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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:900
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/codebert-base
widget:
- source_sentence: Explain the test_code_docs_search_tool logic
sentences:
- "def test_anthropic_call_with_interceptor_tracks_requests(self) -> None:\n \
\ \"\"\"Test that interceptor tracks Anthropic API requests.\"\"\"\n \
\ interceptor = AnthropicTestInterceptor()\n llm = LLM(model=\"anthropic/claude-3-5-haiku-20241022\"\
, interceptor=interceptor)\n\n # Make a simple completion call\n \
\ result = llm.call(\n messages=[{\"role\": \"user\", \"content\"\
: \"Say 'Hello World' and nothing else\"}]\n )\n\n # Verify custom\
\ headers were added\n for request in interceptor.outbound_calls:\n \
\ assert \"X-Anthropic-Interceptor\" in request.headers\n assert\
\ request.headers[\"X-Anthropic-Interceptor\"] == \"anthropic-test-value\"\n \
\ assert \"X-Request-ID\" in request.headers\n assert request.headers[\"\
X-Request-ID\"] == \"test-request-456\"\n\n # Verify response was tracked\n\
\ for response in interceptor.inbound_calls:\n assert \"X-Response-Tracked\"\
\ in response.headers\n assert response.headers[\"X-Response-Tracked\"\
] == \"true\"\n\n # Verify result is valid\n assert result is not\
\ None\n assert isinstance(result, str)\n assert len(result) > 0"
- "def on_inbound(self, message: httpx.Response) -> httpx.Response:\n \"\"\
\"Pass through inbound response.\n\n Args:\n message: The inbound\
\ response.\n\n Returns:\n The response unchanged.\n \
\ \"\"\"\n return message"
- "def test_code_docs_search_tool(mock_adapter):\n mock_adapter.query.return_value\
\ = \"test documentation\"\n\n docs_url = \"https://crewai.com/any-docs-url\"\
\n search_query = \"test documentation\"\n tool = CodeDocsSearchTool(docs_url=docs_url,\
\ adapter=mock_adapter)\n result = tool._run(search_query=search_query)\n \
\ assert \"test documentation\" in result\n mock_adapter.add.assert_called_once_with(docs_url,\
\ data_type=DataType.DOCS_SITE)\n mock_adapter.query.assert_called_once_with(\n\
\ search_query, similarity_threshold=0.6, limit=5\n )\n\n mock_adapter.query.reset_mock()\n\
\ mock_adapter.add.reset_mock()\n\n tool = CodeDocsSearchTool(adapter=mock_adapter)\n\
\ result = tool._run(docs_url=docs_url, search_query=search_query)\n assert\
\ \"test documentation\" in result\n mock_adapter.add.assert_called_once_with(docs_url,\
\ data_type=DataType.DOCS_SITE)\n mock_adapter.query.assert_called_once_with(\n\
\ search_query, similarity_threshold=0.6, limit=5\n )"
- source_sentence: Explain the test_openai_get_client_params_with_base_url_priority
logic
sentences:
- "def test_openai_get_client_params_with_base_url_priority():\n \"\"\"\n \
\ Test that base_url takes priority over api_base in _get_client_params\n \"\
\"\"\n llm = OpenAICompletion(\n model=\"gpt-4o\",\n base_url=\"\
https://priority.openai.com/v1\",\n api_base=\"https://fallback.openai.com/v1\"\
,\n )\n client_params = llm._get_client_params()\n assert client_params[\"\
base_url\"] == \"https://priority.openai.com/v1\""
- "def get_context_window_size(self) -> int:\n \"\"\"Get the context window\
\ size for the LLM.\n\n Returns:\n The number of tokens/characters\
\ the model can handle.\n \"\"\"\n # Default implementation - subclasses\
\ should override with model-specific values\n return DEFAULT_CONTEXT_WINDOW_SIZE"
- "def _inject_date_to_task(self, task: Task) -> None:\n \"\"\"Inject the\
\ current date into the task description if inject_date is enabled.\"\"\"\n \
\ if self.inject_date:\n from datetime import datetime\n\n \
\ try:\n valid_format_codes = [\n \"\
%Y\",\n \"%m\",\n \"%d\",\n \
\ \"%H\",\n \"%M\",\n \"%S\",\n \
\ \"%B\",\n \"%b\",\n \
\ \"%A\",\n \"%a\",\n ]\n is_valid\
\ = any(code in self.date_format for code in valid_format_codes)\n\n \
\ if not is_valid:\n raise ValueError(f\"Invalid date\
\ format: {self.date_format}\")\n\n current_date = datetime.now().strftime(self.date_format)\n\
\ task.description += f\"\\n\\nCurrent Date: {current_date}\"\n\
\ except Exception as e:\n self._logger.log(\"warning\"\
, f\"Failed to inject date: {e!s}\")"
- source_sentence: How to implement async _get_connection?
