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 <!-- at revision 9594580ae943039d0b85feb304404f9b2bb203ce -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown -->
• —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]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval

• —Dataset: dim_768
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 768
  }

Information Retrieval

• —Dataset: dim_512
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 512
  }

Information Retrieval

• —Dataset: dim_256
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 256
  }

Information Retrieval

• —Dataset: dim_128
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 128
  }

Information Retrieval

• —Dataset: dim_64
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 64
  }

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 900 training samples
• —Columns: <code>anchor</code> and <code>positive</code>
• —Approximate statistics based on the first 900 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 13.77 tokens</li><li>max: 356 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 267.71 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>How does putitem work in Python?</code> | <code>def putitem(<br> self,<br> namespace: Sequence[str],<br> /,<br> key: str,<br> value: Mapping[str, Any],<br> index: Literal[False] \| list[str] \| None = None,<br> ttl: int \| None = None,<br> headers: Mapping[str, str] \| None = None,<br> params: QueryParamTypes \| None = None,<br> ) -> None:<br> """Store or update an item.<br><br> Args:<br> namespace: A list of strings representing the namespace path.<br> key: The unique identifier for the item within the namespace.<br> value: A dictionary containing the item's data.<br> index: Controls search indexing - None (use defaults), False (disable), or list of field paths to index.<br> ttl: Optional time-to-live in minutes for the item, or None for no expiration.<br> headers: Optional custom headers to include with the request.<br> params: Optional query parameters to include with the request.<br><br> Returns:<br> None<br><br> ???+ example...</code> | | <code>Explain the RunsClient:<br> """Client for managing runs in LangGraph.<br><br> A run is a single assistant invocation with optional input, config, context, and metadata.<br> This client manages runs, which can be stateful logic</code> | <code>class RunsClient:<br> """Client for managing runs in LangGraph.<br><br> A run is a single assistant invocation with optional input, config, context, and metadata.<br> This client manages runs, which can be stateful (on threads) or stateless.<br><br> ???+ example "Example"<br><br> ``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> ``<br> """<br><br> def _init(self, http: HttpClient) -> None:<br> self.http = http<br><br> @overload<br> def stream(<br> self,<br> threadid: str,<br> assistantid: str,<br> *,<br> input: Input \| None = None,<br> command: Command \| None = None,<br> streammode: StreamMode \| Sequence[StreamMode] = "values",<br> streamsubgraphs: bool = False,<br> streamresumable: bool = False,<br> metadata: Mapping[str, Any] \| None = None,<br> config: Config \| None = None,<br> context: Context \| N...</code> | | <code>Best practices for MyChildDict</code> | <code>class MyChildDict(MyBaseTypedDict):<br> val11: int<br> val11b: int \| None<br> val_11c: int \| None \| str</code> |
• —Loss: <code>MatryoshkaLoss</code> 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

<details><summary>Click to expand</summary>

• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: {}

</details>

Training Logs

• —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}
}

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