Upload folder using huggingface_hub
Browse files- .gitignore +5 -0
- .streamlit/config.toml +5 -0
- app.py +264 -0
- config.ini +87 -0
- data/hkunlp_instructor-large/.gitattributes +34 -0
- data/hkunlp_instructor-large/1_Pooling/config.json +7 -0
- data/hkunlp_instructor-large/2_Dense/config.json +1 -0
- data/hkunlp_instructor-large/2_Dense/pytorch_model.bin +3 -0
- data/hkunlp_instructor-large/README.md +2610 -0
- data/hkunlp_instructor-large/config.json +60 -0
- data/hkunlp_instructor-large/config_sentence_transformers.json +7 -0
- data/hkunlp_instructor-large/modules.json +26 -0
- data/hkunlp_instructor-large/pytorch_model.bin +3 -0
- data/hkunlp_instructor-large/sentence_bert_config.json +4 -0
- data/hkunlp_instructor-large/special_tokens_map.json +107 -0
- data/hkunlp_instructor-large/spiece.model +3 -0
- data/hkunlp_instructor-large/tokenizer.json +0 -0
- data/hkunlp_instructor-large/tokenizer_config.json +112 -0
- data/version.txt +1 -0
- emb.py +171 -0
- history.py +40 -0
- llm.py +132 -0
- requirements.txt +13 -0
- res/lottie/EU.json +1 -0
- res/lottie/Piping.json +0 -0
- settings.py +55 -0
- utils.py +22 -0
.gitignore
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__pycache__/
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docs/
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test.py
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uploader.py
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.env
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.streamlit/config.toml
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[theme]
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primaryColor="#3fb0e8"
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backgroundColor="#192841"
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secondaryBackgroundColor="#3fb0e8"
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textColor="#f9f9f9"
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app.py
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import streamlit as st
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from streamlit_option_menu import option_menu
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from streamlit_lottie import st_lottie
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import json
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from streamlit_chat import message
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from emb import EmbeddingsManager
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from history import HistoryManager
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from llm import LLMManager
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from settings import SettingManager
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import os
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#Defining my parameters
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vector_store="prohelper"
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@st.cache_resource
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def get_EMB():
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emb=EmbeddingsManager(get_settings(),emb="hkunlp/instructor-large")
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emb.set=get_settings()
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return emb
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@st.cache_resource
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def get_History():
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return HistoryManager()
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@st.cache_resource
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def get_llm():
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llm=LLMManager(get_settings())
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llm.set=get_settings()
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return llm
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@st.cache_resource
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def get_settings():
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return SettingManager()
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#Main page config
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st.set_page_config(
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page_title="ProHelper",
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layout="wide",
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initial_sidebar_state="expanded")
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#Data Pull and Functions
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st.markdown("""
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<style>
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.big-font {
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font-size:80px !important;
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}
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</style>
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""", unsafe_allow_html=True)
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#HideStreamlit
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hide_streamlit_style = """
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<style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style>
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"""
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st.markdown(hide_streamlit_style, unsafe_allow_html=True)
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@st.cache_data
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def load_lottiefile(filepath: str):
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with open(filepath,"r") as f:
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return json.load(f)
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# Display conversation history using Streamlit messages
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def display_conversation(history_manager):
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if not history_manager.chat_exists(vector_store):
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history_manager.add_message(vector_store,"Assistant","Hi, how can i help you?")
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message("Hi, how can i help you?",False)
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else:
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for sender,mess in history_manager.get_messages(vector_store):
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is_user=not (sender=="Assistant")
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message(mess, is_user=is_user)
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def clear_text():
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st.session_state.my_text = st.session_state.input
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st.session_state.input = ""
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def process_answer(user_input):
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history=get_History().format_chat(vector_store)
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context=get_EMB().get_formatted_context(vector_store,user_input,history)
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prompt=get_llm().get_prompt(user_input,context,history)
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return get_llm().get_text(prompt),context,history
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def create_setting():
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st.title('ProHelper Settings')
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st.divider()
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st.subheader('*Main LLM Settings*')
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# Create a main container with two columns
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left_column, right_column = st.columns([1, 2])
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# Add Ai assisted search button
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checked = left_column.checkbox('AI assisted search',value=get_settings().ai_assisted_search, help="An additional LLM will pre process the question to get improved search words for RAG")
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#Add max new token settings
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max_new_token = left_column.number_input(label='Max new token', value=get_settings().max_new_token,help="The maximum number of tokens that the LLM can generate")
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get_settings().max_new_token=max_new_token
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#Add topP setting
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top_p = left_column.slider('Top p', min_value=0.0, max_value=1.0, value=get_settings().top_p, step=0.01, help="Used to control the randomness of the generated text.")
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get_settings().top_p=top_p
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#Add temperature setting
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temperature = left_column.slider('Temperature', min_value=0.01, max_value=1.0, value=get_settings().temperature, step=0.01, help="Another parameter used to control the randomness of the generated text.")
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get_settings().temperature=temperature
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#Add repetition penality setting
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repetition_penalty = left_column.slider('Repetition penality', min_value=0.7, max_value=2.0, value=get_settings().repetition_penalty, step=0.01, help="Used to discourage the model from repeating the same phrases or content.")
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get_settings().repetition_penalty=repetition_penalty
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#Add LLM settings
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llm_options = get_settings().listLLMMap
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selected_option = left_column.selectbox('Chat LLM', llm_options, index=get_settings().defaultLLM)
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selected_llm = llm_options.index(selected_option)
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get_settings().defaultLLM=selected_llm
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get_llm().selectLLM(selected_option)
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# Add content to the right column based on the checkbox state
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if checked:
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get_settings().ai_assisted_search=True
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else:
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get_settings().ai_assisted_search=False
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126 |
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#Add prompt setting
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system_prompt = right_column.text_area('System prompt:', get_settings().system_prompt,height=500,help=r"Text that provides context and instructions to the model before it generates a response. The special fields {context}, {history} and {question} will be replaced with their corresponding values")
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get_settings().system_prompt=system_prompt
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129 |
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st.divider()
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st.subheader('*RAG Settings*')
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132 |
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# Create a main container with two columns
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left_column_RAG, right_column_RAG = st.columns([1, 2])
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#Add return documents settings
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n_doc_return = left_column_RAG.number_input(label='N doc return', value=get_settings().n_doc_return,help="The number of documents to be returned by the search")
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get_settings().n_doc_return=n_doc_return
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#Add default search method
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rag_methods = get_settings().available_search_methods
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print(get_settings().search_method)
|
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selected_option_RAG = left_column_RAG.selectbox('Search Methods', rag_methods, index=rag_methods.index(get_settings().search_method))
|
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get_settings().search_method=selected_option_RAG
|
145 |
+
|
146 |
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#Add prompt setting
|
147 |
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RAG_prompt = right_column_RAG.text_area('RAG prompt:', get_settings().default_ai_search_prompt,height=500,help=r"Text that provides context and instructions to the model used for search terms. The special fields {history} and {question} will be replaced with their corresponding values")
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148 |
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get_settings().default_ai_search_prompt=RAG_prompt
|
149 |
+
|
150 |
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#Add max new token settings
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RAG_max_new_token = left_column_RAG.number_input(label='RAG max new token', value=get_settings().RAG_max_new_token,help="The maximum number of tokens that the LLM can generate")
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152 |
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get_settings().RAG_max_new_token=RAG_max_new_token
|
153 |
+
|
154 |
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#Add topP setting
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155 |
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RAG_top_p = left_column_RAG.slider('RAG top p', min_value=0.0, max_value=1.0, value=get_settings().RAG_top_p, step=0.01, help="Used to control the randomness of the generated text.")
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156 |
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get_settings().RAG_top_p=RAG_top_p
|
157 |
+
|
158 |
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#Add temperature setting
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RAG_temperature = left_column_RAG.slider('RAG temperature', min_value=0.01, max_value=1.0, value=get_settings().RAG_temperature, step=0.01, help="Another parameter used to control the randomness of the generated text.")
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get_settings().RAG_temperature=RAG_temperature
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+
|
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#Add repetition penality setting
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RAG_repetition_penalty = left_column_RAG.slider('RAG repetition penality', min_value=0.7, max_value=2.0, value=get_settings().RAG_repetition_penalty, step=0.01, help="Used to discourage the model from repeating the same phrases or content.")
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get_settings().RAG_repetition_penalty=RAG_repetition_penalty
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def create_info():
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#Header
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171 |
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st.title('Welcome to ProHelper')
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st.subheader('*A new tool to help you with process problems*')
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173 |
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st.divider()
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176 |
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#Use Cases
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with st.container():
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178 |
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col1,col2=st.columns(2)
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179 |
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with col1:
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st.header('Knlowledge base:')
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st.markdown(
|
182 |
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"""
|
183 |
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- _Perrys chemical engineers handbook_
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- _Coulson & Richardson's Chemical Engineering Vol.6 Chemical Engineering Design 4th Edition_
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- _Chemical-Process-Equipment-Selection-and-Design-by-Stanley-M.-Walas_
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"""
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)
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with col2:
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current_dir = os.path.dirname(__file__)
|
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lottie_path = os.path.join(current_dir, "res\lottie\Piping.json")
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lottie2 = load_lottiefile(lottie_path)
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st_lottie(lottie2,key='place',height=300,width=300)
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st.divider()
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def main():
|
196 |
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|
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#SIDE BAR------------------------------------------------------------------------------------------------------------------------------
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with st.sidebar:
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selected = option_menu('ProHelper', ["Info", 'About','Settings','Bots'],
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icons=['info-circle','question','gear'],menu_icon='droplet-fill', default_index=0)
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|
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#INTRO PAGE----------------------------------------------------------------------------------------------------------------------------
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if selected=="Info":
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create_info()
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#SETTING PAGE----------------------------------------------------------------------------------------------------------------------------
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207 |
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if selected=="Settings":
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create_setting()
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#ABOUT PAGE----------------------------------------------------------------------------------------------------------------------------
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211 |
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if selected=="About":
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# Header
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st.title('About ProHelper')
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# Content
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st.write("ProHelper is a testing program created by DFO.")
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217 |
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st.write("This program is designed for testing purposes only.")
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218 |
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st.write("For more information, please contact DFO.")
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219 |
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221 |
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#BOTS PAGE-----------------------------------------------------------------------------------------------------------------------------
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222 |
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if selected=="Bots":
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223 |
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if "messages" not in st.session_state.keys():
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224 |
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st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
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225 |
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|
226 |
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# Display or clear chat messages
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227 |
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for message in st.session_state.messages:
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228 |
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with st.chat_message(message["role"]):
|
229 |
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st.write(message["content"])
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230 |
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|
231 |
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# User-provided prompt
|
232 |
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if prompt := st.chat_input("Ask me anything"):
|
233 |
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st.session_state.messages.append({"role": "user", "content": prompt})
|
234 |
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with st.chat_message("user"):
|
235 |
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st.write(prompt)
|
236 |
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|
237 |
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|
238 |
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if st.session_state.messages[-1]["role"] != "assistant":
|
239 |
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prompt=st.session_state.messages[-1]["content"]
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240 |
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with st.chat_message("assistant"):
|
241 |
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with st.spinner("Thinking..."):
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242 |
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response,cont,hist = process_answer(prompt)
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243 |
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placeholder = st.empty()
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244 |
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full_response = ''
|
245 |
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for item in response:
|
246 |
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full_response += item
|
247 |
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placeholder.markdown(full_response)
|
248 |
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placeholder.markdown(full_response)
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249 |
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message = {"role": "assistant", "content": full_response}
|
250 |
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st.session_state.messages.append(message)
|
251 |
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|
252 |
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rag_context = "context"
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253 |
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with st.expander("Sources"):
|
254 |
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st.write(f"Context: {cont}")
|
255 |
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st.write(f"History: {hist}")
|
256 |
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|
257 |
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get_History().add_message(vector_store,"User",prompt)
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258 |
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get_History().add_message(vector_store,"Assistant",full_response)
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259 |
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|
260 |
+
|
261 |
+
|
262 |
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|
263 |
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if __name__ == "__main__":
|
264 |
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main()
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config.ini
ADDED
@@ -0,0 +1,87 @@
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|
1 |
+
[Settings]
|
2 |
+
MAX_NEW_TOKENS = 2048
|
3 |
+
MAX_INPUT_TOKEN_LENGTH = 4096
|
4 |
+
DEFAULT_LLM = 0
|
5 |
+
TEMPERATURE = 0.8
|
6 |
+
TOP_P = 0.95
|
7 |
+
REPETITION_PENALITY = 1.0
|
8 |
+
default_prompt=You are a helpful assistant, you will use the provided context and chat history to answer user questions.
|
9 |
+
Read the given context before answering questions.
|
10 |
+
If you can not answer a user question based on the provided context, inform the user.
|
11 |
+
Enven if the context does not explicitly provide the information needed try to deduce it and provide a definitive answer to yes or no question.
|
12 |
+
Provide a detailed answer to the question.
|
13 |
+
|
14 |
+
CONTEXT:
|
15 |
+
{context}
|
16 |
+
END OF CONTEXT
|
17 |
+
|
18 |
+
CHAT HISTORY:
|
19 |
+
{history}
|
20 |
+
END OF CHAT HISTORY
|
21 |
+
|
22 |
+
USER QUESTION:
|
23 |
+
{question}
|
24 |
+
END OF USER QUESTION
|
25 |
+
|
26 |
+
[Info]
|
27 |
+
version = 0.0.1
|
28 |
+
date = 01/03/2024
|
29 |
+
|
30 |
+
[RAG]
|
31 |
+
methods = MMR,Similarity
|
32 |
+
RAG_MAX_NEW_TOKENS = 500
|
33 |
+
RAG_TEMPERATURE = 0.1
|
34 |
+
RAG_TOP_P = 0.9
|
35 |
+
RAG_REPETITION_PENALITY = 1.0
|
36 |
+
default_ai_assisted_search=True
|
37 |
+
default_search_method=MMR
|
38 |
+
default_returned_docs=10
|
39 |
+
default_text_split_size=1000
|
40 |
+
default_text_overlap=200
|
41 |
+
default_ai_search_prompt=You are an AI assistant, specializing in process engineering and chemistry. Your task is to interpret the following question:
|
42 |
+
{question}
|
43 |
+
considering the following chat history:
|
44 |
+
CHAT HISTORY:
|
45 |
+
{history}
|
46 |
+
END OF CHAT HISTORY
|
47 |
+
Based on the question, suggest the most relevant search terms that could yield the best results. Please provide only the search terms, without any additional text or explanation.
