SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
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': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'I need information on private equity firms in New York, focusing on the technology industry. Also, can you get details on a couple of private equity deals involving companies named "TechSoft" and "Digittal", deals are sized 500 and 700 million in the USA. Lastly, I need more information on a private equity firm named "CapitalGrow".',
    'Retrieve information about a private equity deal',
    'Analyze stock market data for a given company',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6397, 0.3693],
#         [0.6397, 1.0000, 0.3183],
#         [0.3693, 0.3183, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 8,386 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 12 tokens mean: 101.35 tokens max: 128 tokens	min: 6 tokens mean: 22.63 tokens max: 116 tokens	min: 5 tokens mean: 18.8 tokens max: 116 tokens

Samples:

sentence_0	sentence_1	sentence_2
I'm responsible for managing the inventory of the 'NextGen Biosciences' research facility. We need to track the stock of essential items like PCR machines, reagents, and consumables to ensure smooth operation of our ongoing experiments. We have a JSON file that lists our current inventory status. The actions we want to perform include: updating the system when an item is used or restocked, receiving alerts when an item's stock falls below a certain threshold, and generating various reports for stock, consumption, and purchase history. Our JSON inventory list is located at "/path/to/life_sciences_inventory.json". For instance, we recently purchased 5 additional ultra-centrifuges, each with a unique item ID. Moreover, we regularly consume gloves and need to reduce our stock count by 100 units. We want to: 1. Update the inventory for the ultra-centrifuges purchase. 2. Update the inventory for the consumption of gloves. 3. Get alerts for any stock below 10 units. 4. Generate a stock summ...	`Generates alerts for items in the inventory that fall below a specified stock threshold.`	`Update the inventory of equipment on a boat.`
Role definition: Inquirer: A user who raises an inquiry. Response assistant: Communicates with the inquirer and provides answers and solutions. Historical dialog data is as follows: Inquirer: We recently found skeletal remains at a crime scene, and I need your help to determine certain details. I need to know the time since death using the ambient temperature, which was recorded as 10 Celsius, and to determine the cause of death based on the skeletal remains. Additionally, I seek to identify the remains based on the skeletal elements found, which include a skull and femur. Response assistant: Could you please provide more specific details about the corpse involved for estimating time since death and determining the cause of death? Meanwhile, I will proceed with identifying the human remains based on the skull and femur you mentioned. Inquirer: The corpse is mostly decomposed. It is believed to have been at the site for a prolonged period. Please continue your answer given the his...	`Identify human remains based on forensic anthropology`	`Generates a report on the response times for customer complaints.`
`You are Isabella Johansson, and you live in 32286. You want to return the skateboard, garden hose, backpack, keyboard, bed, and also cancel the hose you just ordered (if cancelling one item is not possible, forget about it, you just want to cancel the hose and nothing else). You want to know how much you can get in total as refund. You are extremely brief but patient.`	`Retrieves detailed information about a specific order by its ID in the Order Management System.`	`Cancels a transaction that has been initiated but not yet confirmed.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 2
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: 0.0
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
bf16: False
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Framework Versions

Python: 3.9.6
Sentence Transformers: 5.1.2
Transformers: 4.57.6
PyTorch: 2.8.0
Accelerate: 1.10.1
Datasets: 4.5.0
Tokenizers: 0.22.2

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}
}