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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- dataset_size:900 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: microsoft/codebert-base |
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widget: |
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- source_sentence: Best practices for _invocation_params |
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sentences: |
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- "def after_model(self, state: StateT, runtime: Runtime[ContextT]) -> dict[str,\ |
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\ Any] | None:\n \"\"\"Logic to run after the model is called.\n\n \ |
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\ Args:\n state: The current agent state.\n runtime:\ |
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\ The runtime context.\n\n Returns:\n Agent state updates to\ |
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\ apply after model call.\n \"\"\"" |
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- "def _get_trace_callbacks(\n project_name: str | None = None,\n example_id:\ |
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\ str | UUID | None = None,\n callback_manager: CallbackManager | AsyncCallbackManager\ |
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\ | None = None,\n) -> Callbacks:\n if _tracing_v2_is_enabled():\n project_name_\ |
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\ = project_name or _get_tracer_project()\n tracer = tracing_v2_callback_var.get()\ |
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\ or LangChainTracer(\n project_name=project_name_,\n example_id=example_id,\n\ |
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\ )\n if callback_manager is None:\n cb = cast(\"Callbacks\"\ |
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, [tracer])\n else:\n if not any(\n isinstance(handler,\ |
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\ LangChainTracer)\n for handler in callback_manager.handlers\n\ |
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\ ):\n callback_manager.add_handler(tracer)\n \ |
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\ # If it already has a LangChainTracer, we don't need to add another\ |
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\ one.\n # this would likely mess up the trace hierarchy.\n \ |
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\ cb = callback_manager\n else:\n cb = None\n return cb" |
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- "def _invocation_params(self) -> dict[str, Any]:\n params: dict = {\"model\"\ |
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: self.model, **self.model_kwargs}\n if self.dimensions is not None:\n\ |
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\ params[\"dimensions\"] = self.dimensions\n return params" |
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- source_sentence: How does _approximate_token_counter work in Python? |
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sentences: |
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- "def _approximate_token_counter(messages: Sequence[BaseMessage]) -> int:\n \ |
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\ \"\"\"Wrapper for `count_tokens_approximately` that matches expected signature.\"\ |
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\"\"\n return count_tokens_approximately(messages)" |
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- "def remove_request_headers(request: Any) -> Any:\n for k in request.headers:\n\ |
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\ request.headers[k] = \"**REDACTED**\"\n return request" |
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- "def get_format_instructions(self) -> str:\n \"\"\"Returns formatting instructions\ |
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\ for the given output parser.\"\"\"\n return self.format_instructions" |
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- source_sentence: How to implement _create_thread_and_run? |
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sentences: |
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- "async def on_retriever_end(\n self, documents: Sequence[Document], **kwargs:\ |
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\ Any\n ) -> None:\n \"\"\"Run when the retriever ends running.\n\n\ |
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\ Args:\n documents: The retrieved documents.\n **kwargs:\ |
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\ Additional keyword arguments.\n\n \"\"\"\n if not self.handlers:\n\ |
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\ return\n await ahandle_event(\n self.handlers,\n\ |
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\ \"on_retriever_end\",\n \"ignore_retriever\",\n \ |
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\ documents,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n\ |
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\ tags=self.tags,\n **kwargs,\n )" |
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- "def _create_thread_and_run(self, input_dict: dict, thread: dict) -> Any:\n \ |
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\ params = {\n k: v\n for k, v in input_dict.items()\n\ |
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\ if k\n in (\n \"instructions\",\n \ |
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\ \"model\",\n \"tools\",\n \"parallel_tool_calls\"\ |
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,\n \"top_p\",\n \"temperature\",\n \ |
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\ \"max_completion_tokens\",\n \"max_prompt_tokens\",\n \ |
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\ \"run_metadata\",\n )\n }\n return self.client.beta.threads.create_and_run(\n\ |
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\ assistant_id=self.assistant_id,\n thread=thread,\n \ |
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\ **params,\n )" |
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- "def test_pandas_output_parser_col_no_array() -> None:\n with pytest.raises(OutputParserException):\n\ |
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\ parser.parse(\"column:num_legs\")" |
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- source_sentence: Explain the get_token_ids logic |
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sentences: |
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- "def _runnable(inputs: dict[str, Any]) -> str:\n if inputs[\"text\"] == \"\ |
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foo\":\n return \"first\"\n if \"exception\" not in inputs:\n \ |
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\ msg = \"missing exception\"\n raise ValueError(msg)\n if inputs[\"\ |
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text\"] == \"bar\":\n return \"second\"\n if isinstance(inputs[\"exception\"\ |