|
48 |
+
|
49 |
+
|
50 |
+
[LLM]
|
51 |
+
link1 = mistralai/Mixtral-8x7B-Instruct-v0.1
|
52 |
+
link2 = meta-llama/Llama-2-7b-chat-hf
|
53 |
+
|
54 |
+
[LLM_Map]
|
55 |
+
map1 = Mixtral 7B
|
56 |
+
map2 = Llama 7B
|
57 |
+
|
58 |
+
[Prompt_map]
|
59 |
+
prompt1 =<s>[INST]{sys_prompt}[/INST]
|
60 |
+
prompt2 =[INST]<<SYS>>\n{sys_prompt}\n<</SYS>>\n\n[/INST]
|
61 |
+
|
62 |
+
[EMB]
|
63 |
+
link1 = hkunlp/instructor-large
|
64 |
+
link2 = hkunlp/instructor-xl
|
65 |
+
link3 = intfloat/e5-large-v2
|
66 |
+
link4 = intfloat/e5-base-v2
|
67 |
+
link5 = all-MiniLM-L6-v2
|
68 |
+
|
69 |
+
[EMB_Map]
|
70 |
+
map1 = Instructor Large
|
71 |
+
map2 = Instructor XL
|
72 |
+
map3 = E5 Large
|
73 |
+
map4 = E5 Base
|
74 |
+
map5 = MiniLM
|
75 |
+
|
76 |
+
[EMB_Folder_Map]
|
77 |
+
map1 = InstL
|
78 |
+
map2 = InstXL
|
79 |
+
map3 = E5Large
|
80 |
+
map4 = E5Base
|
81 |
+
map5 = MiniLM
|
82 |
+
|
83 |
+
[Vector_Stores]
|
84 |
+
index1=prohelper
|
85 |
+
|
86 |
+
[Vector_Stores_Map]
|
87 |
+
index1=ProHelper - The assistant is used to guide the user process design
|
data/hkunlp_instructor-large/.gitattributes
ADDED
@@ -0,0 +1,34 @@
|
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|
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|
|
|
|
|
|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
29 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
30 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
31 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
32 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
33 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
34 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
data/hkunlp_instructor-large/1_Pooling/config.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"word_embedding_dimension": 768,
|
3 |
+
"pooling_mode_cls_token": false,
|
4 |
+
"pooling_mode_mean_tokens": true,
|
5 |
+
"pooling_mode_max_tokens": false,
|
6 |
+
"pooling_mode_mean_sqrt_len_tokens": false
|
7 |
+
}
|
data/hkunlp_instructor-large/2_Dense/config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"in_features": 1024, "out_features": 768, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}
|
data/hkunlp_instructor-large/2_Dense/pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a6e82cba0876dacbadccdea565c9e19e29848d994d23968bd1343b8f0f762bdc
|
3 |
+
size 3146603
|
data/hkunlp_instructor-large/README.md
ADDED
@@ -0,0 +1,2610 @@
|
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|
1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
tags:
|
4 |
+
- text-embedding
|
5 |
+
- embeddings
|
6 |
+
- information-retrieval
|
7 |
+
- beir
|
8 |
+
- text-classification
|
9 |
+
- language-model
|
10 |
+
- text-clustering
|
11 |
+
- text-semantic-similarity
|
12 |
+
- text-evaluation
|
13 |
+
- prompt-retrieval
|
14 |
+
- text-reranking
|
15 |
+
- sentence-transformers
|
16 |
+
- feature-extraction
|
17 |
+
- sentence-similarity
|
18 |
+
- transformers
|
19 |
+
- t5
|
20 |
+
- English
|
21 |
+
- Sentence Similarity
|
22 |
+
- natural_questions
|
23 |
+
- ms_marco
|
24 |
+
- fever
|
25 |
+
- hotpot_qa
|
26 |
+
- mteb
|
27 |
+
language: en
|
28 |
+
inference: false
|
29 |
+
license: apache-2.0
|
30 |
+
model-index:
|
31 |
+
- name: INSTRUCTOR
|
32 |
+
results:
|
33 |
+
- task:
|
34 |
+
type: Classification
|
35 |
+
dataset:
|
36 |
+
type: mteb/amazon_counterfactual
|
37 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
38 |
+
config: en
|
39 |
+
split: test
|
40 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
41 |
+
metrics:
|
42 |
+
- type: accuracy
|
43 |
+
value: 88.13432835820896
|
44 |
+
- type: ap
|
45 |
+
value: 59.298209334395665
|
46 |
+
- type: f1
|
47 |
+
value: 83.31769058643586
|
48 |
+
- task:
|
49 |
+
type: Classification
|
50 |
+
dataset:
|
51 |
+
type: mteb/amazon_polarity
|
52 |
+
name: MTEB AmazonPolarityClassification
|
53 |
+
config: default
|
54 |
+
split: test
|
55 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
56 |
+
metrics:
|
57 |
+
- type: accuracy
|
58 |
+
value: 91.526375
|
59 |
+
- type: ap
|
60 |
+
value: 88.16327709705504
|
61 |
+
- type: f1
|
62 |
+
value: 91.51095801287843
|
63 |
+
- task:
|
64 |
+
type: Classification
|
65 |
+
dataset:
|
66 |
+
type: mteb/amazon_reviews_multi
|
67 |
+
name: MTEB AmazonReviewsClassification (en)
|
68 |
+
config: en
|
69 |
+
split: test
|
70 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
71 |
+
metrics:
|
72 |
+
- type: accuracy
|
73 |
+
value: 47.856
|
74 |
+
- type: f1
|
75 |
+
value: 45.41490917650942
|
76 |
+
- task:
|
77 |
+
type: Retrieval
|
78 |
+
dataset:
|
79 |
+
type: arguana
|
80 |
+
name: MTEB ArguAna
|
81 |
+
config: default
|
82 |
+
split: test
|
83 |
+
revision: None
|
84 |
+
metrics:
|
85 |
+
- type: map_at_1
|
86 |
+
value: 31.223
|
87 |
+
- type: map_at_10
|
88 |
+
value: 47.947
|
89 |
+
- type: map_at_100
|
90 |
+
value: 48.742000000000004
|
91 |
+
- type: map_at_1000
|
92 |
+
value: 48.745
|
93 |
+
- type: map_at_3
|
94 |
+
value: 43.137
|
95 |
+
- type: map_at_5
|
96 |
+
value: 45.992
|
97 |
+
- type: mrr_at_1
|
98 |
+
value: 32.432
|
99 |
+
- type: mrr_at_10
|
100 |
+
value: 48.4
|
101 |
+
- type: mrr_at_100
|
102 |
+
value: 49.202
|
103 |
+
- type: mrr_at_1000
|
104 |
+
value: 49.205
|
105 |
+
- type: mrr_at_3
|
106 |
+
value: 43.551
|
107 |
+
- type: mrr_at_5
|
108 |
+
value: 46.467999999999996
|
109 |
+
- type: ndcg_at_1
|
110 |
+
value: 31.223
|
111 |
+
- type: ndcg_at_10
|
112 |
+
value: 57.045
|
113 |
+
- type: ndcg_at_100
|
114 |
+
value: 60.175
|
115 |
+
- type: ndcg_at_1000
|
116 |
+
value: 60.233000000000004
|
117 |
+
- type: ndcg_at_3
|
118 |
+
value: 47.171
|
119 |
+
- type: ndcg_at_5
|
120 |
+
value: 52.322
|
121 |
+
- type: precision_at_1
|
122 |
+
value: 31.223
|
123 |
+
- type: precision_at_10
|
124 |
+
value: 8.599
|
125 |
+
- type: precision_at_100
|
126 |
+
value: 0.991
|
127 |
+
- type: precision_at_1000
|
128 |
+
value: 0.1
|
129 |
+
- type: precision_at_3
|
130 |
+
value: 19.63
|
131 |
+
- type: precision_at_5
|
132 |
+
value: 14.282
|
133 |
+
- type: recall_at_1
|
134 |
+
value: 31.223
|
135 |
+
- type: recall_at_10
|
136 |
+
value: 85.989
|
137 |
+
- type: recall_at_100
|
138 |
+
value: 99.075
|
139 |
+
- type: recall_at_1000
|
140 |
+
value: 99.502
|
141 |
+
- type: recall_at_3
|
142 |
+
value: 58.89
|
143 |
+
- type: recall_at_5
|
144 |
+
value: 71.408
|
145 |
+
- task:
|
146 |
+
type: Clustering
|
147 |
+
dataset:
|
148 |
+
type: mteb/arxiv-clustering-p2p
|
149 |
+
name: MTEB ArxivClusteringP2P
|
150 |
+
config: default
|
151 |
+
split: test
|
152 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
153 |
+
metrics:
|
154 |
+
- type: v_measure
|
155 |
+
value: 43.1621946393635
|
156 |
+
- task:
|
157 |
+
type: Clustering
|
158 |
+
dataset:
|
159 |
+
type: mteb/arxiv-clustering-s2s
|
160 |
+
name: MTEB ArxivClusteringS2S
|
161 |
+
config: default
|
162 |
+
split: test
|
163 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
164 |
+
metrics:
|
165 |
+
- type: v_measure
|
166 |
+
value: 32.56417132407894
|
167 |
+
- task:
|
168 |
+
type: Reranking
|
169 |
+
dataset:
|
170 |
+
type: mteb/askubuntudupquestions-reranking
|
171 |
+
name: MTEB AskUbuntuDupQuestions
|
172 |
+
config: default
|
173 |
+
split: test
|
174 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
175 |
+
metrics:
|
176 |
+
- type: map
|
177 |
+
value: 64.29539304390207
|
178 |
+
- type: mrr
|
179 |
+
value: 76.44484017060196
|
180 |
+
- task:
|
181 |
+
type: STS
|
182 |
+
dataset:
|
183 |
+
type: mteb/biosses-sts
|
184 |
+
name: MTEB BIOSSES
|
185 |
+
config: default
|
186 |
+
split: test
|
187 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
188 |
+
metrics:
|
189 |
+
- type: cos_sim_spearman
|
190 |
+
value: 84.38746499431112
|
191 |
+
- task:
|
192 |
+
type: Classification
|
193 |
+
dataset:
|
194 |
+
type: mteb/banking77
|
195 |
+
name: MTEB Banking77Classification
|
196 |
+
config: default
|
197 |
+
split: test
|
198 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
199 |
+
metrics:
|
200 |
+
- type: accuracy
|
201 |
+
value: 78.51298701298701
|
202 |
+
- type: f1
|
203 |
+
value: 77.49041754069235
|
204 |
+
- task:
|
205 |
+
type: Clustering
|
206 |
+
dataset:
|
207 |
+
type: mteb/biorxiv-clustering-p2p
|
208 |
+
name: MTEB BiorxivClusteringP2P
|
209 |
+
config: default
|
210 |
+
split: test
|
211 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
212 |
+
metrics:
|
213 |
+
- type: v_measure
|
214 |
+
value: 37.61848554098577
|
215 |
+
- task:
|
216 |
+
type: Clustering
|
217 |
+
dataset:
|
218 |
+
type: mteb/biorxiv-clustering-s2s
|
219 |
+
name: MTEB BiorxivClusteringS2S
|
220 |
+
config: default
|
221 |
+
split: test
|
222 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
223 |
+
metrics:
|
224 |
+
- type: v_measure
|
225 |
+
value: 31.32623280148178
|
226 |
+
- task:
|
227 |
+
type: Retrieval
|
228 |
+
dataset:
|
229 |
+
type: BeIR/cqadupstack
|
230 |
+
name: MTEB CQADupstackAndroidRetrieval
|
231 |
+
config: default
|
232 |
+
split: test
|
233 |
+
revision: None
|
234 |
+
metrics:
|
235 |
+
- type: map_at_1
|
236 |
+
value: 35.803000000000004
|
237 |
+
- type: map_at_10
|
238 |
+
value: 48.848
|
239 |
+
- type: map_at_100
|
240 |
+
value: 50.5
|
241 |
+
- type: map_at_1000
|
242 |
+
value: 50.602999999999994
|
243 |
+
- type: map_at_3
|
244 |
+
value: 45.111000000000004
|
245 |
+
- type: map_at_5
|
246 |
+
value: 47.202
|
247 |
+
- type: mrr_at_1
|
248 |
+
value: 44.635000000000005
|
249 |
+
- type: mrr_at_10
|
250 |
+
value: 55.593
|
251 |
+
- type: mrr_at_100
|
252 |
+
value: 56.169999999999995
|
253 |
+
- type: mrr_at_1000
|
254 |
+
value: 56.19499999999999
|
255 |
+
- type: mrr_at_3
|
256 |
+
value: 53.361999999999995
|
257 |
+
- type: mrr_at_5
|
258 |
+
value: 54.806999999999995
|
259 |
+
- type: ndcg_at_1
|
260 |
+
value: 44.635000000000005
|
261 |
+
- type: ndcg_at_10
|
262 |
+
value: 55.899
|
263 |
+
- type: ndcg_at_100
|
264 |
+
value: 60.958
|
265 |
+
- type: ndcg_at_1000
|
266 |
+
value: 62.302
|
267 |
+
- type: ndcg_at_3
|
268 |
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value: 51.051
|
269 |
+
- type: ndcg_at_5
|
270 |
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value: 53.351000000000006
|
271 |
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- type: precision_at_1
|
272 |
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value: 44.635000000000005
|
273 |
+
- type: precision_at_10
|
274 |
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value: 10.786999999999999
|
275 |
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- type: precision_at_100
|
276 |
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value: 1.6580000000000001
|
277 |
+
- type: precision_at_1000
|
278 |
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value: 0.213
|
279 |
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- type: precision_at_3
|
280 |
+
value: 24.893
|
281 |
+
- type: precision_at_5
|
282 |
+
value: 17.740000000000002
|
283 |
+
- type: recall_at_1
|
284 |
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value: 35.803000000000004
|
285 |
+
- type: recall_at_10
|
286 |
+
value: 68.657
|
287 |
+
- type: recall_at_100
|
288 |
+
value: 89.77199999999999
|
289 |
+
- type: recall_at_1000
|
290 |
+
value: 97.67
|
291 |
+
- type: recall_at_3
|
292 |
+
value: 54.066
|
293 |
+
- type: recall_at_5
|
294 |
+
value: 60.788
|
295 |
+
- task:
|
296 |
+
type: Retrieval
|
297 |
+
dataset:
|
298 |
+
type: BeIR/cqadupstack
|
299 |
+
name: MTEB CQADupstackEnglishRetrieval
|
300 |
+
config: default
|
301 |
+
split: test
|
302 |
+
revision: None
|
303 |
+
metrics:
|
304 |
+
- type: map_at_1
|
305 |
+
value: 33.706
|
306 |
+
- type: map_at_10
|
307 |
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value: 44.896
|
308 |
+
- type: map_at_100
|
309 |
+
value: 46.299
|
310 |
+
- type: map_at_1000
|
311 |
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value: 46.44
|
312 |
+
- type: map_at_3
|
313 |
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value: 41.721000000000004
|
314 |
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- type: map_at_5
|
315 |
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value: 43.486000000000004
|
316 |
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- type: mrr_at_1
|
317 |
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value: 41.592
|
318 |
+
- type: mrr_at_10
|
319 |
+
value: 50.529
|
320 |
+
- type: mrr_at_100
|
321 |
+
value: 51.22
|
322 |
+
- type: mrr_at_1000
|
323 |
+
value: 51.258
|
324 |
+
- type: mrr_at_3
|
325 |
+
value: 48.205999999999996
|
326 |
+
- type: mrr_at_5
|
327 |
+
value: 49.528
|
328 |
+
- type: ndcg_at_1
|
329 |
+
value: 41.592
|
330 |
+
- type: ndcg_at_10
|
331 |
+
value: 50.77199999999999
|
332 |
+
- type: ndcg_at_100
|
333 |
+
value: 55.383
|
334 |
+
- type: ndcg_at_1000
|
335 |
+
value: 57.288
|
336 |
+
- type: ndcg_at_3
|
337 |
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value: 46.324
|
338 |
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- type: ndcg_at_5
|
339 |
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value: 48.346000000000004
|
340 |
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- type: precision_at_1
|
341 |
+
value: 41.592
|
342 |
+
- type: precision_at_10
|
343 |
+
value: 9.516
|
344 |
+
- type: precision_at_100
|
345 |
+
value: 1.541
|
346 |
+
- type: precision_at_1000
|
347 |
+
value: 0.2
|
348 |
+
- type: precision_at_3
|
349 |
+
value: 22.399
|
350 |
+
- type: precision_at_5
|
351 |
+
value: 15.770999999999999
|
352 |
+
- type: recall_at_1
|
353 |
+
value: 33.706
|
354 |
+
- type: recall_at_10
|
355 |
+
value: 61.353
|
356 |
+
- type: recall_at_100
|
357 |
+
value: 80.182
|
358 |
+
- type: recall_at_1000
|
359 |
+
value: 91.896
|
360 |
+
- type: recall_at_3
|
361 |
+
value: 48.204
|
362 |
+
- type: recall_at_5
|
363 |
+
value: 53.89699999999999
|
364 |
+
- task:
|
365 |
+
type: Retrieval
|
366 |
+
dataset:
|
367 |
+
type: BeIR/cqadupstack
|
368 |
+
name: MTEB CQADupstackGamingRetrieval
|
369 |
+
config: default
|
370 |
+
split: test
|
371 |
+
revision: None
|
372 |
+
metrics:
|
373 |
+
- type: map_at_1
|
374 |
+
value: 44.424
|
375 |
+
- type: map_at_10
|
376 |
+
value: 57.169000000000004
|
377 |
+
- type: map_at_100
|
378 |
+
value: 58.202
|
379 |
+
- type: map_at_1000
|
380 |
+
value: 58.242000000000004
|
381 |
+
- type: map_at_3
|
382 |
+
value: 53.825
|
383 |
+
- type: map_at_5
|
384 |
+
value: 55.714
|
385 |
+
- type: mrr_at_1
|
386 |
+
value: 50.470000000000006
|
387 |
+
- type: mrr_at_10
|
388 |
+
value: 60.489000000000004
|
389 |
+
- type: mrr_at_100
|
390 |
+
value: 61.096
|
391 |
+
- type: mrr_at_1000
|
392 |
+
value: 61.112
|
393 |
+
- type: mrr_at_3
|
394 |
+
value: 58.192
|
395 |
+
- type: mrr_at_5
|
396 |
+
value: 59.611999999999995
|
397 |
+
- type: ndcg_at_1
|
398 |
+
value: 50.470000000000006
|
399 |
+
- type: ndcg_at_10
|
400 |
+
value: 63.071999999999996
|
401 |
+
- type: ndcg_at_100
|
402 |
+
value: 66.964
|
403 |
+
- type: ndcg_at_1000
|
404 |
+
value: 67.659
|
405 |
+
- type: ndcg_at_3
|
406 |
+
value: 57.74399999999999
|
407 |
+
- type: ndcg_at_5
|
408 |
+
value: 60.367000000000004
|
409 |
+
- type: precision_at_1
|
410 |
+
value: 50.470000000000006
|
411 |
+
- type: precision_at_10
|
412 |
+
value: 10.019
|
413 |
+
- type: precision_at_100
|
414 |
+
value: 1.29
|
415 |
+
- type: precision_at_1000
|
416 |
+
value: 0.13899999999999998
|
417 |
+
- type: precision_at_3
|
418 |
+
value: 25.558999999999997
|
419 |
+
- type: precision_at_5
|
420 |
+
value: 17.467
|
421 |
+
- type: recall_at_1
|
422 |
+
value: 44.424
|
423 |
+
- type: recall_at_10
|
424 |
+
value: 77.02
|
425 |
+
- type: recall_at_100
|
426 |
+
value: 93.738
|
427 |
+
- type: recall_at_1000
|
428 |
+
value: 98.451
|
429 |
+
- type: recall_at_3
|
430 |
+
value: 62.888
|
431 |
+
- type: recall_at_5
|
432 |
+
value: 69.138
|
433 |
+
- task:
|
434 |
+
type: Retrieval
|
435 |
+
dataset:
|
436 |
+
type: BeIR/cqadupstack
|
437 |
+
name: MTEB CQADupstackGisRetrieval
|
438 |
+
config: default
|
439 |
+
split: test
|
440 |
+
revision: None
|
441 |
+
metrics:
|
442 |
+
- type: map_at_1
|
443 |
+
value: 26.294
|
444 |
+
- type: map_at_10
|
445 |
+
value: 34.503
|
446 |
+
- type: map_at_100
|
447 |
+
value: 35.641
|
448 |
+
- type: map_at_1000
|
449 |
+
value: 35.724000000000004
|
450 |
+
- type: map_at_3
|
451 |
+
value: 31.753999999999998
|
452 |
+
- type: map_at_5
|
453 |
+
value: 33.190999999999995
|
454 |
+
- type: mrr_at_1
|
455 |
+
value: 28.362
|
456 |
+
- type: mrr_at_10
|
457 |
+
value: 36.53
|
458 |
+
- type: mrr_at_100
|
459 |
+
value: 37.541000000000004
|
460 |
+
- type: mrr_at_1000
|
461 |
+
value: 37.602000000000004
|
462 |
+
- type: mrr_at_3
|
463 |
+
value: 33.917
|
464 |
+
- type: mrr_at_5
|
465 |
+
value: 35.358000000000004
|
466 |
+
- type: ndcg_at_1
|
467 |
+
value: 28.362
|
468 |
+
- type: ndcg_at_10
|
469 |
+
value: 39.513999999999996
|
470 |
+
- type: ndcg_at_100
|
471 |
+
value: 44.815
|
472 |
+
- type: ndcg_at_1000
|
473 |
+
value: 46.839
|
474 |
+
- type: ndcg_at_3
|
475 |
+
value: 34.02
|
476 |
+
- type: ndcg_at_5
|
477 |
+
value: 36.522
|
478 |
+
- type: precision_at_1
|
479 |
+
value: 28.362
|
480 |
+
- type: precision_at_10
|
481 |
+
value: 6.101999999999999
|
482 |
+
- type: precision_at_100
|
483 |
+
value: 0.9129999999999999
|
484 |
+
- type: precision_at_1000
|
485 |
+
value: 0.11399999999999999
|
486 |
+
- type: precision_at_3
|
487 |
+
value: 14.161999999999999
|
488 |
+
- type: precision_at_5
|
489 |
+
value: 9.966
|
490 |
+
- type: recall_at_1
|
491 |
+
value: 26.294
|
492 |
+
- type: recall_at_10
|
493 |
+
value: 53.098
|
494 |
+
- type: recall_at_100
|
495 |
+
value: 76.877
|
496 |
+
- type: recall_at_1000
|
497 |
+
value: 91.834
|
498 |
+
- type: recall_at_3
|
499 |
+
value: 38.266
|
500 |
+
- type: recall_at_5
|
501 |
+
value: 44.287
|
502 |
+
- task:
|
503 |
+
type: Retrieval
|
504 |
+
dataset:
|
505 |
+
type: BeIR/cqadupstack
|
506 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
507 |
+
config: default
|
508 |
+
split: test
|
509 |
+
revision: None
|
510 |
+
metrics:
|
511 |
+
- type: map_at_1
|
512 |
+
value: 16.407
|
513 |
+
- type: map_at_10
|
514 |
+
value: 25.185999999999996
|
515 |
+
- type: map_at_100
|
516 |
+
value: 26.533
|
517 |
+
- type: map_at_1000
|
518 |
+
value: 26.657999999999998
|
519 |
+
- type: map_at_3
|
520 |
+
value: 22.201999999999998
|
521 |
+
- type: map_at_5
|
522 |
+
value: 23.923
|
523 |
+
- type: mrr_at_1
|
524 |
+
value: 20.522000000000002
|
525 |
+
