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CodeBERT Fine-tuned on CrewAI

Name: codebert-embed-crewai-base
Rating: 2.5 (52 reviews)

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
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.

python

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]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.57
cosine_accuracy@3	0.57
cosine_accuracy@5	0.57
cosine_accuracy@10	0.65
cosine_precision@1	0.57
cosine_precision@3	0.57
cosine_precision@5	0.57
cosine_precision@10	0.325
cosine_recall@1	0.114
cosine_recall@3	0.342
cosine_recall@5	0.57
cosine_recall@10	0.65
cosine_ndcg@10	0.6133
cosine_mrr@10	0.5833
cosine_map@100	0.6323

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy@1	0.56
cosine_accuracy@3	0.56
cosine_accuracy@5	0.56
cosine_accuracy@10	0.68
cosine_precision@1	0.56
cosine_precision@3	0.56
cosine_precision@5	0.56
cosine_precision@10	0.34
cosine_recall@1	0.112
cosine_recall@3	0.336
cosine_recall@5	0.56
cosine_recall@10	0.68
cosine_ndcg@10	0.6249
cosine_mrr@10	0.58
cosine_map@100	0.6328

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy@1	0.54
cosine_accuracy@3	0.54
cosine_accuracy@5	0.54
cosine_accuracy@10	0.67
cosine_precision@1	0.54
cosine_precision@3	0.54
cosine_precision@5	0.54
cosine_precision@10	0.335
cosine_recall@1	0.108
cosine_recall@3	0.324
cosine_recall@5	0.54
cosine_recall@10	0.67
cosine_ndcg@10	0.6103
cosine_mrr@10	0.5617
cosine_map@100	0.6227

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	Value
cosine_accuracy@1	0.47
cosine_accuracy@3	0.47
cosine_accuracy@5	0.47
cosine_accuracy@10	0.58
cosine_precision@1	0.47
cosine_precision@3	0.47
cosine_precision@5	0.47
cosine_precision@10	0.29
cosine_recall@1	0.094
cosine_recall@3	0.282
cosine_recall@5	0.47
cosine_recall@10	0.58
cosine_ndcg@10	0.5295
cosine_mrr@10	0.4883
cosine_map@100	0.5582

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	Value
cosine_accuracy@1	0.5
cosine_accuracy@3	0.5
cosine_accuracy@5	0.5
cosine_accuracy@10	0.6
cosine_precision@1	0.5
cosine_precision@3	0.5
cosine_precision@5	0.5
cosine_precision@10	0.3
cosine_recall@1	0.1
cosine_recall@3	0.3
cosine_recall@5	0.5
cosine_recall@10	0.6
cosine_ndcg@10	0.5541
cosine_mrr@10	0.5167
cosine_map@100	0.5748

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: 13.96 tokens
max: 141 tokens
min: 20 tokens
mean: 254.94 tokens
max: 512 tokens

Samples:

anchor	positive
`Example usage of DeeplyNestedFlow`	class DeeplyNestedFlow(Flow):[object Object] @start()[object Object] def a(self):[object Object] execution_order.append("a")[object Object][object Object] @start()[object Object] def b(self):[object Object] execution_order.append("b")[object Object][object Object] @start()[object Object] def c(self):[object Object] execution_order.append("c")[object Object][object Object] @start()[object Object] def d(self):[object Object] execution_order.append("d")[object Object][object Object] # Nested: (a AND b) OR (c AND d)[object Object] @listen(or_(and_(a, b), and_(c, d)))[object Object] def result(self):[object Object] execution_order.append("result")
`Explain the test_agent_with_knowledge_sources_generate_search_query logic`	def test_agent_with_knowledge_sources_generate_search_query():[object Object] content = "Brandon's favorite color is red and he likes Mexican food."[object Object] string_source = StringKnowledgeSource(content=content)[object Object][object Object] with ([object Object] patch("crewai.knowledge") as mock_knowledge,[object Object] patch([object Object] "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"[object Object] ) as mock_knowledge_storage,[object Object] patch([object Object] "crewai.knowledge.source.base_knowledge_source.KnowledgeStorage"[object Object] ) as mock_base_knowledge_storage,[object Object] patch("crewai.rag.chromadb.client.ChromaDBClient") as mock_chromadb,[object Object] ):[object Object] mock_knowledge_instance = mock_knowledge.return_value[object Object] mock_knowledge_instance.sources = [string_source][object Object] mock_knowledge_instance.query.return_value = [{"content": content}][object Object][object Object] mock_storage_instance = mock_knowledge_storage.return_value[object Object] mock_storage_instance.sources = [string_source][object Object] mock_storage_instance.query.return_value = [{"content": content}]...
`Example usage of agent`	`def agent(self) -> Agent \| None:[object Object] """Get the current agent associated with this memory."""[object Object] return self._agent`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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

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: 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: {'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	7.1051	-	-	-	-	-
1.0	15	-	0.1170	0.06	0.0608	0.0825	0.0762
1.3556	20	6.4716	-	-	-	-	-
2.0	30	5.4463	0.1879	0.1770	0.1625	0.1816	0.1987
2.7111	40	3.7856	-	-	-	-	-
3.0	45	-	0.4987	0.5133	0.4587	0.4249	0.4425
3.3556	50	2.4942	-	-	-	-	-
4.0	60	1.71	0.6133	0.6249	0.6103	0.5295	0.5541

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

bibtex

@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

bibtex

@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

bibtex

@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}
}

	anchor	positive
type	string	string
details	min: 6 tokens mean: 13.96 tokens max: 141 tokens	min: 20 tokens mean: 254.94 tokens max: 512 tokens