init

.env

@@ -0,0 +1,6 @@
+CUDA_VISIBLE_DEVICES=0
+FORCE_CMAKE=1
+CMAKE_ARGS="-DLLAMA_CUBLAS=on"
+no_proxy=localhost,127.0.0.1
+OPENAI_API_KEY=NOONEEED
+OPENAI_API_BASE=http://localhost:1300/v1

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/hkunlp_instructor-base/*gguf filter=lfs diff=lfs merge=lfs -text
+models/*gguf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+/draft_docs
+short_memory.txt

README.md

@@ -1,7 +1,7 @@
 ---
 title: Llama Index Docs Spaces
-emoji: 🚀
-colorFrom: gray
+emoji: 🌍
+colorFrom: purple
 colorTo: indigo
 sdk: streamlit
 sdk_version: 1.28.2

app.py

@@ -0,0 +1,228 @@
+# --------------------------------libraries-----------------------------------
+
+import streamlit as st
+#import torch
+import os
+import logging
+import sys
+from llama_index.callbacks import CallbackManager, LlamaDebugHandler
+from llama_index.llms import LlamaCPP
+from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
+from llama_index.embeddings import InstructorEmbedding
+from llama_index import ServiceContext, VectorStoreIndex, SimpleDirectoryReader
+from tqdm.notebook import tqdm
+from dotenv import load_dotenv
+
+# --------------------------------env variables-----------------------------------
+
+# Load environment variables
+load_dotenv(dotenv_path=".env")
+
+no_proxy = os.getenv("no_proxy")
+OPENAI_API_KEY =  os.getenv("OPENAI_API_KEY")
+OPENAI_API_BASE = os.getenv("OPENAI_API_BASE")
+
+# --------------------------------cache LLM-----------------------------------
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)  
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+llama_debug = LlamaDebugHandler(print_trace_on_end=True)
+callback_manager = CallbackManager([llama_debug])
+# LLM
+@st.cache_resource
+def load_llm_model():
+    if not os.path.exists("models"):
+        st.error("models directory does not exist. Please download and copy paste a model in folder models.")
+        os.makedirs("models")
+        return None  # 
+    llm = LlamaCPP(
+        #model_url="https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q5_K_M.gguf",
+        model_path="models/dolphin-2.1-mistral-7b.Q4_K_S.gguf",
+        temperature=0.0,
+        max_new_tokens=100,
+        context_window=1024,  
+        generate_kwargs={},
+        model_kwargs={"n_gpu_layers": 20}, 
+        messages_to_prompt=messages_to_prompt,
+        completion_to_prompt=completion_to_prompt,
+        verbose=True,
+    )
+    return llm
+
+llm = load_llm_model()
+
+# --------------------------------cache Embedding model-----------------------------------
+
+@st.cache_resource
+def load_emb_model():
+    if not os.path.exists("data"):
+        st.error("Data directory does not exist. Please upload the data.")
+        os.makedirs("data")
+        return None  # 
+    embed_model_inst = InstructorEmbedding("models/hkunlp_instructor-base"
+        #model_name="hkunlp/instructor-base"
+        )
+    service_context = ServiceContext.from_defaults(embed_model=embed_model_inst, llm=llm)
+    documents = SimpleDirectoryReader("data").load_data()
+    print(f"Number of documents: {len(documents)}")
+    index = VectorStoreIndex.from_documents(
+        documents, service_context=service_context, show_progress=True)
+    return index.as_query_engine()
+
+query_engine = load_emb_model()
+
+# ------------------------------------layout----------------------------------------
+
+with st.sidebar:
+    api_server_info = st.text_input("Local LLM API server", OPENAI_API_BASE ,key="openai_api_base")
+    st.title("🤖 Llama Index 📚")
+    if st.button('Clear Memory'):
+        st.session_state.memory = ""
+    st.write("Local LLM API server in this demo is useles, we are loading local model using llama_index integration of llama cpp")
+    st.write("🚀 This app allows you to chat with local LLM using api server or loaded in cache")
+    st.subheader("💻 System Requirements: ")
+    st.markdown("- CPU: the faster the better ")
+    st.markdown("- RAM: 16 GB or higher")
+    st.markdown("- GPU: optional but very useful for Cuda acceleration")
+    st.subheader("Developer Information:")
+    st.write("This app is developed and maintained by **@mohcineelharras**")
+
+# Define your app's tabs
+tab1, tab2, tab3 = st.tabs(["LLM only", "LLM RAG QA with database", "One single document Q&A"])
+
+# -----------------------------------LLM only--------------------------------------------- 
+if 'memory' not in st.session_state:
+    st.session_state.memory = ""
+#token_count = 0
+with tab1:
+    st.title("💬 LLM only")
+    prompt = st.text_input(
+        "Ask your question here",
+        placeholder="Who is Lionel Messi",
+    )
+    template = (
+        "system\n"
+        "You are Dolphin, a helpful AI assistant. Your responses should be based solely on the content of documents you have access to. "
+        "Do not provide information that is not contained in the documents. "
+        "If a question is asked about content not in the documents, respond with 'I do not have that information.' "
+        "Always respond in the same language as the question was asked. Be concise.\n"
+        "user\n"
+        "{prompt}\n"
+        "assistant\n"
+    )
+    if prompt:
+        contextual_prompt = st.session_state.memory + "\n" + prompt
+        formatted_prompt = template.format(prompt=contextual_prompt)
+
+        response = llm.complete(formatted_prompt,max_tokens=100, temperature=0, top_p=0.95, top_k=10)
+        #print(response)
+        text_response = response