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], ValueError):\n raise RuntimeError # noqa: TRY004\n return \"third\"" |
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- "def validate_params(cls, values: dict) -> dict:\n \"\"\"Validate similarity\ |
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\ parameters.\"\"\"\n if values[\"k\"] is None and values[\"similarity_threshold\"\ |
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] is None:\n msg = \"Must specify one of `k` or `similarity_threshold`.\"\ |
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\n raise ValueError(msg)\n return values" |
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- "def get_token_ids(self, text: str) -> list[int]:\n \"\"\"Return the ordered\ |
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\ IDs of the tokens in a text.\n\n Args:\n text: The string\ |
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\ input to tokenize.\n\n Returns:\n A list of IDs corresponding\ |
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\ to the tokens in the text, in order they occur\n in the text.\n\ |
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\ \"\"\"\n if self.custom_get_token_ids is not None:\n \ |
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\ return self.custom_get_token_ids(text)\n return _get_token_ids_default_method(text)" |
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- source_sentence: How does __init__ work in Python? |
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sentences: |
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- "def test_loading_few_shot_prompt_from_json() -> None:\n \"\"\"Test loading\ |
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\ few shot prompt from json.\"\"\"\n with change_directory(EXAMPLE_DIR):\n\ |
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\ prompt = load_prompt(\"few_shot_prompt.json\")\n expected_prompt\ |
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\ = FewShotPromptTemplate(\n input_variables=[\"adjective\"],\n \ |
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\ prefix=\"Write antonyms for the following words.\",\n example_prompt=PromptTemplate(\n\ |
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\ input_variables=[\"input\", \"output\"],\n template=\"\ |
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Input: {input}\\nOutput: {output}\",\n ),\n examples=[\n\ |
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\ {\"input\": \"happy\", \"output\": \"sad\"},\n \ |
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\ {\"input\": \"tall\", \"output\": \"short\"},\n ],\n \ |
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\ suffix=\"Input: {adjective}\\nOutput:\",\n )\n assert prompt ==\ |
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\ expected_prompt" |
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- "def __init__(\n self,\n encoding_name: str = \"gpt2\",\n \ |
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\ model_name: str | None = None,\n allowed_special: Literal[\"all\"] |\ |
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\ AbstractSet[str] = set(),\n disallowed_special: Literal[\"all\"] | Collection[str]\ |
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\ = \"all\",\n **kwargs: Any,\n ) -> None:\n \"\"\"Create a new\ |
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\ `TextSplitter`.\n\n Args:\n encoding_name: The name of the\ |
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\ tiktoken encoding to use.\n model_name: The name of the model to\ |
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\ use. If provided, this will\n override the `encoding_name`.\n\ |
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\ allowed_special: Special tokens that are allowed during encoding.\n\ |
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\ disallowed_special: Special tokens that are disallowed during encoding.\n\ |
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\n Raises:\n ImportError: If the tiktoken package is not installed.\n\ |
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\ \"\"\"\n super().__init__(**kwargs)\n if not _HAS_TIKTOKEN:\n\ |
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\ msg = (\n \"Could not import tiktoken python package.\ |
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\ \"\n \"This is needed in order to for TokenTextSplitter. \"\n\ |
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\ \"Please install it with `pip install tiktoken`.\"\n \ |
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\ )\n raise ImportError(msg)\n\n if model_name is not None:\n\ |
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\ enc = tiktoken.encoding_for_model(model_name)\n else:\n \ |
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\ enc = tiktoken.get_encoding(encoding_name)\n self._tokenizer\ |
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\ = enc\n self._allowed_special = allowed_special\n self._disallowed_special\ |
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\ = disallowed_special" |
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- "def test_fixed_message_response_when_docs_found() -> None:\n fixed_resp =\ |
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\ \"I don't know\"\n answer = \"I know the answer!\"\n llm = FakeListLLM(responses=[answer])\n\ |
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\ retriever = SequentialRetriever(\n sequential_responses=[[Document(page_content=answer)]],\n\ |
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\ )\n memory = ConversationBufferMemory(\n k=1,\n output_key=\"\ |
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answer\",\n memory_key=\"chat_history\",\n return_messages=True,\n\ |
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\ )\n qa_chain = ConversationalRetrievalChain.from_llm(\n llm=llm,\n\ |
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\ memory=memory,\n retriever=retriever,\n return_source_documents=True,\n\ |
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\ rephrase_question=False,\n response_if_no_docs_found=fixed_resp,\n\ |
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\ verbose=True,\n )\n got = qa_chain(\"What is the answer?\")\n \ |
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\ assert got[\"chat_history\"][1].content == answer\n assert got[\"answer\"\ |
