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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- loss:MultipleNegativesRankingLoss
	base_model: sentence-transformers/all-mpnet-base-v2
	datasets: []
	widget:
	- source_sentence: clinician thinks the patient is homeless
	sentences:
	- '- Ms. ___ was homeless at the time of this admission.'
	- This is ___ year old single homeless woman, previously diagnosed with borderline
	personality disorder with chronic affective instability, reactive mood, impulsivity,
	SIB (ingesting objects while hospitalized), recently discharged from ___ on ___,
	___ client, who presented to ___ on a ___ with worsening mood, threats of suicide
	via cutting her legs off, as well as thoughts of wanting to hurt _
	- Patient reports that her apartment is bugged, she has camera in her television,
	and a helicopter is reading minds.
	- source_sentence: assigned a case manager for housing
	sentences:
	- 'Home With Service Facility:'
	- We consulted social work, psychiatry, and the case managers, who are working with
	the hospital attorneys to acquire safer housing options with greater oversight
	from health care professionals. .
	- Has not established care with
	- source_sentence: has been homeless
	sentences:
	- He reports being homeless, living in an empty garage near his sister.
	- To complicate matters, patient's main support/roommate will be moving out of country
	soon, so he will no longer be able to live in his apartment.
	- 'Axis IV: homelessness'
	- source_sentence: homelessness
	sentences:
	- Does not identify any acute stressors, but describes no longer being able to tolerate
	being homeless (lack of food/clothing/showers).
	- Unclear how reliable his group home is administering meds, notably nursing is
	quite limited.
	- Case management assisted in formulated a plan with ___ that would allow the patient's
	___ be the first responder when issues regarding her these two problems arise.
	- source_sentence: assisted…housing benefits
	sentences:
	- As a result, patient is currently homeless.
	- 'Home With Service Facility:'
	- Patient with multiple admissions in the past several months, homeless.
	pipeline_tag: sentence-similarity
	---

	# SentenceTransformer (all-mpnet-base-v2) fine-tuned using clinical naatives

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from
	[sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2).
	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: [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
	- Maximum Sequence Length: 384 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->


	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
	(1): Pooling({'word_embedding_dimension': 768, '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("Shobhank-iiitdwd/Clinical_sentence_transformers_mpnet_base_v2")
	# Run inference
	sentences = [
	'assisted…housing benefits',
	'Home With Service Facility:',
	'Patient with multiple admissions in the past several months, homeless.',
	]
	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.
	-->

	<!--
	## 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

	#### Unnamed Dataset



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

	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `num_train_epochs`: 100
	- `multi_dataset_batch_sampler`: round_robin