sentences:
- "def mock_env():\n with patch.dict(os.environ, {\"CREWAI_PERSONAL_ACCESS_TOKEN\"\
: \"test_token\"}):\n os.environ.pop(\"CREWAI_PLUS_URL\", None)\n \
\ yield"
- "async def _get_connection(self) -> SnowflakeConnection:\n \"\"\"Get a\
\ connection from the pool or create a new one.\"\"\"\n if self._pool_lock\
\ is None:\n raise RuntimeError(\"Pool lock not initialized\")\n \
\ if self._connection_pool is None:\n raise RuntimeError(\"Connection\
\ pool not initialized\")\n async with self._pool_lock:\n if\
\ not self._connection_pool:\n conn = await asyncio.get_event_loop().run_in_executor(\n\
\ self._thread_pool, self._create_connection\n \
\ )\n self._connection_pool.append(conn)\n return\
\ self._connection_pool.pop()"
- "async def _arun(self, selector: str, thread_id: str = \"default\", **kwargs)\
\ -> str:\n \"\"\"Use the async tool.\"\"\"\n try:\n \
\ # Get the current page\n page = await self.get_async_page(thread_id)\n\
\n # Click on the element\n selector_effective = self._selector_effective(selector=selector)\n\
\ from playwright.async_api import TimeoutError as PlaywrightTimeoutError\n\
\n try:\n await page.click(\n selector_effective,\n\
\ strict=self.playwright_strict,\n timeout=self.playwright_timeout,\n\
\ )\n except PlaywrightTimeoutError:\n \
\ return f\"Unable to click on element '{selector}'\"\n except Exception\
\ as click_error:\n return f\"Unable to click on element '{selector}':\
\ {click_error!s}\"\n\n return f\"Clicked element '{selector}'\"\n\
\ except Exception as e:\n return f\"Error clicking on element:\
\ {e!s}\""
- source_sentence: Example usage of test_personal_access_token_from_environment
sentences:
- "async def close(self):\n return None"
- "def test_structured_state_persistence(tmp_path):\n \"\"\"Test persistence\
\ with Pydantic model state.\"\"\"\n db_path = os.path.join(tmp_path, \"test_flows.db\"\
)\n persistence = SQLiteFlowPersistence(db_path)\n\n class StructuredFlow(Flow[TestState]):\n\
\ initial_state = TestState\n\n @start()\n @persist(persistence)\n\
\ def count_up(self):\n self.state.counter += 1\n \
\ self.state.message = f\"Count is {self.state.counter}\"\n\n # Run flow and\
\ verify state changes are saved\n flow = StructuredFlow(persistence=persistence)\n\
\ flow.kickoff()\n\n # Load and verify state\n saved_state = persistence.load_state(flow.state.id)\n\
\ assert saved_state is not None\n assert saved_state[\"counter\"] == 1\n\
\ assert saved_state[\"message\"] == \"Count is 1\""
- "def test_personal_access_token_from_environment(tool):\n assert tool.personal_access_token\
\ == \"test_token\""
- source_sentence: Best practices for handle_a2a_polling_started
sentences:
- "def external_supported_storages() -> dict[str, Any]:\n return {\n \
\ \"mem0\": ExternalMemory._configure_mem0,\n }"
- "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()"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: CodeBERT Fine-tuned on CrewAI
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.57
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.57
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.57
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.65
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.57
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.57
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.57
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.325
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11399999999999996
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3420000000000001
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.57
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.65
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6132795614223119
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5833333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6323349876959563
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.56
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.56
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.56
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.56
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.56
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.34
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11199999999999999
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.336
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.56
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6249193421334678
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5799999999999998
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6328444860345127
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.54
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.67
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.54
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.54
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.54
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.335
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10799999999999997
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.324
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.67
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6103292873112568
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5616666666666664
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.622676615058847
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.47
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.47
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.47
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.47
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.47
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.47
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.29
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09399999999999999
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28200000000000003
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.47
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.58
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5295093969556788
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.48833333333333323
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5581789904714569
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.5
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.5
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.3
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5540994517778899
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5166666666666665
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5748485156077728
name: Cosine Map@100
---
# CodeBERT Fine-tuned on CrewAI
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/codebert-base](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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]])
```
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 768
}
```
| 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 [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| 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 [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| 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 [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| 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 [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| 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 |
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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> execution_order.append("a")<br><br> @start()<br> def b(self):<br> execution_order.append("b")<br><br> @start()<br> def c(self):<br> execution_order.append("c")<br><br> @start()<br> def d(self):<br> execution_order.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> execution_order.append("result")</code> |
| <code>Explain the test_agent_with_knowledge_sources_generate_search_query logic</code> | <code>def test_agent_with_knowledge_sources_generate_search_query():<br> content = "Brandon's favorite color is red and he likes Mexican food."<br> string_source = StringKnowledgeSource(content=content)<br><br> with (<br> patch("crewai.knowledge") as mock_knowledge,<br> patch(<br> "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"<br> ) as mock_knowledge_storage,<br> patch(<br> "crewai.knowledge.source.base_knowledge_source.KnowledgeStorage"<br> ) as mock_base_knowledge_storage,<br> patch("crewai.rag.chromadb.client.ChromaDBClient") as mock_chromadb,<br> ):<br> mock_knowledge_instance = mock_knowledge.return_value<br> mock_knowledge_instance.sources = [string_source]<br> mock_knowledge_instance.query.return_value = [{"content": content}]<br><br> mock_storage_instance = mock_knowledge_storage.return_value<br> mock_storage_instance.sources = [string_source]<br> mock_storage_instance.query.return_value = [{"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>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"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`: {'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`: {}
</details>
### 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}
}
```
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