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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: How to implement __del__? |
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sentences: |
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- "class SampleMultiCrewFlow(Flow[SimpleState]):\n @start()\n def\ |
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\ first_crew(self):\n \"\"\"Run first crew.\"\"\"\n agent\ |
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\ = Agent(\n role=\"first agent\",\n goal=\"first\ |
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\ task\",\n backstory=\"first agent\",\n llm=mock_llm_1,\n\ |
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\ )\n task = Task(\n description=\"First\ |
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\ task\",\n expected_output=\"first result\",\n \ |
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\ agent=agent,\n )\n crew = Crew(\n agents=[agent],\n\ |
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\ tasks=[task],\n share_crew=True,\n \ |
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\ )\n\n result = crew.kickoff()\n\n assert crew._execution_span\ |
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\ is not None\n return str(result.raw)\n\n @listen(first_crew)\n\ |
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\ def second_crew(self, first_result: str):\n \"\"\"Run second\ |
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\ crew.\"\"\"\n agent = Agent(\n role=\"second agent\"\ |
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,\n goal=\"second task\",\n backstory=\"second agent\"\ |
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,\n llm=mock_llm_2,\n )\n task = Task(\n\ |
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\ description=\"Second task\",\n expected_output=\"\ |
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second result\",\n agent=agent,\n )\n crew\ |
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\ = Crew(\n agents=[agent],\n tasks=[task],\n \ |
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\ share_crew=True,\n )\n\n result = crew.kickoff()\n\ |
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\n assert crew._execution_span is not None\n\n self.state.result\ |
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\ = f\"{first_result} + {result.raw}\"\n return self.state.result" |
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- "async def test_anthropic_async_with_tools():\n \"\"\"Test async call with\ |
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\ tools.\"\"\"\n llm = AnthropicCompletion(model=\"claude-sonnet-4-0\")\n\n\ |
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\ tools = [\n {\n \"type\": \"function\",\n \"\ |
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function\": {\n \"name\": \"get_weather\",\n \"\ |
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description\": \"Get the current weather for a location\",\n \"\ |
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parameters\": {\n \"type\": \"object\",\n \ |
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\ \"properties\": {\n \"location\": {\n \ |
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\ \"type\": \"string\",\n \"description\"\ |
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: \"The city and state, e.g. San Francisco, CA\"\n }\n\ |
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\ },\n \"required\": [\"location\"]\n \ |
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\ }\n }\n }\n ]\n\n result = await llm.acall(\n\ |
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\ \"What's the weather in San Francisco?\",\n tools=tools\n )\n\ |
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\ logging.debug(\"result: %s\", result)\n\n assert result is not None\n\ |
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\ assert isinstance(result, str)" |
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- "def __del__(self):\n \"\"\"Cleanup connections on deletion.\"\"\"\n \ |
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\ try:\n if self._connection_pool:\n for conn in\ |
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\ self._connection_pool:\n try:\n conn.close()\n\ |
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\ except Exception: # noqa: PERF203, S110\n \ |
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\ pass\n if self._thread_pool:\n self._thread_pool.shutdown()\n\ |
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\ except Exception: # noqa: S110\n pass" |
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- source_sentence: How does route_to_cycle work in Python? |
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sentences: |
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- "def route_to_cycle(self):\n execution_log.append(\"router_initial\"\ |
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)\n return \"loop\"" |
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- "def _register_system_event_handlers(self, event_bus: CrewAIEventsBus) -> None:\n\ |
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\ \"\"\"Register handlers for system signal events (SIGTERM, SIGINT, etc.).\"\ |
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\"\"\n\n @on_signal\n def handle_signal(source: Any, event: SignalEvent)\ |
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\ -> None:\n \"\"\"Flush trace batch on system signals to prevent data\ |
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\ loss.\"\"\"\n if self.batch_manager.is_batch_initialized():\n \ |
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\ self.batch_manager.finalize_batch()" |
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- "async def aadd(self) -> None:\n \"\"\"Add JSON file content asynchronously.\"\ |
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\"\"\n content_str = (\n str(self.content) if isinstance(self.content,\ |
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\ dict) else self.content\n )\n new_chunks = self._chunk_text(content_str)\n\ |
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\ self.chunks.extend(new_chunks)\n await self._asave_documents()" |
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- source_sentence: Explain the test_evaluate logic |
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sentences: |
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- "def test_flow_copy_state_with_unpickleable_objects():\n \"\"\"Test that _copy_state\ |
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\ handles unpickleable objects like RLock.\n\n Regression test for issue #3828:\ |
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\ Flow should not crash when state contains\n objects that cannot be deep copied\ |
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\ (like threading.RLock).\n \"\"\"\n\n class StateWithRLock(BaseModel):\n\ |
