shubharuidas/codebert-base-code-embed-mrl-langchain-langgraph

codeBert dense retriever

This is a sentence-transformers model finetuned from shubharuidas/codebert-embed-base-dense-retriever. 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: shubharuidas/codebert-embed-base-dense-retriever
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Language: en
• License: apache-2.0

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': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
  (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("shubharuidas/codebert-base-code-embed-mrl-langchain-langgraph")
# Run inference
sentences = [
    'Explain the CheckpointPayload logic',
    'class CheckpointPayload(TypedDict):\n    config: RunnableConfig | None\n    metadata: CheckpointMetadata\n    values: dict[str, Any]\n    next: list[str]\n    parent_config: RunnableConfig | None\n    tasks: list[CheckpointTask]',
    'class _RuntimeOverrides(TypedDict, Generic[ContextT], total=False):\n    context: ContextT\n    store: BaseStore | None\n    stream_writer: StreamWriter\n    previous: Any',
]
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, 0.7282, 0.2122],
#         [0.7282, 1.0000, 0.3511],
#         [0.2122, 0.3511, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.84
cosine_accuracy@3	0.84
cosine_accuracy@5	0.84
cosine_accuracy@10	0.93
cosine_precision@1	0.84
cosine_precision@3	0.84
cosine_precision@5	0.84
cosine_precision@10	0.465
cosine_recall@1	0.168
cosine_recall@3	0.504
cosine_recall@5	0.84
cosine_recall@10	0.93
cosine_ndcg@10	0.8887
cosine_mrr@10	0.855
cosine_map@100	0.8779

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.88
cosine_accuracy@3	0.88
cosine_accuracy@5	0.88
cosine_accuracy@10	0.93
cosine_precision@1	0.88
cosine_precision@3	0.88
cosine_precision@5	0.88
cosine_precision@10	0.465
cosine_recall@1	0.176
cosine_recall@3	0.528
cosine_recall@5	0.88
cosine_recall@10	0.93
cosine_ndcg@10	0.907
cosine_mrr@10	0.8883
cosine_map@100	0.9039

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.87
cosine_accuracy@3	0.87
cosine_accuracy@5	0.87
cosine_accuracy@10	0.92
cosine_precision@1	0.87
cosine_precision@3	0.87
cosine_precision@5	0.87
cosine_precision@10	0.46
cosine_recall@1	0.174
cosine_recall@3	0.522
cosine_recall@5	0.87
cosine_recall@10	0.92
cosine_ndcg@10	0.897
cosine_mrr@10	0.8783
cosine_map@100	0.8959

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.86
cosine_accuracy@3	0.86
cosine_accuracy@5	0.86
cosine_accuracy@10	0.95
cosine_precision@1	0.86
cosine_precision@3	0.86
cosine_precision@5	0.86
cosine_precision@10	0.475
cosine_recall@1	0.172
cosine_recall@3	0.516
cosine_recall@5	0.86
cosine_recall@10	0.95
cosine_ndcg@10	0.9087
cosine_mrr@10	0.875
cosine_map@100	0.895

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.84
cosine_accuracy@3	0.84
cosine_accuracy@5	0.84
cosine_accuracy@10	0.93
cosine_precision@1	0.84
cosine_precision@3	0.84
cosine_precision@5	0.84
cosine_precision@10	0.465
cosine_recall@1	0.168
cosine_recall@3	0.504
cosine_recall@5	0.84
cosine_recall@10	0.93
cosine_ndcg@10	0.8887
cosine_mrr@10	0.855
cosine_map@100	0.8792

Training Details

Training Dataset

Unnamed Dataset

• Size: 900 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 900 samples:

  	anchor	positive
  type	string	string
  details	min: 6 tokens mean: 13.77 tokens max: 356 tokens	min: 14 tokens mean: 267.71 tokens max: 512 tokens

• Samples:

  anchor	positive
  How does put_item work in Python?	def put_item( self, namespace: Sequence[str], /, key: str, value: Mapping[str, Any], index: Literal[False] | list[str] | None = None, ttl: int | None = None, headers: Mapping[str, str] | None = None, params: QueryParamTypes | None = None, ) -> None: """Store or update an item. Args: namespace: A list of strings representing the namespace path. key: The unique identifier for the item within the namespace. value: A dictionary containing the item's data. index: Controls search indexing - None (use defaults), False (disable), or list of field paths to index. ttl: Optional time-to-live in minutes for the item, or None for no expiration. headers: Optional custom headers to include with the request. params: Optional query parameters to include with the request. Returns: None ???+ example...
  Explain the RunsClient: """Client for managing runs in LangGraph. A run is a single assistant invocation with optional input, config, context, and metadata. This client manages runs, which can be stateful logic	class RunsClient: """Client for managing runs in LangGraph. A run is a single assistant invocation with optional input, config, context, and metadata. This client manages runs, which can be stateful (on threads) or stateless. ???+ example "Example" python<br> client = get_client(url="http://localhost:2024")<br> run = await client.runs.create(assistant_id="asst_123", thread_id="thread_456", input={"query": "Hello"})<br> """ def init(self, http: HttpClient) -> None: self.http = http @overload def stream( self, thread_id: str, assistant_id: str, *, input: Input | None = None, command: Command | None = None, stream_mode: StreamMode | Sequence[StreamMode] = "values", stream_subgraphs: bool = False, stream_resumable: bool = False, metadata: Mapping[str, Any] | None = None, config: Config | None = None, context: Context | N...
  Best practices for MyChildDict	class MyChildDict(MyBaseTypedDict): val_11: int val_11b: int | None val_11c: int | None | str

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 2
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• optim: adamw_torch
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 16
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: None
• 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: 42
• data_seed: None
• jit_mode_eval: 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: 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}
• 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
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.7111	10	0.6327	-	-	-	-	-
1.0	15	-	0.8970	0.8979	0.8925	0.8979	0.8641
1.3556	20	0.2227	-	-	-	-	-
2.0	30	0.1692	0.8887	0.907	0.897	0.9087	0.8887

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.2.0
• Transformers: 4.57.6
• PyTorch: 2.9.0+cu126
• Accelerate: 1.12.0
• Datasets: 4.0.0
• 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}