metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:942069
  - loss:CoSENTLoss
base_model: microsoft/mpnet-base
widget:
  - source_sentence: Three women in dress suits walk by a building.
    sentences:
      - Three women are traveling by foot.
      - Two kids are flying on a hovercraft.
      - A man jumps in the ocean.
  - source_sentence: >-
      A man wearing sunglasses is sitting on the steps outside, reading a
      magazine.
    sentences:
      - Men are walking  in different directions.
      - There is a man running.
      - The man is reading a spoon with the words "HELP ME" on it.
  - source_sentence: >-
      A middle-aged man is sitting indian style outside holding a folded paper
      in his hands.
    sentences:
      - A man and woman are looking at produce.
      - A middle aged man is showing off his origami creation.
      - The boy is sitting
  - source_sentence: >-
      Two men playing baseball with the one in the black and red jersey running
      toward base.
    sentences:
      - The person is cooking a hamburger.
      - The man in black and red is sitting in the bleachers.
      - A player fighting in a soccer game.
  - source_sentence: Two men are in an electronics workshop, working on computers or equipment.
    sentences:
      - The men are experts when it comes to electronics.
      - A tall person sitting
      - A man is chasing an SUV that is going in the same direction as him.
datasets:
  - sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on microsoft/mpnet-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: basemodel evaluator
          type: basemodel_evaluator
        metrics:
          - type: pearson_cosine
            value: 0.573397130206215
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5954429501396288
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: finetunedmodel evaluator
          type: finetunedmodel_evaluator
        metrics:
          - type: pearson_cosine
            value: 0.5716395027559762
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6003777834660847
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.5716395027559762
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6003777834660847
            name: Spearman Cosine

SentenceTransformer based on microsoft/mpnet-base

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the all-nli 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- all-nli
Language: en

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("surajvbangera/mediclaim_embedding")
# Run inference
sentences = [
    'Two men are in an electronics workshop, working on computers or equipment.',
    'The men are experts when it comes to electronics.',
    'A tall person sitting',
]
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

Semantic Similarity

Datasets: basemodel_evaluator, finetunedmodel_evaluator and finetunedmodel_evaluator
Evaluated with EmbeddingSimilarityEvaluator

Metric	basemodel_evaluator	finetunedmodel_evaluator
pearson_cosine	0.5734	0.5716
spearman_cosine	0.5954	0.6004

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 942,069 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 17.38 tokens
max: 52 tokens

min: 4 tokens
mean: 10.7 tokens
max: 31 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 17.38 tokens max: 52 tokens	min: 4 tokens mean: 10.7 tokens max: 31 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence1	sentence2	score
`A person on a horse jumps over a broken down airplane.`	`A person is training his horse for a competition.`	`0.5`
`A person on a horse jumps over a broken down airplane.`	`A person is at a diner, ordering an omelette.`	`0.0`
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`1.0`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
Size: 19,657 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 17.56 tokens
max: 45 tokens

min: 5 tokens
mean: 10.51 tokens
max: 25 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 17.56 tokens max: 45 tokens	min: 5 tokens mean: 10.51 tokens max: 25 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence1	sentence2	score
`Two women are embracing while holding to go packages.`	`The sisters are hugging goodbye while holding to go packages after just eating lunch.`	`0.5`
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`1.0`
`Two women are embracing while holding to go packages.`	`The men are fighting outside a deli.`	`0.0`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

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: 5e-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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	basemodel_evaluator_spearman_cosine	finetunedmodel_evaluator_spearman_cosine
-1	-1	-	-	0.0810	-
0.8	100	4.5047	3.9356	0.5954	-
-1	-1	-	-	-	0.6004

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}