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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- dataset_size:900 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: microsoft/codebert-base |
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widget: |
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- source_sentence: Explain the test_code_docs_search_tool logic |
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sentences: |
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- "def test_anthropic_call_with_interceptor_tracks_requests(self) -> None:\n \ |
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\ \"\"\"Test that interceptor tracks Anthropic API requests.\"\"\"\n \ |
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\ interceptor = AnthropicTestInterceptor()\n llm = LLM(model=\"anthropic/claude-3-5-haiku-20241022\"\ |
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, interceptor=interceptor)\n\n # Make a simple completion call\n \ |
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\ result = llm.call(\n messages=[{\"role\": \"user\", \"content\"\ |
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: \"Say 'Hello World' and nothing else\"}]\n )\n\n # Verify custom\ |
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\ headers were added\n for request in interceptor.outbound_calls:\n \ |
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\ assert \"X-Anthropic-Interceptor\" in request.headers\n assert\ |
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\ request.headers[\"X-Anthropic-Interceptor\"] == \"anthropic-test-value\"\n \ |
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\ assert \"X-Request-ID\" in request.headers\n assert request.headers[\"\ |
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X-Request-ID\"] == \"test-request-456\"\n\n # Verify response was tracked\n\ |
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\ for response in interceptor.inbound_calls:\n assert \"X-Response-Tracked\"\ |
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\ in response.headers\n assert response.headers[\"X-Response-Tracked\"\ |
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] == \"true\"\n\n # Verify result is valid\n assert result is not\ |
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\ None\n assert isinstance(result, str)\n assert len(result) > 0" |
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- "def on_inbound(self, message: httpx.Response) -> httpx.Response:\n \"\"\ |
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\"Pass through inbound response.\n\n Args:\n message: The inbound\ |
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\ response.\n\n Returns:\n The response unchanged.\n \ |
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\ \"\"\"\n return message" |
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- "def test_code_docs_search_tool(mock_adapter):\n mock_adapter.query.return_value\ |
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\ = \"test documentation\"\n\n docs_url = \"https://crewai.com/any-docs-url\"\ |
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\n search_query = \"test documentation\"\n tool = CodeDocsSearchTool(docs_url=docs_url,\ |
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\ adapter=mock_adapter)\n result = tool._run(search_query=search_query)\n \ |
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\ assert \"test documentation\" in result\n mock_adapter.add.assert_called_once_with(docs_url,\ |
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\ data_type=DataType.DOCS_SITE)\n mock_adapter.query.assert_called_once_with(\n\ |
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\ search_query, similarity_threshold=0.6, limit=5\n )\n\n mock_adapter.query.reset_mock()\n\ |
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\ mock_adapter.add.reset_mock()\n\n tool = CodeDocsSearchTool(adapter=mock_adapter)\n\ |
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\ result = tool._run(docs_url=docs_url, search_query=search_query)\n assert\ |
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\ \"test documentation\" in result\n mock_adapter.add.assert_called_once_with(docs_url,\ |
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\ data_type=DataType.DOCS_SITE)\n mock_adapter.query.assert_called_once_with(\n\ |
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\ search_query, similarity_threshold=0.6, limit=5\n )" |
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- source_sentence: Explain the test_openai_get_client_params_with_base_url_priority |
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logic |
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sentences: |
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- "def test_openai_get_client_params_with_base_url_priority():\n \"\"\"\n \ |
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\ Test that base_url takes priority over api_base in _get_client_params\n \"\ |
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\"\"\n llm = OpenAICompletion(\n model=\"gpt-4o\",\n base_url=\"\ |
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https://priority.openai.com/v1\",\n api_base=\"https://fallback.openai.com/v1\"\ |
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,\n )\n client_params = llm._get_client_params()\n assert client_params[\"\ |
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base_url\"] == \"https://priority.openai.com/v1\"" |
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- "def get_context_window_size(self) -> int:\n \"\"\"Get the context window\ |
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\ size for the LLM.\n\n Returns:\n The number of tokens/characters\ |
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\ the model can handle.\n \"\"\"\n # Default implementation - subclasses\ |
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\ should override with model-specific values\n return DEFAULT_CONTEXT_WINDOW_SIZE" |
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- "def _inject_date_to_task(self, task: Task) -> None:\n \"\"\"Inject the\ |
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\ current date into the task description if inject_date is enabled.\"\"\"\n \ |
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\ if self.inject_date:\n from datetime import datetime\n\n \ |
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\ try:\n valid_format_codes = [\n \"\ |
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%Y\",\n \"%m\",\n \"%d\",\n \ |
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\ \"%H\",\n \"%M\",\n \"%S\",\n \ |
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\ \"%B\",\n \"%b\",\n \ |
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\ \"%A\",\n \"%a\",\n ]\n is_valid\ |
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\ = any(code in self.date_format for code in valid_format_codes)\n\n \ |
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\ if not is_valid:\n raise ValueError(f\"Invalid date\ |
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\ format: {self.date_format}\")\n\n current_date = datetime.now().strftime(self.date_format)\n\ |
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\ task.description += f\"\\n\\nCurrent Date: {current_date}\"\n\ |
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\ except Exception as e:\n self._logger.log(\"warning\"\ |
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, f\"Failed to inject date: {e!s}\")" |
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- source_sentence: How to implement async _get_connection? |
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sentences: |
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- "def mock_env():\n with patch.dict(os.environ, {\"CREWAI_PERSONAL_ACCESS_TOKEN\"\ |
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: \"test_token\"}):\n os.environ.pop(\"CREWAI_PLUS_URL\", None)\n \ |
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\ yield" |
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- "async def _get_connection(self) -> SnowflakeConnection:\n \"\"\"Get a\ |
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\ connection from the pool or create a new one.\"\"\"\n if self._pool_lock\ |
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\ is None:\n raise RuntimeError(\"Pool lock not initialized\")\n \ |
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\ if self._connection_pool is None:\n raise RuntimeError(\"Connection\ |
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\ pool not initialized\")\n async with self._pool_lock:\n if\ |
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\ not self._connection_pool:\n conn = await asyncio.get_event_loop().run_in_executor(\n\ |
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\ self._thread_pool, self._create_connection\n \ |
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\ )\n self._connection_pool.append(conn)\n return\ |
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\ self._connection_pool.pop()" |
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- "async def _arun(self, selector: str, thread_id: str = \"default\", **kwargs)\ |
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\ -> str:\n \"\"\"Use the async tool.\"\"\"\n try:\n \ |
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\ # Get the current page\n page = await self.get_async_page(thread_id)\n\ |
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\n # Click on the element\n selector_effective = self._selector_effective(selector=selector)\n\ |
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\ from playwright.async_api import TimeoutError as PlaywrightTimeoutError\n\ |
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\n try:\n await page.click(\n selector_effective,\n\ |
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\ strict=self.playwright_strict,\n timeout=self.playwright_timeout,\n\ |
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\ )\n except PlaywrightTimeoutError:\n \ |
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\ return f\"Unable to click on element '{selector}'\"\n except Exception\ |
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\ as click_error:\n return f\"Unable to click on element '{selector}':\ |
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\ {click_error!s}\"\n\n return f\"Clicked element '{selector}'\"\n\ |
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\ except Exception as e:\n return f\"Error clicking on element:\ |
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\ {e!s}\"" |
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- source_sentence: Example usage of test_personal_access_token_from_environment |
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sentences: |
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- "async def close(self):\n return None" |
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- "def test_structured_state_persistence(tmp_path):\n \"\"\"Test persistence\ |
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\ with Pydantic model state.\"\"\"\n db_path = os.path.join(tmp_path, \"test_flows.db\"\ |
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)\n persistence = SQLiteFlowPersistence(db_path)\n\n class StructuredFlow(Flow[TestState]):\n\ |
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\ initial_state = TestState\n\n @start()\n @persist(persistence)\n\ |
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\ def count_up(self):\n self.state.counter += 1\n \ |
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\ self.state.message = f\"Count is {self.state.counter}\"\n\n # Run flow and\ |
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\ verify state changes are saved\n flow = StructuredFlow(persistence=persistence)\n\ |
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\ flow.kickoff()\n\n # Load and verify state\n saved_state = persistence.load_state(flow.state.id)\n\ |
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\ assert saved_state is not None\n assert saved_state[\"counter\"] == 1\n\ |
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\ assert saved_state[\"message\"] == \"Count is 1\"" |
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- "def test_personal_access_token_from_environment(tool):\n assert tool.personal_access_token\ |
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\ == \"test_token\"" |
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- source_sentence: Best practices for handle_a2a_polling_started |
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sentences: |
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- "def external_supported_storages() -> dict[str, Any]:\n return {\n \ |
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\ \"mem0\": ExternalMemory._configure_mem0,\n }" |
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- "def handle_a2a_polling_started(\n self,\n task_id: str,\n \ |
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\ polling_interval: float,\n endpoint: str,\n ) -> None:\n \ |
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\ \"\"\"Handle A2A polling started event with panel display.\"\"\"\n content\ |
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\ = Text()\n content.append(\"A2A Polling Started\\n\", style=\"cyan bold\"\ |
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)\n content.append(\"Task ID: \", style=\"white\")\n content.append(f\"\ |
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{task_id[:8]}...\\n\", style=\"cyan\")\n content.append(\"Interval: \"\ |
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, style=\"white\")\n content.append(f\"{polling_interval}s\\n\", style=\"\ |
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cyan\")\n\n self.print_panel(content, \"⏳ A2A Polling\", \"cyan\")" |
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- "def test_agent_with_knowledge_sources_generate_search_query():\n content =\ |
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\ \"Brandon's favorite color is red and he likes Mexican food.\"\n string_source\ |