+        #---------------------------------------------
+        # text_response = response["choices"][0]["text"]
+        # token_count += response["usage"]["total_tokens"]
+        # st.write("LLM's Response:\n", text_response)
+        # st.write("Token count:\n", token_count)
+        #---------------------------------------------
+        st.write("LLM's Response:\n",text_response)
+        st.session_state.memory = f"Prompt: {contextual_prompt}\nResponse:\n {text_response}"
+        #st.write("Memory:\n", memory)
+        with open("short_memory.txt", 'w') as file:
+            file.write(st.session_state.memory)
+
+# -----------------------------------LLM Q&A-------------------------------------------------    
+
+with tab2:
+    st.title("💬 LLM RAG QA with database")
+    st.write("To consult files that are available in the database, go to https://huggingface.co/spaces/mohcineelharras/llama-index-docs-spaces/blob/main/data")
+    prompt = st.text_input(
+        "Ask your question here",
+        placeholder="How does the blockchain work ?",
+    )
+    if prompt:
+        response = query_engine.query(prompt)
+        st.write("Your prompt: ", prompt)
+        st.write("LLM's Response:\n"+ response.response)
+        with st.expander("Document Similarity Search"):
+            for i, node in enumerate(response.source_nodes):
+                dict_source_i = node.node.metadata
+                dict_source_i.update({"Text":node.node.text})
+                st.write("Source n°"+str(i+1), dict_source_i)
+                st.write()
+
+# -----------------------------------Upload File Q&A-----------------------------------------
+
+def load_emb_uploaded_document(filename):
+    # You may want to add a check to prevent execution during initialization.
+    if 'init' in st.session_state:
+        embed_model_inst = InstructorEmbedding("models/hkunlp_instructor-base")
+        service_context = ServiceContext.from_defaults(embed_model=embed_model_inst, llm=llm)
+        documents = SimpleDirectoryReader(input_files=[filename]).load_data()
+        index = VectorStoreIndex.from_documents(
+            documents, service_context=service_context, show_progress=True)
+        return index.as_query_engine()
+    return None
+
+with tab3:
+    st.title("📝 One single document Q&A with Llama Index using local open llms")
+    uploaded_file = st.file_uploader("Upload an File", type=("txt", "csv", "md","pdf"))
+    question = st.text_input(
+        "Ask something about the files",
+        placeholder="Can you give me a short summary?",
+        disabled=not uploaded_file,
+    )
+
+    if 'init' not in st.session_state:
+        st.session_state.init = True
+
+    if uploaded_file:
+        if not os.path.exists("draft_docs"):
+            st.error("draft_docs directory does not exist. Please download and copy paste a model in folder models.")
+            os.makedirs("draft_docs")
+
+        with open("draft_docs/"+uploaded_file.name, "wb") as f:
+            text = uploaded_file.read()
+            f.write(text)
+        text = uploaded_file.read()
+        # if load_emb_uploaded_document:
+        #     load_emb_uploaded_document.clear()
+        #load_emb_uploaded_document.clear()
+        query_engine = load_emb_uploaded_document("draft_docs/"+uploaded_file.name)
+        st.write("File ",uploaded_file.name, "was loaded successfully")
+
+    if uploaded_file and question and api_server_info:
+        response = prompt = f"""Based on the context presented. Respond to the question below to the best of your ability.
+        \n\n{question}"""
+        response = query_engine.query(prompt)
+        st.write("### Answer")
+        st.write(response.response)
+        with st.expander("Document Similarity Search"):
+            #st.write(len(response.source_nodes))
+            for i, node in enumerate(response.source_nodes):
+                dict_source_i = node.node.metadata
+                dict_source_i.update({"Text":node.node.text})
+                st.write("Source n°"+str(i+1), dict_source_i)
+                #st.write("Source n°"+str(i))
+                #st.write("Meta Data :", node.node.metadata)
+                #st.write("Text :", node.node.text)
+                #st.write()
+    #print("Is File uploaded : ",uploaded_file==True, "Is question asked : ", question==True, "Is question asked : ", api_server_info==True)
+
+st.markdown("""
+<div style="text-align: center; margin-top: 20px;">
+    <a href="https://github.com/mohcineelharras/llama-index-docs" target="_blank" style="margin: 10px; display: inline-block;">
+        <img src="https://img.shields.io/badge/Repository-333?logo=github&style=for-the-badge" alt="Repository" style="vertical-align: middle;">
+    </a>
+    <a href="https://www.linkedin.com/in/mohcine-el-harras" target="_blank" style="margin: 10px; display: inline-block;">
+        <img src="https://img.shields.io/badge/-LinkedIn-0077B5?style=for-the-badge&logo=linkedin" alt="LinkedIn" style="vertical-align: middle;">
+    </a>
+    <a href="https://mohcineelharras.github.io" target="_blank" style="margin: 10px; display: inline-block;">
+        <img src="https://img.shields.io/badge/Visit-Portfolio-9cf?style=for-the-badge" alt="GitHub" style="vertical-align: middle;">
+    </a>
+</div>
+<div style="text-align: center; margin-top: 20px; color: #666; font-size: 0.85em;">
+    © 2023 Mohcine EL HARRAS
+</div>
+""", unsafe_allow_html=True)
+
+
+# -----------------------------------end-----------------------------------------