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\ counter: int = 0\n lock: Optional[threading.RLock] = None\n\n\ |
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\ class FlowWithRLock(Flow[StateWithRLock]):\n @start()\n def\ |
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\ step_1(self):\n self.state.counter += 1\n\n @listen(step_1)\n\ |
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\ def step_2(self):\n self.state.counter += 1\n\n flow =\ |
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\ FlowWithRLock(initial_state=StateWithRLock())\n flow._state.lock = threading.RLock()\n\ |
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\n copied_state = flow._copy_state()\n assert copied_state.counter == 0\n\ |
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\ assert copied_state.lock is not None" |
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- "def test_evaluate(self, crew_planner):\n task_output = TaskOutput(\n \ |
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\ description=\"Task 1\", agent=str(crew_planner.crew.agents[0])\n \ |
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\ )\n\n with mock.patch.object(Task, \"execute_sync\") as execute:\n\ |
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\ execute().pydantic = TaskEvaluationPydanticOutput(quality=9.5)\n\ |
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\ crew_planner.evaluate(task_output)\n assert crew_planner.tasks_scores[0]\ |
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\ == [9.5]" |
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- "class SlowAsyncTool(BaseTool):\n name: str = \"slow_async\"\n \ |
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\ description: str = \"Simulates slow I/O\"\n\n def _run(self,\ |
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\ task_id: int, delay: float) -> str:\n return f\"Task {task_id}\ |
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\ done\"\n\n async def _arun(self, task_id: int, delay: float) -> str:\n\ |
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\ await asyncio.sleep(delay)\n return f\"Task {task_id}\ |
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\ done\"" |
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- source_sentence: Explain the test_clean_action_no_formatting logic |
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sentences: |
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- "def test_task_interpolation_with_hyphens():\n agent = Agent(\n role=\"\ |
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Researcher\",\n goal=\"be an assistant that responds with {interpolation-with-hyphens}\"\ |
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,\n backstory=\"You're an expert researcher, specialized in technology,\ |
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\ software engineering, AI and startups. You work as a freelancer and is now working\ |
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\ on doing research and analysis for a new customer.\",\n allow_delegation=False,\n\ |
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\ )\n task = Task(\n description=\"be an assistant that responds\ |
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\ with {interpolation-with-hyphens}\",\n expected_output=\"The response\ |
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\ should be addressing: {interpolation-with-hyphens}\",\n agent=agent,\n\ |
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\ )\n crew = Crew(\n agents=[agent],\n tasks=[task],\n \ |
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\ verbose=True,\n )\n result = crew.kickoff(inputs={\"interpolation-with-hyphens\"\ |
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: \"say hello world\"})\n assert \"say hello world\" in task.prompt()\n\n \ |
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\ assert result.raw == \"Hello, World!\"" |
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- "class LLMCallCompletedEvent(LLMEventBase):\n \"\"\"Event emitted when a LLM\ |
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\ call completes\"\"\"\n\n type: str = \"llm_call_completed\"\n messages:\ |
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\ str | list[dict[str, Any]] | None = None\n response: Any\n call_type:\ |
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\ LLMCallType\n model: str | None = None" |
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- "def test_clean_action_no_formatting():\n action = \"Ask question to senior\ |
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\ researcher\"\n cleaned_action = parser._clean_action(action)\n assert\ |
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\ cleaned_action == \"Ask question to senior researcher\"" |
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- source_sentence: Example usage of test_status_code_and_content_type |
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sentences: |
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- "class NavigateBackToolInput(BaseModel):\n \"\"\"Input for NavigateBackTool.\"\ |
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\"\"\n\n thread_id: str = Field(\n default=\"default\", description=\"\ |
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Thread ID for the browser session\"\n )" |
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- "def test_status_code_and_content_type(self, mock_bs, mock_get):\n for\ |
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\ status in [200, 201, 301]:\n mock_get.return_value = self.setup_mock_response(\n\ |
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\ f\"<html><body>Status {status}</body></html>\", status_code=status\n\ |
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\ )\n mock_bs.return_value = self.setup_mock_soup(f\"Status\ |
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\ {status}\")\n result = WebPageLoader().load(\n SourceContent(f\"\ |
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https://example.com/{status}\")\n )\n assert result.metadata[\"\ |
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status_code\"] == status\n\n for ctype in [\"text/html\", \"text/plain\"\ |
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, \"application/xhtml+xml\"]:\n mock_get.return_value = self.setup_mock_response(\n\ |
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\ \"<html><body>Content</body></html>\", content_type=ctype\n \ |
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\ )\n mock_bs.return_value = self.setup_mock_soup(\"Content\"\ |
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)\n result = WebPageLoader().load(SourceContent(\"https://example.com\"\ |
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))\n assert result.metadata[\"content_type\"] == ctype" |
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- "def set_crew(self, crew: Any) -> Memory:\n \"\"\"Set the crew for this\ |
