This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-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-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 768, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("vineet10/fm1")
# Run inference
sentences = [
    'This Agreement shall be governed by and construed in accordance with the laws of Indiana. Any dispute arising out of or in connection with this Agreement shall be resolved through good faith negotiations between the Parties and will be subject to the jurisdiction of the courts of Dania.',
    'Under which laws is the Battery Supply Agreement governed and how are disputes resolved?',
    'What events constitute Force Majeure under this Agreement?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	0.8333
cosine_accuracy@5	0.8333
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.2778
cosine_precision@5	0.1667
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	0.8333
cosine_recall@5	0.8333
cosine_recall@10	1.0
cosine_ndcg@10	0.8927
cosine_mrr@10	0.8611
cosine_map@100	0.8611

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	0.8333
cosine_accuracy@5	0.8333
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.2778
cosine_precision@5	0.1667
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	0.8333
cosine_recall@5	0.8333
cosine_recall@10	1.0
cosine_ndcg@10	0.8927
cosine_mrr@10	0.8611
cosine_map@100	0.8611

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	0.8333
cosine_accuracy@5	0.8333
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.2778
cosine_precision@5	0.1667
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	0.8333
cosine_recall@5	0.8333
cosine_recall@10	1.0
cosine_ndcg@10	0.8927
cosine_mrr@10	0.8611
cosine_map@100	0.8611

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	0.8333
cosine_accuracy@5	0.8333
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.2778
cosine_precision@5	0.1667
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	0.8333
cosine_recall@5	0.8333
cosine_recall@10	1.0
cosine_ndcg@10	0.8859
cosine_mrr@10	0.8542
cosine_map@100	0.8542

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	0.8333
cosine_accuracy@5	0.8333
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.2778
cosine_precision@5	0.1667
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	0.8333
cosine_recall@5	0.8333
cosine_recall@10	1.0
cosine_ndcg@10	0.8835
cosine_mrr@10	0.8519
cosine_map@100	0.8519

Training Details

Training Dataset

Unnamed Dataset

Size: 48 training samples
Columns: context and question
Approximate statistics based on the first 1000 samples:
context question
type string string
details
min: 18 tokens
mean: 39.58 tokens
max: 85 tokens
min: 8 tokens
mean: 17.9 tokens
max: 32 tokens

Samples:

context	question
`The Client will pay a flat fee of Rs. 52,000/-, with 50% (Rs. 26,000/-) due upon signing the agreement and the remaining 50% due one week after completion of pre-production. Payment delays will result in proportional delays in data delivery and editing.`	`What are the specified payment terms for the photography services under this contract?`
`Users can report delays to Customer Care and expect an automatic refund within 3-4 business days if services are canceled or rescheduled by the platform.`	`What actions can a user take if the platform is unable to fulfill a successfully placed order?`
`Signed by James Hira, Managing Director of Electric Vehicle Battery Supplier Pvt. Ltd, and Managing Director of Best Car Manufacturer Pvt. Ltd`	`Who signed the Battery Supply Agreement on behalf of the Supplier and the Manufacturer?`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 5
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: 5
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	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0	0	0.8542	0.8611	0.8611	0.8519	0.8611

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: 2.20.0
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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	context	question
type	string	string
details	min: 18 tokens mean: 39.58 tokens max: 85 tokens	min: 8 tokens mean: 17.9 tokens max: 32 tokens