README.md

---
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:453
- loss:CosineSimilarityLoss
widget:
- source_sentence: Termination notice
  sentences:
  - "having value more than Rs 20 crore and original period of completion 12 months\
    \ or more, when there is no reduction in original scope of work by more than 10%,\
    \ and no extension granted on either railway or Contractor\x92s account,"
  - Special Conditions might exist in the contract and supersede the Standard General
    Conditions.
  - Subject to the provisions of the aforesaid Arbitration and Conciliation Act 1996
    and the rules thereunder and relevant para of General Conditions of Contract
- source_sentence: Impact of breach of terms by subcontracting.
  sentences:
  - The contractor shall commence the works within 15 days after the receipt by him
    of an order in wirting to this effect from the Engineer and shall proceed with
    the same with due expection and without delay.
  - Railway may, if satisfied that the works can be completed by the Contractor within
    reasonable short time thereafter, allow the Contractor for further extension of
    time (Proforma at Annexure-VII) as the Engineer may decide
  - On first occasion of noticing exaggerated/ false measurement, Engineer shall recover
    liquidated damages equal to 10% of claimed gross bill value.
- source_sentence: 'Place of Arbitration: The place of arbitration would be within
    the geographical limits of the Division of the Railway'
  sentences:
  - the Railway may grant such extension or extensions of the completion date as may
    be considered reasonable.
  - Location for dispute resolution
  - 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.
- source_sentence:         Special Conditions of Contract must be referred to while
    executing the contract
  sentences:
  - a penal interest of 12% per annum shall be charged for the delay beyond 21(Twenty
    one) days, i.e. from 22nd day after the date of issue of LOA. Further, if the
    60th day happens to be a declared holiday in the concerned office of the Railway,
    submission of PG can be accepted on the next working day.
  -         Contractor should finish the works according to Special conditions of
    Contract.
  - This explains the impact of breaching terms in subcontracting part.
- source_sentence: Additional documents involve General Conditions of Contract, Regulations
    for Tenders and Contracts and Special Conditions of Contract.
  sentences:
  - "At the final stage of completion and commissioning of work, in case the contractor\x92\
    s failure is limited to only some of the works costing not more than 2% of the\
    \ original contract value,"
  -         Any material found during excavation should be reported to the engineer.
  -  If the Contractor shall be dissatisfied by reason of any decision of the Engineer's
    representative, he shall be entitled to refer the matter to the Engineer who shall
    there upon confirm or vary such decision.
---

# SentenceTransformer based on BAAI/bge-large-en

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en](https://huggingface.co./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](https://huggingface.co./BAAI/bge-large-en) <!-- at revision abe7d9d814b775ca171121fb03f394dc42974275 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)

### 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:

```bash
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("Ananthu357/Ananthus-BAAI-for-contracts5.0")
# Run inference
sentences = [
    'Additional documents involve General Conditions of Contract, Regulations for Tenders and Contracts and Special Conditions of Contract.',
    "\xa0If the Contractor shall be dissatisfied by reason of any decision of the Engineer's representative, he shall be entitled to refer the matter to the Engineer who shall there upon confirm or vary such decision.",
    'At the final stage of completion and commissioning of work, in case the contractor\x92s failure is limited to only some of the works costing not more than 2% of the original contract value,',
]
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]
```

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## 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`: 25
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `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`: 25
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step | Training Loss | loss   |
|:-------:|:----:|:-------------:|:------:|
| 3.3448  | 100  | 0.06          | 0.0540 |
| 6.6897  | 200  | 0.0084        | 0.0568 |
| 10.0345 | 300  | 0.0035        | 0.0548 |
| 13.3448 | 400  | 0.0018        | 0.0536 |
| 16.6897 | 500  | 0.0011        | 0.0548 |
| 20.0345 | 600  | 0.001         | 0.0553 |
| 23.3448 | 700  | 0.0009        | 0.0556 |
| 3.3448  | 100  | 0.0014        | 0.0578 |
| 6.6897  | 200  | 0.0038        | 0.0582 |
| 10.0345 | 300  | 0.0025        | 0.0623 |
| 13.3448 | 400  | 0.0014        | 0.0579 |
| 16.6897 | 500  | 0.0008        | 0.0582 |
| 20.0345 | 600  | 0.0006        | 0.0579 |
| 23.3448 | 700  | 0.0006        | 0.0585 |
| 3.3448  | 100  | 0.0029        | 0.0640 |
| 6.6897  | 200  | 0.0048        | 0.0561 |
| 10.0345 | 300  | 0.0018        | 0.0524 |
| 13.3448 | 400  | 0.001         | 0.0522 |
| 16.6897 | 500  | 0.0007        | 0.0514 |
| 20.0345 | 600  | 0.0005        | 0.0519 |
| 23.3448 | 700  | 0.0005        | 0.0522 |


### 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
```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",
}
```

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