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `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
	- `num_train_epochs`: 100
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `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
	- `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`: 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}
	- `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_eval_metrics`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \|
	\|:-------:\|:-----:\|:-------------:\|
	\| 0.6887 \| 500 \| 3.5133 \|
	\| 1.3774 \| 1000 \| 3.2727 \|
	\| 2.0661 \| 1500 \| 3.2238 \|
	\| 2.7548 \| 2000 \| 3.1758 \|
	\| 3.4435 \| 2500 \| 3.1582 \|
	\| 4.1322 \| 3000 \| 3.1385 \|
	\| 4.8209 \| 3500 \| 3.1155 \|
	\| 5.5096 \| 4000 \| 3.1034 \|
	\| 6.1983 \| 4500 \| 3.091 \|
	\| 6.8871 \| 5000 \| 3.0768 \|
	\| 7.5758 \| 5500 \| 3.065 \|
	\| 8.2645 \| 6000 \| 3.0632 \|
	\| 8.9532 \| 6500 \| 3.0566 \|
	\| 9.6419 \| 7000 \| 3.0433 \|
	\| 0.6887 \| 500 \| 3.0536 \|
	\| 1.3774 \| 1000 \| 3.0608 \|
	\| 2.0661 \| 1500 \| 3.0631 \|
	\| 2.7548 \| 2000 \| 3.0644 \|
	\| 3.4435 \| 2500 \| 3.0667 \|
	\| 4.1322 \| 3000 \| 3.07 \|
	\| 4.8209 \| 3500 \| 3.0682 \|
	\| 5.5096 \| 4000 \| 3.0718 \|
	\| 6.1983 \| 4500 \| 3.0719 \|
	\| 6.8871 \| 5000 \| 3.0685 \|
	\| 7.5758 \| 5500 \| 3.0723 \|
	\| 8.2645 \| 6000 \| 3.0681 \|
	\| 8.9532 \| 6500 \| 3.0633 \|
	\| 9.6419 \| 7000 \| 3.0642 \|
	\| 10.3306 \| 7500 \| 3.0511 \|
	\| 11.0193 \| 8000 \| 3.0463 \|
	\| 11.7080 \| 8500 \| 3.0301 \|
	\| 12.3967 \| 9000 \| 3.0163 \|
	\| 13.0854 \| 9500 \| 3.0059 \|
	\| 13.7741 \| 10000 \| 2.9845 \|
	\| 14.4628 \| 10500 \| 2.9705 \|
	\| 15.1515 \| 11000 \| 2.9536 \|
	\| 15.8402 \| 11500 \| 2.9263 \|
	\| 16.5289 \| 12000 \| 2.9199 \|
	\| 17.2176 \| 12500 \| 2.8989 \|
	\| 17.9063 \| 13000 \| 2.8818 \|
	\| 18.5950 \| 13500 \| 2.8735 \|
	\| 19.2837 \| 14000 \| 2.852 \|
	\| 19.9725 \| 14500 \| 2.8315 \|
	\| 20.6612 \| 15000 \| 2.8095 \|
	\| 21.3499 \| 15500 \| 2.7965 \|
	\| 22.0386 \| 16000 \| 2.7802 \|
	\| 22.7273 \| 16500 \| 2.7527 \|
	\| 23.4160 \| 17000 \| 2.7547 \|
	\| 24.1047 \| 17500 \| 2.7377 \|
	\| 24.7934 \| 18000 \| 2.7035 \|
	\| 25.4821 \| 18500 \| 2.7102 \|
	\| 26.1708 \| 19000 \| 2.6997 \|
	\| 26.8595 \| 19500 \| 2.6548 \|
	\| 27.5482 \| 20000 \| 2.6704 \|
	\| 28.2369 \| 20500 \| 2.6624 \|
	\| 28.9256 \| 21000 \| 2.6306 \|
	\| 29.6143 \| 21500 \| 2.6358 \|
	\| 30.3030 \| 22000 \| 2.634 \|
	\| 30.9917 \| 22500 \| 2.6089 \|
	\| 31.6804 \| 23000 \| 2.607 \|
	\| 32.3691 \| 23500 \| 2.6246 \|
	\| 33.0579 \| 24000 \| 2.5947 \|
	\| 33.7466 \| 24500 \| 2.5798 \|
	\| 34.4353 \| 25000 \| 2.6025 \|
	\| 35.1240 \| 25500 \| 2.5824 \|
	\| 35.8127 \| 26000 \| 2.5698 \|
	\| 36.5014 \| 26500 \| 2.5711 \|
	\| 37.1901 \| 27000 \| 2.5636 \|
	\| 37.8788 \| 27500 \| 2.5387 \|
	\| 38.5675 \| 28000 \| 2.5472 \|
	\| 39.2562 \| 28500 \| 2.5455 \|
	\| 39.9449 \| 29000 \| 2.5204 \|
	\| 40.6336 \| 29500 \| 2.524 \|
	\| 41.3223 \| 30000 \| 2.5246 \|
	\| 42.0110 \| 30500 \| 2.5125 \|
