metadata
base_model: BAAI/bge-large-en
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:458
- loss:CosineSimilarityLoss
widget:
- source_sentence: What does the document say about GST ?
sentences:
- >-
If any ambiguity arises as to the meaning and intent of any portion of
the Specifications and Drawings or as to execution or quality of any
work or material, or as to the measurements of the works the decision of
the Engineer thereon shall be final subject to the appeal
- >-
For tenders costing more than Rs 20 crore wherein eligibility criteria
includes bid capacity also, the tenderer will be qualified only if its
available bid capacity is equal to or more than the total bid value of
the present tender. The available bid capacity shall be calculated.
- >-
Tenderers will examine the various provisions of The Central Goods and
Services Tax Act, 2017(CGST)/ Integrated Goods and Services Tax Act,
2017(IGST)/ Union Territory Goods and Services Tax Act, 2017(UTGST)/
- source_sentence: What is the deadline to submit the proposed project schedule?
sentences:
- >-
The Contractor who has been awarded the work shall as soon as possible
but not later than 30 days after the date of receipt of the acceptance
letter
- "\_ \_ \_ \_ Special Conditions can modify the Standard General Conditions."
- >-
Limited Tenders shall mean tenders invited from all or some contractors
on the approved or select list of contractors with the Railway
- source_sentence: >-
These Regulations for Tenders and Contracts shall be read in conjunction
with the Standard General Conditions of Contract which are referred to
herein and shall be subject to modifications additions or suppression by
Special Conditions of Contract and/or Special Specifications, if any,
annexed to the Tender Forms.
sentences:
- >-
unless the Contractor has made a claim in writing in respect thereof
before the issue of the Maintenance Certificate under this clause.
- There shall be no modification expected.
- Indemnification clause
- source_sentence: No claim certificate
sentences:
- >-
Subcontracting will in no way relieve the Contractor to execute the work
as per terms of the contract.
- Final Supplementary Agreement
- Client can transfer the liability to the contractor
- source_sentence: What is the deadline to submit the proposed project schedule?
sentences:
- "\_ \_ \_ \_ The Contractor shall at his own expense provide with sheds, storehouses and yards in such situations and in such numbers"
- >-
This clause defines the Contractor's responsibility for subcontractor
performance.
- >-
Any item of work carried out by the Contractor on the instructions of
the Engineer which is not included in the accepted Schedules of Rates
shall be executed at the rates set forth in the Schedule of Rates of
Railway.
SentenceTransformer based on BAAI/bge-large-en
This is a sentence-transformers model finetuned from BAAI/bge-large-en. It maps sentences & paragraphs to a 1024-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-large-en
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 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': 1024, '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("Ananthu357/Ananthus-BAAI-for-contracts6.0")
# Run inference
sentences = [
'What is the deadline to submit the proposed project schedule?',
'Any item of work carried out by the Contractor on the instructions of the Engineer which is not included in the accepted Schedules of Rates shall be executed at the rates set forth in the Schedule of Rates of Railway.',
'\xa0 \xa0 \xa0 \xa0 The Contractor shall at his own expense provide with sheds, storehouses and yards in such situations and in such numbers',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 25warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 25max_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
: Truefp16_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
: Falseignore_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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss |
---|---|---|---|
3.3448 | 100 | 0.0661 | 0.0479 |
6.6897 | 200 | 0.0106 | 0.0539 |
10.0345 | 300 | 0.004 | 0.0505 |
13.3448 | 400 | 0.0022 | 0.0506 |
16.6897 | 500 | 0.0012 | 0.0512 |
20.0345 | 600 | 0.001 | 0.0512 |
23.3448 | 700 | 0.0009 | 0.0516 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- 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",
}