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\ memory instance.\"\"\"\n self.crew = crew\n return self" |
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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 (LR=2e-05) |
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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: 0.04 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.04 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.04 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.06 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.04 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.04 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.04 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.03 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.008 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.024 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.04 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.06 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.050819890355577976 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.04333333333333334 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.06130275691848844 |
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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: 0.01 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.01 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.01 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.01 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.01 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.01 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.01 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.005 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.002 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.006 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.01 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.01 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.01 |
|
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.01 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.019316331411936505 |
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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: 0.01 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.01 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.01 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.03 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.01 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.01 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.01 |
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name: Cosine Precision@5 |
|
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- type: cosine_precision@10 |
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value: 0.015 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.002 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.006 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.01 |
|
|
name: Cosine Recall@5 |
|
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- type: cosine_recall@10 |
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value: 0.03 |
|
|
name: Cosine Recall@10 |
|
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- type: cosine_ndcg@10 |
|
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value: 0.020819890355577977 |
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|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.013333333333333334 |
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|
name: Cosine Mrr@10 |
|
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- type: cosine_map@100 |
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value: 0.028978936077832484 |
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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 128 |
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type: dim_128 |
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.01 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.01 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.01 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.01 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.01 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.01 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.01 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.005 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.002 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.006 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.01 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.01 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.01 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.01 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.027544667112101906 |
|
|
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 64 |
|
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type: dim_64 |
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.05 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.05 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.05 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.07 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.05 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.05 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.05 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.035 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.01 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.03 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.05 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.07 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.06081989035557797 |
|
|
name: Cosine Ndcg@10 |
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|
- type: cosine_mrr@10 |
|
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value: 0.05333333333333334 |
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|
name: Cosine Mrr@10 |
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|
- type: cosine_map@100 |
|
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value: 0.0838507480466874 |
|
|
name: Cosine Map@100 |
|
|
--- |
|
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|
|
|
# CodeBERT Fine-tuned on CrewAI (LR=2e-05) |
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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. |
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## Model Details |
|
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 --> |
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- **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 |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'}) |
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(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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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("itsanan/codebert-finetuned-crewai-base") |
