SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-2.2-Pre

This is a sentence-transformers model finetuned from Shuu12121/CodeModernBERT-Owl-2.2-Pre. 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/CodeModernBERT-Owl-2.2-Pre
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}) with Transformer model: 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    '<pre>\nField extraction metadata on the property.\n</pre>\n\n<code>.google.cloud.documentai.v1beta3.FieldExtractionMetadata field_extraction_metadata = 9;\n</code>\n\n@return Whether the fieldExtractionMetadata field is set.',
    '@java.lang.Override\n  public boolean hasFieldExtractionMetadata() {\n    return ((bitField0_ & 0x00000001) != 0);\n  }',
    'pub fn poller(self) -> impl lro::Poller<(), crate::model::DeleteSitemapMetadata> {\n            type Operation =\n                lro::internal::Operation<wkt::Empty, crate::model::DeleteSitemapMetadata>;\n            let polling_error_policy = self.0.stub.get_polling_error_policy(&self.0.options);\n            let polling_backoff_policy = self.0.stub.get_polling_backoff_policy(&self.0.options);\n\n            let stub = self.0.stub.clone();\n            let mut options = self.0.options.clone();\n            options.set_retry_policy(gax::retry_policy::NeverRetry);\n            let query = move |name| {\n                let stub = stub.clone();\n                let options = options.clone();\n                async {\n                    let op = GetOperation::new(stub)\n                        .set_name(name)\n                        .with_options(options)\n                        .send()\n                        .await?;\n                    Ok(Operation::new(op))\n                }\n            };\n\n            let start = move || async {\n                let op = self.send().await?;\n                Ok(Operation::new(op))\n            };\n\n            lro::internal::new_unit_response_poller(\n                polling_error_policy,\n                polling_backoff_policy,\n                start,\n                query,\n            )\n        }',
]
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

Size: 2,732,400 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 8 tokens
mean: 70.49 tokens
max: 1024 tokens

min: 5 tokens
mean: 138.25 tokens
max: 1024 tokens

min: 1.0
mean: 1.0
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 8 tokens mean: 70.49 tokens max: 1024 tokens	min: 5 tokens mean: 138.25 tokens max: 1024 tokens	min: 1.0 mean: 1.0 max: 1.0

Samples:

sentence_0	sentence_1	label
`Prints the specified pkg. If is_main is not set, nested package notation is used.`	pub fn print_package( &mut self, resolve: &Resolve, pkg: PackageId, is_main: bool, ) -> Result<()> { let pkg = &resolve.packages[pkg]; self.print_package_outer(pkg)?; if is_main { self.output.semicolon(); self.output.newline(); } else { self.output.indent_start(); } for (name, id) in pkg.interfaces.iter() { self.print_interface_outer(resolve, id, name)?; self.output.indent_start(); self.print_interface(resolve, id)?; self.output.indent_end(); if is_main { self.output.newline(); } } for (name, id) in pkg.worlds.iter() { self.print_docs(&resolve.worlds[id].docs); self.print_stability(&resolve.worlds[id].stability); self.output.keyword("world"); self.output.str(" "); self.print_name_type(name, TypeKind:...	`1.0`
`An alternative descriptive name for the user.`	`pub fn nick_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self { self.nick_name = ::std::option::Option::Some(input.into()); self }`	`1.0`
`Indicates whether the match is case sensitive.`	`pub fn case_sensitive(mut self, input: bool) -> Self { self.case_sensitive = ::std::option::Option::Some(input); self }`	`1.0`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 150
per_device_eval_batch_size: 150
num_train_epochs: 5
fp16: True
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: 150
per_device_eval_batch_size: 150
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: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
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
use_ipex: False
bf16: False
fp16: True
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: 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
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: round_robin

Training Logs