- type: mrr_at_10
|
526 |
+
value: 29.522
|
527 |
+
- type: mrr_at_100
|
528 |
+
value: 30.644
|
529 |
+
- type: mrr_at_1000
|
530 |
+
value: 30.713
|
531 |
+
- type: mrr_at_3
|
532 |
+
value: 26.679000000000002
|
533 |
+
- type: mrr_at_5
|
534 |
+
value: 28.483000000000004
|
535 |
+
- type: ndcg_at_1
|
536 |
+
value: 20.522000000000002
|
537 |
+
- type: ndcg_at_10
|
538 |
+
value: 30.656
|
539 |
+
- type: ndcg_at_100
|
540 |
+
value: 36.864999999999995
|
541 |
+
- type: ndcg_at_1000
|
542 |
+
value: 39.675
|
543 |
+
- type: ndcg_at_3
|
544 |
+
value: 25.319000000000003
|
545 |
+
- type: ndcg_at_5
|
546 |
+
value: 27.992
|
547 |
+
- type: precision_at_1
|
548 |
+
value: 20.522000000000002
|
549 |
+
- type: precision_at_10
|
550 |
+
value: 5.795999999999999
|
551 |
+
- type: precision_at_100
|
552 |
+
value: 1.027
|
553 |
+
- type: precision_at_1000
|
554 |
+
value: 0.13999999999999999
|
555 |
+
- type: precision_at_3
|
556 |
+
value: 12.396
|
557 |
+
- type: precision_at_5
|
558 |
+
value: 9.328
|
559 |
+
- type: recall_at_1
|
560 |
+
value: 16.407
|
561 |
+
- type: recall_at_10
|
562 |
+
value: 43.164
|
563 |
+
- type: recall_at_100
|
564 |
+
value: 69.695
|
565 |
+
- type: recall_at_1000
|
566 |
+
value: 89.41900000000001
|
567 |
+
- type: recall_at_3
|
568 |
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value: 28.634999999999998
|
569 |
+
- type: recall_at_5
|
570 |
+
value: 35.308
|
571 |
+
- task:
|
572 |
+
type: Retrieval
|
573 |
+
dataset:
|
574 |
+
type: BeIR/cqadupstack
|
575 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
576 |
+
config: default
|
577 |
+
split: test
|
578 |
+
revision: None
|
579 |
+
metrics:
|
580 |
+
- type: map_at_1
|
581 |
+
value: 30.473
|
582 |
+
- type: map_at_10
|
583 |
+
value: 41.676
|
584 |
+
- type: map_at_100
|
585 |
+
value: 43.120999999999995
|
586 |
+
- type: map_at_1000
|
587 |
+
value: 43.230000000000004
|
588 |
+
- type: map_at_3
|
589 |
+
value: 38.306000000000004
|
590 |
+
- type: map_at_5
|
591 |
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value: 40.355999999999995
|
592 |
+
- type: mrr_at_1
|
593 |
+
value: 37.536
|
594 |
+
- type: mrr_at_10
|
595 |
+
value: 47.643
|
596 |
+
- type: mrr_at_100
|
597 |
+
value: 48.508
|
598 |
+
- type: mrr_at_1000
|
599 |
+
value: 48.551
|
600 |
+
- type: mrr_at_3
|
601 |
+
value: 45.348
|
602 |
+
- type: mrr_at_5
|
603 |
+
value: 46.744
|
604 |
+
- type: ndcg_at_1
|
605 |
+
value: 37.536
|
606 |
+
- type: ndcg_at_10
|
607 |
+
value: 47.823
|
608 |
+
- type: ndcg_at_100
|
609 |
+
value: 53.395
|
610 |
+
- type: ndcg_at_1000
|
611 |
+
value: 55.271
|
612 |
+
- type: ndcg_at_3
|
613 |
+
value: 42.768
|
614 |
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- type: ndcg_at_5
|
615 |
+
value: 45.373000000000005
|
616 |
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- type: precision_at_1
|
617 |
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value: 37.536
|
618 |
+
- type: precision_at_10
|
619 |
+
value: 8.681
|
620 |
+
- type: precision_at_100
|
621 |
+
value: 1.34
|
622 |
+
- type: precision_at_1000
|
623 |
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value: 0.165
|
624 |
+
- type: precision_at_3
|
625 |
+
value: 20.468
|
626 |
+
- type: precision_at_5
|
627 |
+
value: 14.495
|
628 |
+
- type: recall_at_1
|
629 |
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value: 30.473
|
630 |
+
- type: recall_at_10
|
631 |
+
value: 60.092999999999996
|
632 |
+
- type: recall_at_100
|
633 |
+
value: 82.733
|
634 |
+
- type: recall_at_1000
|
635 |
+
value: 94.875
|
636 |
+
- type: recall_at_3
|
637 |
+
value: 45.734
|
638 |
+
- type: recall_at_5
|
639 |
+
value: 52.691
|
640 |
+
- task:
|
641 |
+
type: Retrieval
|
642 |
+
dataset:
|
643 |
+
type: BeIR/cqadupstack
|
644 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
645 |
+
config: default
|
646 |
+
split: test
|
647 |
+
revision: None
|
648 |
+
metrics:
|
649 |
+
- type: map_at_1
|
650 |
+
value: 29.976000000000003
|
651 |
+
- type: map_at_10
|
652 |
+
value: 41.097
|
653 |
+
- type: map_at_100
|
654 |
+
value: 42.547000000000004
|
655 |
+
- type: map_at_1000
|
656 |
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value: 42.659000000000006
|
657 |
+
- type: map_at_3
|
658 |
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value: 37.251
|
659 |
+
- type: map_at_5
|
660 |
+
value: 39.493
|
661 |
+
- type: mrr_at_1
|
662 |
+
value: 37.557
|
663 |
+
- type: mrr_at_10
|
664 |
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value: 46.605000000000004
|
665 |
+
- type: mrr_at_100
|
666 |
+
value: 47.487
|
667 |
+
- type: mrr_at_1000
|
668 |
+
value: 47.54
|
669 |
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- type: mrr_at_3
|
670 |
+
value: 43.721
|
671 |
+
- type: mrr_at_5
|
672 |
+
value: 45.411
|
673 |
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- type: ndcg_at_1
|
674 |
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value: 37.557
|
675 |
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- type: ndcg_at_10
|
676 |
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value: 47.449000000000005
|
677 |
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- type: ndcg_at_100
|
678 |
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value: 53.052
|
679 |
+
- type: ndcg_at_1000
|
680 |
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value: 55.010999999999996
|
681 |
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- type: ndcg_at_3
|
682 |
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value: 41.439
|
683 |
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- type: ndcg_at_5
|
684 |
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value: 44.292
|
685 |
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- type: precision_at_1
|
686 |
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value: 37.557
|
687 |
+
- type: precision_at_10
|
688 |
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value: 8.847
|
689 |
+
- type: precision_at_100
|
690 |
+
value: 1.357
|
691 |
+
- type: precision_at_1000
|
692 |
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value: 0.16999999999999998
|
693 |
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- type: precision_at_3
|
694 |
+
value: 20.091
|
695 |
+
- type: precision_at_5
|
696 |
+
value: 14.384
|
697 |
+
- type: recall_at_1
|
698 |
+
value: 29.976000000000003
|
699 |
+
- type: recall_at_10
|
700 |
+
value: 60.99099999999999
|
701 |
+
- type: recall_at_100
|
702 |
+
value: 84.245
|
703 |
+
- type: recall_at_1000
|
704 |
+
value: 96.97200000000001
|
705 |
+
- type: recall_at_3
|
706 |
+
value: 43.794
|
707 |
+
- type: recall_at_5
|
708 |
+
value: 51.778999999999996
|
709 |
+
- task:
|
710 |
+
type: Retrieval
|
711 |
+
dataset:
|
712 |
+
type: BeIR/cqadupstack
|
713 |
+
name: MTEB CQADupstackRetrieval
|
714 |
+
config: default
|
715 |
+
split: test
|
716 |
+
revision: None
|
717 |
+
metrics:
|
718 |
+
- type: map_at_1
|
719 |
+
value: 28.099166666666665
|
720 |
+
- type: map_at_10
|
721 |
+
value: 38.1365
|
722 |
+
- type: map_at_100
|
723 |
+
value: 39.44491666666667
|
724 |
+
- type: map_at_1000
|
725 |
+
value: 39.55858333333334
|
726 |
+
- type: map_at_3
|
727 |
+
value: 35.03641666666666
|
728 |
+
- type: map_at_5
|
729 |
+
value: 36.79833333333334
|
730 |
+
- type: mrr_at_1
|
731 |
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value: 33.39966666666667
|
732 |
+
- type: mrr_at_10
|
733 |
+
value: 42.42583333333333
|
734 |
+
- type: mrr_at_100
|
735 |
+
value: 43.28575
|
736 |
+
- type: mrr_at_1000
|
737 |
+
value: 43.33741666666667
|
738 |
+
- type: mrr_at_3
|
739 |
+
value: 39.94975
|
740 |
+
- type: mrr_at_5
|
741 |
+
value: 41.41633333333334
|
742 |
+
- type: ndcg_at_1
|
743 |
+
value: 33.39966666666667
|
744 |
+
- type: ndcg_at_10
|
745 |
+
value: 43.81741666666667
|
746 |
+
- type: ndcg_at_100
|
747 |
+
value: 49.08166666666667
|
748 |
+
- type: ndcg_at_1000
|
749 |
+
value: 51.121166666666674
|
750 |
+
- type: ndcg_at_3
|
751 |
+
value: 38.73575
|
752 |
+
- type: ndcg_at_5
|
753 |
+
value: 41.18158333333333
|
754 |
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- type: precision_at_1
|
755 |
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value: 33.39966666666667
|
756 |
+
- type: precision_at_10
|
757 |
+
value: 7.738916666666667
|
758 |
+
- type: precision_at_100
|
759 |
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value: 1.2265833333333331
|
760 |
+
- type: precision_at_1000
|
761 |
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value: 0.15983333333333336
|
762 |
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- type: precision_at_3
|
763 |
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value: 17.967416666666665
|
764 |
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- type: precision_at_5
|
765 |
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value: 12.78675
|
766 |
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- type: recall_at_1
|
767 |
+
value: 28.099166666666665
|
768 |
+
- type: recall_at_10
|
769 |
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value: 56.27049999999999
|
770 |
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- type: recall_at_100
|
771 |
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value: 78.93291666666667
|
772 |
+
- type: recall_at_1000
|
773 |
+
value: 92.81608333333334
|
774 |
+
- type: recall_at_3
|
775 |
+
value: 42.09775
|
776 |
+
- type: recall_at_5
|
777 |
+
value: 48.42533333333334
|
778 |
+
- task:
|
779 |
+
type: Retrieval
|
780 |
+
dataset:
|
781 |
+
type: BeIR/cqadupstack
|
782 |
+
name: MTEB CQADupstackStatsRetrieval
|
783 |
+
config: default
|
784 |
+
split: test
|
785 |
+
revision: None
|
786 |
+
metrics:
|
787 |
+
- type: map_at_1
|
788 |
+
value: 23.663
|
789 |
+
- type: map_at_10
|
790 |
+
value: 30.377
|
791 |
+
- type: map_at_100
|
792 |
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value: 31.426
|
793 |
+
- type: map_at_1000
|
794 |
+
value: 31.519000000000002
|
795 |
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- type: map_at_3
|
796 |
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value: 28.069
|
797 |
+
- type: map_at_5
|
798 |
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value: 29.256999999999998
|
799 |
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- type: mrr_at_1
|
800 |
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value: 26.687
|
801 |
+
- type: mrr_at_10
|
802 |
+
value: 33.107
|
803 |
+
- type: mrr_at_100
|
804 |
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value: 34.055
|
805 |
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- type: mrr_at_1000
|
806 |
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value: 34.117999999999995
|
807 |
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- type: mrr_at_3
|
808 |
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value: 31.058000000000003
|
809 |
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- type: mrr_at_5
|
810 |
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value: 32.14
|
811 |
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- type: ndcg_at_1
|
812 |
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value: 26.687
|
813 |
+
- type: ndcg_at_10
|
814 |
+
value: 34.615
|
815 |
+
- type: ndcg_at_100
|
816 |
+
value: 39.776
|
817 |
+
- type: ndcg_at_1000
|
818 |
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value: 42.05
|
819 |
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- type: ndcg_at_3
|
820 |
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value: 30.322
|
821 |
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- type: ndcg_at_5
|
822 |
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value: 32.157000000000004
|
823 |
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- type: precision_at_1
|
824 |
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value: 26.687
|
825 |
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- type: precision_at_10
|
826 |
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value: 5.491
|
827 |
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- type: precision_at_100
|
828 |
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value: 0.877
|
829 |
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- type: precision_at_1000
|
830 |
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value: 0.11499999999999999
|
831 |
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- type: precision_at_3
|
832 |
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value: 13.139000000000001
|
833 |
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- type: precision_at_5
|
834 |
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value: 9.049
|
835 |
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- type: recall_at_1
|
836 |
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value: 23.663
|
837 |
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- type: recall_at_10
|
838 |
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value: 45.035
|
839 |
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- type: recall_at_100
|
840 |
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value: 68.554
|
841 |
+
- type: recall_at_1000
|
842 |
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value: 85.077
|
843 |
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- type: recall_at_3
|
844 |
+
value: 32.982
|
845 |
+
- type: recall_at_5
|
846 |
+
value: 37.688
|
847 |
+
- task:
|
848 |
+
type: Retrieval
|
849 |
+
dataset:
|
850 |
+
type: BeIR/cqadupstack
|
851 |
+
name: MTEB CQADupstackTexRetrieval
|
852 |
+
config: default
|
853 |
+
split: test
|
854 |
+
revision: None
|
855 |
+
metrics:
|
856 |
+
- type: map_at_1
|
857 |
+
value: 17.403
|
858 |
+
- type: map_at_10
|
859 |
+
value: 25.197000000000003
|
860 |
+
- type: map_at_100
|
861 |
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value: 26.355
|
862 |
+
- type: map_at_1000
|
863 |
+
value: 26.487
|
864 |
+
- type: map_at_3
|
865 |
+
value: 22.733
|
866 |
+
- type: map_at_5
|
867 |
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value: 24.114
|
868 |
+
- type: mrr_at_1
|
869 |
+
value: 21.37
|
870 |
+
- type: mrr_at_10
|
871 |
+
value: 29.091
|
872 |
+
- type: mrr_at_100
|
873 |
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value: 30.018
|
874 |
+
- type: mrr_at_1000
|
875 |
+
value: 30.096
|
876 |
+
- type: mrr_at_3
|
877 |
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value: 26.887
|
878 |
+
- type: mrr_at_5
|
879 |
+
value: 28.157
|
880 |
+
- type: ndcg_at_1
|
881 |
+
value: 21.37
|
882 |
+
- type: ndcg_at_10
|
883 |
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value: 30.026000000000003
|
884 |
+
- type: ndcg_at_100
|
885 |
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value: 35.416
|
886 |
+
- type: ndcg_at_1000
|
887 |
+
value: 38.45
|
888 |
+
- type: ndcg_at_3
|
889 |
+
value: 25.764
|
890 |
+
- type: ndcg_at_5
|
891 |
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value: 27.742
|
892 |
+
- type: precision_at_1
|
893 |
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value: 21.37
|
894 |
+
- type: precision_at_10
|
895 |
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value: 5.609
|
896 |
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- type: precision_at_100
|
897 |
+
value: 0.9860000000000001
|
898 |
+
- type: precision_at_1000
|
899 |
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value: 0.14300000000000002
|
900 |
+
- type: precision_at_3
|
901 |
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value: 12.423
|
902 |
+
- type: precision_at_5
|
903 |
+
value: 9.009
|
904 |
+
- type: recall_at_1
|
905 |
+
value: 17.403
|
906 |
+
- type: recall_at_10
|
907 |
+
value: 40.573
|
908 |
+
- type: recall_at_100
|
909 |
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value: 64.818
|
910 |
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- type: recall_at_1000
|
911 |
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value: 86.53699999999999
|
912 |
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- type: recall_at_3
|
913 |
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value: 28.493000000000002
|
914 |
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- type: recall_at_5
|
915 |
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value: 33.660000000000004
|
916 |
+
- task:
|
917 |
+
type: Retrieval
|
918 |
+
dataset:
|
919 |
+
type: BeIR/cqadupstack
|
920 |
+
name: MTEB CQADupstackUnixRetrieval
|
921 |
+
config: default
|
922 |
+
split: test
|
923 |
+
revision: None
|
924 |
+
metrics:
|
925 |
+
- type: map_at_1
|
926 |
+
value: 28.639
|
927 |
+
- type: map_at_10
|
928 |
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value: 38.951
|
929 |
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|
930 |
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value: 40.238
|
931 |
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- type: map_at_1000
|
932 |
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value: 40.327
|
933 |
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|
934 |
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value: 35.842
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935 |
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|
936 |
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value: 37.617
|
937 |
+
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|
938 |
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value: 33.769
|
939 |
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|
940 |
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value: 43.088
|
941 |
+
- type: mrr_at_100
|
942 |
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value: 44.03
|
943 |
+
- type: mrr_at_1000
|
944 |
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value: 44.072
|
945 |
+
- type: mrr_at_3
|
946 |
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value: 40.656
|
947 |
+
- type: mrr_at_5
|
948 |
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value: 42.138999999999996
|
949 |
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- type: ndcg_at_1
|
950 |
+
value: 33.769
|
951 |
+
- type: ndcg_at_10
|
952 |
+
value: 44.676
|
953 |
+
- type: ndcg_at_100
|
954 |
+
value: 50.416000000000004
|
955 |
+
- type: ndcg_at_1000
|
956 |
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value: 52.227999999999994
|
957 |
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- type: ndcg_at_3
|
958 |