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\ = StringKnowledgeSource(content=content)\n\n with (\n patch(\"crewai.knowledge\"\ |
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) as mock_knowledge,\n patch(\n \"crewai.knowledge.storage.knowledge_storage.KnowledgeStorage\"\ |
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\n ) as mock_knowledge_storage,\n patch(\n \"crewai.knowledge.source.base_knowledge_source.KnowledgeStorage\"\ |
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\n ) as mock_base_knowledge_storage,\n patch(\"crewai.rag.chromadb.client.ChromaDBClient\"\ |
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) as mock_chromadb,\n ):\n mock_knowledge_instance = mock_knowledge.return_value\n\ |
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\ mock_knowledge_instance.sources = [string_source]\n mock_knowledge_instance.query.return_value\ |
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\ = [{\"content\": content}]\n\n mock_storage_instance = mock_knowledge_storage.return_value\n\ |
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\ mock_storage_instance.sources = [string_source]\n mock_storage_instance.query.return_value\ |
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\ = [{\"content\": content}]\n mock_storage_instance.save.return_value\ |
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\ = None\n\n mock_chromadb_instance = mock_chromadb.return_value\n \ |
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\ mock_chromadb_instance.add_documents.return_value = None\n\n mock_base_knowledge_storage.return_value\ |
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\ = mock_storage_instance\n\n agent = Agent(\n role=\"Information\ |
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\ Agent with extensive role description that is longer than 80 characters\",\n\ |
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\ goal=\"Provide information based on knowledge sources\",\n \ |
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\ backstory=\"You have access to specific knowledge sources.\",\n \ |
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\ llm=LLM(model=\"gpt-4o-mini\"),\n knowledge_sources=[string_source],\n\ |
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\ )\n\n task = Task(\n description=\"What is Brandon's\ |
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\ favorite color?\",\n expected_output=\"The answer to the question,\ |
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\ in a format like this: `{{name: str, favorite_color: str}}`\",\n \ |
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\ agent=agent,\n )\n\n crew = Crew(agents=[agent], tasks=[task])\n\ |
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\ result = crew.kickoff()\n\n # Updated assertion to check the JSON\ |
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\ content\n assert \"Brandon\" in str(agent.knowledge_search_query)\n \ |
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\ assert \"favorite color\" in str(agent.knowledge_search_query)\n\n \ |
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\ assert \"red\" in result.raw.lower()" |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: CodeBERT Fine-tuned on CrewAI |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.57 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.57 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.57 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.65 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.57 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.57 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.57 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.325 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.11399999999999996 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.3420000000000001 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.57 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.65 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.6132795614223119 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.5833333333333334 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6323349876959563 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.56 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.56 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.56 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.68 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.56 |
|
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.56 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.56 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.34 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.11199999999999999 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.336 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.56 |
|
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.68 |
|
|
name: Cosine Recall@10 |
|
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- type: cosine_ndcg@10 |
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value: 0.6249193421334678 |
|
|
name: Cosine Ndcg@10 |
|
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- type: cosine_mrr@10 |
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value: 0.5799999999999998 |
|
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6328444860345127 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
|
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- type: cosine_accuracy@1 |
|
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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: |
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type: information-retrieval |
|
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name: Information Retrieval |
|
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dataset: |
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name: dim 128 |
|
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type: dim_128 |
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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 |
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|
- **Output Dimensionality:** 768 dimensions |
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|
- **Similarity Function:** Cosine Similarity |
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|
<!-- - **Training Dataset:** Unknown --> |
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|
- **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}) |
|
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) |
|
|
``` |
|
|
|
|
|
## 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]]) |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
|
--> |
|
|
|
|
|
## 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 | |
|
|
|
|
|
<!-- |
|
|
## Bias, Risks and Limitations |
|
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Recommendations |
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
|
--> |
|
|
|
|
|
## 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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