Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,925 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:900
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: microsoft/codebert-base
+widget:
+- source_sentence: How to implement __del__?
+  sentences:
+  - "class SampleMultiCrewFlow(Flow[SimpleState]):\n        @start()\n        def\
+    \ first_crew(self):\n            \"\"\"Run first crew.\"\"\"\n            agent\
+    \ = Agent(\n                role=\"first agent\",\n                goal=\"first\
+    \ task\",\n                backstory=\"first agent\",\n                llm=mock_llm_1,\n\
+    \            )\n            task = Task(\n                description=\"First\
+    \ task\",\n                expected_output=\"first result\",\n               \
+    \ agent=agent,\n            )\n            crew = Crew(\n                agents=[agent],\n\
+    \                tasks=[task],\n                share_crew=True,\n           \
+    \ )\n\n            result = crew.kickoff()\n\n            assert crew._execution_span\
+    \ is not None\n            return str(result.raw)\n\n        @listen(first_crew)\n\
+    \        def second_crew(self, first_result: str):\n            \"\"\"Run second\
+    \ crew.\"\"\"\n            agent = Agent(\n                role=\"second agent\"\
+    ,\n                goal=\"second task\",\n                backstory=\"second agent\"\
+    ,\n                llm=mock_llm_2,\n            )\n            task = Task(\n\
+    \                description=\"Second task\",\n                expected_output=\"\
+    second result\",\n                agent=agent,\n            )\n            crew\
+    \ = Crew(\n                agents=[agent],\n                tasks=[task],\n  \
+    \              share_crew=True,\n            )\n\n            result = crew.kickoff()\n\
+    \n            assert crew._execution_span is not None\n\n            self.state.result\
+    \ = f\"{first_result} + {result.raw}\"\n            return self.state.result"
+  - "async def test_anthropic_async_with_tools():\n    \"\"\"Test async call with\
+    \ tools.\"\"\"\n    llm = AnthropicCompletion(model=\"claude-sonnet-4-0\")\n\n\
+    \    tools = [\n        {\n            \"type\": \"function\",\n            \"\
+    function\": {\n                \"name\": \"get_weather\",\n                \"\
+    description\": \"Get the current weather for a location\",\n                \"\
+    parameters\": {\n                    \"type\": \"object\",\n                 \
+    \   \"properties\": {\n                        \"location\": {\n             \
+    \               \"type\": \"string\",\n                            \"description\"\
+    : \"The city and state, e.g. San Francisco, CA\"\n                        }\n\
+    \                    },\n                    \"required\": [\"location\"]\n  \
+    \              }\n            }\n        }\n    ]\n\n    result = await llm.acall(\n\
+    \        \"What's the weather in San Francisco?\",\n        tools=tools\n    )\n\
+    \    logging.debug(\"result: %s\", result)\n\n    assert result is not None\n\
+    \    assert isinstance(result, str)"
+  - "def __del__(self):\n        \"\"\"Cleanup connections on deletion.\"\"\"\n  \
+    \      try:\n            if self._connection_pool:\n                for conn in\
+    \ self._connection_pool:\n                    try:\n                        conn.close()\n\
+    \                    except Exception:  # noqa: PERF203, S110\n              \
+    \          pass\n            if self._thread_pool:\n                self._thread_pool.shutdown()\n\
+    \        except Exception:  # noqa: S110\n            pass"
+- source_sentence: How does route_to_cycle work in Python?