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value: 39.494
|
959 |
+
- type: ndcg_at_5
|
960 |
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value: 42.013
|
961 |
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- type: precision_at_1
|
962 |
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value: 33.769
|
963 |
+
- type: precision_at_10
|
964 |
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value: 7.668
|
965 |
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- type: precision_at_100
|
966 |
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value: 1.18
|
967 |
+
- type: precision_at_1000
|
968 |
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value: 0.145
|
969 |
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- type: precision_at_3
|
970 |
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value: 18.221
|
971 |
+
- type: precision_at_5
|
972 |
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value: 12.966
|
973 |
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- type: recall_at_1
|
974 |
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value: 28.639
|
975 |
+
- type: recall_at_10
|
976 |
+
value: 57.687999999999995
|
977 |
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- type: recall_at_100
|
978 |
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value: 82.541
|
979 |
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- type: recall_at_1000
|
980 |
+
value: 94.896
|
981 |
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- type: recall_at_3
|
982 |
+
value: 43.651
|
983 |
+
- type: recall_at_5
|
984 |
+
value: 49.925999999999995
|
985 |
+
- task:
|
986 |
+
type: Retrieval
|
987 |
+
dataset:
|
988 |
+
type: BeIR/cqadupstack
|
989 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
990 |
+
config: default
|
991 |
+
split: test
|
992 |
+
revision: None
|
993 |
+
metrics:
|
994 |
+
- type: map_at_1
|
995 |
+
value: 29.57
|
996 |
+
- type: map_at_10
|
997 |
+
value: 40.004
|
998 |
+
- type: map_at_100
|
999 |
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value: 41.75
|
1000 |
+
- type: map_at_1000
|
1001 |
+
value: 41.97
|
1002 |
+
- type: map_at_3
|
1003 |
+
value: 36.788
|
1004 |
+
- type: map_at_5
|
1005 |
+
value: 38.671
|
1006 |
+
- type: mrr_at_1
|
1007 |
+
value: 35.375
|
1008 |
+
- type: mrr_at_10
|
1009 |
+
value: 45.121
|
1010 |
+
- type: mrr_at_100
|
1011 |
+
value: 45.994
|
1012 |
+
- type: mrr_at_1000
|
1013 |
+
value: 46.04
|
1014 |
+
- type: mrr_at_3
|
1015 |
+
value: 42.227
|
1016 |
+
- type: mrr_at_5
|
1017 |
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value: 43.995
|
1018 |
+
- type: ndcg_at_1
|
1019 |
+
value: 35.375
|
1020 |
+
- type: ndcg_at_10
|
1021 |
+
value: 46.392
|
1022 |
+
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|
1023 |
+
value: 52.196
|
1024 |
+
- type: ndcg_at_1000
|
1025 |
+
value: 54.274
|
1026 |
+
- type: ndcg_at_3
|
1027 |
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value: 41.163
|
1028 |
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|
1029 |
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value: 43.813
|
1030 |
+
- type: precision_at_1
|
1031 |
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value: 35.375
|
1032 |
+
- type: precision_at_10
|
1033 |
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value: 8.676
|
1034 |
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- type: precision_at_100
|
1035 |
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value: 1.678
|
1036 |
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- type: precision_at_1000
|
1037 |
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value: 0.253
|
1038 |
+
- type: precision_at_3
|
1039 |
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value: 19.104
|
1040 |
+
- type: precision_at_5
|
1041 |
+
value: 13.913
|
1042 |
+
- type: recall_at_1
|
1043 |
+
value: 29.57
|
1044 |
+
- type: recall_at_10
|
1045 |
+
value: 58.779
|
1046 |
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- type: recall_at_100
|
1047 |
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value: 83.337
|
1048 |
+
- type: recall_at_1000
|
1049 |
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value: 95.979
|
1050 |
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- type: recall_at_3
|
1051 |
+
value: 44.005
|
1052 |
+
- type: recall_at_5
|
1053 |
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value: 50.975
|
1054 |
+
- task:
|
1055 |
+
type: Retrieval
|
1056 |
+
dataset:
|
1057 |
+
type: BeIR/cqadupstack
|
1058 |
+
name: MTEB CQADupstackWordpressRetrieval
|
1059 |
+
config: default
|
1060 |
+
split: test
|
1061 |
+
revision: None
|
1062 |
+
metrics:
|
1063 |
+
- type: map_at_1
|
1064 |
+
value: 20.832
|
1065 |
+
- type: map_at_10
|
1066 |
+
value: 29.733999999999998
|
1067 |
+
- type: map_at_100
|
1068 |
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value: 30.727
|
1069 |
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- type: map_at_1000
|
1070 |
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value: 30.843999999999998
|
1071 |
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- type: map_at_3
|
1072 |
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value: 26.834999999999997
|
1073 |
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|
1074 |
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value: 28.555999999999997
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1075 |
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- type: mrr_at_1
|
1076 |
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value: 22.921
|
1077 |
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|
1078 |
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value: 31.791999999999998
|
1079 |
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- type: mrr_at_100
|
1080 |
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value: 32.666000000000004
|
1081 |
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- type: mrr_at_1000
|
1082 |
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value: 32.751999999999995
|
1083 |
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- type: mrr_at_3
|
1084 |
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value: 29.144
|
1085 |
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- type: mrr_at_5
|
1086 |
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value: 30.622
|
1087 |
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- type: ndcg_at_1
|
1088 |
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value: 22.921
|
1089 |
+
- type: ndcg_at_10
|
1090 |
+
value: 34.915
|
1091 |
+
- type: ndcg_at_100
|
1092 |
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value: 39.744
|
1093 |
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- type: ndcg_at_1000
|
1094 |
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value: 42.407000000000004
|
1095 |
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- type: ndcg_at_3
|
1096 |
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value: 29.421000000000003
|
1097 |
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- type: ndcg_at_5
|
1098 |
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value: 32.211
|
1099 |
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- type: precision_at_1
|
1100 |
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value: 22.921
|
1101 |
+
- type: precision_at_10
|
1102 |
+
value: 5.675
|
1103 |
+
- type: precision_at_100
|
1104 |
+
value: 0.872
|
1105 |
+
- type: precision_at_1000
|
1106 |
+
value: 0.121
|
1107 |
+
- type: precision_at_3
|
1108 |
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value: 12.753999999999998
|
1109 |
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- type: precision_at_5
|
1110 |
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value: 9.353
|
1111 |
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- type: recall_at_1
|
1112 |
+
value: 20.832
|
1113 |
+
- type: recall_at_10
|
1114 |
+
value: 48.795
|
1115 |
+
- type: recall_at_100
|
1116 |
+
value: 70.703
|
1117 |
+
- type: recall_at_1000
|
1118 |
+
value: 90.187
|
1119 |
+
- type: recall_at_3
|
1120 |
+
value: 34.455000000000005
|
1121 |
+
- type: recall_at_5
|
1122 |
+
value: 40.967
|
1123 |
+
- task:
|
1124 |
+
type: Retrieval
|
1125 |
+
dataset:
|
1126 |
+
type: climate-fever
|
1127 |
+
name: MTEB ClimateFEVER
|
1128 |
+
config: default
|
1129 |
+
split: test
|
1130 |
+
revision: None
|
1131 |
+
metrics:
|
1132 |
+
- type: map_at_1
|
1133 |
+
value: 10.334
|
1134 |
+
- type: map_at_10
|
1135 |
+
value: 19.009999999999998
|
1136 |
+
- type: map_at_100
|
1137 |
+
value: 21.129
|
1138 |
+
- type: map_at_1000
|
1139 |
+
value: 21.328
|
1140 |
+
- type: map_at_3
|
1141 |
+
value: 15.152
|
1142 |
+
- type: map_at_5
|
1143 |
+
value: 17.084
|
1144 |
+
- type: mrr_at_1
|
1145 |
+
value: 23.453
|
1146 |
+
- type: mrr_at_10
|
1147 |
+
value: 36.099
|
1148 |
+
- type: mrr_at_100
|
1149 |
+
value: 37.069
|
1150 |
+
- type: mrr_at_1000
|
1151 |
+
value: 37.104
|
1152 |
+
- type: mrr_at_3
|
1153 |
+
value: 32.096000000000004
|
1154 |
+
- type: mrr_at_5
|
1155 |
+
value: 34.451
|
1156 |
+
- type: ndcg_at_1
|
1157 |
+
value: 23.453
|
1158 |
+
- type: ndcg_at_10
|
1159 |
+
value: 27.739000000000004
|
1160 |
+
- type: ndcg_at_100
|
1161 |
+
value: 35.836
|
1162 |
+
- type: ndcg_at_1000
|
1163 |
+
value: 39.242
|
1164 |
+
- type: ndcg_at_3
|
1165 |
+
value: 21.263
|
1166 |
+
- type: ndcg_at_5
|
1167 |
+
value: 23.677
|
1168 |
+
- type: precision_at_1
|
1169 |
+
value: 23.453
|
1170 |
+
- type: precision_at_10
|
1171 |
+
value: 9.199
|
1172 |
+
- type: precision_at_100
|
1173 |
+
value: 1.791
|
1174 |
+
- type: precision_at_1000
|
1175 |
+
value: 0.242
|
1176 |
+
- type: precision_at_3
|
1177 |
+
value: 16.2
|
1178 |
+
- type: precision_at_5
|
1179 |
+
value: 13.147
|
1180 |
+
- type: recall_at_1
|
1181 |
+
value: 10.334
|
1182 |
+
- type: recall_at_10
|
1183 |
+
value: 35.177
|
1184 |
+
- type: recall_at_100
|
1185 |
+
value: 63.009
|
1186 |
+
- type: recall_at_1000
|
1187 |
+
value: 81.938
|
1188 |
+
- type: recall_at_3
|
1189 |
+
value: 19.914
|
1190 |
+
- type: recall_at_5
|
1191 |
+
value: 26.077
|
1192 |
+
- task:
|
1193 |
+
type: Retrieval
|
1194 |
+
dataset:
|
1195 |
+
type: dbpedia-entity
|
1196 |
+
name: MTEB DBPedia
|
1197 |
+
config: default
|
1198 |
+
split: test
|
1199 |
+
revision: None
|
1200 |
+
metrics:
|
1201 |
+
- type: map_at_1
|
1202 |
+
value: 8.212
|
1203 |
+
- type: map_at_10
|
1204 |
+
value: 17.386
|
1205 |
+
- type: map_at_100
|
1206 |
+
value: 24.234
|
1207 |
+
- type: map_at_1000
|
1208 |
+
value: 25.724999999999998
|
1209 |
+
- type: map_at_3
|
1210 |
+
value: 12.727
|
1211 |
+
- type: map_at_5
|
1212 |
+
value: 14.785
|
1213 |
+
- type: mrr_at_1
|
1214 |
+
value: 59.25
|
1215 |
+
- type: mrr_at_10
|
1216 |
+
value: 68.687
|
1217 |
+
- type: mrr_at_100
|
1218 |
+
value: 69.133
|
1219 |
+
- type: mrr_at_1000
|
1220 |
+
value: 69.14099999999999
|
1221 |
+
- type: mrr_at_3
|
1222 |
+
value: 66.917
|
1223 |
+
- type: mrr_at_5
|
1224 |
+
value: 67.742
|
1225 |
+
- type: ndcg_at_1
|
1226 |
+
value: 48.625
|
1227 |
+
- type: ndcg_at_10
|
1228 |
+
value: 36.675999999999995
|
1229 |
+
- type: ndcg_at_100
|
1230 |
+
value: 41.543
|
1231 |
+
- type: ndcg_at_1000
|
1232 |
+
value: 49.241
|
1233 |
+
- type: ndcg_at_3
|
1234 |
+
value: 41.373
|
1235 |
+
- type: ndcg_at_5
|
1236 |
+
value: 38.707
|
1237 |
+
- type: precision_at_1
|
1238 |
+
value: 59.25
|
1239 |
+
- type: precision_at_10
|
1240 |
+
value: 28.525
|
1241 |
+
- type: precision_at_100
|
1242 |
+
value: 9.027000000000001
|
1243 |
+
- type: precision_at_1000
|
1244 |
+
value: 1.8339999999999999
|
1245 |
+
- type: precision_at_3
|
1246 |
+
value: 44.833
|
1247 |
+
- type: precision_at_5
|
1248 |
+
value: 37.35
|
1249 |
+
- type: recall_at_1
|
1250 |
+
value: 8.212
|
1251 |
+
- type: recall_at_10
|
1252 |
+
value: 23.188
|
1253 |
+
- type: recall_at_100
|
1254 |
+
value: 48.613
|
1255 |
+
- type: recall_at_1000
|
1256 |
+
value: 73.093
|
1257 |
+
- type: recall_at_3
|
1258 |
+
value: 14.419
|
1259 |
+
- type: recall_at_5
|
1260 |
+
value: 17.798
|
1261 |
+
- task:
|
1262 |
+
type: Classification
|
1263 |
+
dataset:
|
1264 |
+
type: mteb/emotion
|
1265 |
+
name: MTEB EmotionClassification
|
1266 |
+
config: default
|
1267 |
+
split: test
|
1268 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
1269 |
+
metrics:
|
1270 |
+
- type: accuracy
|
1271 |
+
value: 52.725
|
1272 |
+
- type: f1
|
1273 |
+
value: 46.50743309855908
|
1274 |
+
- task:
|
1275 |
+
type: Retrieval
|
1276 |
+
dataset:
|
1277 |
+
type: fever
|
1278 |
+
name: MTEB FEVER
|
1279 |
+
config: default
|
1280 |
+
split: test
|
1281 |
+
revision: None
|
1282 |
+
metrics:
|
1283 |
+
- type: map_at_1
|
1284 |
+
value: 55.086
|
1285 |
+
- type: map_at_10
|
1286 |
+
value: 66.914
|
1287 |
+
- type: map_at_100
|
1288 |
+
value: 67.321
|
1289 |
+
- type: map_at_1000
|
1290 |
+
value: 67.341
|
1291 |
+
- type: map_at_3
|
1292 |
+
value: 64.75800000000001
|
1293 |
+
- type: map_at_5
|
1294 |
+
value: 66.189
|
1295 |
+
- type: mrr_at_1
|
1296 |
+
value: 59.28600000000001
|
1297 |
+
- type: mrr_at_10
|
1298 |
+
value: 71.005
|
1299 |
+
- type: mrr_at_100
|
1300 |
+
value: 71.304
|
1301 |
+
- type: mrr_at_1000
|
1302 |
+
value: 71.313
|
1303 |
+
- type: mrr_at_3
|
1304 |
+
value: 69.037
|
1305 |
+
- type: mrr_at_5
|
1306 |
+
value: 70.35
|
1307 |
+
- type: ndcg_at_1
|
1308 |
+
value: 59.28600000000001
|
1309 |
+
- type: ndcg_at_10
|
1310 |
+
value: 72.695
|
1311 |
+
- type: ndcg_at_100
|
1312 |
+
value: 74.432
|
1313 |
+
- type: ndcg_at_1000
|
1314 |
+
value: 74.868
|
1315 |
+
- type: ndcg_at_3
|
1316 |
+
value: 68.72200000000001
|
1317 |
+
- type: ndcg_at_5
|
1318 |
+
value: 71.081
|
1319 |
+
- type: precision_at_1
|
1320 |
+
value: 59.28600000000001
|
1321 |
+
- type: precision_at_10
|
1322 |
+
value: 9.499
|
1323 |
+
- type: precision_at_100
|
1324 |
+
value: 1.052
|
1325 |
+
- type: precision_at_1000
|
1326 |
+
value: 0.11100000000000002
|
1327 |
+
- type: precision_at_3
|
1328 |
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value: 27.503
|
1329 |
+
- type: precision_at_5
|
1330 |
+
value: 17.854999999999997
|
1331 |
+
- type: recall_at_1
|
1332 |
+
value: 55.086
|
1333 |
+
- type: recall_at_10
|
1334 |
+
value: 86.453
|
1335 |
+
- type: recall_at_100
|
1336 |
+
value: 94.028
|
1337 |
+
- type: recall_at_1000
|
1338 |
+
value: 97.052
|
1339 |
+
- type: recall_at_3
|
1340 |
+
value: 75.821
|
1341 |
+
- type: recall_at_5
|
1342 |
+
value: 81.6
|
1343 |
+
- task:
|
1344 |
+
type: Retrieval
|
1345 |
+
dataset:
|
1346 |
+
type: fiqa
|
1347 |
+
name: MTEB FiQA2018
|
1348 |
+
config: default
|
1349 |
+
split: test
|
1350 |
+
revision: None
|
1351 |
+
metrics:
|
1352 |
+
- type: map_at_1
|
1353 |
+
value: 22.262999999999998
|
1354 |
+
- type: map_at_10
|
1355 |
+
value: 37.488
|
1356 |
+
- type: map_at_100
|
1357 |
+
value: 39.498
|
1358 |
+
- type: map_at_1000
|
1359 |
+
value: 39.687
|
1360 |
+
- type: map_at_3
|
1361 |
+
value: 32.529
|
1362 |
+
- type: map_at_5
|
1363 |
+
value: 35.455
|
1364 |
+
- type: mrr_at_1
|
1365 |
+
value: 44.907000000000004
|
1366 |
+
- type: mrr_at_10
|
1367 |
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value: 53.239000000000004
|
1368 |
+
- type: mrr_at_100
|
1369 |
+
value: 54.086
|
1370 |
+
- type: mrr_at_1000
|
1371 |
+
value: 54.122
|
1372 |
+
- type: mrr_at_3
|
1373 |
+
value: 51.235
|
1374 |
+
- type: mrr_at_5
|
1375 |
+
value: 52.415
|
1376 |
+
- type: ndcg_at_1
|
1377 |
+
value: 44.907000000000004
|
1378 |
+
- type: ndcg_at_10
|
1379 |
+
value: 45.446
|
1380 |
+
- type: ndcg_at_100
|
1381 |
+
value: 52.429
|
1382 |
+
- type: ndcg_at_1000
|
1383 |
+
value: 55.169000000000004
|
1384 |
+
- type: ndcg_at_3
|
1385 |
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value: 41.882000000000005
|
1386 |
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- type: ndcg_at_5
|
1387 |
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value: 43.178
|
1388 |
+
- type: precision_at_1
|
1389 |
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value: 44.907000000000004
|
1390 |
+
- type: precision_at_10
|
1391 |
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value: 12.931999999999999
|
1392 |
+
- type: precision_at_100
|
1393 |
+
value: 2.025
|
1394 |
+
- type: precision_at_1000
|
1395 |
+
value: 0.248
|
1396 |
+
- type: precision_at_3
|
1397 |
+
value: 28.652
|
1398 |
+
- type: precision_at_5
|
1399 |
+
value: 21.204
|
1400 |
+
- type: recall_at_1
|
1401 |
+
value: 22.262999999999998
|
1402 |
+
- type: recall_at_10
|
1403 |
+
value: 52.447
|
1404 |
+
- type: recall_at_100
|
1405 |
+
value: 78.045
|
1406 |
+
- type: recall_at_1000
|
1407 |
+
value: 94.419
|
1408 |
+
- type: recall_at_3
|
1409 |
+
value: 38.064
|
1410 |
+
- type: recall_at_5
|
1411 |
+
value: 44.769
|
1412 |
+
- task:
|
1413 |
+
type: Retrieval
|
1414 |
+
dataset:
|
1415 |
+
type: hotpotqa
|
1416 |
+
name: MTEB HotpotQA
|
1417 |
+
config: default
|
1418 |
+
split: test
|
1419 |
+
revision: None
|
1420 |
+
metrics:
|
1421 |
+
- type: map_at_1
|
1422 |
+
value: 32.519
|
1423 |
+
- type: map_at_10
|
1424 |
+
value: 45.831
|
1425 |
+
- type: map_at_100
|
1426 |
+
value: 46.815
|
1427 |
+
- type: map_at_1000
|
1428 |
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value: 46.899
|
1429 |
+
- type: map_at_3
|
1430 |
+
value: 42.836
|
1431 |
+
- type: map_at_5
|
1432 |
+
value: 44.65
|
1433 |
+
- type: mrr_at_1
|
1434 |
+
value: 65.037
|
1435 |
+
- type: mrr_at_10
|
1436 |
+
value: 72.16
|
1437 |
+
- type: mrr_at_100
|
1438 |
+
value: 72.51100000000001
|
1439 |
+
- type: mrr_at_1000
|
1440 |
+
value: 72.53
|
1441 |
+
- type: mrr_at_3
|
1442 |
+
value: 70.682
|
1443 |
+
- type: mrr_at_5
|
1444 |
+
value: 71.54599999999999
|
1445 |
+
- type: ndcg_at_1
|
1446 |
+
value: 65.037
|
1447 |
+
- type: ndcg_at_10
|
1448 |
+
value: 55.17999999999999
|
1449 |
+
- type: ndcg_at_100
|
1450 |
+
value: 58.888
|
1451 |
+
- type: ndcg_at_1000
|
1452 |
+
value: 60.648
|
1453 |
+
- type: ndcg_at_3
|
1454 |
+
value: 50.501
|
1455 |
+
- type: ndcg_at_5
|
1456 |
+
value: 52.977
|
1457 |
+
- type: precision_at_1
|
1458 |
+
value: 65.037
|
1459 |
+
- type: precision_at_10
|
1460 |
+
value: 11.530999999999999
|
1461 |
+
- type: precision_at_100
|
1462 |
+
value: 1.4460000000000002
|
1463 |
+
- type: precision_at_1000
|
1464 |
+
value: 0.168
|
1465 |
+
- type: precision_at_3
|
1466 |
+
value: 31.483
|
1467 |
+
- type: precision_at_5
|
1468 |
