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 +1,946 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:900
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: microsoft/codebert-base
+widget:
+- source_sentence: Explain the test_code_docs_search_tool logic
+  sentences:
+  - "def test_anthropic_call_with_interceptor_tracks_requests(self) -> None:\n   \
+    \     \"\"\"Test that interceptor tracks Anthropic API requests.\"\"\"\n     \
+    \   interceptor = AnthropicTestInterceptor()\n        llm = LLM(model=\"anthropic/claude-3-5-haiku-20241022\"\
+    , interceptor=interceptor)\n\n        # Make a simple completion call\n      \
+    \  result = llm.call(\n            messages=[{\"role\": \"user\", \"content\"\
+    : \"Say 'Hello World' and nothing else\"}]\n        )\n\n        # Verify custom\
+    \ headers were added\n        for request in interceptor.outbound_calls:\n   \
+    \         assert \"X-Anthropic-Interceptor\" in request.headers\n            assert\
+    \ request.headers[\"X-Anthropic-Interceptor\"] == \"anthropic-test-value\"\n \
+    \           assert \"X-Request-ID\" in request.headers\n            assert request.headers[\"\
+    X-Request-ID\"] == \"test-request-456\"\n\n        # Verify response was tracked\n\
+    \        for response in interceptor.inbound_calls:\n            assert \"X-Response-Tracked\"\
+    \ in response.headers\n            assert response.headers[\"X-Response-Tracked\"\
+    ] == \"true\"\n\n        # Verify result is valid\n        assert result is not\
+    \ None\n        assert isinstance(result, str)\n        assert len(result) > 0"
+  - "def on_inbound(self, message: httpx.Response) -> httpx.Response:\n        \"\"\
+    \"Pass through inbound response.\n\n        Args:\n            message: The inbound\
+    \ response.\n\n        Returns:\n            The response unchanged.\n       \
+    \ \"\"\"\n        return message"
+  - "def test_code_docs_search_tool(mock_adapter):\n    mock_adapter.query.return_value\
+    \ = \"test documentation\"\n\n    docs_url = \"https://crewai.com/any-docs-url\"\
+    \n    search_query = \"test documentation\"\n    tool = CodeDocsSearchTool(docs_url=docs_url,\
+    \ adapter=mock_adapter)\n    result = tool._run(search_query=search_query)\n \
+    \   assert \"test documentation\" in result\n    mock_adapter.add.assert_called_once_with(docs_url,\
+    \ data_type=DataType.DOCS_SITE)\n    mock_adapter.query.assert_called_once_with(\n\
+    \        search_query, similarity_threshold=0.6, limit=5\n    )\n\n    mock_adapter.query.reset_mock()\n\
+    \    mock_adapter.add.reset_mock()\n\n    tool = CodeDocsSearchTool(adapter=mock_adapter)\n\
+    \    result = tool._run(docs_url=docs_url, search_query=search_query)\n    assert\
+    \ \"test documentation\" in result\n    mock_adapter.add.assert_called_once_with(docs_url,\
+    \ data_type=DataType.DOCS_SITE)\n    mock_adapter.query.assert_called_once_with(\n\
+    \        search_query, similarity_threshold=0.6, limit=5\n    )"
+- source_sentence: Explain the test_openai_get_client_params_with_base_url_priority
+    logic
+  sentences:
+  - "def test_openai_get_client_params_with_base_url_priority():\n    \"\"\"\n   \
+    \ Test that base_url takes priority over api_base in _get_client_params\n    \"\
+    \"\"\n    llm = OpenAICompletion(\n        model=\"gpt-4o\",\n        base_url=\"\
+    https://priority.openai.com/v1\",\n        api_base=\"https://fallback.openai.com/v1\"\
+    ,\n    )\n    client_params = llm._get_client_params()\n    assert client_params[\"\
+    base_url\"] == \"https://priority.openai.com/v1\""
+  - "def get_context_window_size(self) -> int:\n        \"\"\"Get the context window\
+    \ size for the LLM.\n\n        Returns:\n            The number of tokens/characters\
+    \ the model can handle.\n        \"\"\"\n        # Default implementation - subclasses\
+    \ should override with model-specific values\n        return DEFAULT_CONTEXT_WINDOW_SIZE"
+  - "def _inject_date_to_task(self, task: Task) -> None:\n        \"\"\"Inject the\
+    \ current date into the task description if inject_date is enabled.\"\"\"\n  \
+    \      if self.inject_date:\n            from datetime import datetime\n\n   \
+    \         try:\n                valid_format_codes = [\n                    \"\
+    %Y\",\n                    \"%m\",\n                    \"%d\",\n            \
+    \        \"%H\",\n                    \"%M\",\n                    \"%S\",\n \
+    \                   \"%B\",\n                    \"%b\",\n                   \
+    \ \"%A\",\n                    \"%a\",\n                ]\n                is_valid\
+    \ = any(code in self.date_format for code in valid_format_codes)\n\n         \
+    \       if not is_valid:\n                    raise ValueError(f\"Invalid date\
+    \ format: {self.date_format}\")\n\n                current_date = datetime.now().strftime(self.date_format)\n\
+    \                task.description += f\"\\n\\nCurrent Date: {current_date}\"\n\
+    \            except Exception as e:\n                self._logger.log(\"warning\"\
+    , f\"Failed to inject date: {e!s}\")"
+- source_sentence: How to implement async _get_connection?
+  sentences:
+  - "def mock_env():\n    with patch.dict(os.environ, {\"CREWAI_PERSONAL_ACCESS_TOKEN\"\
+    : \"test_token\"}):\n        os.environ.pop(\"CREWAI_PLUS_URL\", None)\n     \
+    \   yield"
+  - "async def _get_connection(self) -> SnowflakeConnection:\n        \"\"\"Get a\
+    \ connection from the pool or create a new one.\"\"\"\n        if self._pool_lock\
+    \ is None:\n            raise RuntimeError(\"Pool lock not initialized\")\n  \
+    \      if self._connection_pool is None:\n            raise RuntimeError(\"Connection\
+    \ pool not initialized\")\n        async with self._pool_lock:\n            if\
+    \ not self._connection_pool:\n                conn = await asyncio.get_event_loop().run_in_executor(\n\
+    \                    self._thread_pool, self._create_connection\n            \
+    \    )\n                self._connection_pool.append(conn)\n            return\
+    \ self._connection_pool.pop()"
+  - "async def _arun(self, selector: str, thread_id: str = \"default\", **kwargs)\
+    \ -> str:\n        \"\"\"Use the async tool.\"\"\"\n        try:\n           \
+    \ # Get the current page\n            page = await self.get_async_page(thread_id)\n\
+    \n            # Click on the element\n            selector_effective = self._selector_effective(selector=selector)\n\
+    \            from playwright.async_api import TimeoutError as PlaywrightTimeoutError\n\
+    \n            try:\n                await page.click(\n                    selector_effective,\n\
+    \                    strict=self.playwright_strict,\n                    timeout=self.playwright_timeout,\n\
+    \                )\n            except PlaywrightTimeoutError:\n             \
+    \   return f\"Unable to click on element '{selector}'\"\n            except Exception\
+    \ as click_error:\n                return f\"Unable to click on element '{selector}':\
+    \ {click_error!s}\"\n\n            return f\"Clicked element '{selector}'\"\n\
+    \        except Exception as e:\n            return f\"Error clicking on element:\
+    \ {e!s}\""
+- source_sentence: Example usage of test_personal_access_token_from_environment
+  sentences:
+  - "async def close(self):\n                        return None"
+  - "def test_structured_state_persistence(tmp_path):\n    \"\"\"Test persistence\
+    \ with Pydantic model state.\"\"\"\n    db_path = os.path.join(tmp_path, \"test_flows.db\"\
+    )\n    persistence = SQLiteFlowPersistence(db_path)\n\n    class StructuredFlow(Flow[TestState]):\n\
+    \        initial_state = TestState\n\n        @start()\n        @persist(persistence)\n\
+    \        def count_up(self):\n            self.state.counter += 1\n          \
+    \  self.state.message = f\"Count is {self.state.counter}\"\n\n    # Run flow and\
+    \ verify state changes are saved\n    flow = StructuredFlow(persistence=persistence)\n\
+    \    flow.kickoff()\n\n    # Load and verify state\n    saved_state = persistence.load_state(flow.state.id)\n\
+    \    assert saved_state is not None\n    assert saved_state[\"counter\"] == 1\n\
+    \    assert saved_state[\"message\"] == \"Count is 1\""
+  - "def test_personal_access_token_from_environment(tool):\n    assert tool.personal_access_token\
+    \ == \"test_token\""
+- source_sentence: Best practices for handle_a2a_polling_started
+  sentences:
+  - "def external_supported_storages() -> dict[str, Any]:\n        return {\n    \
+    \        \"mem0\": ExternalMemory._configure_mem0,\n        }"
+  - "def handle_a2a_polling_started(\n        self,\n        task_id: str,\n     \
+    \   polling_interval: float,\n        endpoint: str,\n    ) -> None:\n       \
+    \ \"\"\"Handle A2A polling started event with panel display.\"\"\"\n        content\
+    \ = Text()\n        content.append(\"A2A Polling Started\\n\", style=\"cyan bold\"\
+    )\n        content.append(\"Task ID: \", style=\"white\")\n        content.append(f\"\
+    {task_id[:8]}...\\n\", style=\"cyan\")\n        content.append(\"Interval: \"\
+    , style=\"white\")\n        content.append(f\"{polling_interval}s\\n\", style=\"\
+    cyan\")\n\n        self.print_panel(content, \"⏳ A2A Polling\", \"cyan\")"
+  - "def test_agent_with_knowledge_sources_generate_search_query():\n    content =\
+    \ \"Brandon's favorite color is red and he likes Mexican food.\"\n    string_source\
+    \ = StringKnowledgeSource(content=content)\n\n    with (\n        patch(\"crewai.knowledge\"\
+    ) as mock_knowledge,\n        patch(\n            \"crewai.knowledge.storage.knowledge_storage.KnowledgeStorage\"\
+    \n        ) as mock_knowledge_storage,\n        patch(\n            \"crewai.knowledge.source.base_knowledge_source.KnowledgeStorage\"\
+    \n        ) as mock_base_knowledge_storage,\n        patch(\"crewai.rag.chromadb.client.ChromaDBClient\"\
+    ) as mock_chromadb,\n    ):\n        mock_knowledge_instance = mock_knowledge.return_value\n\
+    \        mock_knowledge_instance.sources = [string_source]\n        mock_knowledge_instance.query.return_value\
+    \ = [{\"content\": content}]\n\n        mock_storage_instance = mock_knowledge_storage.return_value\n\
+    \        mock_storage_instance.sources = [string_source]\n        mock_storage_instance.query.return_value\
+    \ = [{\"content\": content}]\n        mock_storage_instance.save.return_value\
+    \ = None\n\n        mock_chromadb_instance = mock_chromadb.return_value\n    \
+    \    mock_chromadb_instance.add_documents.return_value = None\n\n        mock_base_knowledge_storage.return_value\
+    \ = mock_storage_instance\n\n        agent = Agent(\n            role=\"Information\
+    \ Agent with extensive role description that is longer than 80 characters\",\n\
+    \            goal=\"Provide information based on knowledge sources\",\n      \
+    \      backstory=\"You have access to specific knowledge sources.\",\n       \
+    \     llm=LLM(model=\"gpt-4o-mini\"),\n            knowledge_sources=[string_source],\n\
+    \        )\n\n        task = Task(\n            description=\"What is Brandon's\
+    \ favorite color?\",\n            expected_output=\"The answer to the question,\
+    \ in a format like this: `{{name: str, favorite_color: str}}`\",\n           \
+    \ agent=agent,\n        )\n\n        crew = Crew(agents=[agent], tasks=[task])\n\
+    \        result = crew.kickoff()\n\n        # Updated assertion to check the JSON\
+    \ content\n        assert \"Brandon\" in str(agent.knowledge_search_query)\n \
+    \       assert \"favorite color\" in str(agent.knowledge_search_query)\n\n   \
+    \     assert \"red\" in result.raw.lower()"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: CodeBERT Fine-tuned on CrewAI
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.57
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.57
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.57
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.65
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.57
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.57
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.57
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.325
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11399999999999996
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3420000000000001
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.57
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.65
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6132795614223119
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5833333333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6323349876959563
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.56
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.56
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.56
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.68
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.56
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.56
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.56
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.34
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11199999999999999
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.336
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.56
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.68
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6249193421334678
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5799999999999998
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6328444860345127
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.54
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.54
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.54
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.67
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.54
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.54
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.54
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.335
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10799999999999997
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.324
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.54
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.67
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6103292873112568
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5616666666666664
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.622676615058847
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.47
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.47
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.47
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.58
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.47
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.47
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.47
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.29
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.09399999999999999
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.28200000000000003
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.47
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.58
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5295093969556788
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.48833333333333323
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5581789904714569
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.5
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.5
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.3
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.6
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5540994517778899
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5166666666666665
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5748485156077728
+      name: Cosine Map@100
+---
+
+# CodeBERT Fine-tuned on CrewAI
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("itsanan/codebert-embed-crewai-base")
+# Run inference
+sentences = [
+    'Best practices for handle_a2a_polling_started',
+    'def handle_a2a_polling_started(\n        self,\n        task_id: str,\n        polling_interval: float,\n        endpoint: str,\n    ) -> None:\n        """Handle A2A polling started event with panel display."""\n        content = Text()\n        content.append("A2A Polling Started\\n", style="cyan bold")\n        content.append("Task ID: ", style="white")\n        content.append(f"{task_id[:8]}...\\n", style="cyan")\n        content.append("Interval: ", style="white")\n        content.append(f"{polling_interval}s\\n", style="cyan")\n\n        self.print_panel(content, "⏳ A2A Polling", "cyan")',
+    'def test_agent_with_knowledge_sources_generate_search_query():\n    content = "Brandon\'s favorite color is red and he likes Mexican food."\n    string_source = StringKnowledgeSource(content=content)\n\n    with (\n        patch("crewai.knowledge") as mock_knowledge,\n        patch(\n            "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"\n        ) as mock_knowledge_storage,\n        patch(\n            "crewai.knowledge.source.base_knowledge_source.KnowledgeStorage"\n        ) as mock_base_knowledge_storage,\n        patch("crewai.rag.chromadb.client.ChromaDBClient") as mock_chromadb,\n    ):\n        mock_knowledge_instance = mock_knowledge.return_value\n        mock_knowledge_instance.sources = [string_source]\n        mock_knowledge_instance.query.return_value = [{"content": content}]\n\n        mock_storage_instance = mock_knowledge_storage.return_value\n        mock_storage_instance.sources = [string_source]\n        mock_storage_instance.query.return_value = [{"content": content}]\n        mock_storage_instance.save.return_value = None\n\n        mock_chromadb_instance = mock_chromadb.return_value\n        mock_chromadb_instance.add_documents.return_value = None\n\n        mock_base_knowledge_storage.return_value = mock_storage_instance\n\n        agent = Agent(\n            role="Information Agent with extensive role description that is longer than 80 characters",\n            goal="Provide information based on knowledge sources",\n            backstory="You have access to specific knowledge sources.",\n            llm=LLM(model="gpt-4o-mini"),\n            knowledge_sources=[string_source],\n        )\n\n        task = Task(\n            description="What is Brandon\'s favorite color?",\n            expected_output="The answer to the question, in a format like this: `{{name: str, favorite_color: str}}`",\n            agent=agent,\n        )\n\n        crew = Crew(agents=[agent], tasks=[task])\n        result = crew.kickoff()\n\n        # Updated assertion to check the JSON content\n        assert "Brandon" in str(agent.knowledge_search_query)\n        assert "favorite color" in str(agent.knowledge_search_query)\n\n        assert "red" in result.raw.lower()',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.7350, 0.6480],
+#         [0.7350, 1.0000, 0.8133],
+#         [0.6480, 0.8133, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 768
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.57       |
+| cosine_accuracy@3   | 0.57       |
+| cosine_accuracy@5   | 0.57       |
+| cosine_accuracy@10  | 0.65       |
+| cosine_precision@1  | 0.57       |
+| cosine_precision@3  | 0.57       |
+| cosine_precision@5  | 0.57       |
+| cosine_precision@10 | 0.325      |
+| cosine_recall@1     | 0.114      |
+| cosine_recall@3     | 0.342      |
+| cosine_recall@5     | 0.57       |
+| cosine_recall@10    | 0.65       |
+| **cosine_ndcg@10**  | **0.6133** |
+| cosine_mrr@10       | 0.5833     |
+| cosine_map@100      | 0.6323     |
+
+#### Information Retrieval
+
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 512
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.56       |
+| cosine_accuracy@3   | 0.56       |
+| cosine_accuracy@5   | 0.56       |
+| cosine_accuracy@10  | 0.68       |
+| cosine_precision@1  | 0.56       |
+| cosine_precision@3  | 0.56       |
+| cosine_precision@5  | 0.56       |
+| cosine_precision@10 | 0.34       |
+| cosine_recall@1     | 0.112      |
+| cosine_recall@3     | 0.336      |
+| cosine_recall@5     | 0.56       |
+| cosine_recall@10    | 0.68       |
+| **cosine_ndcg@10**  | **0.6249** |
+| cosine_mrr@10       | 0.58       |
+| cosine_map@100      | 0.6328     |
+
+#### Information Retrieval
+
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 256
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.54       |
+| cosine_accuracy@3   | 0.54       |
+| cosine_accuracy@5   | 0.54       |
+| cosine_accuracy@10  | 0.67       |
+| cosine_precision@1  | 0.54       |
+| cosine_precision@3  | 0.54       |
+| cosine_precision@5  | 0.54       |
+| cosine_precision@10 | 0.335      |
+| cosine_recall@1     | 0.108      |
+| cosine_recall@3     | 0.324      |
+| cosine_recall@5     | 0.54       |
+| cosine_recall@10    | 0.67       |
+| **cosine_ndcg@10**  | **0.6103** |
+| cosine_mrr@10       | 0.5617     |
+| cosine_map@100      | 0.6227     |
+
+#### Information Retrieval
+
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 128
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.47       |
+| cosine_accuracy@3   | 0.47       |
+| cosine_accuracy@5   | 0.47       |
+| cosine_accuracy@10  | 0.58       |
+| cosine_precision@1  | 0.47       |
+| cosine_precision@3  | 0.47       |
+| cosine_precision@5  | 0.47       |
+| cosine_precision@10 | 0.29       |
+| cosine_recall@1     | 0.094      |
+| cosine_recall@3     | 0.282      |
+| cosine_recall@5     | 0.47       |
+| cosine_recall@10    | 0.58       |
+| **cosine_ndcg@10**  | **0.5295** |
+| cosine_mrr@10       | 0.4883     |
+| cosine_map@100      | 0.5582     |
+
+#### Information Retrieval
+
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 64
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.5        |
+| cosine_accuracy@3   | 0.5        |
+| cosine_accuracy@5   | 0.5        |
+| cosine_accuracy@10  | 0.6        |
+| cosine_precision@1  | 0.5        |
+| cosine_precision@3  | 0.5        |
+| cosine_precision@5  | 0.5        |
+| cosine_precision@10 | 0.3        |
+| cosine_recall@1     | 0.1        |
+| cosine_recall@3     | 0.3        |
+| cosine_recall@5     | 0.5        |
+| cosine_recall@10    | 0.6        |
+| **cosine_ndcg@10**  | **0.5541** |
+| cosine_mrr@10       | 0.5167     |
+| cosine_map@100      | 0.5748     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 900 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 900 samples:
+  |         | anchor                                                                             | positive                                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.96 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 254.94 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                 | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Example usage of DeeplyNestedFlow</code>                                         | <code>class DeeplyNestedFlow(Flow):<br>        @start()<br>        def a(self):<br>            execution_order.append("a")<br><br>        @start()<br>        def b(self):<br>            execution_order.append("b")<br><br>        @start()<br>        def c(self):<br>            execution_order.append("c")<br><br>        @start()<br>        def d(self):<br>            execution_order.append("d")<br><br>        # Nested: (a AND b) OR (c AND d)<br>        @listen(or_(and_(a, b), and_(c, d)))<br>        def result(self):<br>            execution_order.append("result")</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  | <code>Explain the test_agent_with_knowledge_sources_generate_search_query logic</code> | <code>def test_agent_with_knowledge_sources_generate_search_query():<br>    content = "Brandon's favorite color is red and he likes Mexican food."<br>    string_source = StringKnowledgeSource(content=content)<br><br>    with (<br>        patch("crewai.knowledge") as mock_knowledge,<br>        patch(<br>            "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"<br>        ) as mock_knowledge_storage,<br>        patch(<br>            "crewai.knowledge.source.base_knowledge_source.KnowledgeStorage"<br>        ) as mock_base_knowledge_storage,<br>        patch("crewai.rag.chromadb.client.ChromaDBClient") as mock_chromadb,<br>    ):<br>        mock_knowledge_instance = mock_knowledge.return_value<br>        mock_knowledge_instance.sources = [string_source]<br>        mock_knowledge_instance.query.return_value = [{"content": content}]<br><br>        mock_storage_instance = mock_knowledge_storage.return_value<br>        mock_storage_instance.sources = [string_source]<br>        mock_storage_instance.query.return_value = [{"content": content}]...</code> |
+  | <code>Example usage of agent</code>                                                    | <code>def agent(self) -> Agent \| None:<br>        """Get the current agent associated with this memory."""<br>        return self._agent</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: None
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step   | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.7111  | 10     | 7.1051        | -                      | -                      | -                      | -                      | -                     |
+| 1.0     | 15     | -             | 0.1170                 | 0.06                   | 0.0608                 | 0.0825                 | 0.0762                |
+| 1.3556  | 20     | 6.4716        | -                      | -                      | -                      | -                      | -                     |
+| 2.0     | 30     | 5.4463        | 0.1879                 | 0.1770                 | 0.1625                 | 0.1816                 | 0.1987                |
+| 2.7111  | 40     | 3.7856        | -                      | -                      | -                      | -                      | -                     |
+| 3.0     | 45     | -             | 0.4987                 | 0.5133                 | 0.4587                 | 0.4249                 | 0.4425                |
+| 3.3556  | 50     | 2.4942        | -                      | -                      | -                      | -                      | -                     |
+| **4.0** | **60** | **1.71**      | **0.6133**             | **0.6249**             | **0.6103**             | **0.5295**             | **0.5541**            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.2
+- Transformers: 4.57.6
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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 sha256:38ca05ca80a43728805e6934348811d7f6dcca269fdcd0f0f8892ff187327672
+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 +1,58 @@
+{
+  "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>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}