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MANMEET75/
pubmedbert-base-embedding-Chatbot-Matryoshk

Name: pubmedbert-base-embedding-Chatbot-Matryoshk
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pubmedbert-base-embedding Chatbot Matryoshka

This is a sentence-transformers model finetuned from NeuML/pubmedbert-base-embeddings. 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: NeuML/pubmedbert-base-embeddings
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

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: BertModel 
  (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("MANMEET75/pubmedbert-base-embedding-Chatbot-Matryoshk")
# Run inference
sentences = [
    "I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.",
    'Bharti, what languages can you understand and respond to?',
    'Bharti, can you provide tips for effective online communication?',
]
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

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7674
cosine_accuracy@3	0.907
cosine_accuracy@5	0.9302
cosine_accuracy@10	0.9302
cosine_precision@1	0.7674
cosine_precision@3	0.3023
cosine_precision@5	0.186
cosine_precision@10	0.093
cosine_recall@1	0.7674
cosine_recall@3	0.907
cosine_recall@5	0.9302
cosine_recall@10	0.9302
cosine_ndcg@10	0.8564
cosine_mrr@10	0.8314
cosine_map@100	0.835

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6977
cosine_accuracy@3	0.8837
cosine_accuracy@5	0.9302
cosine_accuracy@10	0.9302
cosine_precision@1	0.6977
cosine_precision@3	0.2946
cosine_precision@5	0.186
cosine_precision@10	0.093
cosine_recall@1	0.6977
cosine_recall@3	0.8837
cosine_recall@5	0.9302
cosine_recall@10	0.9302
cosine_ndcg@10	0.832
cosine_mrr@10	0.7984
cosine_map@100	0.8017

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7907
cosine_accuracy@3	0.8837
cosine_accuracy@5	0.907
cosine_accuracy@10	0.907
cosine_precision@1	0.7907
cosine_precision@3	0.2946
cosine_precision@5	0.1814
cosine_precision@10	0.0907
cosine_recall@1	0.7907
cosine_recall@3	0.8837
cosine_recall@5	0.907
cosine_recall@10	0.907
cosine_ndcg@10	0.8533
cosine_mrr@10	0.8353
cosine_map@100	0.8392

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6744
cosine_accuracy@3	0.814
cosine_accuracy@5	0.8837
cosine_accuracy@10	0.907
cosine_precision@1	0.6744
cosine_precision@3	0.2713
cosine_precision@5	0.1767
cosine_precision@10	0.0907
cosine_recall@1	0.6744
cosine_recall@3	0.814
cosine_recall@5	0.8837
cosine_recall@10	0.907
cosine_ndcg@10	0.7942
cosine_mrr@10	0.7576
cosine_map@100	0.76

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6047
cosine_accuracy@3	0.7442
cosine_accuracy@5	0.7907
cosine_accuracy@10	0.8605
cosine_precision@1	0.6047
cosine_precision@3	0.2481
cosine_precision@5	0.1581
cosine_precision@10	0.086
cosine_recall@1	0.6047
cosine_recall@3	0.7442
cosine_recall@5	0.7907
cosine_recall@10	0.8605
cosine_ndcg@10	0.722
cosine_mrr@10	0.6786
cosine_map@100	0.6823

Training Details

Training Dataset

Unnamed Dataset

Size: 530 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 12 tokens
mean: 36.83 tokens
max: 107 tokens
min: 7 tokens
mean: 18.54 tokens
max: 30 tokens

Samples:

positive	anchor
`BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below [object Object].`	`What are the benefits of the BharatPe speaker?`
`BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below [object Object].`	`What advantages does the BharatPe speaker offer?`
`BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below [object Object].`	`Can you outline the benefits of using the BharatPe speaker?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 10
lr_scheduler_type: cosine
warmup_ratio: 0.1
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_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: 10
max_steps: -1
lr_scheduler_type: cosine
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: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
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: True
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_fused
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.9412	1	-	0.4829	0.5338	0.5921	0.3235	0.6100
1.8824	2	-	0.5767	0.6175	0.6588	0.4176	0.6793
2.8235	3	-	0.6337	0.6776	0.6979	0.5083	0.7263
3.7647	4	-	0.6588	0.7257	0.7297	0.5840	0.7612
4.7059	5	-	0.7049	0.7766	0.7643	0.6151	0.7902
5.6471	6	-	0.7374	0.8257	0.7890	0.6519	0.7956
6.5882	7	-	0.7573	0.8261	0.7912	0.6689	0.7978
7.5294	8	-	0.7590	0.8275	0.7958	0.6811	0.8233
8.4706	9	-	0.76	0.8392	0.7998	0.6823	0.8234
9.4118	10	4.944	0.7600	0.8392	0.8017	0.6823	0.8350

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.32.1
Datasets: 2.19.1
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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}

	positive	anchor
type	string	string
details	min: 12 tokens mean: 36.83 tokens max: 107 tokens	min: 7 tokens mean: 18.54 tokens max: 30 tokens