SentenceTransformer based on BAAI/bge-base-en-v1.5 (Finetuned by OSLLMAI)
This is a SentenceTransformers model finetuned by OSLLMAI from BAAI/bge-base-en-v1.5. It maps sentences and paragraphs into a 768-dimensional dense vector space. The model is ideal for tasks such as semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. It uses cosine similarity as the similarity function and supports a maximum sequence length of 512 tokens.
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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
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("osllmai/bge-base-en-v1.5-finetuned_osllmai_v1")
# Run inference
sentences = [
'Early this year, there was a buzz on Motorola inviting T-Mobile Moto X owners to take part in a soak test for possible future update. Motorola seemed skeptical in disclosing facts at that point of time but since Moto G was recently upgraded to Android 4.4.2; enthusiasts anticipated the same for T-Mobile Moto X. And it turned out to be true.\nNews Update\nThis T-Mobile version of Moto X is now receiving the upgrade which is a file size of 147.6 MB. The Android 4.4.2 is the latest version of KitKat that includes all the goodies from the earlier installments, plus a few additions. The good news is, Motorola has customized the whole package and made a few tweaks into the update. The Software Version bumped to 161.44.25 and the notable changes are listed as below:\n- It added substantial support for services like printing photos, Google Docs, Gmail messages and other such content via Wi-Fi, Bluetooth and hosted services such as HP ePrinters and Google Cloud Print.\n- It fixed all the bugs identified during the preliminary runs, including the ones that caused a few users to experience short battery life after upgrading to KitKat.\n- Another bug that caused delays in synchronizing email services like Microsoft Exchange was resolved, thus adding to the convenience of the user.\nThis is a noteworthy upgrade, considering the fact that bugs and errors were fixed. Mobile addicts across the world will rejoice, for they can experience the smartness of Android KitKat flawlessly in their devices. This is significant development in terms of update.\nThis variant is an unlocked GSM device so chances are, you can use it on networks of other service providers. In all probability, the update should not be affected and the installation should hardly take much time. The T-Mobile Moto X Android update is now available for manual download. It is accessible in the following sequential way:\n- Click on Settings\n- Click on About Phone\n- Click on System Updates\n- Click on Download\nRecommendations\nFor ensuring a successful installation, it is highly recommended to install this update with at least 50% battery and a strong connectivity; preferably Wi-Fi. Follow the notification message and select download-> once the download is over, select Install-> Once the installation is over, and the phone will automatically restart. This marks the completion of the installation process. The phone is now updated to 161.44.25 – This build is same as the soak test.\nThis upgrade is free in the carrier network and Motorola and Google has collaborated for a back up service for those in trouble. In case of distress, a user can contact them through the Moto X web interface and avail the service. There is still no news on other carrier variants of this update but we can safely hope that it will roll out very soon. Though the upgrade doesn’t appeal in terms of version number but it is definitely significant for users to live with the latest KitKat.',
'What are some of the notable changes in the T-Mobile Moto X update?',
'Who is the editor of "The Routledge Handbook of Tourism Geographies"?',
]
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]
Training Details
Training Dataset
Unnamed Dataset
Evaluation Dataset
Unnamed Dataset
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: steps
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
num_train_epochs: 30
warmup_ratio: 0.1
bf16: 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: 10
per_device_eval_batch_size: 10
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_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: 30
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: True
fp16: False
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
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
Training Logs
Click to expand
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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}
}