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# Run inference |
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sentences = [ |
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'Example usage of test_status_code_and_content_type', |
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'def test_status_code_and_content_type(self, mock_bs, mock_get):\n for status in [200, 201, 301]:\n mock_get.return_value = self.setup_mock_response(\n f"<html><body>Status {status}</body></html>", status_code=status\n )\n mock_bs.return_value = self.setup_mock_soup(f"Status {status}")\n result = WebPageLoader().load(\n SourceContent(f"https://example.com/{status}")\n )\n assert result.metadata["status_code"] == status\n\n for ctype in ["text/html", "text/plain", "application/xhtml+xml"]:\n mock_get.return_value = self.setup_mock_response(\n "<html><body>Content</body></html>", content_type=ctype\n )\n mock_bs.return_value = self.setup_mock_soup("Content")\n result = WebPageLoader().load(SourceContent("https://example.com"))\n assert result.metadata["content_type"] == ctype', |
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'def set_crew(self, crew: Any) -> Memory:\n """Set the crew for this memory instance."""\n self.crew = crew\n return self', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.9009, 0.9087], |
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# [0.9009, 1.0000, 0.9053], |
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# [0.9087, 0.9053, 1.0000]]) |
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``` |
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|
|
<!-- |
|
|
### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
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{ |
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"truncate_dim": 768 |
|
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} |
|
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``` |
|
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|
|
|
| Metric | Value | |
|
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.04 | |
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| cosine_accuracy@3 | 0.04 | |
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| cosine_accuracy@5 | 0.04 | |
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| cosine_accuracy@10 | 0.06 | |
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| cosine_precision@1 | 0.04 | |
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| cosine_precision@3 | 0.04 | |
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| cosine_precision@5 | 0.04 | |
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| cosine_precision@10 | 0.03 | |
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| cosine_recall@1 | 0.008 | |
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| cosine_recall@3 | 0.024 | |
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| cosine_recall@5 | 0.04 | |
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| cosine_recall@10 | 0.06 | |
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| **cosine_ndcg@10** | **0.0508** | |
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| cosine_mrr@10 | 0.0433 | |
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| cosine_map@100 | 0.0613 | |
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|
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#### Information Retrieval |
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* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
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|
"truncate_dim": 512 |
|
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} |
|
|
``` |
|
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|
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|
| Metric | Value | |
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|:--------------------|:---------| |
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| cosine_accuracy@1 | 0.01 | |
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| cosine_accuracy@3 | 0.01 | |
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| cosine_accuracy@5 | 0.01 | |
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| cosine_accuracy@10 | 0.01 | |
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| cosine_precision@1 | 0.01 | |
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| cosine_precision@3 | 0.01 | |
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| cosine_precision@5 | 0.01 | |
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| cosine_precision@10 | 0.005 | |
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| cosine_recall@1 | 0.002 | |
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| cosine_recall@3 | 0.006 | |
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| cosine_recall@5 | 0.01 | |
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| cosine_recall@10 | 0.01 | |
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| **cosine_ndcg@10** | **0.01** | |
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| cosine_mrr@10 | 0.01 | |
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| cosine_map@100 | 0.0193 | |
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|
|
|
#### Information Retrieval |
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* Dataset: `dim_256` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
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|
"truncate_dim": 256 |
|
|
} |
|
|
``` |
|
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|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.01 | |
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| cosine_accuracy@3 | 0.01 | |
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| cosine_accuracy@5 | 0.01 | |
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| cosine_accuracy@10 | 0.03 | |
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| cosine_precision@1 | 0.01 | |
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| cosine_precision@3 | 0.01 | |
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| cosine_precision@5 | 0.01 | |
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| cosine_precision@10 | 0.015 | |
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| cosine_recall@1 | 0.002 | |
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| cosine_recall@3 | 0.006 | |
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| cosine_recall@5 | 0.01 | |
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| cosine_recall@10 | 0.03 | |
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| **cosine_ndcg@10** | **0.0208** | |
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| cosine_mrr@10 | 0.0133 | |
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| cosine_map@100 | 0.029 | |
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|
|
|
#### Information Retrieval |
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* Dataset: `dim_128` |
