This is a sentence-transformers model finetuned from nreimers/MiniLM-L6-H384-uncased. It maps sentences & paragraphs to a 128-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: nreimers/MiniLM-L6-H384-uncased
Maximum Sequence Length: 31 tokens
Output Dimensionality: 128 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

ColBERT(
  (0): Transformer({'max_seq_length': 31, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'are emg pickups any good?',
    "EMGs are a one trick pony, and only sound good for high gain applications. Sort of, they definitely aren't as flexible as most passive options, but most metal oriented passive pickups have the same issue. A lot of guitarists forget that EMG makes more pickups than just the 81/85 set.",
    "Among guitar and bass accessories, the company sells active humbucker pickups, such as the EMG 81, the EMG 85, the EMG 60, and the EMG 89. They also produce passive pickups such as the EMG-HZ Series, which include SRO-OC1's and SC Sets.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 128]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 1,893,949 training samples
Columns: question, answer, and negative
Approximate statistics based on the first 1000 samples:
question answer negative
type string string string
details
min: 9 tokens
mean: 13.01 tokens
max: 27 tokens
min: 16 tokens
mean: 31.78 tokens
max: 32 tokens
min: 14 tokens
mean: 31.66 tokens
max: 32 tokens

Samples:

question	answer	negative
`what is the relationship between humility and thankfulness?`	`how gratitude can influence humility and vice versa. Humility is characterized by low self-focus, secure sense of self, and increased valuation of others. Gratitude is marked by a sense that one has benefited from the actions of another.`	`-hum-, root. -hum- comes from Latin, where it has the meaning "ground. '' This meaning is found in such words as: exhume, humble, humiliate, humility, humus, posthumous.`
`what is the difference between usb a b c?`	`The USB-A has a much larger physical connector than the Type C, Type C is around the same size as a micro-USB connector. Unlike, Type A, you won't need to try and insert it, flip it over and then flip it over once more just to find the right orientation when trying to make a connection.`	`First the transfer rates: USB 2.0 offers transfer rates of 480 Mbps and USB 3.0 offers transfer rates of 4.8 Gbps - that's 10 times faster. ... USB 2.0 provided up to 500 mA whereas USB 3.0 provides up to 900 mA, allowing power hungry devices to now be bus powered.`
`how hyaluronic acid is made?`	`Hyaluronic acid is a substance that is naturally present in the human body. It is found in the highest concentrations in fluids in the eyes and joints. The hyaluronic acid that is used as medicine is extracted from rooster combs or made by bacteria in the laboratory.`	`Hyaluronic acid helps your skin hang on to the moisture. 2. ... Hyaluronic acid by itself is non-comedogenic (doesn't clog pores), but you should be careful when choosing a hyaluronic acid serum that the ingredient list doesn't contain any sneaky pore-clogging ingredients you're not expecting.`

Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

Unnamed Dataset

Size: 5,000 evaluation samples
Columns: question, answer, and negative_1
Approximate statistics based on the first 1000 samples:
question answer negative_1
type string string string
details
min: 9 tokens
mean: 12.96 tokens
max: 22 tokens
min: 19 tokens
mean: 31.7 tokens
max: 32 tokens
min: 14 tokens
mean: 31.43 tokens
max: 32 tokens

Samples:

question	answer	negative_1
`are tefal ingenio pans suitable for induction hobs?`	`Tefal Ingenio is a revolutionary concept that brings a whole new take on versatility. ... The frying pans also feature Tefal's iconic Thermo-Spot which lets you know when the pan has reached optimal cooking temperature. The Ingenio Induction range is compatible with all hobs and is also dishwasher safe.`	`Tefal Ingenio is a revolutionary concept that brings a whole new take on versatility. ... The frying pans also feature Tefal's iconic Thermo-Spot which lets you know when the pan has reached optimal cooking temperature. The Ingenio Induction range is compatible with all hobs and is also dishwasher safe.`
`how many continuing education hours is acls?`	`The ACLS, PALS, and NRP certification courses are approved for 8 CEUs/CMEs, and recertification courses are approved for 4 CEUs/CMEs. The BLS certification course is approved for 4 CEUs/CMEs and the recertification course is approved for 2 CEUs/CMEs. For more information, please visit our Accreditation page.`	`The foremost difference between the two is their advancement level. Essentially, ACLS is a sophisticated and more advanced course and builds upon the major fundamentals developed during BLS. The main purpose of BLS and ACLS certification are well explained in this article.`
`what are the health benefits of drinking peppermint tea?`	`['Makes you Stress Free. When it comes to relieving stress and anxiety, peppermint tea is one of the best allies. ... ', 'Sleep-Friendly. ... ', 'Aids in Weight Loss. ... ', 'Cure for an Upset Stomach. ... ', 'Improves Digestion. ... ', 'Boosts Immune System. ... ', 'Fights Bad Breath.']`	`Peppermint tea is a popular herbal tea that is naturally calorie- and caffeine-free. Some research has suggested that the oils in peppermint may have a number of other health benefits, such as fresher breath, better digestion, and reduced pain from headaches. Peppermint tea also has antibacterial properties.`

Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 3e-06
num_train_epochs: 1
warmup_ratio: 0.1
seed: 12
bf16: True
dataloader_num_workers: 12
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 3e-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
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: 12
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: 12
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
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}
tp_size: 0
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: None
hub_always_push: False
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.0001	1	10.8061
0.0270	200	8.9391
0.0541	400	5.1795
0.0811	600	2.3951
0.1081	800	1.6927
0.1352	1000	1.404
0.1622	1200	1.2496
0.1892	1400	1.1613
0.2162	1600	1.0843
0.2433	1800	1.0427
0.2703	2000	1.0005
0.2973	2200	0.9695
0.3244	2400	0.9325
0.3514	2600	0.9122
0.3784	2800	0.8832
0.4055	3000	0.8689
0.4325	3200	0.8626
0.4595	3400	0.8452
0.4866	3600	0.8329
0.5136	3800	0.8132
0.5406	4000	0.8111
0.5676	4200	0.7952
0.5947	4400	0.7892
0.6217	4600	0.7772
0.6487	4800	0.7793
0.6758	5000	0.7705
0.7028	5200	0.7692
0.7298	5400	0.7625
0.7569	5600	0.7595
0.7839	5800	0.7405
0.8109	6000	0.7513
0.8380	6200	0.7396
0.8650	6400	0.7312
0.8920	6600	0.7325
0.9190	6800	0.7371
0.9461	7000	0.7422
0.9731	7200	0.7296

Framework Versions

Python: 3.11.0
Sentence Transformers: 4.0.1
Transformers: 4.50.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}

	question	answer	negative
type	string	string	string
details	min: 9 tokens mean: 13.01 tokens max: 27 tokens	min: 16 tokens mean: 31.78 tokens max: 32 tokens	min: 14 tokens mean: 31.66 tokens max: 32 tokens

ayushexel/colbert-MiniLM-L6-H384-1-epoch-gooaq-1995000

ColBERT based on nreimers/MiniLM-L6-H384-uncased

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

ayushexel/
colbert-MiniLM-L6-H384-1-epoch-gooaq-1995000