! Catch you later. Have a great
	one!'
	- '[4:25 pm on 26 December, 2023] Evan: Evan created a painting that reflects a
	sense of joy and freedom.'
	- source_sentence: When is Evan planning a big family reunion?
	sentences:
	- '[3:55 pm on 6 June, 2023] Evan: Awesome, Sam! Let me know how it goes. Making
	small changes can really help you live a healthier life. Don''t forget - every
	step matters!'
	- '[1:45 pm on 9 December, 2023] Evan: Evan is planning a big family reunion next
	summer to create more memories with family.'
	- '[2:42 pm on 2 April, 2023] Andrew: Thanks! Fingers crossed for the apartment
	and that furry friend.'
	- source_sentence: When did Evan finish the painting that's hanging in the exhibit?
	sentences:
	- '[6:48 pm on 17 December, 2023] Evan: Yeah, trying something new and succeeding
	gives a great feeling of accomplishment. Give it a go, even if it''s just a little
	thing. You''ll be amazed!'
	- '[7:52 pm on 7 August, 2023] Evan: Yep, Sam! Consistency and perseverance will
	get us far. Great chat!'
	- '[1:24 pm on 25 May, 2023] Maria: Maria is busy at the shelter preparing for a
	fundraiser to cover basic needs for the homeless.'
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on intfloat/e5-large-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) <!-- at revision f169b11e22de13617baa190a028a32f3493550b6 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Supported Modality: Text
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
	(1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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("ainativestudio/locomo-e5-large-finetuned")
	# Run inference
	sentences = [
	"When did Evan finish the painting that's hanging in the exhibit?",
	"[6:48 pm on 17 December, 2023] Evan: Yeah, trying something new and succeeding gives a great feeling of accomplishment. Give it a go, even if it's just a little thing. You'll be amazed!",
	'[7:52 pm on 7 August, 2023] Evan: Yep, Sam! Consistency and perseverance will get us far. Great chat!',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities)
	# tensor([[ 1.0000, -0.9972, -0.9972],
	# [-0.9972, 1.0000, 1.0000],
	# [-0.9972, 1.0000, 1.0000]])
	```
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 2,806 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
	* Approximate statistics based on the first 100 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:---------\|:---------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| modality \| text \| text \| text \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 12.6 tokens</li><li>max: 20 tokens</li></ul> \| <ul><li>min: 24 tokens</li><li>mean: 35.69 tokens</li><li>max: 79 tokens</li></ul> \| <ul><li>min: 21 tokens</li><li>mean: 40.93 tokens</li><li>max: 92 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:-----------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>When did Caroline pass the adoption interview?</code> \| <code>[9:55 am on 22 October, 2023] Caroline: Caroline passed the adoption agency interviews last Friday and is excited about building her own family through adoption.</code> \| <code>[7:55 pm on 9 June, 2023] Melanie: Melanie is supportive of Caroline and proud of her for spreading awareness and inspiring others in the LGBTQ community.</code> \|
	\| <code>How many dogs does Andrew have?</code> \| <code>[6:12 pm on 19 October, 2023] Andrew: Andrew adopted another pup from a shelter and named him Buddy.</code> \| <code>[1:10 pm on 27 March, 2023] Andrew: Fox Hollow is a great trail to hike on weekends; the views are awesome!</code> \|
	\| <code>Why did Audrey think positive reinforcement training is important for pets?</code> \| <code>[2:03 pm on 11 May, 2023] Audrey: Audrey believes in using positive reinforcement rather than punishment to train pets.</code> \| <code>[5:41 pm on 3 May, 2023] Audrey: Audrey's dogs wear party hats for fun and treats.</code> \|
	* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

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

	- `per_device_train_batch_size`: 2
	- `per_device_eval_batch_size`: 2
	- `multi_dataset_batch_sampler`: round_robin

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 2
	- `per_device_eval_batch_size`: 2
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_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
	- `num_train_epochs`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: None
	- `warmup_ratio`: 0.0
	- `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
	- `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
	- `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`: 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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `parallelism_config`: 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
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `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`: None
	- `hub_always_push`: False
	- `hub_revision`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `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
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: no
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: True
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.3564 \| 500 \| 4.226 \|
	\| 0.7128 \| 1000 \| 4.0481 \|
	\| 1.0691 \| 1500 \| 4.004 \|
	\| 1.4255 \| 2000 \| 3.9519 \|
	\| 1.7819 \| 2500 \| 3.9643 \|
	\| 2.1383 \| 3000 \| 3.9683 \|
	\| 2.4947 \| 3500 \| 3.9387 \|
	\| 2.8510 \| 4000 \| 3.9134 \|


	### Training Time
	- Training: 1.1 hours

	### Framework Versions
	- Python: 3.14.2
	- Sentence Transformers: 5.5.1
	- Transformers: 4.57.6
	- PyTorch: 2.10.0
	- Accelerate: 1.13.0
	- Datasets: 5.0.0
	- Tokenizers: 0.22.2

	## 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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