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:616
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: Fulfilment of contractual obligations
    sentences:
      - >-
        Should a tenderer find discrepancies in or omissions from the drawings
        or any of the Tender Forms or should he be in doubt as to their meaning
      - >-
        Period of Maintenance shall mean the specified period of maintenance
        from the date of completion of the works, as certified by the Engineer.
      - >-
        A copy of certificate stating that they are not liable to be
        disqualified and all their statements/documents
  - source_sentence: Is time is of essence in the contract?
    sentences:
      - >-
        In exceptional cases where accommodation is provided to the Contractor
        at the Railway's discretion, recoveries shall be made at such rates
      - and the works must be completed not later than the dates
      - >-
        The successful bidder shall submit the Performance Guarantee (PG) in any
        of the following forms, amounting to 5% of the contract value
  - source_sentence: Is there a way to claim consequential losses?
    sentences:
      - "provision has been made in Clauses 7(j), 8, 18, 22(5), 39, 43(2), 45(i)(a), 55, 55-A(5), 57, 57A,61(1), 61(2) and 62(1) of Standard General Conditions of Contract or in any Clause (stated as excepted matter) of the Special Conditions of the Contract, shall be deemed as \x91excepted matters\x92 (matters not arbitrable) and decisions of the Railway authority"
      - >-
        All sums payable by way of compensation under any of these conditions
        shall be considered as reasonable compensation
      - Third party liability relationship is present in this contract.
  - source_sentence: Valuables found during works
    sentences:
      - >-
        The contractor will indemnify, defend, save and hold harmless the
        Authority and its officers, servants, agents, Government
        INstrumentalities and Government owned and/or controlled
        entities/enterprises, against any and all suits, proceedings, actions,
        demands and third party claims for any loss, damage, cost and expense of
        whatever kind and nature, whether arising out of any breach by the
        contractor of any its obligations inder this agrreement, including any
        errors or deficiencies in the design documents, or tort or on any other
        ground whatsoever, except to the extent that any such suits,
        proceedings, actions, demands and claims have arisen due to any
        negligent act or omission, or breach or default of this agreement on the
        part of the authority Indemnified persons.
      - >-
        his position as an independent contractor specifying engineering
        organization available with details of partners / staff / engineers
        employed with qualifications and experience
      - >-
        All gold, silver, oil, other minerals of any description, all precious
        stones, coins, treasures relics antiquities and other similar things
        which shall be found in or upon the site shall be the property of the
        Railway
  - source_sentence: Project schedules like Bar chart, CPM, PERT
    sentences:
      - "\_All temporary works necessary for the proper execution of the works shall be provided and maintained by the Contractor"
      - Can the excavated material be directly used in construction.
      - >-
        Nothing stated herein shall preclude the Contractor in achieving earlier
        completion of item or whole of the works than indicated in the
        programme.

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-contracts8.0")
# Run inference
sentences = [
    'Project schedules like Bar chart, CPM, PERT',
    '\xa0All temporary works necessary for the proper execution of the works shall be provided and maintained by the Contractor',
    'Nothing stated herein shall preclude the Contractor in achieving earlier completion of item or whole of the works than indicated in the programme.',
]
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: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 15
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
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: 15
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: 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	loss
2.4615	100	0.0629	0.0440
4.9231	200	0.012	0.0504
7.3333	300	0.0052	0.0462
9.7949	400	0.0031	0.0489
12.2051	500	0.0016	0.0479

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
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",
}