SentenceTransformer (all-mpnet-base-v2) fine-tuned using clinical naatives
This is a sentence-transformers model finetuned from 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
- Maximum Sequence Length: 384 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
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]
Training Details
Training Dataset
Unnamed Dataset
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 64per_device_eval_batch_size
: 64num_train_epochs
: 100multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 100max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Click to expand
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 |
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
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Model tree for Shobhank-iiitdwd/Clinical_sentence_transformers_mpnet_base_v2
Base model
sentence-transformers/all-mpnet-base-v2
Finetuned
this model