+
value: 20.845
|
1469 |
+
- type: recall_at_1
|
1470 |
+
value: 32.519
|
1471 |
+
- type: recall_at_10
|
1472 |
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value: 57.657000000000004
|
1473 |
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- type: recall_at_100
|
1474 |
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value: 72.30199999999999
|
1475 |
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- type: recall_at_1000
|
1476 |
+
value: 84.024
|
1477 |
+
- type: recall_at_3
|
1478 |
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value: 47.225
|
1479 |
+
- type: recall_at_5
|
1480 |
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value: 52.113
|
1481 |
+
- task:
|
1482 |
+
type: Classification
|
1483 |
+
dataset:
|
1484 |
+
type: mteb/imdb
|
1485 |
+
name: MTEB ImdbClassification
|
1486 |
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config: default
|
1487 |
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split: test
|
1488 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
1489 |
+
metrics:
|
1490 |
+
- type: accuracy
|
1491 |
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value: 88.3168
|
1492 |
+
- type: ap
|
1493 |
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value: 83.80165516037135
|
1494 |
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- type: f1
|
1495 |
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value: 88.29942471066407
|
1496 |
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- task:
|
1497 |
+
type: Retrieval
|
1498 |
+
dataset:
|
1499 |
+
type: msmarco
|
1500 |
+
name: MTEB MSMARCO
|
1501 |
+
config: default
|
1502 |
+
split: dev
|
1503 |
+
revision: None
|
1504 |
+
metrics:
|
1505 |
+
- type: map_at_1
|
1506 |
+
value: 20.724999999999998
|
1507 |
+
- type: map_at_10
|
1508 |
+
value: 32.736
|
1509 |
+
- type: map_at_100
|
1510 |
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value: 33.938
|
1511 |
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- type: map_at_1000
|
1512 |
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value: 33.991
|
1513 |
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- type: map_at_3
|
1514 |
+
value: 28.788000000000004
|
1515 |
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- type: map_at_5
|
1516 |
+
value: 31.016
|
1517 |
+
- type: mrr_at_1
|
1518 |
+
value: 21.361
|
1519 |
+
- type: mrr_at_10
|
1520 |
+
value: 33.323
|
1521 |
+
- type: mrr_at_100
|
1522 |
+
value: 34.471000000000004
|
1523 |
+
- type: mrr_at_1000
|
1524 |
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value: 34.518
|
1525 |
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- type: mrr_at_3
|
1526 |
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value: 29.453000000000003
|
1527 |
+
- type: mrr_at_5
|
1528 |
+
value: 31.629
|
1529 |
+
- type: ndcg_at_1
|
1530 |
+
value: 21.361
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1662 |
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- task:
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1724 |
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name: MTEB NQ
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1728 |
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revision: None
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1730 |
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1731 |
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value: 27.378000000000004
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1732 |
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1795 |
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metrics:
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1800 |
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1861 |
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1862 |
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1891 |
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1930 |
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value: 2.16
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1934 |
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1935 |
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1936 |
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1937 |
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1938 |
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1939 |
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value: 4.263
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1940 |
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1941 |
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value: 19.922
|
1942 |
+
- type: recall_at_100
|
1943 |
+
value: 43.808
|
1944 |
+
- type: recall_at_1000
|
1945 |
+
value: 72.14500000000001
|
1946 |
+
- type: recall_at_3
|
1947 |
+
value: 9.493
|
1948 |
+
- type: recall_at_5
|
1949 |
+
value: 13.767999999999999
|
1950 |
+
- task:
|
1951 |
+
type: STS
|
1952 |
+
dataset:
|
1953 |
+
type: mteb/sickr-sts
|
1954 |
+
name: MTEB SICK-R
|
1955 |
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config: default
|
1956 |
+
split: test
|
1957 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1958 |
+
metrics:
|
1959 |
+
- type: cos_sim_spearman
|
1960 |
+
value: 81.27446313317233
|
1961 |
+
- task:
|
1962 |
+
type: STS
|
1963 |
+
dataset:
|
1964 |
+
type: mteb/sts12-sts
|
1965 |
+
name: MTEB STS12
|
1966 |
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config: default
|
1967 |
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split: test
|
1968 |
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revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1969 |
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metrics:
|
1970 |
+
- type: cos_sim_spearman
|
1971 |
+
value: 76.27963301217527
|
1972 |
+
- task:
|
1973 |
+
type: STS
|
1974 |
+
dataset:
|
1975 |
+
type: mteb/sts13-sts
|
1976 |
+
name: MTEB STS13
|
1977 |
+
config: default
|
1978 |
+
split: test
|
1979 |
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1980 |
+
metrics:
|
1981 |
+
- type: cos_sim_spearman
|
1982 |
+
value: 88.18495048450949
|
1983 |
+
- task:
|
1984 |
+
type: STS
|
1985 |
+
dataset:
|
1986 |
+
type: mteb/sts14-sts
|
1987 |
+
name: MTEB STS14
|
1988 |
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config: default
|
1989 |
+
split: test
|
1990 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
1991 |
+
metrics:
|
1992 |
+
- type: cos_sim_spearman
|
1993 |
+
value: 81.91982338692046
|
1994 |
+
- task:
|
1995 |
+
type: STS
|
1996 |
+
dataset:
|
1997 |
+
type: mteb/sts15-sts
|
1998 |
+
name: MTEB STS15
|
1999 |
+
config: default
|
2000 |
+
split: test
|
2001 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
2002 |
+
metrics:
|
2003 |
+
- type: cos_sim_spearman
|
2004 |
+
value: 89.00896818385291
|
2005 |
+
- task:
|
2006 |
+
type: STS
|
2007 |
+
dataset:
|
2008 |
+
type: mteb/sts16-sts
|
2009 |
+
name: MTEB STS16
|
2010 |
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config: default
|
2011 |
+
split: test
|
2012 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
2013 |
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metrics:
|
2014 |
+
- type: cos_sim_spearman
|
2015 |
+
value: 85.48814644586132
|
2016 |
+
- task:
|
2017 |
+
type: STS
|
2018 |
+
dataset:
|
2019 |
+
type: mteb/sts17-crosslingual-sts
|
2020 |
+
name: MTEB STS17 (en-en)
|
2021 |
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config: en-en
|
2022 |
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split: test
|
2023 |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
2024 |
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metrics:
|
2025 |
+
- type: cos_sim_spearman
|
2026 |
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value: 90.30116926966582
|
2027 |
+
- task:
|
2028 |
+
type: STS
|
2029 |
+
dataset:
|
2030 |
+
type: mteb/sts22-crosslingual-sts
|
2031 |
+
name: MTEB STS22 (en)
|
2032 |
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config: en
|
2033 |
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split: test
|
2034 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
2035 |
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metrics:
|
2036 |
+
- type: cos_sim_spearman
|
2037 |
+
value: 67.74132963032342
|
2038 |
+
- task:
|
2039 |
+
type: STS
|
2040 |
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dataset:
|
2041 |
+
type: mteb/stsbenchmark-sts
|
2042 |
+
name: MTEB STSBenchmark
|
2043 |
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config: default
|
2044 |
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split: test
|
2045 |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2046 |
+
metrics:
|
2047 |
+
- type: cos_sim_spearman
|
2048 |
+
value: 86.87741355780479
|
2049 |
+
- task:
|
2050 |
+
type: Reranking
|
2051 |
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dataset:
|
2052 |
+
type: mteb/scidocs-reranking
|
2053 |
+
name: MTEB SciDocsRR
|
2054 |
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config: default
|
2055 |
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split: test
|
2056 |
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revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2057 |
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metrics:
|
2058 |
+
- type: map
|
2059 |
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value: 82.0019012295875
|
2060 |
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- type: mrr
|
2061 |
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value: 94.70267024188593
|
2062 |
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- task:
|
2063 |
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type: Retrieval
|
2064 |
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dataset:
|
2065 |
+
type: scifact
|
2066 |
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name: MTEB SciFact
|
2067 |
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config: default
|
2068 |
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split: test
|
2069 |
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revision: None
|
2070 |
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metrics:
|
2071 |
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- type: map_at_1
|
2072 |
+
value: 50.05
|
2073 |
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- type: map_at_10
|
2074 |
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value: 59.36
|
2075 |
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- type: map_at_100
|
2076 |
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value: 59.967999999999996
|
2077 |
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- type: map_at_1000
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2078 |
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value: 60.023
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2079 |
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- type: map_at_3
|
2080 |
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value: 56.515
|
2081 |
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- type: map_at_5
|
2082 |
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value: 58.272999999999996
|
2083 |
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- type: mrr_at_1
|
2084 |
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value: 53
|
2085 |
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- type: mrr_at_10
|
2086 |
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value: 61.102000000000004
|
2087 |
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- type: mrr_at_100
|
2088 |
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value: 61.476
|
2089 |
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- type: mrr_at_1000
|
2090 |
+
value: 61.523
|
2091 |
+
- type: mrr_at_3
|
2092 |
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value: 58.778
|
2093 |
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- type: mrr_at_5
|
2094 |
+
value: 60.128
|
2095 |
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- type: ndcg_at_1
|
2096 |
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value: 53
|
2097 |
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- type: ndcg_at_10
|
2098 |
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value: 64.43100000000001
|
2099 |
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- type: ndcg_at_100
|
2100 |
+
value: 66.73599999999999
|
2101 |
+
- type: ndcg_at_1000
|
2102 |
+
value: 68.027
|
2103 |
+
- type: ndcg_at_3
|
2104 |
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value: 59.279
|
2105 |
+
- type: ndcg_at_5
|
2106 |
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value: 61.888
|
2107 |
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- type: precision_at_1
|
2108 |
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value: 53
|
2109 |
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- type: precision_at_10
|
2110 |
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value: 8.767
|
2111 |
+
- type: precision_at_100
|
2112 |
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value: 1.01
|
2113 |
+
- type: precision_at_1000
|
2114 |
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value: 0.11100000000000002
|
2115 |
+
- type: precision_at_3
|
2116 |
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value: 23.444000000000003
|
2117 |
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- type: precision_at_5
|
2118 |
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value: 15.667
|
2119 |
+
- type: recall_at_1
|
2120 |
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value: 50.05
|
2121 |
+
- type: recall_at_10
|
2122 |
+
value: 78.511
|
2123 |
+
- type: recall_at_100
|
2124 |
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value: 88.5
|
2125 |
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- type: recall_at_1000
|
2126 |
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value: 98.333
|
2127 |
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- type: recall_at_3
|
2128 |
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value: 64.117
|
2129 |
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- type: recall_at_5
|
2130 |
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value: 70.867
|
2131 |
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- task:
|
2132 |
+
type: PairClassification
|
2133 |
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dataset:
|
2134 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2135 |
+
name: MTEB SprintDuplicateQuestions
|
2136 |
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config: default
|
2137 |
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split: test
|
2138 |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2139 |
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metrics:
|
2140 |
+
- type: cos_sim_accuracy
|
2141 |
+
value: 99.72178217821782
|
2142 |
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- type: cos_sim_ap
|
2143 |
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value: 93.0728601593541
|
2144 |
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- type: cos_sim_f1
|
2145 |
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value: 85.6727976766699
|
2146 |
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- type: cos_sim_precision
|
2147 |
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value: 83.02063789868667
|
2148 |
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- type: cos_sim_recall
|
2149 |
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value: 88.5
|
2150 |
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- type: dot_accuracy
|
2151 |
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value: 99.72178217821782
|
2152 |
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- type: dot_ap
|
2153 |
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value: 93.07287396168348
|
2154 |
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- type: dot_f1
|
2155 |
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value: 85.6727976766699
|
2156 |
+
- type: dot_precision
|
2157 |
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value: 83.02063789868667
|
2158 |
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- type: dot_recall
|
2159 |
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value: 88.5
|
2160 |
+
- type: euclidean_accuracy
|
2161 |
+
value: 99.72178217821782
|
2162 |
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- type: euclidean_ap
|
2163 |
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value: 93.07285657982895
|
2164 |
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- type: euclidean_f1
|
2165 |
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value: 85.6727976766699
|
2166 |
+
- type: euclidean_precision
|
2167 |
+
value: 83.02063789868667
|
2168 |
+
- type: euclidean_recall
|
2169 |
+
value: 88.5
|
2170 |
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- type: manhattan_accuracy
|
2171 |
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value: 99.72475247524753
|
2172 |
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- type: manhattan_ap
|
2173 |
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value: 93.02792973059809
|
2174 |
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- type: manhattan_f1
|
2175 |
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value: 85.7727737973388
|
2176 |
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- type: manhattan_precision
|
2177 |
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value: 87.84067085953879
|
2178 |
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- type: manhattan_recall
|
2179 |
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value: 83.8
|
2180 |
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- type: max_accuracy
|
2181 |
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value: 99.72475247524753
|
2182 |
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- type: max_ap
|
2183 |
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value: 93.07287396168348
|
2184 |
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- type: max_f1
|
2185 |
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value: 85.7727737973388
|
2186 |
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- task:
|
2187 |
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type: Clustering
|
2188 |
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dataset:
|
2189 |
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type: mteb/stackexchange-clustering
