SentenceTransformer based on BAAI/bge-base-en-v1.5
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.
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
andquestion
- 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:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 5warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falsebatch_sampler
: no_duplicatesmulti_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
@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
@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}
}
- Downloads last month
- 3
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for vineet10/fm1
Base model
BAAI/bge-base-en-v1.5
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.833
- Cosine Accuracy@3 on dim 768self-reported0.833
- Cosine Accuracy@5 on dim 768self-reported0.833
- Cosine Accuracy@10 on dim 768self-reported1.000
- Cosine Precision@1 on dim 768self-reported0.833
- Cosine Precision@3 on dim 768self-reported0.278
- Cosine Precision@5 on dim 768self-reported0.167
- Cosine Precision@10 on dim 768self-reported0.100
- Cosine Recall@1 on dim 768self-reported0.833
- Cosine Recall@3 on dim 768self-reported0.833