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] == answer" |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: codeBert Base |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.83 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.85 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.86 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.94 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.83 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.83 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.83 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.45299999999999996 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.16599999999999998 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.498 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.83 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9059999999999999 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8712089918828809 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8532738095238095 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.861635686929646 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.85 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.86 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.87 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
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value: 0.95 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
|
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value: 0.85 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.84 |
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name: Cosine Precision@3 |
|
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- type: cosine_precision@5 |
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value: 0.8419999999999999 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.45299999999999996 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.16999999999999996 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.504 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8419999999999999 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9059999999999999 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8775797199885595 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8699404761904762 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8692738075020783 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.86 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.89 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
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value: 0.93 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
|
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value: 0.86 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.85 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
|
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value: 0.85 |
|
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name: Cosine Precision@5 |
|
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- type: cosine_precision@10 |
|
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value: 0.45 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.17199999999999996 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.51 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
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value: 0.85 |
|
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name: Cosine Recall@5 |
|
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- type: cosine_recall@10 |
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value: 0.9 |
|
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name: Cosine Recall@10 |
|
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- type: cosine_ndcg@10 |
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value: 0.8789938349894767 |
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name: Cosine Ndcg@10 |
|
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- type: cosine_mrr@10 |
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value: 0.8805952380952381 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8726611807317667 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
|
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value: 0.84 |
|
|
name: Cosine Accuracy@1 |
|
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- type: cosine_accuracy@3 |
|
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value: 0.87 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
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value: 0.88 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.93 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.84 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.8366666666666667 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.8419999999999999 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.455 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.16799999999999998 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.502 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.8419999999999999 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.91 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.8777095006932575 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.8630000000000001 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.8661619081282643 |
|
|
name: Cosine Map@100 |
|
|
- task: |
|
|
type: information-retrieval |
|
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name: Information Retrieval |
|
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dataset: |
|
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name: dim 64 |
|
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type: dim_64 |
|
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.78 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.81 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.81 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.93 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.78 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.7866666666666667 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.7859999999999999 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.44799999999999995 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.15599999999999997 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.472 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.7859999999999999 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.8959999999999999 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.8445404597381452 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.8120634920634922 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.8308457034802883 |
|
|
name: Cosine Map@100 |
|
|
--- |
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# codeBert Base |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'}) |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("killdollar/codebert-embed-base-dense-retriever") |
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# Run inference |
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sentences = [ |
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'How does __init__ work in Python?', |
