SentenceTransformer based on BAAI/bge-small-en-v1.5
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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("smokxy/embedding_finetuned")
# Run inference
sentences = [
'What is the requirement of Aadhaar for crop loan or Kisan Credit Card (KCC) under the Interest Subvention Scheme?',
"'6.3.1 Aadhaar has been made mandatory for availing Crop insurance from Kharif 2017 season onwards. Therefore, all banks are advised to mandatorily obtain Aadhaar number of their farmers and the same applies for non-loanee farmers enrolled through banks/Insurance companies/insurance intermediaries. 6.3.2 Farmers not having Aadhaar ID may also enrol under PMFBY subject to their enrolment for Aadhaar and submission of proof of such enrolment as per notification No. 334.dated 8th February, 2017 issued by GOI under Section 7 of Aadhaar Act 2016(Targeted Delivery of Financial and other Subsidies, Benefits and Services). Copy of the notification may be perused on www.pmfby.gov.in. This may be subject to further directions issued by Govt. from time to time. 6.3.3 All banks have to compulsorily take Aadhaar/Aadhaar enrolment number as per notification under Aadhaar Act before sanction of crop loan/KCC under Interest Subvention Scheme. Hence the coverage of loanee farmers without Aadhaar does not arise and such accounts need to be reviewed by the concerned bank branch regularly.'",
"' Date……………………………… ……………………………… Signature of Branch Manager with branch seal Name…………………………………… … Designation …………………………………… ……………………………… ……………………………… Signature of Authorized Person in zonal office Name………………………………… Designation …………………………………… 5. Promoter's request letter List of Enclosures 1. Recommendation 9. List of shareholders addressed to the Bank Manager on original letter head of FPO confirmed by promoter and bank with amount of CGC sought on Bank's Original letterhead with date and dispatch number duly signed by the Branch Manager on each page. 2. Sanction letter of 6. Implementation Schedule 10. Affidavit of promoters that confirmed by the bank. they have not availed CGC from any other institution for sanctioned Credit Facility. sanctioning authority addressed to recommending branch. 3. Bank's approved 7. Up-to-date statement of account of 11. Field inspection report of Term loan and Cash Credit (if Sanctioned). Bank official as on recent date. Appraisal/Process note bearing signature of sanctioning authority. 4. Potential Impact on 8. a).Equity Certificate, C.A/CS * Pin Code at Column No. 1. a), certificate/RCS certificate 2. b), 2. c), 4. a) and 9. a) is Mandatory b). FORM-2, FORM-5 and FORM-23 filed with ROC for Company/RCS. small farmer producers 1. Social Impact, 2. Environmental Impact 3.'",
]
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 | 0.89 |
cosine_accuracy@10 | 0.93 |
cosine_precision@1 | 0.51 |
cosine_precision@5 | 0.178 |
cosine_precision@10 | 0.093 |
cosine_recall@1 | 0.51 |
cosine_recall@5 | 0.89 |
cosine_recall@10 | 0.93 |
cosine_ndcg@5 | 0.7199 |
cosine_ndcg@10 | 0.7332 |
cosine_ndcg@100 | 0.7507 |
cosine_mrr@5 | 0.6627 |
cosine_mrr@10 | 0.6684 |
cosine_mrr@100 | 0.6731 |
cosine_map@100 | 0.6731 |
dot_accuracy@1 | 0.51 |
dot_accuracy@5 | 0.89 |
dot_accuracy@10 | 0.93 |
dot_precision@1 | 0.51 |
dot_precision@5 | 0.178 |
dot_precision@10 | 0.093 |
dot_recall@1 | 0.51 |
dot_recall@5 | 0.89 |
dot_recall@10 | 0.93 |
dot_ndcg@5 | 0.7199 |
dot_ndcg@10 | 0.7332 |
dot_ndcg@100 | 0.7507 |
dot_mrr@5 | 0.6627 |
dot_mrr@10 | 0.6684 |
dot_mrr@100 | 0.6731 |
dot_map@100 | 0.6731 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsgradient_accumulation_steps
: 4learning_rate
: 1e-05weight_decay
: 0.01num_train_epochs
: 1.0warmup_ratio
: 0.1load_best_model_at_end
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 4eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.01adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1.0max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | val_evaluator_cosine_map@100 |
---|---|---|---|---|
0.531 | 15 | 0.5565 | 0.0661 | 0.6731 |
0.9912 | 28 | - | 0.0661 | 0.6731 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.43.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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",
}
GISTEmbedLoss
@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}
}
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Model tree for smokxy/embedding_finetuned
Base model
BAAI/bge-small-en-v1.5Evaluation results
- Cosine Accuracy@1 on val evaluatorself-reported0.510
- Cosine Accuracy@5 on val evaluatorself-reported0.890
- Cosine Accuracy@10 on val evaluatorself-reported0.930
- Cosine Precision@1 on val evaluatorself-reported0.510
- Cosine Precision@5 on val evaluatorself-reported0.178
- Cosine Precision@10 on val evaluatorself-reported0.093
- Cosine Recall@1 on val evaluatorself-reported0.510
- Cosine Recall@5 on val evaluatorself-reported0.890
- Cosine Recall@10 on val evaluatorself-reported0.930
- Cosine Ndcg@5 on val evaluatorself-reported0.720