This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. 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: BAAI/bge-small-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("SamagraDataGov/embedding_finetuned_test")
# Run inference
sentences = [
    'Who is considered as the nodal agency for engagement with the Ministry of Agriculture and Farmers Welfare and Insurance Companies?',
    "'8.1    CSCs under Ministry of Electronics and Information Technology (MeITY) have been engaged to enrol    non-loanee farmers. The Insurance Companies are required to enter into a separate agreement with    CSC and pay service charges as fixed by DAC&FW, GOI per farmer per village per season. No other    agreement or payment is required to be made for this purpose. Nodal agency for engagement with    Ministry of Agriculture and Farmers Welfare and Insurance Companies will be CSC-SPV, a company    established under MeITY for carrying out e-governance initiatives of GoI.  8.2    No charges/fee shall be borne or paid by the farmers being enrolled through CSCs i.e. CSC-SPV and    CSC-VLE  8.3    As per IRDA circular, no separate qualification/certification will be required for the VLEs of CSCs to    facilitate enrolment of non-loanee farmers.  8.4    All empanelled Insurance Companies will compulsorily be required to enter into an agreement with    CSC for enrolment of non-loanee farmers and for provision of other defined services to farmers.   8.5    Other designated intermediaries may be linked with the Portal in due course.   8.6    Empanelled Insurance Companies have to necessarily register on the portal and submit list and details    of agents/intermediaries engaged for enrolment of non-loanee farmers in the beginning of each    season  within 10 days of award of work in the State.  Further all agents/intermediaries have to work    strictly as per the provisions of the Scheme and IRDA regulations'",
    "' 13.4 Laxmanrao  Imandar  National  Academy  for  Co-operative  Research  &  Development (LINAC), Gurugram promoted by NCDC is designated as Nodal Training Institution at central level for FPOs registered under Co-operative Societies Act and promoted by NCDC. The LINAC will work in partnership with other reputed national and regional training institutions like NIAM, VAMNICOM, MANAGE, NIRD, NCCT, IRMA, ASCI, State and Central Agriculture Universities,  KVK, very reputed National level Management and Skill Development Institutions/Universities etc.  The LINAC in consultation with NCDC and DAC&FW will prepare a training module and training schedule for the ensuing year, which will be got approved by N-PMAFSC. As regards training expenses, in case of LINAC being nodal agency, the LINAC through NCDC will claim the expenses from DAC&FW and will also submit the utilization certificate through NCDC after the training programme is over.  13.5 DAC&FW in due course may also identify and designate other training institute(s)  as additional Nodal Training Institute at central level, which will undertake training and skill development partnering with other national and regional level institutes.   13.6 The central Nodal Training Institutes will ensure that training programme be held  preferably in same State/UT wherein FPO trainees located are proposed to participate to reduce the burden on transportation(TA/DA) cost. While formulating the training schedule, Nodal Training Institutes will ensure that BoDs, CEOs/Managers and other stakeholders etc. are trained twice in a year. Nodal Training Institutes will have to make boarding and lodging arrangements for the  trainees and will also reimburse to and fro journey tickets to the extent of sleeper class train tickets and/or ordinary bus fare. Nodal Training Institutions will also evolve methodology to monitor and track the performance of trainees and their FPO organization to ensure effectiveness of training being provided.'",
]
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: val_evaluator
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.51
cosine_accuracy@5	0.9
cosine_accuracy@10	0.96
cosine_precision@1	0.51
cosine_precision@5	0.18
cosine_precision@10	0.096
cosine_recall@1	0.51
cosine_recall@5	0.9
cosine_recall@10	0.96
cosine_ndcg@5	0.7319
cosine_ndcg@10	0.7503
cosine_ndcg@100	0.759
cosine_mrr@5	0.6745
cosine_mrr@10	0.6815
cosine_mrr@100	0.6834
cosine_map@100	0.6834
dot_accuracy@1	0.51
dot_accuracy@5	0.9
dot_accuracy@10	0.96
dot_precision@1	0.51
dot_precision@5	0.18
dot_precision@10	0.096
dot_recall@1	0.51
dot_recall@5	0.9
dot_recall@10	0.96
dot_ndcg@5	0.7319
dot_ndcg@10	0.7503
dot_ndcg@100	0.759
dot_mrr@5	0.6745
dot_mrr@10	0.6815
dot_mrr@100	0.6834
dot_map@100	0.6834

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 1e-05
weight_decay: 0.01
num_train_epochs: 1.0
warmup_ratio: 0.1
load_best_model_at_end: 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: 32
per_device_eval_batch_size: 32
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: 1e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1.0
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: False
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: 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
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	val_evaluator_cosine_map@100
0.5172	15	2.0908	1.008	0.6834
1.0	29	-	1.0080	0.6834

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.1
Transformers: 4.43.4
PyTorch: 2.4.1+cu121
Accelerate: 0.33.0
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",
}

GISTEmbedLoss

bibtex

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, 
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}