Saideepthi55/sentencetransformer-ft

MPNet base trained on AllNLI triplets

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Saideepthi55/sentencetransformer-ft")
# 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: all-nli-dev
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.6562
dot_accuracy	0.5342
manhattan_accuracy	0.7076
euclidean_accuracy	0.6584
max_accuracy	0.7076

Triplet

• Dataset: all-nli-dev
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9804
dot_accuracy	0.0189
manhattan_accuracy	0.9811
euclidean_accuracy	0.9802
max_accuracy	0.9811

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. 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:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

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(C@Hc3ccccc3)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(C@Hc4ccccc4)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:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• 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: 8
• per_device_eval_batch_size: 8
• 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	loss	all-nli-dev_max_accuracy
0	0	-	-	0.7076
0.0174	64	-	-	0.7156
0.0068	100	2.7336	2.6486	0.7524
0.0136	200	2.4965	1.9213	0.8162
0.0204	300	1.9042	1.7761	0.822
0.0272	400	1.6856	1.7172	0.8371
0.0340	500	1.6117	1.6916	0.8507
0.0408	600	1.5673	1.6809	0.8976
0.0477	700	1.5984	1.7052	0.9329
0.0545	800	1.5828	1.6841	0.9391
0.0613	900	1.5375	1.6534	0.9267
0.0681	1000	1.5561	1.6619	0.9509
0.0749	1100	1.4911	1.6538	0.9556
0.0817	1200	1.5075	1.6498	0.966
0.0885	1300	1.4722	1.6468	0.946
0.0953	1400	1.4806	1.6981	0.9631
0.1021	1500	1.4788	1.6335	0.9662
0.1089	1600	1.4668	1.6668	0.9731
0.1157	1700	1.4383	1.6473	0.9711
0.1225	1800	1.4549	1.6462	0.9713
0.1294	1900	1.4394	1.6184	0.9718
0.1362	2000	1.3861	1.6156	0.9676
0.1430	2100	1.4111	1.6045	0.9711
0.1498	2200	1.4286	1.6056	0.9782
0.1566	2300	1.4669	1.6174	0.9764
0.1634	2400	1.3761	1.6182	0.9776
0.1702	2500	1.4119	1.6150	0.9738
0.1770	2600	1.3625	1.5984	0.9776
0.1838	2700	1.3726	1.6092	0.9807
0.1906	2800	1.3265	1.6059	0.9789
0.1974	2900	1.3925	1.6004	0.978
0.2042	3000	1.3524	1.5964	0.9773
0.2111	3100	1.342	1.6213	0.9787
0.2179	3200	1.3478	1.6016	0.9822
0.2247	3300	1.3888	1.6038	0.9793
0.2315	3400	1.3328	1.5977	0.9813
0.2383	3500	1.372	1.6114	0.9824
0.2451	3600	1.3046	1.6082	0.9824
0.2519	3700	1.3857	1.5922	0.9824
0.2587	3800	1.3236	1.6127	0.9809
0.2655	3900	1.2929	1.5935	0.9824
0.2723	4000	1.3889	1.6047	0.9831
0.2791	4100	1.3509	1.6030	0.9844
0.2859	4200	1.3455	1.6099	0.9824
0.2928	4300	1.337	1.5939	0.984
0.2996	4400	1.3302	1.6057	0.9827
0.3064	4500	1.3377	1.6254	0.9833
0.3132	4600	1.3221	1.6020	0.9849
0.3200	4700	1.3209	1.6146	0.9824
0.3268	4800	1.354	1.6022	0.9824
0.3336	4900	1.3213	1.6136	0.9822
0.3404	5000	1.3484	1.5920	0.9807
0.3472	5100	1.3412	1.6106	0.978
0.3540	5200	1.3532	1.6001	0.9784
0.3608	5300	1.2984	1.6192	0.9762
0.3676	5400	1.3621	1.5850	0.98
0.3745	5500	1.2839	1.6158	0.9807
0.3813	5600	1.3664	1.6030	0.9831
0.3881	5700	1.327	1.6168	0.9822
0.3949	5800	1.3123	1.6040	0.982
0.4017	5900	1.3019	1.6092	0.9824
0.4085	6000	1.3908	1.5935	0.9829
0.4153	6100	1.3136	1.5916	0.9791
0.4221	6200	1.32	1.6091	0.9807
0.4289	6300	1.3018	1.6052	0.9827
0.4357	6400	1.3144	1.6083	0.9816
0.4425	6500	1.2865	1.6015	0.9829
0.4493	6600	1.2946	1.5882	0.9818
0.4562	6700	1.3245	1.5949	0.9824
0.4630	6800	1.3278	1.6081	0.9831
0.4698	6900	1.2842	1.6086	0.9836
0.4766	7000	1.3231	1.6170	0.9811

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.0
• Transformers: 4.44.2
• PyTorch: 2.4.0+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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}
}