data/doctest.txt

@@ -0,0 +1,3 @@
+Hi my name is Mohcine,
+I am 25 years old
+I am a freelancer

models/dolphin-2.1-mistral-7b.Q4_K_S.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6fa0795eeac9ac8835a7f85ed398cf1a0881d3c9f40ee4bab51a5fd8838f68f9
+size 4140384992

models/hkunlp_instructor-base/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

models/hkunlp_instructor-base/1_Pooling/config.json

@@ -0,0 +1,9 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
+}

models/hkunlp_instructor-base/2_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 768, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}

models/hkunlp_instructor-base/2_Dense/pytorch_model.bin

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+oid sha256:b4e9301dfb3d947e4e2bcc6c8c9c8da58d1c07c11db7eb5e9e1a14749883f719
+size 2360171

models/hkunlp_instructor-base/README.md

@@ -0,0 +1,2610 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- text-embedding
+- embeddings
+- information-retrieval
+- beir
+- text-classification
+- language-model
+- text-clustering
+- text-semantic-similarity
+- text-evaluation
+- prompt-retrieval
+- text-reranking
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- t5
+- English
+- Sentence Similarity
+- natural_questions
+- ms_marco
+- fever
+- hotpot_qa
+- mteb
+language: en
+inference: false
+license: apache-2.0
+model-index:
+- name: final_base_results
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 86.2089552238806
+    - type: ap
+      value: 55.76273850794966
+    - type: f1
+      value: 81.26104211414781
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 88.35995000000001
+    - type: ap
+      value: 84.18839957309655
+    - type: f1
+      value: 88.317619250081
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 44.64
+    - type: f1
+      value: 42.48663956478136
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.383000000000003
+    - type: map_at_10
+      value: 43.024
+    - type: map_at_100
+      value: 44.023
+    - type: map_at_1000
+      value: 44.025999999999996
+    - type: map_at_3
+      value: 37.684
+    - type: map_at_5
+      value: 40.884
+    - type: mrr_at_1
+      value: 28.094
+    - type: mrr_at_10
+      value: 43.315
+    - type: mrr_at_100
+      value: 44.313
+    - type: mrr_at_1000
+      value: 44.317
+    - type: mrr_at_3
+      value: 37.862
+    - type: mrr_at_5
+      value: 41.155
+    - type: ndcg_at_1
+      value: 27.383000000000003
+    - type: ndcg_at_10
+      value: 52.032000000000004
+    - type: ndcg_at_100
+      value: 56.19499999999999
+    - type: ndcg_at_1000
+      value: 56.272
+    - type: ndcg_at_3
+      value: 41.166000000000004
+    - type: ndcg_at_5
+      value: 46.92
+    - type: precision_at_1
+      value: 27.383000000000003
+    - type: precision_at_10
+      value: 8.087
+    - type: precision_at_100
+      value: 0.989
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 17.093
+    - type: precision_at_5
+      value: 13.044
+    - type: recall_at_1
+      value: 27.383000000000003
+    - type: recall_at_10
+      value: 80.868
+    - type: recall_at_100
+      value: 98.86200000000001
+    - type: recall_at_1000
+      value: 99.431
+    - type: recall_at_3
+      value: 51.28
+    - type: recall_at_5
+      value: 65.22
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 39.68441054431849
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 29.188539728343844
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 63.173362687519784
+    - type: mrr
+      value: 76.18860748362133
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_spearman
+      value: 82.30789953771232
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 77.03571428571428
+    - type: f1
+      value: 75.87384305045917
