SentenceTransformer based on Shuu12121/CodeModernBERT-Owl

This is a sentence-transformers model finetuned from Shuu12121/CodeModernBERT-Owl. 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
Maximum Sequence Length: 2048 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': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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 = [
    'Write combining tables to filesystem as python code.',
    'def _do_write(fname, variable, version, date, table):\n        \n        # pylint: disable=R0914\n        #         Too many local variables (19/15) (col 4)\n        print("writing {} ..".format(fname))\n        import unicodedata\n        import datetime\n        import string\n        utc_now = datetime.datetime.utcnow()\n        indent = 4\n        with open(fname, \'w\') as fout:\n            fout.write(\n                \'\\n\'\n                "# Generated: {iso_utc}\\n"\n                "# Source: {version}\\n"\n                "# Date: {date}\\n"\n                "{variable} = (".format(iso_utc=utc_now.isoformat(),\n                                        version=version,\n                                        date=date,\n                                        variable=variable,\n                                        variable_proper=variable.title()))\n            for start, end in table:\n                ucs_start, ucs_end = unichr(start), unichr(end)\n                hex_start, hex_end = (\'0x{0:04x}\'.format(start),\n                                      \'0x{0:04x}\'.format(end))\n                try:\n                    name_start = string.capwords(unicodedata.name(ucs_start))\n                except ValueError:\n                    name_start = u\'\'\n                try:\n                    name_end = string.capwords(unicodedata.name(ucs_end))\n                except ValueError:\n                    name_end = u\'\'\n                fout.write(\'\\n\' + (\' \' * indent))\n                fout.write(\'({0}, {1},),\'.format(hex_start, hex_end))\n                fout.write(\'  # {0:24s}..{1}\'.format(\n                    name_start[:24].rstrip() or \'(nil)\',\n                    name_end[:24].rstrip()))\n            fout.write(\'\\n)\\n\')\n        print("complete.")',
    'def RGB_to_HSV(cobj, *args, **kwargs):\n    \n    var_R = cobj.rgb_r\n    var_G = cobj.rgb_g\n    var_B = cobj.rgb_b\n\n    var_max = max(var_R, var_G, var_B)\n    var_min = min(var_R, var_G, var_B)\n\n    var_H = __RGB_to_Hue(var_R, var_G, var_B, var_min, var_max)\n\n    if var_max == 0:\n        var_S = 0\n    else:\n        var_S = 1.0 - (var_min / var_max)\n\n    var_V = var_max\n\n    return HSVColor(\n        var_H, var_S, var_V)',
]
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]

Evaluation

Metrics

Information Retrieval

Dataset: code-docstring-retrieval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5568
cosine_accuracy@3	0.8609
cosine_accuracy@5	0.9019
cosine_accuracy@10	0.9297
cosine_precision@1	0.5568
cosine_precision@3	0.287
cosine_precision@5	0.1804
cosine_precision@10	0.093
cosine_recall@1	0.5568
cosine_recall@3	0.8609
cosine_recall@5	0.9019
cosine_recall@10	0.9297
cosine_ndcg@10	0.7695
cosine_mrr@10	0.7155
cosine_map@100	0.7173

Training Details

Training Dataset

Unnamed Dataset

Size: 1,455,632 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: 5 tokens
mean: 96.39 tokens
max: 2048 tokens

min: 27 tokens
mean: 159.11 tokens
max: 889 tokens

min: 1.0
mean: 1.0
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 5 tokens mean: 96.39 tokens max: 2048 tokens	min: 27 tokens mean: 159.11 tokens max: 889 tokens	min: 1.0 mean: 1.0 max: 1.0

Samples:

sentence_0	sentence_1	label
`Set the text for this element. Arguments: text (str): The text cls (str): The class of the text, defaults to current (leave this unless you know what you are doing). There may be only one text content element of each class associated with the element.`	`def settext(self, text, cls='current'): self.replace(TextContent, value=text, cls=cls)`	`1.0`
`Associate a document with this element. Arguments: doc (:class:Document): A document Each element must be associated with a FoLiA document.`	`def setdocument(self, doc): assert isinstance(doc, Document) if not self.doc: self.doc = doc if self.id: if self.id in doc: raise DuplicateIDError(self.id) else: self.doc.index[id] = self for e in self: #recursive for all children if isinstance(e,AbstractElement): e.setdocument(doc)`	`1.0`
`Tests whether a new element of this class can be added to the parent. This method is mostly for internal use. This will use the OCCURRENCES property, but may be overidden by subclasses for more customised behaviour. Parameters: parent (:class:AbstractElement): The element that is being added to set (str or None): The set raiseexceptions (bool): Raise an exception if the element can't be added? Returns: bool Raises: ValueError`	def addable(Class, parent, set=None, raiseexceptions=True): if not parent.class.accepts(Class, raiseexceptions, parent): return False if Class.OCCURRENCES > 0: #check if the parent doesn't have too many already count = parent.count(Class,None,True,[True, AbstractStructureElement]) #never descend into embedded structure annotatioton if count >= Class.OCCURRENCES: if raiseexceptions: if parent.id: extra = ' (id=' + parent.id + ')' else: extra = '' raise DuplicateAnnotationError("Unable to add another object of type " + Class.name + " to " + parent.class.name + " " + extra + ". There are already " + str(count) + " instances of this class, which is the maximum.") else: return False if Class.OCCURRENCES_PER_SET > 0 and set and Class.R...	`1.0`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
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: steps
prediction_loss_only: True
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
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}
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: round_robin

Training Logs