|
2190 |
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name: MTEB StackExchangeClustering
|
2191 |
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config: default
|
2192 |
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split: test
|
2193 |
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2194 |
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metrics:
|
2195 |
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- type: v_measure
|
2196 |
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value: 68.77583615550819
|
2197 |
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- task:
|
2198 |
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type: Clustering
|
2199 |
+
dataset:
|
2200 |
+
type: mteb/stackexchange-clustering-p2p
|
2201 |
+
name: MTEB StackExchangeClusteringP2P
|
2202 |
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config: default
|
2203 |
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split: test
|
2204 |
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revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2205 |
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metrics:
|
2206 |
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- type: v_measure
|
2207 |
+
value: 36.151636938606956
|
2208 |
+
- task:
|
2209 |
+
type: Reranking
|
2210 |
+
dataset:
|
2211 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2212 |
+
name: MTEB StackOverflowDupQuestions
|
2213 |
+
config: default
|
2214 |
+
split: test
|
2215 |
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revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2216 |
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metrics:
|
2217 |
+
- type: map
|
2218 |
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value: 52.16607939471187
|
2219 |
+
- type: mrr
|
2220 |
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value: 52.95172046091163
|
2221 |
+
- task:
|
2222 |
+
type: Summarization
|
2223 |
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dataset:
|
2224 |
+
type: mteb/summeval
|
2225 |
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name: MTEB SummEval
|
2226 |
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config: default
|
2227 |
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split: test
|
2228 |
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2229 |
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metrics:
|
2230 |
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- type: cos_sim_pearson
|
2231 |
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value: 31.314646669495666
|
2232 |
+
- type: cos_sim_spearman
|
2233 |
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value: 31.83562491439455
|
2234 |
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- type: dot_pearson
|
2235 |
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value: 31.314590842874157
|
2236 |
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- type: dot_spearman
|
2237 |
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value: 31.83363065810437
|
2238 |
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- task:
|
2239 |
+
type: Retrieval
|
2240 |
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dataset:
|
2241 |
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type: trec-covid
|
2242 |
+
name: MTEB TRECCOVID
|
2243 |
+
config: default
|
2244 |
+
split: test
|
2245 |
+
revision: None
|
2246 |
+
metrics:
|
2247 |
+
- type: map_at_1
|
2248 |
+
value: 0.198
|
2249 |
+
- type: map_at_10
|
2250 |
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value: 1.3010000000000002
|
2251 |
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- type: map_at_100
|
2252 |
+
value: 7.2139999999999995
|
2253 |
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- type: map_at_1000
|
2254 |
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value: 20.179
|
2255 |
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- type: map_at_3
|
2256 |
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value: 0.528
|
2257 |
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- type: map_at_5
|
2258 |
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value: 0.8019999999999999
|
2259 |
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- type: mrr_at_1
|
2260 |
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value: 72
|
2261 |
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|
2262 |
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value: 83.39999999999999
|
2263 |
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- type: mrr_at_100
|
2264 |
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value: 83.39999999999999
|
2265 |
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- type: mrr_at_1000
|
2266 |
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value: 83.39999999999999
|
2267 |
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- type: mrr_at_3
|
2268 |
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value: 81.667
|
2269 |
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- type: mrr_at_5
|
2270 |
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value: 83.06700000000001
|
2271 |
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- type: ndcg_at_1
|
2272 |
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value: 66
|
2273 |
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- type: ndcg_at_10
|
2274 |
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value: 58.059000000000005
|
2275 |
+
- type: ndcg_at_100
|
2276 |
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value: 44.316
|
2277 |
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- type: ndcg_at_1000
|
2278 |
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value: 43.147000000000006
|
2279 |
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- type: ndcg_at_3
|
2280 |
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value: 63.815999999999995
|
2281 |
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- type: ndcg_at_5
|
2282 |
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value: 63.005
|
2283 |
+
- type: precision_at_1
|
2284 |
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value: 72
|
2285 |
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- type: precision_at_10
|
2286 |
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value: 61.4
|
2287 |
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- type: precision_at_100
|
2288 |
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value: 45.62
|
2289 |
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- type: precision_at_1000
|
2290 |
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value: 19.866
|
2291 |
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- type: precision_at_3
|
2292 |
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value: 70
|
2293 |
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- type: precision_at_5
|
2294 |
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value: 68.8
|
2295 |
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- type: recall_at_1
|
2296 |
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value: 0.198
|
2297 |
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- type: recall_at_10
|
2298 |
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value: 1.517
|
2299 |
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- type: recall_at_100
|
2300 |
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value: 10.587
|
2301 |
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- type: recall_at_1000
|
2302 |
+
value: 41.233
|
2303 |
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- type: recall_at_3
|
2304 |
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value: 0.573
|
2305 |
+
- type: recall_at_5
|
2306 |
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value: 0.907
|
2307 |
+
- task:
|
2308 |
+
type: Retrieval
|
2309 |
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dataset:
|
2310 |
+
type: webis-touche2020
|
2311 |
+
name: MTEB Touche2020
|
2312 |
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config: default
|
2313 |
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split: test
|
2314 |
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revision: None
|
2315 |
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metrics:
|
2316 |
+
- type: map_at_1
|
2317 |
+
value: 1.894
|
2318 |
+
- type: map_at_10
|
2319 |
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value: 8.488999999999999
|
2320 |
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- type: map_at_100
|
2321 |
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value: 14.445
|
2322 |
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- type: map_at_1000
|
2323 |
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value: 16.078
|
2324 |
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- type: map_at_3
|
2325 |
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value: 4.589
|
2326 |
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- type: map_at_5
|
2327 |
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value: 6.019
|
2328 |
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- type: mrr_at_1
|
2329 |
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value: 22.448999999999998
|
2330 |
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- type: mrr_at_10
|
2331 |
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value: 39.82
|
2332 |
+
- type: mrr_at_100
|
2333 |
+
value: 40.752
|
2334 |
+
- type: mrr_at_1000
|
2335 |
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value: 40.771
|
2336 |
+
- type: mrr_at_3
|
2337 |
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value: 34.354
|
2338 |
+
- type: mrr_at_5
|
2339 |
+
value: 37.721
|
2340 |
+
- type: ndcg_at_1
|
2341 |
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value: 19.387999999999998
|
2342 |
+
- type: ndcg_at_10
|
2343 |
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value: 21.563
|
2344 |
+
- type: ndcg_at_100
|
2345 |
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value: 33.857
|
2346 |
+
- type: ndcg_at_1000
|
2347 |
+
value: 46.199
|
2348 |
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- type: ndcg_at_3
|
2349 |
+
value: 22.296
|
2350 |
+
- type: ndcg_at_5
|
2351 |
+
value: 21.770999999999997
|
2352 |
+
- type: precision_at_1
|
2353 |
+
value: 22.448999999999998
|
2354 |
+
- type: precision_at_10
|
2355 |
+
value: 19.796
|
2356 |
+
- type: precision_at_100
|
2357 |
+
value: 7.142999999999999
|
2358 |
+
- type: precision_at_1000
|
2359 |
+
value: 1.541
|
2360 |
+
- type: precision_at_3
|
2361 |
+
value: 24.490000000000002
|
2362 |
+
- type: precision_at_5
|
2363 |
+
value: 22.448999999999998
|
2364 |
+
- type: recall_at_1
|
2365 |
+
value: 1.894
|
2366 |
+
- type: recall_at_10
|
2367 |
+
value: 14.931
|
2368 |
+
- type: recall_at_100
|
2369 |
+
value: 45.524
|
2370 |
+
- type: recall_at_1000
|
2371 |
+
value: 83.243
|
2372 |
+
- type: recall_at_3
|
2373 |
+
value: 5.712
|
2374 |
+
- type: recall_at_5
|
2375 |
+
value: 8.386000000000001
|
2376 |
+
- task:
|
2377 |
+
type: Classification
|
2378 |
+
dataset:
|
2379 |
+
type: mteb/toxic_conversations_50k
|
2380 |
+
name: MTEB ToxicConversationsClassification
|
2381 |
+
config: default
|
2382 |
+
split: test
|
2383 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2384 |
+
metrics:
|
2385 |
+
- type: accuracy
|
2386 |
+
value: 71.049
|
2387 |
+
- type: ap
|
2388 |
+
value: 13.85116971310922
|
2389 |
+
- type: f1
|
2390 |
+
value: 54.37504302487686
|
2391 |
+
- task:
|
2392 |
+
type: Classification
|
2393 |
+
dataset:
|
2394 |
+
type: mteb/tweet_sentiment_extraction
|
2395 |
+
name: MTEB TweetSentimentExtractionClassification
|
2396 |
+
config: default
|
2397 |
+
split: test
|
2398 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2399 |
+
metrics:
|
2400 |
+
- type: accuracy
|
2401 |
+
value: 64.1312959818902
|
2402 |
+
- type: f1
|
2403 |
+
value: 64.11413877009383
|
2404 |
+
- task:
|
2405 |
+
type: Clustering
|
2406 |
+
dataset:
|
2407 |
+
type: mteb/twentynewsgroups-clustering
|
2408 |
+
name: MTEB TwentyNewsgroupsClustering
|
2409 |
+
config: default
|
2410 |
+
split: test
|
2411 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2412 |
+
metrics:
|
2413 |
+
- type: v_measure
|
2414 |
+
value: 54.13103431861502
|
2415 |
+
- task:
|
2416 |
+
type: PairClassification
|
2417 |
+
dataset:
|
2418 |
+
type: mteb/twittersemeval2015-pairclassification
|
2419 |
+
name: MTEB TwitterSemEval2015
|
2420 |
+
config: default
|
2421 |
+
split: test
|
2422 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2423 |
+
metrics:
|
2424 |
+
- type: cos_sim_accuracy
|
2425 |
+
value: 87.327889372355
|
2426 |
+
- type: cos_sim_ap
|
2427 |
+
value: 77.42059895975699
|
2428 |
+
- type: cos_sim_f1
|
2429 |
+
value: 71.02706903250873
|
2430 |
+
- type: cos_sim_precision
|
2431 |
+
value: 69.75324344950394
|
2432 |
+
- type: cos_sim_recall
|
2433 |
+
value: 72.34828496042216
|
2434 |
+
- type: dot_accuracy
|
2435 |
+
value: 87.327889372355
|
2436 |
+
- type: dot_ap
|
2437 |
+
value: 77.4209479346677
|
2438 |
+
- type: dot_f1
|
2439 |
+
value: 71.02706903250873
|
2440 |
+
- type: dot_precision
|
2441 |
+
value: 69.75324344950394
|
2442 |
+
- type: dot_recall
|
2443 |
+
value: 72.34828496042216
|
2444 |
+
- type: euclidean_accuracy
|
2445 |
+
value: 87.327889372355
|
2446 |
+
- type: euclidean_ap
|
2447 |
+
value: 77.42096495861037
|
2448 |
+
- type: euclidean_f1
|
2449 |
+
value: 71.02706903250873
|
2450 |
+
- type: euclidean_precision
|
2451 |
+
value: 69.75324344950394
|
2452 |
+
- type: euclidean_recall
|
2453 |
+
value: 72.34828496042216
|
2454 |
+
- type: manhattan_accuracy
|
2455 |
+
value: 87.31000774870358
|
2456 |
+
- type: manhattan_ap
|
2457 |
+
value: 77.38930750711619
|
2458 |
+
- type: manhattan_f1
|
2459 |
+
value: 71.07935314027831
|
2460 |
+
- type: manhattan_precision
|
2461 |
+
value: 67.70957726295677
|
2462 |
+
- type: manhattan_recall
|
2463 |
+
value: 74.80211081794195
|
2464 |
+
- type: max_accuracy
|
2465 |
+
value: 87.327889372355
|
2466 |
+
- type: max_ap
|
2467 |
+
value: 77.42096495861037
|
2468 |
+
- type: max_f1
|
2469 |
+
value: 71.07935314027831
|
2470 |
+
- task:
|
2471 |
+
type: PairClassification
|
2472 |
+
dataset:
|
2473 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2474 |
+
name: MTEB TwitterURLCorpus
|
2475 |
+
config: default
|
2476 |
+
split: test
|
2477 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2478 |
+
metrics:
|
2479 |
+
- type: cos_sim_accuracy
|
2480 |
+
value: 89.58939729110878
|
2481 |
+
- type: cos_sim_ap
|
2482 |
+
value: 87.17594155025475
|
2483 |
+
- type: cos_sim_f1
|
2484 |
+
value: 79.21146953405018
|
2485 |
+
- type: cos_sim_precision
|
2486 |
+
value: 76.8918527109307
|
2487 |
+
- type: cos_sim_recall
|
2488 |
+
value: 81.67539267015707
|
2489 |
+
- type: dot_accuracy
|
2490 |
+
value: 89.58939729110878
|
2491 |
+
- type: dot_ap
|
2492 |
+
value: 87.17593963273593
|
2493 |
+
- type: dot_f1
|
2494 |
+
value: 79.21146953405018
|
2495 |
+
- type: dot_precision
|
2496 |
+
value: 76.8918527109307
|
2497 |
+
- type: dot_recall
|
2498 |
+
value: 81.67539267015707
|
2499 |
+
- type: euclidean_accuracy
|
2500 |
+
value: 89.58939729110878
|
2501 |
+
- type: euclidean_ap
|
2502 |
+
value: 87.17592466925834
|
2503 |
+
- type: euclidean_f1
|
2504 |
+
value: 79.21146953405018
|
2505 |
+
- type: euclidean_precision
|
2506 |
+
value: 76.8918527109307
|
2507 |
+
- type: euclidean_recall
|
2508 |
+
value: 81.67539267015707
|
2509 |
+
- type: manhattan_accuracy
|
2510 |
+
value: 89.62626615438352
|
2511 |
+
- type: manhattan_ap
|
2512 |
+
value: 87.16589873161546
|
2513 |
+
- type: manhattan_f1
|
2514 |
+
value: 79.25143598295348
|
2515 |
+
- type: manhattan_precision
|
2516 |
+
value: 76.39494177323712
|
2517 |
+
- type: manhattan_recall
|
2518 |
+
value: 82.32984293193716
|
2519 |
+
- type: max_accuracy
|
2520 |
+
value: 89.62626615438352
|
2521 |
+
- type: max_ap
|
2522 |
+
value: 87.17594155025475
|
2523 |
+
- type: max_f1
|
2524 |
+
value: 79.25143598295348
|
2525 |
+
---
|
2526 |
+
|
2527 |
+
# hkunlp/instructor-large
|
2528 |
+
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
|
2529 |
+
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
|
2530 |
+
|
2531 |
+
**************************** **Updates** ****************************
|
2532 |
+
|
2533 |
+
* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
|
2534 |
+
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
|
2535 |
+
|
2536 |
+
## Quick start
|
2537 |
+
<hr />
|
2538 |
+
|
2539 |
+
## Installation
|
2540 |
+
```bash
|
2541 |
+
pip install InstructorEmbedding
|
2542 |
+
```
|
2543 |
+
|
2544 |
+
## Compute your customized embeddings
|
2545 |
+
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
|
2546 |
+
```python
|
2547 |
+
from InstructorEmbedding import INSTRUCTOR
|
2548 |
+
model = INSTRUCTOR('hkunlp/instructor-large')
|
2549 |
+
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
|
2550 |
+
instruction = "Represent the Science title:"
|
2551 |
+
embeddings = model.encode([[instruction,sentence]])
|
2552 |
+
print(embeddings)
|
2553 |
+
```
|
2554 |
+
|
2555 |
+
## Use cases
|
2556 |
+
<hr />
|
2557 |
+
|
2558 |
+
## Calculate embeddings for your customized texts
|
2559 |
+
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
|
2560 |
+
|
2561 |
+
Represent the `domain` `text_type` for `task_objective`:
|
2562 |
+
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
|
2563 |
+
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
|
2564 |
+
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
|
2565 |
+
|
2566 |
+
## Calculate Sentence similarities
|
2567 |
+
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
|
2568 |
+
```python
|
2569 |
+
from sklearn.metrics.pairwise import cosine_similarity
|
2570 |
+
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
|
2571 |
+
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
|
2572 |
+
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
|
2573 |
+
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
|
2574 |
+
embeddings_a = model.encode(sentences_a)
|
2575 |
+
embeddings_b = model.encode(sentences_b)
|
2576 |
+
similarities = cosine_similarity(embeddings_a,embeddings_b)
|
2577 |
+
print(similarities)
|
2578 |
+
```
|
2579 |
+
|
2580 |
+
## Information Retrieval
|
2581 |
+
You can also use **customized embeddings** for information retrieval.