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 32.98041170516364
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 25.71652988451154
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.739999999999995
+    - type: map_at_10
+      value: 46.197
+    - type: map_at_100
+      value: 47.814
+    - type: map_at_1000
+      value: 47.934
+    - type: map_at_3
+      value: 43.091
+    - type: map_at_5
+      value: 44.81
+    - type: mrr_at_1
+      value: 41.059
+    - type: mrr_at_10
+      value: 52.292
+    - type: mrr_at_100
+      value: 52.978
+    - type: mrr_at_1000
+      value: 53.015
+    - type: mrr_at_3
+      value: 49.976
+    - type: mrr_at_5
+      value: 51.449999999999996
+    - type: ndcg_at_1
+      value: 41.059
+    - type: ndcg_at_10
+      value: 52.608
+    - type: ndcg_at_100
+      value: 57.965
+    - type: ndcg_at_1000
+      value: 59.775999999999996
+    - type: ndcg_at_3
+      value: 48.473
+    - type: ndcg_at_5
+      value: 50.407999999999994
+    - type: precision_at_1
+      value: 41.059
+    - type: precision_at_10
+      value: 9.943
+    - type: precision_at_100
+      value: 1.6070000000000002
+    - type: precision_at_1000
+      value: 0.20500000000000002
+    - type: precision_at_3
+      value: 23.413999999999998
+    - type: precision_at_5
+      value: 16.481
+    - type: recall_at_1
+      value: 33.739999999999995
+    - type: recall_at_10
+      value: 63.888999999999996
+    - type: recall_at_100
+      value: 85.832
+    - type: recall_at_1000
+      value: 97.475
+    - type: recall_at_3
+      value: 51.953
+    - type: recall_at_5
+      value: 57.498000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.169999999999998
+    - type: map_at_10
+      value: 41.455
+    - type: map_at_100
+      value: 42.716
+    - type: map_at_1000
+      value: 42.847
+    - type: map_at_3
+      value: 38.568999999999996
+    - type: map_at_5
+      value: 40.099000000000004
+    - type: mrr_at_1
+      value: 39.427
+    - type: mrr_at_10
+      value: 47.818
+    - type: mrr_at_100
+      value: 48.519
+    - type: mrr_at_1000
+      value: 48.558
+    - type: mrr_at_3
+      value: 45.86
+    - type: mrr_at_5
+      value: 46.936
+    - type: ndcg_at_1
+      value: 39.427
+    - type: ndcg_at_10
+      value: 47.181
+    - type: ndcg_at_100
+      value: 51.737
+    - type: ndcg_at_1000
+      value: 53.74
+    - type: ndcg_at_3
+      value: 43.261
+    - type: ndcg_at_5
+      value: 44.891
+    - type: precision_at_1
+      value: 39.427
+    - type: precision_at_10
+      value: 8.847
+    - type: precision_at_100
+      value: 1.425
+    - type: precision_at_1000
+      value: 0.189
+    - type: precision_at_3
+      value: 20.785999999999998
+    - type: precision_at_5
+      value: 14.560999999999998
+    - type: recall_at_1
+      value: 31.169999999999998
+    - type: recall_at_10
+      value: 56.971000000000004
+    - type: recall_at_100
+      value: 76.31400000000001
+    - type: recall_at_1000
+      value: 88.93900000000001
+    - type: recall_at_3
+      value: 45.208
+    - type: recall_at_5
+      value: 49.923
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.682
+    - type: map_at_10
+      value: 52.766000000000005
+    - type: map_at_100
+      value: 53.84100000000001
+    - type: map_at_1000
+      value: 53.898
+    - type: map_at_3
+      value: 49.291000000000004
+    - type: map_at_5
+      value: 51.365
+    - type: mrr_at_1
+      value: 45.266
+    - type: mrr_at_10
+      value: 56.093
+    - type: mrr_at_100
+      value: 56.763
+    - type: mrr_at_1000
+      value: 56.793000000000006
+    - type: mrr_at_3
+      value: 53.668000000000006
+    - type: mrr_at_5
+      value: 55.1
+    - type: ndcg_at_1
+      value: 45.266
+    - type: ndcg_at_10