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'def __init__(\n self,\n encoding_name: str = "gpt2",\n model_name: str | None = None,\n allowed_special: Literal["all"] | AbstractSet[str] = set(),\n disallowed_special: Literal["all"] | Collection[str] = "all",\n **kwargs: Any,\n ) -> None:\n """Create a new `TextSplitter`.\n\n Args:\n encoding_name: The name of the tiktoken encoding to use.\n model_name: The name of the model to use. If provided, this will\n override the `encoding_name`.\n allowed_special: Special tokens that are allowed during encoding.\n disallowed_special: Special tokens that are disallowed during encoding.\n\n Raises:\n ImportError: If the tiktoken package is not installed.\n """\n super().__init__(**kwargs)\n if not _HAS_TIKTOKEN:\n msg = (\n "Could not import tiktoken python package. "\n "This is needed in order to for TokenTextSplitter. "\n "Please install it with `pip install tiktoken`."\n )\n raise ImportError(msg)\n\n if model_name is not None:\n enc = tiktoken.encoding_for_model(model_name)\n else:\n enc = tiktoken.get_encoding(encoding_name)\n self._tokenizer = enc\n self._allowed_special = allowed_special\n self._disallowed_special = disallowed_special', |
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'def test_fixed_message_response_when_docs_found() -> None:\n fixed_resp = "I don\'t know"\n answer = "I know the answer!"\n llm = FakeListLLM(responses=[answer])\n retriever = SequentialRetriever(\n sequential_responses=[[Document(page_content=answer)]],\n )\n memory = ConversationBufferMemory(\n k=1,\n output_key="answer",\n memory_key="chat_history",\n return_messages=True,\n )\n qa_chain = ConversationalRetrievalChain.from_llm(\n llm=llm,\n memory=memory,\n retriever=retriever,\n return_source_documents=True,\n rephrase_question=False,\n response_if_no_docs_found=fixed_resp,\n verbose=True,\n )\n got = qa_chain("What is the answer?")\n assert got["chat_history"][1].content == answer\n assert got["answer"] == answer', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.7336, 0.0979], |
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# [0.7336, 1.0000, 0.1742], |
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# [0.0979, 0.1742, 1.0000]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
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```json |
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{ |
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"truncate_dim": 768 |
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} |
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``` |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.83 | |
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| cosine_accuracy@3 | 0.85 | |
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| cosine_accuracy@5 | 0.86 | |
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| cosine_accuracy@10 | 0.94 | |
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| cosine_precision@1 | 0.83 | |
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| cosine_precision@3 | 0.83 | |
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| cosine_precision@5 | 0.83 | |
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| cosine_precision@10 | 0.453 | |
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| cosine_recall@1 | 0.166 | |
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| cosine_recall@3 | 0.498 | |
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| cosine_recall@5 | 0.83 | |
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| cosine_recall@10 | 0.906 | |
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| **cosine_ndcg@10** | **0.8712** | |
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| cosine_mrr@10 | 0.8533 | |
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| cosine_map@100 | 0.8616 | |
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#### Information Retrieval |
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* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
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```json |
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{ |
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"truncate_dim": 512 |
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} |
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``` |
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| Metric | Value | |
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| cosine_accuracy@1 | 0.85 | |
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| cosine_accuracy@3 | 0.86 | |
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| cosine_accuracy@5 | 0.87 | |
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| cosine_accuracy@10 | 0.95 | |
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| cosine_precision@1 | 0.85 | |
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| cosine_precision@3 | 0.84 | |
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| cosine_precision@5 | 0.842 | |
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| cosine_precision@10 | 0.453 | |
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| cosine_recall@1 | 0.17 | |
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| cosine_recall@3 | 0.504 | |
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| cosine_recall@5 | 0.842 | |
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| cosine_recall@10 | 0.906 | |
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| **cosine_ndcg@10** | **0.8776** | |
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| cosine_mrr@10 | 0.8699 | |
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| cosine_map@100 | 0.8693 | |
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#### Information Retrieval |
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* Dataset: `dim_256` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
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```json |
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{ |
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"truncate_dim": 256 |
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} |
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``` |
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| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.86 | |
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| cosine_accuracy@3 | 0.89 | |
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| cosine_accuracy@5 | 0.9 | |
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| cosine_accuracy@10 | 0.93 | |
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| cosine_precision@1 | 0.86 | |
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| cosine_precision@3 | 0.85 | |
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| cosine_precision@5 | 0.85 | |
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| cosine_precision@10 | 0.45 | |
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| cosine_recall@1 | 0.172 | |
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| cosine_recall@3 | 0.51 | |
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| cosine_recall@5 | 0.85 | |
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| cosine_recall@10 | 0.9 | |