	\| 42.6997 \| 31000 \| 2.5042 \|
	\| 43.3884 \| 31500 \| 2.5165 \|
	\| 44.0771 \| 32000 \| 2.5187 \|
	\| 44.7658 \| 32500 \| 2.4975 \|
	\| 45.4545 \| 33000 \| 2.5048 \|
	\| 46.1433 \| 33500 \| 2.521 \|
	\| 46.8320 \| 34000 \| 2.4825 \|
	\| 47.5207 \| 34500 \| 2.5034 \|
	\| 48.2094 \| 35000 \| 2.5049 \|
	\| 48.8981 \| 35500 \| 2.4886 \|
	\| 49.5868 \| 36000 \| 2.4992 \|
	\| 50.2755 \| 36500 \| 2.5099 \|
	\| 50.9642 \| 37000 \| 2.489 \|
	\| 51.6529 \| 37500 \| 2.4825 \|
	\| 52.3416 \| 38000 \| 2.4902 \|
	\| 53.0303 \| 38500 \| 2.4815 \|
	\| 53.7190 \| 39000 \| 2.4723 \|
	\| 54.4077 \| 39500 \| 2.4921 \|
	\| 55.0964 \| 40000 \| 2.4763 \|
	\| 55.7851 \| 40500 \| 2.4692 \|
	\| 56.4738 \| 41000 \| 2.4831 \|
	\| 57.1625 \| 41500 \| 2.4705 \|
	\| 57.8512 \| 42000 \| 2.4659 \|
	\| 58.5399 \| 42500 \| 2.4804 \|
	\| 59.2287 \| 43000 \| 2.4582 \|
	\| 59.9174 \| 43500 \| 2.4544 \|
	\| 60.6061 \| 44000 \| 2.4712 \|
	\| 61.2948 \| 44500 \| 2.4478 \|
	\| 61.9835 \| 45000 \| 2.4428 \|
	\| 62.6722 \| 45500 \| 2.4558 \|
	\| 63.3609 \| 46000 \| 2.4428 \|
	\| 64.0496 \| 46500 \| 2.4399 \|
	\| 64.7383 \| 47000 \| 2.4529 \|
	\| 65.4270 \| 47500 \| 2.4374 \|
	\| 66.1157 \| 48000 \| 2.4543 \|
	\| 66.8044 \| 48500 \| 2.4576 \|
	\| 67.4931 \| 49000 \| 2.4426 \|
	\| 68.1818 \| 49500 \| 2.4698 \|
	\| 68.8705 \| 50000 \| 2.4604 \|
	\| 69.5592 \| 50500 \| 2.4515 \|
	\| 70.2479 \| 51000 \| 2.4804 \|
	\| 70.9366 \| 51500 \| 2.4545 \|
	\| 71.6253 \| 52000 \| 2.4523 \|
	\| 72.3140 \| 52500 \| 2.4756 \|
	\| 73.0028 \| 53000 \| 2.4697 \|
	\| 73.6915 \| 53500 \| 2.4536 \|
	\| 74.3802 \| 54000 \| 2.4866 \|
	\| 75.0689 \| 54500 \| 2.471 \|
	\| 75.7576 \| 55000 \| 2.483 \|
	\| 76.4463 \| 55500 \| 2.5002 \|
	\| 77.1350 \| 56000 \| 2.4849 \|
	\| 77.8237 \| 56500 \| 2.4848 \|
	\| 78.5124 \| 57000 \| 2.5047 \|
	\| 79.2011 \| 57500 \| 2.5143 \|
	\| 79.8898 \| 58000 \| 2.4879 \|
	\| 80.5785 \| 58500 \| 2.5093 \|
	\| 81.2672 \| 59000 \| 2.5247 \|
	\| 81.9559 \| 59500 \| 2.4915 \|
	\| 82.6446 \| 60000 \| 2.5124 \|
	\| 83.3333 \| 60500 \| 2.5056 \|
	\| 84.0220 \| 61000 \| 2.4767 \|
	\| 84.7107 \| 61500 \| 2.5068 \|
	\| 85.3994 \| 62000 \| 2.5173 \|
	\| 86.0882 \| 62500 \| 2.4911 \|
	\| 86.7769 \| 63000 \| 2.526 \|
	\| 87.4656 \| 63500 \| 2.5313 \|
	\| 88.1543 \| 64000 \| 2.5312 \|
	\| 88.8430 \| 64500 \| 2.5735 \|
	\| 89.5317 \| 65000 \| 2.5873 \|
	\| 90.2204 \| 65500 \| 2.6395 \|
	\| 90.9091 \| 66000 \| 2.7914 \|
	\| 91.5978 \| 66500 \| 2.6729 \|
	\| 92.2865 \| 67000 \| 2.9846 \|
	\| 92.9752 \| 67500 \| 2.9259 \|
	\| 93.6639 \| 68000 \| 2.8845 \|
	\| 94.3526 \| 68500 \| 2.9906 \|
	\| 95.0413 \| 69000 \| 2.9534 \|
	\| 95.7300 \| 69500 \| 2.9857 \|
	\| 96.4187 \| 70000 \| 3.0559 \|
	\| 97.1074 \| 70500 \| 2.9919 \|
	\| 97.7961 \| 71000 \| 3.0435 \|
	\| 98.4848 \| 71500 \| 3.0534 \|
	\| 99.1736 \| 72000 \| 3.0169 \|
	\| 99.8623 \| 72500 \| 3.0264 \|

	</details>

	### Framework Versions
	- Python: 3.10.11
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.0.1
	- Accelerate: 0.31.0
	- Datasets: 2.19.1
	- Tokenizers: 0.19.1