|
2582 |
+
```python
|
2583 |
+
import numpy as np
|
2584 |
+
from sklearn.metrics.pairwise import cosine_similarity
|
2585 |
+
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
|
2586 |
+
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
|
2587 |
+
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
|
2588 |
+
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
|
2589 |
+
query_embeddings = model.encode(query)
|
2590 |
+
corpus_embeddings = model.encode(corpus)
|
2591 |
+
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
|
2592 |
+
retrieved_doc_id = np.argmax(similarities)
|
2593 |
+
print(retrieved_doc_id)
|
2594 |
+
```
|
2595 |
+
|
2596 |
+
## Clustering
|
2597 |
+
Use **customized embeddings** for clustering texts in groups.
|
2598 |
+
```python
|
2599 |
+
import sklearn.cluster
|
2600 |
+
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
|
2601 |
+
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
|
2602 |
+
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
|
2603 |
+
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
|
2604 |
+
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
|
2605 |
+
embeddings = model.encode(sentences)
|
2606 |
+
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
|
2607 |
+
clustering_model.fit(embeddings)
|
2608 |
+
cluster_assignment = clustering_model.labels_
|
2609 |
+
print(cluster_assignment)
|
2610 |
+
```
|
data/hkunlp_instructor-large/config.json
ADDED
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/scratch/acd13578qu/metatrain_models/enhanced_large/checkpoint-300/",
|
3 |
+
"architectures": [
|
4 |
+
"T5EncoderModel"
|
5 |
+
],
|
6 |
+
"d_ff": 4096,
|
7 |
+
"d_kv": 64,
|
8 |
+
"d_model": 1024,
|
9 |
+
"decoder_start_token_id": 0,
|
10 |
+
"dense_act_fn": "relu",
|
11 |
+
"dropout_rate": 0.1,
|
12 |
+
"eos_token_id": 1,
|
13 |
+
"feed_forward_proj": "relu",
|
14 |
+
"initializer_factor": 1.0,
|
15 |
+
"is_encoder_decoder": true,
|
16 |
+
"is_gated_act": false,
|
17 |
+
"layer_norm_epsilon": 1e-06,
|
18 |
+
"model_type": "t5",
|
19 |
+
"n_positions": 512,
|
20 |
+
"num_decoder_layers": 24,
|
21 |
+
"num_heads": 16,
|
22 |
+
"num_layers": 24,
|
23 |
+
"output_past": true,
|
24 |
+
"pad_token_id": 0,
|
25 |
+
"relative_attention_max_distance": 128,
|
26 |
+
"relative_attention_num_buckets": 32,
|
27 |
+
"task_specific_params": {
|
28 |
+
"summarization": {
|
29 |
+
"early_stopping": true,
|
30 |
+
"length_penalty": 2.0,
|
31 |
+
"max_length": 200,
|
32 |
+
"min_length": 30,
|
33 |
+
"no_repeat_ngram_size": 3,
|
34 |
+
"num_beams": 4,
|
35 |
+
"prefix": "summarize: "
|
36 |
+
},
|
37 |
+
"translation_en_to_de": {
|
38 |
+
"early_stopping": true,
|
39 |
+
"max_length": 300,
|
40 |
+
"num_beams": 4,
|
41 |
+
"prefix": "translate English to German: "
|
42 |
+
},
|
43 |
+
"translation_en_to_fr": {
|
44 |
+
"early_stopping": true,
|
45 |
+
"max_length": 300,
|
46 |
+
"num_beams": 4,
|
47 |
+
"prefix": "translate English to French: "
|
48 |
+
},
|
49 |
+
"translation_en_to_ro": {
|
50 |
+
"early_stopping": true,
|
51 |
+
"max_length": 300,
|
52 |
+
"num_beams": 4,
|
53 |
+
"prefix": "translate English to Romanian: "
|
54 |
+
}
|
55 |
+
},
|
56 |
+
"torch_dtype": "float32",
|
57 |
+
"transformers_version": "4.20.0.dev0",
|
58 |
+
"use_cache": true,
|
59 |
+
"vocab_size": 32128
|
60 |
+
}
|
data/hkunlp_instructor-large/config_sentence_transformers.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.0",
|
4 |
+
"transformers": "4.7.0",
|
5 |
+
"pytorch": "1.9.0+cu102"
|
6 |
+
}
|
7 |
+
}
|
data/hkunlp_instructor-large/modules.json
ADDED
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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[
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
12 |
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|
13 |
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|
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|
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|
16 |
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|
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|
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|
19 |
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|
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|
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|
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|
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|
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|
25 |
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}
|
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|
data/hkunlp_instructor-large/pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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|
|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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|
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size 1339823867
|
data/hkunlp_instructor-large/sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
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|
|
1 |
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{
|
2 |
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|
3 |
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"do_lower_case": false
|
4 |
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}
|
data/hkunlp_instructor-large/special_tokens_map.json
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@@ -0,0 +1,107 @@
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|
data/hkunlp_instructor-large/spiece.model
ADDED
@@ -0,0 +1,3 @@
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data/hkunlp_instructor-large/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
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|
data/hkunlp_instructor-large/tokenizer_config.json
ADDED
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97 |
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|
98 |
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|
99 |
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|
100 |
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|
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|
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|
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|
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|
105 |
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|
106 |
+
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|
107 |
+
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|
108 |
+
"pad_token": "<pad>",
|
109 |
+
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|
110 |
+
"tokenizer_class": "T5Tokenizer",
|
111 |
+
"unk_token": "<unk>"
|
112 |
+
}
|
data/version.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1
|
emb.py
ADDED
@@ -0,0 +1,171 @@
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|
|
|
1 |
+
import os
|
2 |
+
from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader
|
3 |
+
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
4 |
+
from langchain.embeddings import SentenceTransformerEmbeddings
|
5 |
+
from langchain.vectorstores import Chroma
|
6 |
+
import configparser
|
7 |
+
from tqdm import tqdm
|
8 |
+
from langchain.vectorstores import Pinecone
|
9 |
+
from langchain.schema import Document
|
10 |
+
import pinecone
|
11 |
+
from dotenv import load_dotenv
|
12 |
+
from llm import LLMManager
|
13 |
+
|
14 |
+
class EmbeddingsManager:
|
15 |
+
|
16 |
+
def __init__(self,settings, emb="hkunlp/instructor-large"):
|
17 |
+
|
18 |
+
#Loading env variables
|
19 |
+
load_dotenv()
|
20 |
+
|
21 |
+
#Loading config file
|
22 |
+
self.config=configparser.ConfigParser()
|
23 |
+
self.config.read("config.ini")
|
24 |
+
|
25 |
+
#Loading settingManager
|
26 |
+
self.set=settings
|
27 |
+
|
28 |
+
#Loading default parameters for search
|
29 |
+
self.search_method=self.set.search_method
|
30 |
+
self.n_doc_return=self.set.n_doc_return
|
31 |
+
self.ai_assisted_search=self.config.getboolean('RAG','default_ai_assisted_search')
|
32 |
+
self.available_search_methods=self.set.available_search_methods
|
33 |
+
self.text_split_size=self.config.getint('RAG','default_text_split_size')
|
34 |
+
self.text_overlap=self.config.getint('RAG','default_text_overlap')
|
35 |
+
|
36 |
+
#Loading available Vector Stores
|
37 |
+
self.vector_stores=self.get_vector_list()
|
38 |
+
self.vector_stores_map=self.get_vector_map_list()
|
39 |
+
|
40 |
+
#Selecting the embeddings model
|
41 |
+
self.embedding_model_name=emb
|
42 |
+
|
43 |
+
#Initing
|
44 |
+
current_dir = os.path.dirname(__file__)
|
45 |
+
data_dir = os.path.join(current_dir, "data")
|
46 |
+
os.environ['TRANSFORMERS_CACHE'] = data_dir
|
47 |
+
PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
|
48 |
+
PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV')
|
49 |
+
self.embeddings_model = SentenceTransformerEmbeddings(model_name=self.embedding_model_name, cache_folder=data_dir)
|
50 |
+
pinecone.init(api_key=PINECONE_API_KEY,environment=PINECONE_API_ENV)
|
51 |
+
|
52 |
+
#This function used to get the list of emb
|
53 |
+
def get_emb_list(self):
|
54 |
+
"""Returns a list of the available Embedding models"""
|
55 |
+
emb_map_section = 'EMB'
|
56 |
+
if emb_map_section in self.config:
|
57 |
+
return [self.config.get(emb_map_section, emb) for emb in self.config[emb_map_section]]
|
58 |
+
else:
|
59 |
+
return []
|
60 |
+
|
61 |
+
#This function used to get the list of available VectorStores
|
62 |
+
def get_vector_list(self):
|
63 |
+
"""Returns a list of the available Vector Stores"""
|
64 |
+
section = 'Vector_Stores'
|
65 |
+
if section in self.config:
|
66 |
+
return [self.config.get(section, vector) for vector in self.config[section]]
|
67 |
+
else:
|
68 |
+
return []
|
69 |
+
|
70 |
+
#This function used to get the map of available VectorStores
|
71 |
+
def get_vector_map_list(self):
|
72 |
+
"""Returns a list of the available Vector Stores"""
|
73 |
+
section = 'Vector_Stores_Map'
|
74 |
+
if section in self.config:
|
75 |
+
return [self.config.get(section, vector) for vector in self.config[section]]
|
76 |
+
else:
|
77 |
+
return []
|
78 |
+
|
79 |
+
#This function is used to get the relevant context
|
80 |
+
def get_context(self,index, query, history):
|
81 |
+
"""Returns the relevant context for the LLM"""
|
82 |
+
|
83 |
+
docsearch = Pinecone.from_existing_index(index, self.embeddings_model)
|
84 |
+
|
85 |
+
if self.set.ai_assisted_search:
|
86 |
+
prompt=self.set.default_ai_search_prompt
|
87 |
+
prompt=prompt.format(question=query,history=history)
|
88 |
+
print(prompt)
|
89 |
+
llm=LLMManager(self.set)
|
90 |
+
queryterms=llm.get_query_terms(prompt)
|
91 |
+
query=queryterms+"\n"+query
|
92 |
+
|
93 |
+
#print("new query input={new_query}".format(new_query=query))
|
94 |
+
|
95 |
+
|
96 |
+
if self.set.search_method=="MMR":
|
97 |
+
return docsearch.max_marginal_relevance_search(query, k=self.set.n_doc_return,fetch_metadata=True)
|
98 |
+
|
99 |
+
elif self.set.search_method=="Similarity":
|
100 |
+
return docsearch.similarity_search(query, k=self.set.n_doc_return,fetch_metadata=True)
|
101 |
+
|
102 |
+
else:
|
103 |
+
return docsearch.max_marginal_relevance_search(query, k=self.set.n_doc_return,fetch_metadata=True)
|
104 |
+
|
105 |
+
|
106 |
+
#This function is used to get the relevant context
|
107 |
+
def get_context_search(self,index, query):
|
108 |
+
"""Returns the relevant context for the LLM"""
|
109 |
+
|
110 |
+
docsearch = Pinecone.from_existing_index(index, self.embeddings_model)
|
111 |
+
|
112 |
+
if self.set.search_method=="MMR":
|
113 |
+
return docsearch.max_marginal_relevance_search(query, k=2,fetch_metadata=True)
|
114 |
+
|
115 |
+
elif self.set.search_method=="Similarity":
|
116 |
+
return docsearch.similarity_search(query, k=2,fetch_metadata=True)
|
117 |
+
|
118 |
+
else:
|
119 |
+
return docsearch.max_marginal_relevance_search(query, k=self.n_doc_return,fetch_metadata=True)
|
120 |
+
|
121 |
+
#This function is used to get the relevant context formatted
|
122 |
+
def get_formatted_context(self,index, query,history):
|
123 |
+
"""Returns the relevant context for the LLM formatted"""
|
124 |
+
|
125 |
+
formatted=""
|
126 |
+
docs=self.get_context(index, query,history)
|
127 |
+
for doc in docs:
|
128 |
+
formatted+="DOCUMENT NAME={doc_name}\nDOCUMENT CONTENT={doc_content}\n\n".format(doc_name=doc.metadata["source"],doc_content=doc.page_content)
|
129 |
+
return formatted
|
130 |
+
|
131 |
+
|
132 |
+
|
133 |
+
#This function is used to add documents to an existing vector store
|
134 |
+
def generate_vector_store(self, index):
|
135 |
+
"""Adds a document to the vector store on Pinecone."""
|
136 |
+
|
137 |
+
documents = []
|
138 |
+
for root, dirs, files in os.walk("docs"):
|
139 |
+
for file in files:
|
140 |
+
if file.endswith(".pdf"):
|
141 |
+
print("Uploading "+file.replace(".pdf",""))
|
142 |
+
documents.clear()
|
143 |
+
loader = PDFMinerLoader(os.path.join(root, file))
|
144 |
+
documents.extend(loader.load())
|
145 |
+
text_splitter = RecursiveCharacterTextSplitter(chunk_size=self.text_split_size, chunk_overlap=self.text_overlap)
|
146 |
+
texts = text_splitter.split_documents(documents)
|
147 |
+
docsearch = Pinecone.from_documents(texts, embedding=self.embeddings_model, index_name=index)
|
148 |
+
os.remove(os.path.join(root, file))
|
149 |
+
|
150 |
+
return "Ok"
|
151 |
+
|
152 |
+
|
153 |
+
# Example Usage:
|
154 |
+
if __name__ == "__main__":
|
155 |
+
|
156 |
+
"""This is an example of how to add document to the vectorstore on Pinecone"""
|
157 |
+
from settings import SettingManager
|
158 |
+
set= SettingManager()
|
159 |
+
emb_manager = EmbeddingsManager(set,emb="hkunlp/instructor-large")
|
160 |
+
print(emb_manager.generate_vector_store("prohelper"))
|
161 |
+
|
162 |
+
|
163 |
+
#"""This is an example of how to retrive context and display all values retrived"""
|
164 |
+
#emb_manager = EmbeddingsManager()
|
165 |
+
#docs=emb_manager.get_context(index="prohelper",query="Could you explain to me what is esrs?")
|
166 |
+
#for i in docs:
|
167 |
+
# print("---------------------------------------------------------")
|
168 |
+
# print(i.metadata["Doc"])
|
169 |
+
# print(" ")
|
170 |
+
# print(i.page_content)
|
171 |
+
#llm_manager.selectLLM("Mixtral 7B")
|
history.py
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from dotenv import load_dotenv
|
2 |
+
import configparser
|
3 |
+
|
4 |
+
class HistoryManager:
|
5 |
+
|
6 |
+
def __init__(self):
|
7 |
+
|
8 |
+
#Loading env variables
|
9 |
+
try:
|
10 |
+
load_dotenv()
|
11 |
+
except:
|
12 |
+
print("No .env file")
|
13 |
+
|
14 |
+
#Loading config file
|
15 |
+
self.config=configparser.ConfigParser()
|
16 |
+
self.config.read("config.ini")
|
17 |
+
|
18 |
+
self.chat_history = {}
|
19 |
+
|
20 |
+
def add_message(self, chat_id, sender, message):
|
21 |
+
if chat_id not in self.chat_history:
|
22 |
+
self.chat_history[chat_id] = []
|
23 |
+
self.chat_history[chat_id].append((sender, message))
|
24 |
+
|
25 |
+
def get_messages(self, chat_id):
|
26 |
+
return self.chat_history.get(chat_id, [])
|
27 |
+
|
28 |
+
def clear_chat(self, chat_id):
|
29 |
+
if chat_id in self.chat_history:
|
30 |
+
del self.chat_history[chat_id]
|
31 |
+
|
32 |
+
def format_chat(self, chat_id):
|
33 |
+
formatted_chat = ""
|
34 |
+
messages = self.get_messages(chat_id)
|
35 |
+
for sender, message in messages:
|
36 |
+
formatted_chat += f"{sender} message= {message}\n"
|
37 |
+
return formatted_chat
|
38 |
+
|
39 |
+
def chat_exists(self, chat_id):
|
40 |
+
return chat_id in self.chat_history
|
llm.py
ADDED
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from huggingface_hub import InferenceClient
|
2 |
+
from dotenv import load_dotenv
|
3 |
+
import configparser
|
4 |
+
import os
|
5 |
+
|
6 |
+
|
7 |
+
class LLMManager:
|
8 |
+
def __init__(self, settings):
|
9 |
+
|
10 |
+
#Loading HF Token
|
11 |
+
try:
|
12 |
+
load_dotenv()
|
13 |
+
except:
|
14 |
+
print("No .env file")
|
15 |
+
|
16 |
+
#Initing HuggingFace Inference client
|
17 |
+
HF_TOKEN = os.environ.get('HF_TOKEN')
|
18 |
+
self.client = InferenceClient(token=HF_TOKEN)
|
19 |
+
|
20 |
+
#Creating and loading config file
|
21 |
+
self.config=configparser.ConfigParser()
|
22 |
+
self.config.read("config.ini")
|
23 |
+
|
24 |
+
#getting setting
|
25 |
+
self.set=settings
|
26 |
+
|
27 |
+
#Loading default index for LLM
|
28 |
+
self.defaultLLM=self.set.defaultLLM
|
29 |
+
|
30 |
+
#Loading available LLM
|
31 |
+
self.listLLM=self.get_llm()
|
32 |
+
self.listLLMMap=self.get_llm_map()
|
33 |
+
|
34 |
+
#Setting the model
|
35 |
+
self.currentLLM=self.listLLM[self.defaultLLM]
|
36 |
+
|
37 |
+
#Function used to select the LLM
|
38 |
+
def selectLLM(self, llm):
|
39 |
+
print("Selected {llm} LLM")
|
40 |
+
llmIndex=self.listLLMMap.index(llm)
|
41 |
+
self.currentLLM=self.listLLM[llmIndex]
|
42 |
+
|
43 |
+
#Function used to get a list of available LLM
|
44 |
+
def get_llm(self):
|
45 |
+
llm_section = 'LLM'
|
46 |
+
if llm_section in self.config:
|
47 |
+
return [self.config.get(llm_section, llm) for llm in self.config[llm_section]]
|
48 |
+
else:
|
49 |
+
return []
|
50 |
+
|
51 |
+
#Function used to get a list of available LLM
|
52 |
+
def get_llm_prompts(self):
|
53 |
+
prompt_section = 'Prompt_map'
|
54 |
+
if prompt_section in self.config:
|
55 |
+
return [self.config.get(prompt_section, llm) for llm in self.config[prompt_section]]
|
56 |
+
else:
|
57 |
+
return []
|
58 |
+
|
59 |
+
#Function used to get the list of llm Map
|
60 |
+
def get_llm_map(self):
|
61 |
+
llm_map_section = 'LLM_Map'
|
62 |
+
if llm_map_section in self.config:
|
63 |
+
return [self.config.get(llm_map_section, llm) for llm in self.config[llm_map_section]]
|
64 |
+
else:
|
65 |
+
return []
|
66 |
+
|
67 |
+
#This function is used to retrive the reply to a question
|
68 |
+
def get_text(self, question):
|
69 |
+
|
70 |
+
print("temp={temp}".format(temp=self.set.temperature))
|
71 |
+
print("Repetition={rep}".format(rep=self.set.repetition_penalty))
|
72 |
+
generate_kwargs = dict(
|
73 |
+
temperature=self.set.temperature,
|
74 |
+
max_new_tokens=self.set.max_new_token,
|
75 |
+
top_p=self.set.top_p,
|
76 |
+
repetition_penalty=self.set.repetition_penalty,
|
77 |
+
do_sample=True,
|
78 |
+
seed=42,
|
79 |
+
)
|
80 |
+
|
81 |
+
stream = self.client.text_generation(model=self.currentLLM, prompt=question, **generate_kwargs,stream=False, details=False, return_full_text=False)
|
82 |
+
#output = ""
|
83 |
+
return stream
|
84 |
+
#for response in stream:
|
85 |
+
# output += response.token.text
|
86 |
+
# yield output
|
87 |
+
#return output
|
88 |
+
|
89 |
+
#this function is used to retrive the best search terms
|
90 |
+
def get_query_terms(self, question):
|
91 |
+
generate_kwargs = dict(
|
92 |
+
temperature=self.set.RAG_temperature,
|
93 |
+
max_new_tokens=self.set.RAG_max_new_token,
|
94 |
+
top_p=self.set.RAG_top_p,
|
95 |
+
repetition_penalty=self.set.RAG_repetition_penalty,
|
96 |
+
do_sample=True,
|
97 |
+
)
|
98 |
+
stream = self.client.text_generation(model=self.currentLLM, prompt=question, **generate_kwargs,stream=False, details=False, return_full_text=False)
|
99 |
+
return stream
|
100 |
+
|
101 |
+
|
102 |
+
#This function is used to generate the prompt for the LLM
|
103 |
+
def get_prompt(self,user_input,rag_contex,chat_history, system_prompt=None):
|
104 |
+
"""Returns the formatted prompt for a specific LLM"""
|
105 |
+
|
106 |
+
prompts=self.get_llm_prompts()
|
107 |
+
prompt=""
|
108 |
+
|
109 |
+
if system_prompt==None:
|
110 |
+
system_prompt=self.set.system_prompt
|
111 |
+
else:
|
112 |
+
print("System prompt set to : \n {sys_prompt}".format(sys_prompt=system_prompt))
|
113 |
+
|
114 |
+
try:
|
115 |
+
prompt= prompts[self.listLLM.index(self.currentLLM)].format(sys_prompt=system_prompt)
|
116 |
+
except Exception:
|
117 |
+
print("Warning prompt map for {llm} has not been defined".format(llm=self.currentLLM))
|
118 |
+
prompt="{sys_prompt}".format(sys_prompt=system_prompt)
|
119 |
+
|
120 |
+
print("Prompt={pro}".format(pro=prompt))
|
121 |
+
return prompt.format(context=rag_contex,history=chat_history,question=user_input)
|
122 |
+
|
123 |
+
|
124 |
+
# Example Usage:
|
125 |
+
#if __name__ == "__main__":
|
126 |
+
# llm_manager = LLMManager()
|
127 |
+
# print(llm_manager.config.get('Prompt_map', 'prompt1').format(
|
128 |
+
# system_prompt="Sei una brava IA",
|
129 |
+
# history="",
|
130 |
+
# context="",
|
131 |
+
# question=""))
|
132 |
+
#llm_manager.selectLLM("Mixtral 7B")
|
requirements.txt
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
huggingface_hub
|
2 |
+
streamlit
|
3 |
+
python-dotenv
|
4 |
+
langchain
|
5 |
+
chromadb==0.3.26
|
6 |
+
tqdm
|
7 |
+
pydantic==1.10.11
|
8 |
+
pdfminer.six==20221105
|
9 |
+
sentence_transformers==2.2.2
|
10 |
+
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Ellipse","hd":false},{"ty":"st","c":{"a":0,"k":[0,0,0,1],"ix":3},"o":{"a":0,"k":100,"ix":4},"w":{"a":0,"k":0,"ix":5},"lc":1,"lj":1,"ml":4,"bm":0,"nm":"Kontur 1","mn":"ADBE Vector Graphic - Stroke","hd":false},{"ty":"fl","c":{"k":[{"s":[0.161,0.165,0.608,1],"t":0,"i":{"x":[1],"y":[1]},"o":{"x":[0],"y":[0]}},{"s":[0.161,0.165,0.608,1],"t":105,"i":{"x":[1],"y":[1]},"o":{"x":[0],"y":[0]}}]},"o":{"a":0,"k":100,"ix":5},"r":1,"bm":0,"nm":"Fläche 1","mn":"ADBE Vector Graphic - Fill","hd":false},{"ty":"tr","p":{"a":0,"k":[0,0],"ix":2},"a":{"a":0,"k":[0,0],"ix":1},"s":{"a":0,"k":[100,100],"ix":3},"r":{"a":0,"k":0,"ix":6},"o":{"a":0,"k":100,"ix":7},"sk":{"a":0,"k":0,"ix":4},"sa":{"a":0,"k":0,"ix":5},"nm":"Transformieren"}],"nm":"Ellipse 1","np":3,"cix":2,"bm":0,"ix":1,"mn":"ADBE Vector Group","hd":false}],"ip":0,"op":240,"st":0,"bm":0},{"ddd":0,"ind":14,"ty":4,"nm":"Bubble 1","sr":1,"ks":{"o":{"a":0,"k":100,"ix":11},"r":{"a":0,"k":0,"ix":10},"p":{"a":1,"k":[{"i":{"x":0.667,"y":1},"o":{"x":0.333,"y":0},"t":7,"s":[250,250,0],"to":[-28.333,-30,0],"ti":[28.333,30,0]},{"i":{"x":0.667,"y":0.667},"o":{"x":0.333,"y":0.333},"t":20,"s":[80,70,0],"to":[0,0,0],"ti":[0,0,0]},{"i":{"x":0.667,"y":1},"o":{"x":0.333,"y":0},"t":27,"s":[80,70,0],"to":[28.333,30,0],"ti":[-28.333,-30,0]},{"t":56,"s":[250,250,0]}],"ix":2,"l":2},"a":{"a":0,"k":[0,0,0],"ix":1,"l":2},"s":{"a":1,"k":[{"i":{"x":[0.667,0.667,0.667],"y":[0.81,0.81,-6.583]},"o":{"x":[0.333,0.333,0.333],"y":[0,0,0]},"t":0,"s":[0,0,100]},{"i":{"x":[0.667,0.667,0.667],"y":[1,1,1]},"o":{"x":[0.333,0.333,0.333],"y":[-3.033,-3.033,15.167]},"t":20,"s":[8,8,100]},{"t":60,"s":[7,7,100]}],"ix":6,"l":2}},"ao":0,"shapes":[{"ty":"gr","it":[{"d":1,"ty":"el","s":{"a":0,"k":[500,500],"ix":2},"p":{"a":0,"k":[0,0],"ix":3},"nm":"Elliptischer Pfad 1","mn":"ADBE Vector Shape - Ellipse","hd":false},{"ty":"st","c":{"a":0,"k":[0,0,0,1],"ix":3},"o":{"a":0,"k":100,"ix":4},"w":{"a":0,"k":0,"ix":5},"lc":1,"lj":1,"ml":4,"bm":0,"nm":"Kontur 1","mn":"ADBE Vector Graphic - Stroke","hd":false},{"ty":"fl","c":{"k":[{"s":[0.161,0.165,0.608,1],"t":0,"i":{"x":[1],"y":[1]},"o":{"x":[0],"y":[0]}},{"s":[0.161,0.165,0.608,1],"t":105,"i":{"x":[1],"y":[1]},"o":{"x":[0],"y":[0]}}]},"o":{"a":0,"k":100,"ix":5},"r":1,"bm":0,"nm":"Fläche 1","mn":"ADBE Vector Graphic - Fill","hd":false},{"ty":"tr","p":{"a":0,"k":[0,0],"ix":2},"a":{"a":0,"k":[0,0],"ix":1},"s":{"a":0,"k":[100,100],"ix":3},"r":{"a":0,"k":0,"ix":6},"o":{"a":0,"k":100,"ix":7},"sk":{"a":0,"k":0,"ix":4},"sa":{"a":0,"k":0,"ix":5},"nm":"Transformieren"}],"nm":"Ellipse 1","np":3,"cix":2,"bm":0,"ix":1,"mn":"ADBE Vector Group","hd":false}],"ip":0,"op":240,"st":0,"bm":0}],"markers":[]}
|
res/lottie/Piping.json
ADDED
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settings.py
ADDED
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1 |
+
import configparser
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2 |
+
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3 |
+
class SettingManager:
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4 |
+
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5 |
+
def __init__(self):
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6 |
+
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7 |
+
#Accessing default config
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8 |
+
self.config=configparser.ConfigParser()
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9 |
+
self.config.read("config.ini")
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10 |
+
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11 |
+
#getting default params
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12 |
+
self.ai_assisted_search=self.config.getboolean('RAG','default_ai_assisted_search')
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13 |
+
self.max_new_token=self.config.getint("Settings", "MAX_NEW_TOKENS")
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14 |
+
self.top_p=self.config.getfloat("Settings", "TOP_P")
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15 |
+
self.defaultLLM=self.config.getint('Settings', 'DEFAULT_LLM')
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16 |
+
self.temperature=self.config.getfloat("Settings", "TEMPERATURE")
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17 |
+
self.repetition_penalty=self.config.getfloat("Settings", "REPETITION_PENALITY")
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18 |
+
self.system_prompt=self.config.get("Settings", "default_prompt")
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19 |
+
self.n_doc_return=self.config.getint('RAG','default_returned_docs')
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20 |
+
self.available_search_methods=self.config.get('RAG','methods').split(',')
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21 |
+
self.search_method=self.config.get('RAG','default_search_method')
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22 |
+
self.default_ai_search_prompt=self.config.get("RAG", "default_ai_search_prompt")
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23 |
+
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24 |
+
self.RAG_max_new_token=self.config.getint("RAG", "RAG_MAX_NEW_TOKENS")
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25 |
+
self.RAG_top_p=self.config.getfloat("RAG", "RAG_TOP_P")
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26 |
+
self.RAG_temperature=self.config.getfloat("RAG", "RAG_TEMPERATURE")
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27 |
+
self.RAG_repetition_penalty=self.config.getfloat("RAG", "RAG_REPETITION_PENALITY")
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28 |
+
|
29 |
+
#Loading available LLM
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30 |
+
self.listLLM=self.get_llm()
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31 |
+
self.listLLMMap=self.get_llm_map()
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32 |
+
|
33 |
+
#Function used to get a list of available LLM
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34 |
+
def get_llm(self):
|
35 |
+
llm_section = 'LLM'
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36 |
+
if llm_section in self.config:
|
37 |
+
return [self.config.get(llm_section, llm) for llm in self.config[llm_section]]
|
38 |
+
else:
|
39 |
+
return []
|
40 |
+
|
41 |
+
#Function used to get a list of available LLM
|
42 |
+
def get_llm_prompts(self):
|
43 |
+
prompt_section = 'Prompt_map'
|
44 |
+
if prompt_section in self.config:
|
45 |
+
return [self.config.get(prompt_section, llm) for llm in self.config[prompt_section]]
|
46 |
+
else:
|
47 |
+
return []
|
48 |
+
|
49 |
+
#Function used to get the list of llm Map
|
50 |
+
def get_llm_map(self):
|
51 |
+
llm_map_section = 'LLM_Map'
|
52 |
+
if llm_map_section in self.config:
|
53 |
+
return [self.config.get(llm_map_section, llm) for llm in self.config[llm_map_section]]
|
54 |
+
else:
|
55 |
+
return []
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utils.py
ADDED
@@ -0,0 +1,22 @@
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|
1 |
+
import os
|
2 |
+
|
3 |
+
#This function is used to retrive the latest version of a certain Embeddings
|
4 |
+
def getLatestEMBVersion(emb):
|
5 |
+
dir=os.path.join("data",emb)
|
6 |
+
folders = [f for f in os.listdir(dir) if os.path.isdir(os.path.join(dir, f))]
|
7 |
+
|
8 |
+
max_version = 0
|
9 |
+
max_version_folder = None
|
10 |
+
|
11 |
+
for folder in folders:
|
12 |
+
try:
|
13 |
+
# Extract the version number from the folder name
|
14 |
+
version = int(folder[1:])
|
15 |
+
if version > max_version:
|
16 |
+
max_version = version
|
17 |
+
max_version_folder = folder
|
18 |
+
except ValueError:
|
19 |
+
# Ignore folders that don't match the expected format
|
20 |
+
pass
|
21 |
+
|
22 |
+
return max_version_folder
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