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| **cosine_ndcg@10** | **0.879** | |
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| cosine_mrr@10 | 0.8806 | |
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| cosine_map@100 | 0.8727 | |
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#### Information Retrieval |
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* Dataset: `dim_128` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
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```json |
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{ |
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"truncate_dim": 128 |
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} |
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``` |
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| Metric | Value | |
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| cosine_accuracy@1 | 0.84 | |
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| cosine_accuracy@3 | 0.87 | |
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| cosine_accuracy@5 | 0.88 | |
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| cosine_accuracy@10 | 0.93 | |
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| cosine_precision@1 | 0.84 | |
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| cosine_precision@3 | 0.8367 | |
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| cosine_precision@5 | 0.842 | |
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| cosine_precision@10 | 0.455 | |
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| cosine_recall@1 | 0.168 | |
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| cosine_recall@3 | 0.502 | |
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| cosine_recall@5 | 0.842 | |
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| cosine_recall@10 | 0.91 | |
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| **cosine_ndcg@10** | **0.8777** | |
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| cosine_mrr@10 | 0.863 | |
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| cosine_map@100 | 0.8662 | |
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
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```json |
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{ |
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"truncate_dim": 64 |
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} |
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``` |
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| Metric | Value | |
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| cosine_accuracy@1 | 0.78 | |
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| cosine_accuracy@3 | 0.81 | |
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| cosine_accuracy@5 | 0.81 | |
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| cosine_accuracy@10 | 0.93 | |
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| cosine_precision@1 | 0.78 | |
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| cosine_precision@3 | 0.7867 | |
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| cosine_precision@5 | 0.786 | |
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| cosine_precision@10 | 0.448 | |
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| cosine_recall@1 | 0.156 | |
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| cosine_recall@3 | 0.472 | |
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| cosine_recall@5 | 0.786 | |
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| cosine_recall@10 | 0.896 | |
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| **cosine_ndcg@10** | **0.8445** | |
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| cosine_mrr@10 | 0.8121 | |
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| cosine_map@100 | 0.8308 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 900 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 900 samples: |
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| | anchor | positive | |
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|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 13.15 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 239.87 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Explain the test_qdrant_similarity_search_with_relevance_scores logic</code> | <code>def test_qdrant_similarity_search_with_relevance_scores(<br> batch_size: int,<br> content_payload_key: str,<br> metadata_payload_key: str,<br> vector_name: str \| None,<br>) -> None:<br> """Test end to end construction and search."""<br> texts = ["foo", "bar", "baz"]<br> docsearch = Qdrant.from_texts(<br> texts,<br> ConsistentFakeEmbeddings(),<br> location=":memory:",<br> content_payload_key=content_payload_key,<br> metadata_payload_key=metadata_payload_key,<br> batch_size=batch_size,<br> vector_name=vector_name,<br> )<br> output = docsearch.similarity_search_with_relevance_scores("foo", k=3)<br><br> assert all(<br> (score <= 1 or np.isclose(score, 1)) and score >= 0 for _, score in output<br> )</code> | |
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| <code>How to implement LangChainPendingDeprecationWarning?</code> | <code>class LangChainPendingDeprecationWarning(PendingDeprecationWarning):<br> """A class for issuing deprecation warnings for LangChain users."""</code> | |
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| <code>Example usage of random_name</code> | <code>def random_name() -> str:<br> """Generate a random name."""<br> adjective = random.choice(adjectives) # noqa: S311<br> noun = random.choice(nouns) # noqa: S311<br> number = random.randint(1, 100) # noqa: S311<br> return f"{adjective}-{noun}-{number}"</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `project`: huggingface |
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- `trackio_space_id`: trackio |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `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 | 6.8447 | - | - | - | - | - | |
|
|
| 1.0 | 15 | - | 0.1025 | 0.0367 | 0.0548 | 0.0502 | 0.1185 | |
|
|
| 0.7111 | 10 | 4.8545 | - | - | - | - | - | |
|
|
| 1.0 | 15 | - | 0.2250 | 0.3047 | 0.2895 | 0.2892 | 0.3178 | |
|
|
| 0.7111 | 10 | 1.9011 | - | - | - | - | - | |
|
|
| 1.0 | 15 | - | 0.6530 | 0.6393 | 0.6269 | 0.6631 | 0.6658 | |
|
|
| 1.3556 | 20 | 0.6349 | - | - | - | - | - | |
|
|
| 2.0 | 30 | 0.1887 | 0.8480 | 0.8643 | 0.8641 | 0.8532 | 0.7974 | |
|
|
| 2.7111 | 40 | 0.0959 | - | - | - | - | - | |
|
|
| 3.0 | 45 | - | 0.8688 | 0.8774 | 0.8754 | 0.8725 | 0.8457 | |
|
|
| 3.3556 | 50 | 0.0359 | - | - | - | - | - | |
|
|
| **4.0** | **60** | **0.0515** | **0.8712** | **0.8776** | **0.879** | **0.8777** | **0.8445** | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.12 |
|
|
- Sentence Transformers: 5.2.0 |
|
|
- Transformers: 4.57.3 |
|
|
- 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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