+      value: 58.836
+    - type: ndcg_at_100
+      value: 62.863
+    - type: ndcg_at_1000
+      value: 63.912
+    - type: ndcg_at_3
+      value: 53.19199999999999
+    - type: ndcg_at_5
+      value: 56.125
+    - type: precision_at_1
+      value: 45.266
+    - type: precision_at_10
+      value: 9.492
+    - type: precision_at_100
+      value: 1.236
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 23.762
+    - type: precision_at_5
+      value: 16.414
+    - type: recall_at_1
+      value: 39.682
+    - type: recall_at_10
+      value: 73.233
+    - type: recall_at_100
+      value: 90.335
+    - type: recall_at_1000
+      value: 97.452
+    - type: recall_at_3
+      value: 58.562000000000005
+    - type: recall_at_5
+      value: 65.569
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.743
+    - type: map_at_10
+      value: 34.016000000000005
+    - type: map_at_100
+      value: 35.028999999999996
+    - type: map_at_1000
+      value: 35.113
+    - type: map_at_3
+      value: 31.763
+    - type: map_at_5
+      value: 33.013999999999996
+    - type: mrr_at_1
+      value: 28.927000000000003
+    - type: mrr_at_10
+      value: 36.32
+    - type: mrr_at_100
+      value: 37.221
+    - type: mrr_at_1000
+      value: 37.281
+    - type: mrr_at_3
+      value: 34.105000000000004
+    - type: mrr_at_5
+      value: 35.371
+    - type: ndcg_at_1
+      value: 28.927000000000003
+    - type: ndcg_at_10
+      value: 38.474000000000004
+    - type: ndcg_at_100
+      value: 43.580000000000005
+    - type: ndcg_at_1000
+      value: 45.64
+    - type: ndcg_at_3
+      value: 34.035
+    - type: ndcg_at_5
+      value: 36.186
+    - type: precision_at_1
+      value: 28.927000000000003
+    - type: precision_at_10
+      value: 5.74
+    - type: precision_at_100
+      value: 0.8710000000000001
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 14.124
+    - type: precision_at_5
+      value: 9.74
+    - type: recall_at_1
+      value: 26.743
+    - type: recall_at_10
+      value: 49.955
+    - type: recall_at_100
+      value: 73.904
+    - type: recall_at_1000
+      value: 89.133
+    - type: recall_at_3
+      value: 38.072
+    - type: recall_at_5
+      value: 43.266
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.928
+    - type: map_at_10
+      value: 23.549
+    - type: map_at_100
+      value: 24.887
+    - type: map_at_1000
+      value: 25.018
+    - type: map_at_3
+      value: 21.002000000000002
+    - type: map_at_5
+      value: 22.256
+    - type: mrr_at_1
+      value: 21.02
+    - type: mrr_at_10
+      value: 27.898
+    - type: mrr_at_100
+      value: 29.018
+    - type: mrr_at_1000
+      value: 29.099999999999998
+    - type: mrr_at_3
+      value: 25.456
+    - type: mrr_at_5
+      value: 26.625
+    - type: ndcg_at_1
+      value: 21.02
+    - type: ndcg_at_10
+      value: 28.277
+    - type: ndcg_at_100
+      value: 34.54
+    - type: ndcg_at_1000
+      value: 37.719
+    - type: ndcg_at_3
+      value: 23.707
+    - type: ndcg_at_5
+      value: 25.482
+    - type: precision_at_1
+      value: 21.02
+    - type: precision_at_10
+      value: 5.361
+    - type: precision_at_100
+      value: 0.9809999999999999
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 11.401
+    - type: precision_at_5
+      value: 8.209
+    - type: recall_at_1
+      value: 16.928
+    - type: recall_at_10
+      value: 38.601
+    - type: recall_at_100
+      value: 65.759
+    - type: recall_at_1000
+      value: 88.543
+    - type: recall_at_3
+      value: 25.556
+    - type: recall_at_5
+      value: 30.447000000000003
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.549000000000003
+    - type: map_at_10
+      value: 38.426
+    - type: map_at_100
+      value: 39.845000000000006