+  sentences:
+  - "def route_to_cycle(self):\n            execution_log.append(\"router_initial\"\
+    )\n            return \"loop\""
+  - "def _register_system_event_handlers(self, event_bus: CrewAIEventsBus) -> None:\n\
+    \        \"\"\"Register handlers for system signal events (SIGTERM, SIGINT, etc.).\"\
+    \"\"\n\n        @on_signal\n        def handle_signal(source: Any, event: SignalEvent)\
+    \ -> None:\n            \"\"\"Flush trace batch on system signals to prevent data\
+    \ loss.\"\"\"\n            if self.batch_manager.is_batch_initialized():\n   \
+    \             self.batch_manager.finalize_batch()"
+  - "async def aadd(self) -> None:\n        \"\"\"Add JSON file content asynchronously.\"\
+    \"\"\n        content_str = (\n            str(self.content) if isinstance(self.content,\
+    \ dict) else self.content\n        )\n        new_chunks = self._chunk_text(content_str)\n\
+    \        self.chunks.extend(new_chunks)\n        await self._asave_documents()"
+- source_sentence: Explain the test_evaluate logic
+  sentences:
+  - "def test_flow_copy_state_with_unpickleable_objects():\n    \"\"\"Test that _copy_state\
+    \ handles unpickleable objects like RLock.\n\n    Regression test for issue #3828:\
+    \ Flow should not crash when state contains\n    objects that cannot be deep copied\
+    \ (like threading.RLock).\n    \"\"\"\n\n    class StateWithRLock(BaseModel):\n\
+    \        counter: int = 0\n        lock: Optional[threading.RLock] = None\n\n\
+    \    class FlowWithRLock(Flow[StateWithRLock]):\n        @start()\n        def\
+    \ step_1(self):\n            self.state.counter += 1\n\n        @listen(step_1)\n\
+    \        def step_2(self):\n            self.state.counter += 1\n\n    flow =\
+    \ FlowWithRLock(initial_state=StateWithRLock())\n    flow._state.lock = threading.RLock()\n\
+    \n    copied_state = flow._copy_state()\n    assert copied_state.counter == 0\n\
+    \    assert copied_state.lock is not None"
+  - "def test_evaluate(self, crew_planner):\n        task_output = TaskOutput(\n \
+    \           description=\"Task 1\", agent=str(crew_planner.crew.agents[0])\n \
+    \       )\n\n        with mock.patch.object(Task, \"execute_sync\") as execute:\n\
+    \            execute().pydantic = TaskEvaluationPydanticOutput(quality=9.5)\n\
+    \            crew_planner.evaluate(task_output)\n            assert crew_planner.tasks_scores[0]\
+    \ == [9.5]"
+  - "class SlowAsyncTool(BaseTool):\n            name: str = \"slow_async\"\n    \
+    \        description: str = \"Simulates slow I/O\"\n\n            def _run(self,\
+    \ task_id: int, delay: float) -> str:\n                return f\"Task {task_id}\
+    \ done\"\n\n            async def _arun(self, task_id: int, delay: float) -> str:\n\
+    \                await asyncio.sleep(delay)\n                return f\"Task {task_id}\
+    \ done\""
+- source_sentence: Explain the test_clean_action_no_formatting logic
+  sentences:
+  - "def test_task_interpolation_with_hyphens():\n    agent = Agent(\n        role=\"\
+    Researcher\",\n        goal=\"be an assistant that responds with {interpolation-with-hyphens}\"\
+    ,\n        backstory=\"You're an expert researcher, specialized in technology,\
+    \ software engineering, AI and startups. You work as a freelancer and is now working\
+    \ on doing research and analysis for a new customer.\",\n        allow_delegation=False,\n\
+    \    )\n    task = Task(\n        description=\"be an assistant that responds\
+    \ with {interpolation-with-hyphens}\",\n        expected_output=\"The response\
+    \ should be addressing: {interpolation-with-hyphens}\",\n        agent=agent,\n\
+    \    )\n    crew = Crew(\n        agents=[agent],\n        tasks=[task],\n   \
+    \     verbose=True,\n    )\n    result = crew.kickoff(inputs={\"interpolation-with-hyphens\"\
+    : \"say hello world\"})\n    assert \"say hello world\" in task.prompt()\n\n \
+    \   assert result.raw == \"Hello, World!\""
+  - "class LLMCallCompletedEvent(LLMEventBase):\n    \"\"\"Event emitted when a LLM\
+    \ call completes\"\"\"\n\n    type: str = \"llm_call_completed\"\n    messages:\
+    \ str | list[dict[str, Any]] | None = None\n    response: Any\n    call_type:\
+    \ LLMCallType\n    model: str | None = None"
+  - "def test_clean_action_no_formatting():\n    action = \"Ask question to senior\
+    \ researcher\"\n    cleaned_action = parser._clean_action(action)\n    assert\
+    \ cleaned_action == \"Ask question to senior researcher\""
+- source_sentence: Example usage of test_status_code_and_content_type
+  sentences:
+  - "class NavigateBackToolInput(BaseModel):\n    \"\"\"Input for NavigateBackTool.\"\
+    \"\"\n\n    thread_id: str = Field(\n        default=\"default\", description=\"\
+    Thread ID for the browser session\"\n    )"
+  - "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"
+  - "def set_crew(self, crew: Any) -> Memory:\n        \"\"\"Set the crew for this\
+    \ memory instance.\"\"\"\n        self.crew = crew\n        return self"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: CodeBERT Fine-tuned on CrewAI (LR=2e-05)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.04
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.04
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.04
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.06
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.04
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.04
