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:574
- loss:CosineSimilarityLoss
widget:
- source_sentence: What is mentioned regarding the patent errors?
  sentences:
  - the Schedule to the Indian Medical Council Act
  - 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.
  - Omissions and Descrepancies
- source_sentence: Is there a way to claim consequential losses?
  sentences:
  - The Railway reserves the right of not to invite tenders for any of Railway work
    or works or to invite open or limited tenders
  - entitle the Contractor to damages or compensation therefor, but in any such case,
    the Railway may grant such extension or extensions of the completion date as may
    be considered reasonable.
  - "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."
- source_sentence: Does the contract document contain a indemnification clause provision?
  sentences:
  - The partners of the firm to which the Letter of Acceptance (LOA) is issued, shall
    be jointly and severally liable to the Railway for execution of the contract
  - Contractor awarded the work shall submit a detailed program of work indicating
    the time schedule
  - All notices, communications, reference and complaints made by the Railway or the
    Engineer or the Engineer's Representative or the Contractor inter-se concerning
    the works shall be in writing or e-mail on registered e mail IDs and no notice,
    communication, reference or complaint not in writing or through e-mail, shall
    be recognized.
- source_sentence: Force Majeure
  sentences:
  - 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.
  - Act of God
  - 'Instructions: The Engineer shall direct the order in which the several parts
    of the works shall be executed'
- source_sentence: Interpretation of Standard General Conditions of contract
  sentences:
  - The Contractor shall at his own expense provide himself with sheds, storehouses
    and yards in such situations and in such numbers as in the opinion of the Engineer
    is requisite for carrying on the works and the Contractor
  - What are the additional documents that have to be read along with the Standard
    General Conditions of Contract?
  - the necessity arises for the execution of such items of works that the accepted
    Schedule of Rates does not include rate or rates for the extra work involved.
---

# 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-contracts7.0")
# Run inference
sentences = [
    'Interpretation of Standard General Conditions of contract',
    'What are the additional documents that have to be read along with the Standard General Conditions of Contract?',
    'The Contractor shall at his own expense provide himself with sheds, storehouses and yards in such situations and in such numbers as in the opinion of the Engineer is requisite for carrying on the works and the Contractor',
]
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`: 15
- `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`: 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

</details>

### Training Logs
| Epoch   | Step | Training Loss | loss   |
|:-------:|:----:|:-------------:|:------:|
| 2.7778  | 100  | 0.0571        | 0.0611 |
| 5.5556  | 200  | 0.0073        | 0.0604 |
| 8.3333  | 300  | 0.0031        | 0.0578 |
| 11.1111 | 400  | 0.0019        | 0.0589 |
| 13.8889 | 500  | 0.0013        | 0.0587 |


### 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
```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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