This is a sentence-transformers model finetuned from Shuu12121/NightOwl. 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: Shuu12121/NightOwl
Maximum Sequence Length: 1024 tokens
Output Dimensionality: 768 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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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})
)

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 = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8824, 0.8289],
#         [0.8824, 1.0000, 0.8247],
#         [0.8289, 0.8247, 1.0000]])

Evaluation

Metrics

Information Retrieval

Datasets: csn-s1-python, csn-s1-go, csn-s1-java, csn-s1-javascript, csn-s1-php and csn-s1-ruby
Evaluated with InformationRetrievalEvaluator

Metric	csn-s1-python	csn-s1-go	csn-s1-java	csn-s1-javascript	csn-s1-php	csn-s1-ruby
cosine_accuracy@1	0.855	0.865	0.691	0.74	0.732	0.799
cosine_accuracy@3	0.954	0.952	0.845	0.837	0.802	0.907
cosine_accuracy@5	0.969	0.969	0.885	0.861	0.812	0.928
cosine_accuracy@10	0.981	0.979	0.918	0.887	0.819	0.944
cosine_precision@1	0.855	0.865	0.691	0.74	0.732	0.799
cosine_precision@3	0.318	0.3173	0.2817	0.279	0.2673	0.3023
cosine_precision@5	0.1938	0.1938	0.177	0.1722	0.1624	0.1856
cosine_precision@10	0.0981	0.0979	0.0918	0.0887	0.0819	0.0944
cosine_recall@1	0.855	0.865	0.691	0.74	0.732	0.799
cosine_recall@3	0.954	0.952	0.845	0.837	0.802	0.907
cosine_recall@5	0.969	0.969	0.885	0.861	0.812	0.928
cosine_recall@10	0.981	0.979	0.918	0.887	0.819	0.944
cosine_ndcg@10	0.9251	0.928	0.811	0.816	0.7808	0.8771
cosine_mrr@10	0.9064	0.911	0.7759	0.7929	0.768	0.855
cosine_map@100	0.9068	0.9123	0.7779	0.7952	0.7689	0.8565

Training Details

Training Dataset

Unnamed Dataset

Columns: anchor, positive, negative_1, negative_2, negative_3, negative_4, negative_5, negative_6, and negative_7
Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 100.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc", 7 "query_to_query", 8 "doc_to_query", 9 "doc_to_doc" 10 ], 11 "partition_mode": "joint", 12 "hardness_mode": null, 13 "hardness_strength": 0.0 14}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 3e-05
weight_decay: 0.01
max_steps: 200000
lr_scheduler_type: cosine
warmup_ratio: 0.005
bf16: True
gradient_checkpointing: 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: 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: 3e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3.0
max_steps: 200000
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.005
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}
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
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: True
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: False
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: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs