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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:900
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/codebert-base
widget:
- source_sentence: 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 |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 900 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 900 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.86 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 253.07 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How to implement LLMCallCompletedEvent?</code> | <code>class LLMCallCompletedEvent(LLMEventBase):<br> """Event emitted when a LLM call completes"""<br><br> type: str = "llm_call_completed"<br> messages: str \| list[dict[str, Any]] \| None = None<br> response: Any<br> call_type: LLMCallType<br> model: str \| None = None</code> |
| <code>How does get_llm_response work in Python?</code> | <code>def get_llm_response(<br> llm: LLM \| BaseLLM,<br> messages: list[LLMMessage],<br> callbacks: list[TokenCalcHandler],<br> printer: Printer,<br> from_task: Task \| None = None,<br> from_agent: Agent \| LiteAgent \| None = None,<br> response_model: type[BaseModel] \| None = None,<br> executor_context: CrewAgentExecutor \| LiteAgent \| None = None,<br>) -> str:<br> """Call the LLM and return the response, handling any invalid responses.<br><br> Args:<br> llm: The LLM instance to call.<br> messages: The messages to send to the LLM.<br> callbacks: List of callbacks for the LLM call.<br> printer: Printer instance for output.<br> from_task: Optional task context for the LLM call.<br> from_agent: Optional agent context for the LLM call.<br> response_model: Optional Pydantic model for structured outputs.<br> executor_context: Optional executor context for hook invocation.<br><br> Returns:<br> The response from the LLM as a string.<br><br> Raises:<br> Exception: If an error ...</code> |
| <code>Example usage of _run</code> | <code>def _run(<br> self,<br> **kwargs: Any,<br> ) -> Any:<br> website_url: str \| None = kwargs.get("website_url", self.website_url)<br> if website_url is None:<br> raise ValueError("Website URL must be provided.")<br><br> page = requests.get(<br> website_url,<br> timeout=15,<br> headers=self.headers,<br> cookies=self.cookies if self.cookies else {},<br> )<br><br> page.encoding = page.apparent_encoding<br> parsed = BeautifulSoup(page.text, "html.parser")<br><br> text = "The following text is scraped website content:\n\n"<br> text += parsed.get_text(" ")<br> text = re.sub("[ \t]+", " ", text)<br> return re.sub("\\s+\n\\s+", "\n", text)</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 32
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 20
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 20
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| **0.9956** | **7** | **-** | **0.04** | **0.04** | **0.03** | **0.0262** | **0.0308** |
| 1.2844 | 10 | 7.098 | - | - | - | - | - |
| 1.8533 | 14 | - | 0.0362 | 0.02 | 0.0354 | 0.0154 | 0.0508 |
| 2.5689 | 20 | 6.5515 | - | - | - | - | - |
| 2.7111 | 21 | - | 0.0508 | 0.01 | 0.0208 | 0.01 | 0.0608 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.2
- Transformers: 4.57.6
- PyTorch: 2.9.0+cu126
- Accelerate: 1.12.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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