This is a sentence-transformers model finetuned from microsoft/mpnet-base on the sxc_med_llm_chemical_gen dataset. 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: microsoft/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sxc_med_llm_chemical_gen
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, '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})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Saideepthi55/sentencetransformer_ftmodel_on_chemical_dataset")
# Run inference
sentences = [
    'With a molecule represented by the SMILES string CNNNCC(=O)N[C@H](C)C[C@@H](C)NCc1ccc2c(c1)CCC2, propose adjustments that can increase its logP value while keeping the output molecule structurally related to the input molecule.',
    'Given a molecule expressed in SMILES string, help me optimize it according to my requirements.',
    'In line with your criteria, I\'ve optimized the molecule and present it as "C[C@H](C[C@@H](C)NC(=O)COC(C)(C)C)NCc1ccc2c(c1)CCC2".',
]
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]

Evaluation

Metrics

Triplet

Dataset: sxc_med_llm_chemical_gen
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9838
dot_accuracy	0.0162
manhattan_accuracy	0.9827
euclidean_accuracy	0.9836
max_accuracy	0.9838

Training Details

Training Dataset

sxc_med_llm_chemical_gen

Dataset: sxc_med_llm_chemical_gen at ae28874
Size: 117,502 training samples
Columns: input, instruction, and output
Approximate statistics based on the first 1000 samples:
input instruction output
type string string string
details
min: 2 tokens
mean: 50.03 tokens
max: 156 tokens
min: 6 tokens
mean: 16.19 tokens
max: 69 tokens
min: 3 tokens
mean: 67.94 tokens
max: 321 tokens

Samples:

input	instruction	output
	`List the countries of the Middle East`	`Egypt, Israel, Iran, Iraq, Jordan, Kuwait, Lebanon, Qatar, Saudi Arabia, Syria, Turkey, The United Arab Emirates, Oman, and Yemen.`
`Support me in refining the molecule CCC(C)=C(CC)c1ccc(Nc2nc3nonc3nc2N2CCC(Oc3ccc(Cl)cc3)CC2)cc1 to have a more drug-like nature, ensuring it remains close to the input molecule. Please inform me of the QED value of the optimized molecule.`	`Help me adapt a molecular structure denoted in SMILES string based on my preferences.`	`I've optimized the molecule according to your requirements, yielding the structure "COC(=O)c1ccc(Nc2nc3nonc3nc2N2CCC(Oc3ccc(Cl)cc3)CC2)cc1" and an estimated QED value of 0.4.`
	`Create a social media strategy to attract followers on Twitter.`	The social media strategy for Twitter should involve Tweeting on a regular basis, with content that is engaging and relevant to the target audience. This can include retweetable quotes, links to videos and articles, sharing photos/images, polls, and so on. It is also beneficial for the information to include hashtags relevant to the content and any relevant topics of the industry. [object Object][object Object]It is also important to leverage Twitter influencers and accounts, as well as engaging with potential followers regularly and DM them when necessary. Additionally, running campaigns, contests and giveaways to gain more followers should also be part of the strategy. Finally, hashtag research should be conducted to increase the reach of the tweets.

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

sxc_med_llm_chemical_gen

Dataset: sxc_med_llm_chemical_gen at ae28874
Size: 4,500 evaluation samples
Columns: input, instruction, and output
Approximate statistics based on the first 1000 samples:
input instruction output
type string string string
details
min: 2 tokens
mean: 68.49 tokens
max: 165 tokens
min: 6 tokens
mean: 16.89 tokens
max: 36 tokens
min: 3 tokens
mean: 69.16 tokens
max: 205 tokens

Samples:

input	instruction	output
`With a molecule represented by the SMILES string COc1ccc(CN(C(=O)Nc2ccc([object Object]c3ccccc3)c(Cl)c2)C2CC2)cc1, propose adjustments that can decrease its blood-brain barrier permeability (BBBP) value by at least 0.1 compared to the pre-optimized value while keeping the output molecule structurally related to the input molecule. Please inform me of the BBBP value of the optimized molecule.`	`Help me adapt a molecular structure denoted in SMILES string based on my preferences.`	`I've optimized the molecule according to your requirements, yielding the structure "CC(C)c1cccc(Oc2ccc(CN(C(=O)Nc3ccc([object Object]c4ccccc4)c(Cl)c3)C3CC3)cc2)c1" and an estimated BBBP value of 0.34.`
`Which modifications can be applied to the molecule Cc1nn(-c2cc3cc(F)ccc3s2)c2ncc(C(=O)N3CCCC3c3nnc4n3CCCCC4)cc12 to increase its water solubility value while ensuring it remains similar to the original molecule?`	`Aid me in refining a molecular structure written in SMILES notation based on my criteria.`	`I've optimized the molecule according to your requirements, resulting in the following structure: "Cc1nn(C)c2ncc(C(=O)N3CCCC3c3nnc4n3CCCCC4)cc12".`
	`Describe the benefits of having a mentor.`	`Having a mentor can provide tremendous benefits, including guidance, advice, support and connection to a network of contacts. Mentors can offer valuable feedback, help to create goals and strategies, and provide the accountability necessary to carry out the desired goals. They can also provide a fresh perspective which can help to create new ideas and solutions.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 1
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: True
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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sxc_med_llm_chemical_gen_max_accuracy
0	0	-	-	0.7076
0.0136	100	4.1705	3.6314	0.7469
0.0272	200	3.0088	2.4771	0.8191
0.0408	300	2.3803	2.3765	0.8716
0.0545	400	2.2281	2.3122	0.9316
0.0681	500	2.1647	2.2997	0.9436
0.0817	600	2.1289	2.2663	0.9511
0.0953	700	2.0663	2.2601	0.9629
0.1089	800	2.065	2.2500	0.9687
0.1225	900	2.0399	2.2595	0.9693
0.1362	1000	1.9939	2.2375	0.9707
0.1498	1100	1.9858	2.2220	0.9684
0.1634	1200	2.0069	2.2265	0.9758
0.1770	1300	1.9591	2.2073	0.9702
0.1906	1400	1.9288	2.2078	0.976
0.2042	1500	1.9466	2.2036	0.9758
0.2179	1600	1.9175	2.2086	0.9764
0.2315	1700	1.8835	2.2329	0.9796
0.2451	1800	1.9134	2.2003	0.9796
0.2587	1900	1.8809	2.2003	0.9811
0.2723	2000	1.9263	2.2039	0.9824
0.2859	2100	1.9101	2.2084	0.9804
0.2996	2200	1.8846	2.2052	0.9831
0.3132	2300	1.8842	2.1903	0.9818
0.3268	2400	1.8945	2.1984	0.9807
0.3404	2500	1.9217	2.1859	0.9829
0.3540	2600	1.8704	2.1995	0.982
0.3676	2700	1.889	2.2038	0.9822
0.3813	2800	1.875	2.2079	0.9829
0.3949	2900	1.8792	2.1975	0.9833
0.4085	3000	1.882	2.1895	0.9796
0.4221	3100	1.8886	2.2115	0.9831
0.4357	3200	1.8629	2.2040	0.9838
0.4493	3300	1.8647	2.1973	0.9836
0.4630	3400	1.8888	2.1961	0.9838
0.4766	3500	1.8692	2.2027	0.9829
0.4902	3600	1.8846	2.1954	0.9838
0.5038	3700	1.8482	2.1888	0.9822
0.5174	3800	1.8527	2.1873	0.9824
0.5310	3900	1.8378	2.1940	0.9811
0.5447	4000	1.8679	2.2008	0.9833
0.5583	4100	1.8421	2.1845	0.9842
0.5719	4200	1.8325	2.1948	0.9847
0.5855	4300	1.8675	2.1750	0.9836
0.5991	4400	1.8483	2.1828	0.9831
0.6127	4500	1.854	2.1886	0.9831
0.6264	4600	1.827	2.1876	0.9824
0.6400	4700	1.8863	2.1849	0.9836
0.6536	4800	1.8919	2.1816	0.984
0.6672	4900	1.8211	2.1830	0.9847
0.6808	5000	1.8345	2.1847	0.9842
0.6944	5100	1.8685	2.1855	0.9853
0.7081	5200	1.85	2.1864	0.9844
0.7217	5300	1.8222	2.1875	0.9842
0.7353	5400	1.8179	2.1923	0.9844
0.7489	5500	1.7992	2.1909	0.9851
0.7625	5600	1.8495	2.1811	0.9847
0.7761	5700	1.808	2.1763	0.9842
0.7898	5800	1.8293	2.1861	0.9849
0.8034	5900	1.8184	2.1845	0.9851
0.8170	6000	1.8256	2.1956	0.9849
0.8306	6100	1.7904	2.1916	0.9842
0.8442	6200	1.8028	2.1918	0.9847
0.8578	6300	1.8316	2.1917	0.9856
0.8715	6400	1.7951	2.1929	0.9851
0.8851	6500	1.8175	2.1866	0.9847
0.8987	6600	1.8071	2.1899	0.9853
0.9123	6700	1.8632	2.1905	0.9844
0.9259	6800	1.8441	2.1885	0.984
0.9395	6900	1.8243	2.1865	0.9836
0.9532	7000	1.8055	2.1852	0.9842
0.9668	7100	1.8227	2.1843	0.984
0.9804	7200	1.8287	2.1831	0.984
0.9940	7300	1.8379	2.1838	0.9838

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.1
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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	input	instruction	output
type	string	string	string
details	min: 2 tokens mean: 50.03 tokens max: 156 tokens	min: 6 tokens mean: 16.19 tokens max: 69 tokens	min: 3 tokens mean: 67.94 tokens max: 321 tokens