itsanan/codebert-embed-crewai-base

CodeBERT Fine-tuned on CrewAI

This is a sentence-transformers model finetuned from microsoft/codebert-base. 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: microsoft/codebert-base <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 -->
• —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("itsanan/codebert-embed-crewai-base")
# Run inference
sentences = [
    'Best practices for handle_a2a_polling_started',
    'def handle_a2a_polling_started(\n        self,\n        task_id: str,\n        polling_interval: float,\n        endpoint: str,\n    ) -> None:\n        """Handle A2A polling started event with panel display."""\n        content = Text()\n        content.append("A2A Polling Started\\n", style="cyan bold")\n        content.append("Task ID: ", style="white")\n        content.append(f"{task_id[:8]}...\\n", style="cyan")\n        content.append("Interval: ", style="white")\n        content.append(f"{polling_interval}s\\n", style="cyan")\n\n        self.print_panel(content, "⏳ A2A Polling", "cyan")',
    'def test_agent_with_knowledge_sources_generate_search_query():\n    content = "Brandon\'s favorite color is red and he likes Mexican food."\n    string_source = StringKnowledgeSource(content=content)\n\n    with (\n        patch("crewai.knowledge") as mock_knowledge,\n        patch(\n            "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"\n        ) as mock_knowledge_storage,\n        patch(\n            "crewai.knowledge.source.base_knowledge_source.KnowledgeStorage"\n        ) as mock_base_knowledge_storage,\n        patch("crewai.rag.chromadb.client.ChromaDBClient") as mock_chromadb,\n    ):\n        mock_knowledge_instance = mock_knowledge.return_value\n        mock_knowledge_instance.sources = [string_source]\n        mock_knowledge_instance.query.return_value = [{"content": content}]\n\n        mock_storage_instance = mock_knowledge_storage.return_value\n        mock_storage_instance.sources = [string_source]\n        mock_storage_instance.query.return_value = [{"content": content}]\n        mock_storage_instance.save.return_value = None\n\n        mock_chromadb_instance = mock_chromadb.return_value\n        mock_chromadb_instance.add_documents.return_value = None\n\n        mock_base_knowledge_storage.return_value = mock_storage_instance\n\n        agent = Agent(\n            role="Information Agent with extensive role description that is longer than 80 characters",\n            goal="Provide information based on knowledge sources",\n            backstory="You have access to specific knowledge sources.",\n            llm=LLM(model="gpt-4o-mini"),\n            knowledge_sources=[string_source],\n        )\n\n        task = Task(\n            description="What is Brandon\'s favorite color?",\n            expected_output="The answer to the question, in a format like this: `{{name: str, favorite_color: str}}`",\n            agent=agent,\n        )\n\n        crew = Crew(agents=[agent], tasks=[task])\n        result = crew.kickoff()\n\n        # Updated assertion to check the JSON content\n        assert "Brandon" in str(agent.knowledge_search_query)\n        assert "favorite color" in str(agent.knowledge_search_query)\n\n        assert "red" in result.raw.lower()',
]
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, 0.7350, 0.6480],
#         [0.7350, 1.0000, 0.8133],
#         [0.6480, 0.8133, 1.0000]])

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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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: 13.96 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 254.94 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | anchor | positive | |:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Example usage of DeeplyNestedFlow</code> | <code>class DeeplyNestedFlow(Flow):<br> @start()<br> def a(self):<br> executionorder.append("a")<br><br> @start()<br> def b(self):<br> executionorder.append("b")<br><br> @start()<br> def c(self):<br> executionorder.append("c")<br><br> @start()<br> def d(self):<br> executionorder.append("d")<br><br> # Nested: (a AND b) OR (c AND d)<br> @listen(or(and(a, b), and(c, d)))<br> def result(self):<br> executionorder.append("result")</code> | | <code>Explain the testagentwithknowledgesourcesgeneratesearchquery logic</code> | <code>def testagentwithknowledgesourcesgeneratesearchquery():<br> content = "Brandon's favorite color is red and he likes Mexican food."<br> stringsource = StringKnowledgeSource(content=content)<br><br> with (<br> patch("crewai.knowledge") as mockknowledge,<br> patch(<br> "crewai.knowledge.storage.knowledgestorage.KnowledgeStorage"<br> ) as mockknowledgestorage,<br> patch(<br> "crewai.knowledge.source.baseknowledgesource.KnowledgeStorage"<br> ) as mockbaseknowledgestorage,<br> patch("crewai.rag.chromadb.client.ChromaDBClient") as mockchromadb,<br> ):<br> mockknowledgeinstance = mockknowledge.returnvalue<br> mockknowledgeinstance.sources = [stringsource]<br> mockknowledgeinstance.query.returnvalue = [{"content": content}]<br><br> mockstorageinstance = mockknowledgestorage.returnvalue<br> mockstorageinstance.sources = [stringsource]<br> mockstorageinstance.query.returnvalue = [{"content": content}]...</code> | | <code>Example usage of agent</code> | <code>def agent(self) -> Agent \| None:<br> """Get the current agent associated with this memory."""<br> return self._agent</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: 4
• —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: 4
• —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: 5.2.2
• —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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