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BGE micro v2 ESG

Name: bge-micro-v2-esg-v2
Availability: InStock

This is a sentence-transformers model finetuned from TaylorAI/bge-micro-v2. It maps sentences & paragraphs to a 384-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: TaylorAI/bge-micro-v2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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': 384, '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("elsayovita/bge-micro-v2-esg-v2")
# Run inference
sentences = [
    'Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised',
    "Question: What is the company's commitment towards its employees' health and well-being based on the provided context information?",
    'What types of skills does NetLink focus on developing through their training and development opportunities for employees?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_384
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.755
cosine_accuracy@3	0.8992
cosine_accuracy@5	0.9237
cosine_accuracy@10	0.9447
cosine_precision@1	0.755
cosine_precision@3	0.2997
cosine_precision@5	0.1847
cosine_precision@10	0.0945
cosine_recall@1	0.021
cosine_recall@3	0.025
cosine_recall@5	0.0257
cosine_recall@10	0.0262
cosine_ndcg@10	0.1891
cosine_mrr@10	0.8309
cosine_map@100	0.0231

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7496
cosine_accuracy@3	0.8958
cosine_accuracy@5	0.9187
cosine_accuracy@10	0.9418
cosine_precision@1	0.7496
cosine_precision@3	0.2986
cosine_precision@5	0.1837
cosine_precision@10	0.0942
cosine_recall@1	0.0208
cosine_recall@3	0.0249
cosine_recall@5	0.0255
cosine_recall@10	0.0262
cosine_ndcg@10	0.1882
cosine_mrr@10	0.8262
cosine_map@100	0.023

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7356
cosine_accuracy@3	0.8875
cosine_accuracy@5	0.9106
cosine_accuracy@10	0.9342
cosine_precision@1	0.7356
cosine_precision@3	0.2958
cosine_precision@5	0.1821
cosine_precision@10	0.0934
cosine_recall@1	0.0204
cosine_recall@3	0.0247
cosine_recall@5	0.0253
cosine_recall@10	0.0259
cosine_ndcg@10	0.1858
cosine_mrr@10	0.8144
cosine_map@100	0.0227

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6972
cosine_accuracy@3	0.8494
cosine_accuracy@5	0.8831
cosine_accuracy@10	0.9132
cosine_precision@1	0.6972
cosine_precision@3	0.2831
cosine_precision@5	0.1766
cosine_precision@10	0.0913
cosine_recall@1	0.0194
cosine_recall@3	0.0236
cosine_recall@5	0.0245
cosine_recall@10	0.0254
cosine_ndcg@10	0.1788
cosine_mrr@10	0.7793
cosine_map@100	0.0217

Information Retrieval

Dataset: dim_32
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5974
cosine_accuracy@3	0.7523
cosine_accuracy@5	0.797
cosine_accuracy@10	0.8448
cosine_precision@1	0.5974
cosine_precision@3	0.2508
cosine_precision@5	0.1594
cosine_precision@10	0.0845
cosine_recall@1	0.0166
cosine_recall@3	0.0209
cosine_recall@5	0.0221
cosine_recall@10	0.0235
cosine_ndcg@10	0.1593
cosine_mrr@10	0.685
cosine_map@100	0.0191

Training Details

Training Dataset

Unnamed Dataset

Size: 11,863 training samples
Columns: context and question
Approximate statistics based on the first 1000 samples:
context question
type string string
details
min: 13 tokens
mean: 40.74 tokens
max: 277 tokens
min: 11 tokens
mean: 24.4 tokens
max: 62 tokens

Samples:

context	question
`The engagement with key stakeholders involves various topics and methods throughout the year`	`Question: What does the engagement with key stakeholders involve throughout the year?`
`For unitholders and analysts, the focus is on business and operations, the release of financial results, and the overall performance and announcements`	`Question: What is the focus for unitholders and analysts in terms of business and operations, financial results, performance, and announcements?`
`These are communicated through press releases and other required disclosures via SGXNet and NetLink's website`	`What platform is used to communicate press releases and required disclosures for NetLink?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 384, 5 256, 6 128, 7 64, 8 32 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: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
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: 4
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: True
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
eval_on_start: 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_32_cosine_map@100	dim_384_cosine_map@100	dim_64_cosine_map@100
0.4313	10	5.2501	-	-	-	-	-
0.8625	20	3.4967	-	-	-	-	-
1.0350	24	-	0.0221	0.0224	0.0185	0.0226	0.0210
1.2264	30	3.1196	-	-	-	-	-
1.6577	40	2.4428	-	-	-	-	-
2.0458	49	-	0.0226	0.0229	0.0189	0.0230	0.0215
2.0216	50	2.2222	-	-	-	-	-
2.4528	60	2.3441	-	-	-	-	-
2.8841	70	2.0096	-	-	-	-	-
3.0566	74	-	0.0227	0.0230	0.0191	0.0231	0.0217
3.2480	80	2.3019	-	-	-	-	-
3.6792	90	1.9538	-	-	-	-	-
3.7655	92	-	0.0227	0.023	0.0191	0.0231	0.0217

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.4.0+cu121
Accelerate: 0.32.1
Datasets: 2.21.0
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}
}

	context	question
type	string	string
details	min: 13 tokens mean: 40.74 tokens max: 277 tokens	min: 11 tokens mean: 24.4 tokens max: 62 tokens