254 lines
9.4 KiB
Python
254 lines
9.4 KiB
Python
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# pylint: disable=ungrouped-imports
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"""
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---------------------------------------------------------------------------
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NOTE:
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Custom Implementation of an Retriever based on the LLaMA Model, which is compatible with Haystack Retriever Pipeline.
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Calls under the hood the MODEL SERVICE.
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NOTE: SEE functions embed_queries and embed_documents for pooling strategy and layer extraction
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---------------------------------------------------------------------------
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"""
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from typing import List, Dict, Union, Optional, Any, Literal, Callable
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import logging
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from pathlib import Path
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from copy import deepcopy
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from requests.exceptions import HTTPError
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import numpy as np
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from tqdm import tqdm
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import pandas as pd
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from huggingface_hub import hf_hub_download
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from haystack.errors import HaystackError
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from haystack.schema import Document, FilterType
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from haystack.document_stores import BaseDocumentStore
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from haystack.telemetry import send_event
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from haystack.lazy_imports import LazyImport
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from haystack.nodes.retriever import DenseRetriever
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logger = logging.getLogger(__name__)
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with LazyImport(message="Run 'pip install farm-haystack[inference]'") as torch_and_transformers_import:
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import torch
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from haystack.modeling.utils import initialize_device_settings # pylint: disable=ungrouped-imports
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from transformers import AutoConfig
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import sys
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sys.path.append("../..")
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from api.embeddingsServiceCaller import EmbeddingServiceCaller
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_EMBEDDING_ENCODERS: Dict[str, Callable] = {
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"llama": {}
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}
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class LlamaRetriever(DenseRetriever):
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def __init__(
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self,
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model_format = "llama",
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document_store: Optional[BaseDocumentStore] = None,
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model_version: Optional[str] = None,
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use_gpu: bool = True,
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batch_size: int = 32,
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max_seq_len: int = 512,
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pooling_strategy: str = "reduce_mean",
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emb_extraction_layer: int = -1,
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top_k: int = 10,
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progress_bar: bool = True,
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devices: Optional[List[Union[str, "torch.device"]]] = None,
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use_auth_token: Optional[Union[str, bool]] = None,
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scale_score: bool = True,
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embed_meta_fields: Optional[List[str]] = None,
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api_key: Optional[str] = None,
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azure_api_version: str = "2022-12-01",
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azure_base_url: Optional[str] = None,
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azure_deployment_name: Optional[str] = None,
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api_base: str = "https://api.openai.com/v1",
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openai_organization: Optional[str] = None,
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):
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torch_and_transformers_import.check()
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if embed_meta_fields is None:
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embed_meta_fields = []
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super().__init__()
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self.devices, _ = initialize_device_settings(devices=devices, use_cuda=use_gpu, multi_gpu=True)
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if batch_size < len(self.devices):
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logger.warning("Batch size is less than the number of devices.All gpus will not be utilized.")
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self.document_store = document_store
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self.model_version = model_version
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self.use_gpu = use_gpu
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self.batch_size = batch_size
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self.max_seq_len = max_seq_len
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self.pooling_strategy = pooling_strategy
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self.emb_extraction_layer = emb_extraction_layer
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self.top_k = top_k
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self.progress_bar = progress_bar
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self.use_auth_token = use_auth_token
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self.scale_score = scale_score
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self.api_key = api_key
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self.api_base = api_base
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self.api_version = azure_api_version
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self.azure_base_url = azure_base_url
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self.azure_deployment_name = azure_deployment_name
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self.openai_organization = openai_organization
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self.model_format= model_format
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self.emb_caller= EmbeddingServiceCaller()
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self.embed_meta_fields = embed_meta_fields
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def retrieve(
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self,
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query: str,
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filters: Optional[FilterType] = None,
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top_k: Optional[int] = None,
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index: Optional[str] = None,
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headers: Optional[Dict[str, str]] = None,
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scale_score: Optional[bool] = None,
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document_store: Optional[BaseDocumentStore] = None,
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) -> List[Document]:
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document_store = document_store or self.document_store
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if document_store is None:
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raise ValueError(
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"This Retriever was not initialized with a Document Store. Provide one to the retrieve() method."
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)
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if top_k is None:
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top_k = self.top_k
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if index is None:
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index = document_store.index
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if scale_score is None:
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scale_score = self.scale_score
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query_emb = self.embed_queries(queries=[query])
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documents = document_store.query_by_embedding(
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query_emb=query_emb, filters=filters, top_k=top_k, index=index, headers=headers, scale_score=scale_score
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)
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return documents
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def retrieve_batch(
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self,
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queries: List[str],
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filters: Optional[Union[FilterType, List[Optional[FilterType]]]] = None,
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top_k: Optional[int] = None,
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index: Optional[str] = None,
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headers: Optional[Dict[str, str]] = None,
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batch_size: Optional[int] = None,
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scale_score: Optional[bool] = None,
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document_store: Optional[BaseDocumentStore] = None,
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) -> List[List[Document]]:
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document_store = document_store or self.document_store
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if document_store is None:
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raise ValueError(
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"This Retriever was not initialized with a Document Store. Provide one to the retrieve_batch() method."
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)
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if top_k is None:
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top_k = self.top_k
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if batch_size is None:
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batch_size = self.batch_size
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if index is None:
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index = document_store.index
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if scale_score is None:
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scale_score = self.scale_score
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query_embs: np.ndarray = self.embed_queries(queries=queries)
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batched_query_embs: List[np.ndarray] = []
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for i in range(0, len(query_embs), batch_size):
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batched_query_embs.extend(query_embs[i : i + batch_size])
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documents = document_store.query_by_embedding_batch(
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query_embs=batched_query_embs,
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top_k=top_k,
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filters=filters,
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index=index,
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headers=headers,
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scale_score=scale_score,
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)
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return documents
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def embed_queries(self, queries: List[str]) -> np.ndarray:
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if isinstance(queries, str):
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queries = [queries]
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assert isinstance(queries, list), "Expecting a list of texts, i.e. create_embeddings(texts=['text1',...])"
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return np.array(self.emb_caller.get_embeddings(queries[0]))
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def embed_documents(self, documents: List[Document]) -> np.ndarray:
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documents = self._preprocess_documents(documents)
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embeddings=[]
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for doc in documents:
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embeddings.append(self.emb_caller.get_embeddings(doc.content))
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return np.array(embeddings)
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def _preprocess_documents(self, docs: List[Document]) -> List[Document]:
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linearized_docs = []
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for doc in docs:
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doc = deepcopy(doc)
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if doc.content_type == "table":
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if isinstance(doc.content, pd.DataFrame):
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doc.content = doc.content.to_csv(index=False)
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else:
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raise HaystackError("Documents of type 'table' need to have a pd.DataFrame as content field")
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meta_data_fields = []
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for key in self.embed_meta_fields:
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if key in doc.meta and doc.meta[key]:
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if isinstance(doc.meta[key], list):
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meta_data_fields.extend([item for item in doc.meta[key]])
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else:
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meta_data_fields.append(doc.meta[key])
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meta_data_fields = [str(field) for field in meta_data_fields]
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doc.content = "\n".join(meta_data_fields + [doc.content])
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linearized_docs.append(doc)
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return linearized_docs
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@staticmethod
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def _infer_model_format(model_name_or_path: str, use_auth_token: Optional[Union[str, bool]]) -> str:
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valid_openai_model_name = model_name_or_path in ["ada", "babbage", "davinci", "curie"] or any(
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m in model_name_or_path for m in ["-ada-", "-babbage-", "-davinci-", "-curie-"]
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)
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if valid_openai_model_name:
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return "openai"
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if model_name_or_path in ["small", "medium", "large", "multilingual-22-12", "finance-sentiment"]:
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return "cohere"
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if Path(model_name_or_path).exists():
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if Path(f"{model_name_or_path}/config_sentence_transformers.json").exists():
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return "sentence_transformers"
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else:
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try:
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hf_hub_download(
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repo_id=model_name_or_path,
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filename="config_sentence_transformers.json",
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use_auth_token=use_auth_token,
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)
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return "sentence_transformers"
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except HTTPError:
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pass
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config = AutoConfig.from_pretrained(model_name_or_path, use_auth_token=use_auth_token)
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if config.model_type == "retribert":
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return "retribert"
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return "farm"
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def train(
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self,
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training_data: List[Dict[str, Any]],
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learning_rate: float = 2e-5,
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n_epochs: int = 1,
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num_warmup_steps: Optional[int] = None,
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batch_size: int = 16,
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train_loss: Literal["mnrl", "margin_mse"] = "mnrl",
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num_workers: int = 0,
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use_amp: bool = False,
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**kwargs,
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) -> None:
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pass
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