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* 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 |
|
|
} |
|
|
``` |
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|
|
|
| Metric | Value | |
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|:--------------------|:---------| |
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| cosine_accuracy@1 | 0.01 | |
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| cosine_accuracy@3 | 0.01 | |
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| cosine_accuracy@5 | 0.01 | |
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| cosine_accuracy@10 | 0.01 | |
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| cosine_precision@1 | 0.01 | |
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| cosine_precision@3 | 0.01 | |
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| cosine_precision@5 | 0.01 | |
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| cosine_precision@10 | 0.005 | |
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| cosine_recall@1 | 0.002 | |
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| cosine_recall@3 | 0.006 | |
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| cosine_recall@5 | 0.01 | |
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| cosine_recall@10 | 0.01 | |
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| **cosine_ndcg@10** | **0.01** | |
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| cosine_mrr@10 | 0.01 | |
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| cosine_map@100 | 0.0275 | |
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|
|
|
#### Information Retrieval |
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|
|
|
* 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 |
|
|
} |
|
|
``` |
|
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|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
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| cosine_accuracy@1 | 0.05 | |
|
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| cosine_accuracy@3 | 0.05 | |
|
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| cosine_accuracy@5 | 0.05 | |
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| cosine_accuracy@10 | 0.07 | |
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| cosine_precision@1 | 0.05 | |
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| cosine_precision@3 | 0.05 | |
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| cosine_precision@5 | 0.05 | |
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| cosine_precision@10 | 0.035 | |
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| cosine_recall@1 | 0.01 | |
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| cosine_recall@3 | 0.03 | |
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| cosine_recall@5 | 0.05 | |
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| cosine_recall@10 | 0.07 | |
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| **cosine_ndcg@10** | **0.0608** | |
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| cosine_mrr@10 | 0.0533 | |
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| cosine_map@100 | 0.0839 | |
|
|
|
|
|
<!-- |
|
|
## Bias, Risks and Limitations |
|
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|
|
|
*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 |
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|
|
|
* 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: 13.86 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 253.07 tokens</li><li>max: 512 tokens</li></ul> | |
|
|
* Samples: |
|
|
| anchor | positive | |
|
|
|:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>How to implement LLMCallCompletedEvent?</code> | <code>class LLMCallCompletedEvent(LLMEventBase):<br> """Event emitted when a LLM call completes"""<br><br> type: str = "llm_call_completed"<br> messages: str \| list[dict[str, Any]] \| None = None<br> response: Any<br> call_type: LLMCallType<br> model: str \| None = None</code> | |
|
|
| <code>How does get_llm_response work in Python?</code> | <code>def get_llm_response(<br> llm: LLM \| BaseLLM,<br> messages: list[LLMMessage],<br> callbacks: list[TokenCalcHandler],<br> printer: Printer,<br> from_task: Task \| None = None,<br> from_agent: Agent \| LiteAgent \| None = None,<br> response_model: type[BaseModel] \| None = None,<br> executor_context: CrewAgentExecutor \| LiteAgent \| None = None,<br>) -> str:<br> """Call the LLM and return the response, handling any invalid responses.<br><br> Args:<br> llm: The LLM instance to call.<br> messages: The messages to send to the LLM.<br> callbacks: List of callbacks for the LLM call.<br> printer: Printer instance for output.<br> from_task: Optional task context for the LLM call.<br> from_agent: Optional agent context for the LLM call.<br> response_model: Optional Pydantic model for structured outputs.<br> executor_context: Optional executor context for hook invocation.<br><br> Returns:<br> The response from the LLM as a string.<br><br> Raises:<br> Exception: If an error ...</code> | |
|
|
| <code>Example usage of _run</code> | <code>def _run(<br> self,<br> **kwargs: Any,<br> ) -> Any:<br> website_url: str \| None = kwargs.get("website_url", self.website_url)<br> if website_url is None:<br> raise ValueError("Website URL must be provided.")<br><br> page = requests.get(<br> website_url,<br> timeout=15,<br> headers=self.headers,<br> cookies=self.cookies if self.cookies else {},<br> )<br><br> page.encoding = page.apparent_encoding<br> parsed = BeautifulSoup(page.text, "html.parser")<br><br> text = "The following text is scraped website content:\n\n"<br> text += parsed.get_text(" ")<br> text = re.sub("[ \t]+", " ", text)<br> return re.sub("\\s+\n\\s+", "\n", text)</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`: steps |
|
|
- `per_device_train_batch_size`: 4 |
|
|
- `per_device_eval_batch_size`: 4 |
|
|
- `gradient_accumulation_steps`: 32 |
|
|
- `learning_rate`: 2e-05 |
|
|
- `weight_decay`: 0.01 |
|
|
- `num_train_epochs`: 20 |
|
|
- `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`: steps |
|
|
- `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`: 32 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
|
|
- `learning_rate`: 2e-05 |
|
|
- `weight_decay`: 0.01 |
|
|
- `adam_beta1`: 0.9 |
|
|
- `adam_beta2`: 0.999 |
|
|
- `adam_epsilon`: 1e-08 |
|
|
- `max_grad_norm`: 1.0 |
|
|
- `num_train_epochs`: 20 |
|
|
- `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 | |
|
|
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| **0.9956** | **7** | **-** | **0.04** | **0.04** | **0.03** | **0.0262** | **0.0308** | |
|
|
| 1.2844 | 10 | 7.098 | - | - | - | - | - | |
|
|
| 1.8533 | 14 | - | 0.0362 | 0.02 | 0.0354 | 0.0154 | 0.0508 | |
|
|
| 2.5689 | 20 | 6.5515 | - | - | - | - | - | |
|
|
| 2.7111 | 21 | - | 0.0508 | 0.01 | 0.0208 | 0.01 | 0.0608 | |
|
|
|
|
|
* 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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