README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:360
- loss:CosineSimilarityLoss
base_model: BAAI/bge-large-en
datasets: []
widget:
- source_sentence: Deadline for submitting project schedule.
  sentences:
  - Variation
  - "The Railway shall have the right to let other contracts in connection with the\
    \ works. The Contractor shall afford other Contractors reasonable opportunity\
    \ for the storage of their materials and the execution of their works and shall\
    \ properly connect and coordinate his work with theirs. If any part of the Contractor\x92\
    s work depends upon proper execution or result upon the work of another Contractor(s),\
    \ the Contractor shall inspect and promptly report to the Engineer any defects\
    \ in such works that render it unsuitable for such proper execution and results.\
    \ The Contractor's failure so-to inspect and report shall constitute an acceptance\
    \ of the other Contractor's work as fit and proper for the reception of his work,\
    \ except as to defects which may develop in the other Contractor's work after\
    \ the execution of his work."
  - The quantities set out in the accepted Schedule of Rates with items of works quantified
    are the estimated quantities of the works
- source_sentence:  What is the deadline to submit the proposed project schedule?
  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,"
  - Can the stones/rocks/bounders obtained during excavation be used for construction
    if found technically satisfactory?
  - Chart/PERT/CPM. He shall also submit the details of organisation (in terms of
    labour and supervisors), plant and machinery that he intends to utilize (from
    time to time) for execution of the work within stipulated date of completion.
- source_sentence: "Does the contract document contain a \x91third-party liability\
    \ relationship\x92 provision?"
  sentences:
  - The Contractor shall indemnify and save harmless the Railway from and against
    all actions, suit, proceedings, losses, costs, damages, charges, claims and demands
    of every nature and description brought or recovered against the Railways by reason
    of any act or omission of the Contractor, his agents or employees, in the execution
    of the works or in his guarding of the same. All sums payable by way of compensation
    under any of these conditions shall be considered as reasonable compensation to
    be applied to the actual loss or damage sustained, and whether or not any damage
    shall have been sustained.
  - the Railway shall not in any way be liable for the supply of materials or for
    the non-supply thereof for any reasons whatsoever nor for any loss or damage arising
    in consequence of such delay or non-supply.
  - The Railway shall have the right to let other contracts in connection with the
    works.
- source_sentence: Liquidated Damages
  sentences:
  - The Contractor shall commence the works within 15 days after the receipt by him
    of an order in writing to this effect from the Engineer and shall proceed with
    the same with due expedition and without delay
  - Any bribe, commission, gift or advantage given, promised or offered by or on behalf
    of the Contractor or his partner or agent or servant or anyone on his behalf
  - purpose of works either free of cost or pay thecost of the same.
- source_sentence: What is mentioned regarding the patent errors?
  sentences:
  - the Security Deposit already with railways under the contract shall be forfeited.
  - This clause mentions Special Conditions, which might be additional documents relevant
    to the contract.
  - shall take upon himself and provide for the risk of any error which may subsequently
    be discovered and shall make no subsequent claim on account thereof.
pipeline_tag: sentence-similarity
---

# 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-contracts")
# Run inference
sentences = [
    'What is mentioned regarding the patent errors?',
    'shall take upon himself and provide for the risk of any error which may subsequently be discovered and shall make no subsequent claim on account thereof.',
    'This clause mentions Special Conditions, which might be additional documents relevant to the contract.',
]
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`: 40
- `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`: 40
- `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   |
|:-------:|:----:|:-------------:|:------:|
| 3.5652  | 100  | 0.0564        | 0.0940 |
| 7.1304  | 200  | 0.0122        | 0.0713 |
| 10.4348 | 300  | 0.0051        | 0.0655 |
| 14.0    | 400  | 0.0026        | 0.0678 |
| 17.3043 | 500  | 0.001         | 0.0668 |
| 20.8696 | 600  | 0.0009        | 0.0666 |
| 24.1739 | 700  | 0.0008        | 0.0671 |
| 27.7391 | 800  | 0.0007        | 0.0674 |
| 31.0435 | 900  | 0.0007        | 0.0671 |


### 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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