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.04
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.03
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.008
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.024
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.04
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.06
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.050819890355577976
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.04333333333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.06130275691848844
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.01
+      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: 0.019316331411936505
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.01
+      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: 0.03
+      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: 0.015
+      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: 0.03
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.020819890355577977
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.013333333333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.028978936077832484
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.01
+      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:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    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
+    - type: cosine_mrr@10
+      value: 0.05333333333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.0838507480466874
+      name: Cosine Map@100
+---
+
+# CodeBERT Fine-tuned on CrewAI (LR=2e-05)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("itsanan/codebert-finetuned-crewai-base")
+# Run inference
+sentences = [
+    'Example usage of test_status_code_and_content_type',
+    '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',
+    'def set_crew(self, crew: Any) -> Memory:\n        """Set the crew for this memory instance."""\n        self.crew = crew\n        return self',
+]
+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, 0.9009, 0.9087],
+#         [0.9009, 1.0000, 0.9053],
+#         [0.9087, 0.9053, 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   | 0.04       |
+| cosine_accuracy@3   | 0.04       |
+| cosine_accuracy@5   | 0.04       |
+| cosine_accuracy@10  | 0.06       |
+| cosine_precision@1  | 0.04       |
+| cosine_precision@3  | 0.04       |
+| cosine_precision@5  | 0.04       |
+| cosine_precision@10 | 0.03       |
+| cosine_recall@1     | 0.008      |
+| cosine_recall@3     | 0.024      |
+| cosine_recall@5     | 0.04       |
+| cosine_recall@10    | 0.06       |
+| **cosine_ndcg@10**  | **0.0508** |
+| cosine_mrr@10       | 0.0433     |
+| cosine_map@100      | 0.0613     |
+
+#### 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   | 0.01     |
+| cosine_accuracy@3   | 0.01     |
+| cosine_accuracy@5   | 0.01     |
+| cosine_accuracy@10  | 0.01     |
+| cosine_precision@1  | 0.01     |
+| cosine_precision@3  | 0.01     |
+| cosine_precision@5  | 0.01     |
+| cosine_precision@10 | 0.005    |
+| cosine_recall@1     | 0.002    |
+| cosine_recall@3     | 0.006    |
+| cosine_recall@5     | 0.01     |
+| cosine_recall@10    | 0.01     |
+| **cosine_ndcg@10**  | **0.01** |
+| cosine_mrr@10       | 0.01     |
+| cosine_map@100      | 0.0193   |
+
+#### 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   | 0.01       |
+| cosine_accuracy@3   | 0.01       |
+| cosine_accuracy@5   | 0.01       |
+| cosine_accuracy@10  | 0.03       |
+| cosine_precision@1  | 0.01       |
+| cosine_precision@3  | 0.01       |
+| cosine_precision@5  | 0.01       |
+| cosine_precision@10 | 0.015      |
+| cosine_recall@1     | 0.002      |
+| cosine_recall@3     | 0.006      |
+| cosine_recall@5     | 0.01       |
+| cosine_recall@10    | 0.03       |
+| **cosine_ndcg@10**  | **0.0208** |
+| cosine_mrr@10       | 0.0133     |
+| cosine_map@100      | 0.029      |
+
+#### 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   | 0.01     |
+| cosine_accuracy@3   | 0.01     |
+| cosine_accuracy@5   | 0.01     |
+| cosine_accuracy@10  | 0.01     |
+| cosine_precision@1  | 0.01     |
+| cosine_precision@3  | 0.01     |
+| cosine_precision@5  | 0.01     |
+| cosine_precision@10 | 0.005    |
+| cosine_recall@1     | 0.002    |
+| cosine_recall@3     | 0.006    |
+| cosine_recall@5     | 0.01     |
+| cosine_recall@10    | 0.01     |
+| **cosine_ndcg@10**  | **0.01** |
+| cosine_mrr@10       | 0.01     |
+| cosine_map@100      | 0.0275   |
+
+#### 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   | 0.05       |
+| cosine_accuracy@3   | 0.05       |
+| cosine_accuracy@5   | 0.05       |
+| cosine_accuracy@10  | 0.07       |
+| cosine_precision@1  | 0.05       |
+| cosine_precision@3  | 0.05       |
+| cosine_precision@5  | 0.05       |
+| cosine_precision@10 | 0.035      |
+| cosine_recall@1     | 0.01       |
+| cosine_recall@3     | 0.03       |
+| cosine_recall@5     | 0.05       |
+| cosine_recall@10    | 0.07       |
+| **cosine_ndcg@10**  | **0.0608** |
+| cosine_mrr@10       | 0.0533     |
+| cosine_map@100      | 0.0839     |
+
+<!--
+## 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: 13.86 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.2",
+    "transformers": "4.57.6",
+    "pytorch": "2.9.0+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5537e7b6f78ecfc3d89e96962b30e25d1242b3d138de128fa2dd5cbaf314f8ec
+size 498604904

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}