+    - type: map_at_1000
+      value: 39.956
+    - type: map_at_3
+      value: 35.372
+    - type: map_at_5
+      value: 37.204
+    - type: mrr_at_1
+      value: 35.034
+    - type: mrr_at_10
+      value: 44.041000000000004
+    - type: mrr_at_100
+      value: 44.95
+    - type: mrr_at_1000
+      value: 44.997
+    - type: mrr_at_3
+      value: 41.498000000000005
+    - type: mrr_at_5
+      value: 43.077
+    - type: ndcg_at_1
+      value: 35.034
+    - type: ndcg_at_10
+      value: 44.218
+    - type: ndcg_at_100
+      value: 49.958000000000006
+    - type: ndcg_at_1000
+      value: 52.019000000000005
+    - type: ndcg_at_3
+      value: 39.34
+    - type: ndcg_at_5
+      value: 41.892
+    - type: precision_at_1
+      value: 35.034
+    - type: precision_at_10
+      value: 7.911
+    - type: precision_at_100
+      value: 1.26
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_3
+      value: 18.511
+    - type: precision_at_5
+      value: 13.205
+    - type: recall_at_1
+      value: 28.549000000000003
+    - type: recall_at_10
+      value: 56.035999999999994
+    - type: recall_at_100
+      value: 79.701
+    - type: recall_at_1000
+      value: 93.149
+    - type: recall_at_3
+      value: 42.275
+    - type: recall_at_5
+      value: 49.097
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.391000000000002
+    - type: map_at_10
+      value: 39.48
+    - type: map_at_100
+      value: 40.727000000000004
+    - type: map_at_1000
+      value: 40.835
+    - type: map_at_3
+      value: 36.234
+    - type: map_at_5
+      value: 37.877
+    - type: mrr_at_1
+      value: 35.959
+    - type: mrr_at_10
+      value: 44.726
+    - type: mrr_at_100
+      value: 45.531
+    - type: mrr_at_1000
+      value: 45.582
+    - type: mrr_at_3
+      value: 42.047000000000004
+    - type: mrr_at_5
+      value: 43.611
+    - type: ndcg_at_1
+      value: 35.959
+    - type: ndcg_at_10
+      value: 45.303
+    - type: ndcg_at_100
+      value: 50.683
+    - type: ndcg_at_1000
+      value: 52.818
+    - type: ndcg_at_3
+      value: 39.987
+    - type: ndcg_at_5
+      value: 42.243
+    - type: precision_at_1
+      value: 35.959
+    - type: precision_at_10
+      value: 8.241999999999999
+    - type: precision_at_100
+      value: 1.274
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 18.836
+    - type: precision_at_5
+      value: 13.196
+    - type: recall_at_1
+      value: 29.391000000000002
+    - type: recall_at_10
+      value: 57.364000000000004
+    - type: recall_at_100
+      value: 80.683
+    - type: recall_at_1000
+      value: 94.918
+    - type: recall_at_3
+      value: 42.263
+    - type: recall_at_5
+      value: 48.634
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.791749999999997
+    - type: map_at_10
+      value: 35.75541666666667
+    - type: map_at_100
+      value: 37.00791666666667
+    - type: map_at_1000
+      value: 37.12408333333333
+    - type: map_at_3
+      value: 33.02966666666667
+    - type: map_at_5
+      value: 34.56866666666667
+    - type: mrr_at_1
+      value: 31.744333333333337
+    - type: mrr_at_10
+      value: 39.9925
+    - type: mrr_at_100
+      value: 40.86458333333333
+    - type: mrr_at_1000
+      value: 40.92175000000001
+    - type: mrr_at_3
+      value: 37.68183333333334
+    - type: mrr_at_5
+      value: 39.028499999999994
+    - type: ndcg_at_1
+      value: 31.744333333333337
+    - type: ndcg_at_10
+      value: 40.95008333333334
+    - type: ndcg_at_100
+      value: 46.25966666666667
+    - type: ndcg_at_1000
+      value: 48.535333333333334
+    - type: ndcg_at_3
+      value: 36.43333333333333
+    - type: ndcg_at_5
+      value: 38.602333333333334
+    - type: precision_at_1
+      value: 31.744333333333337
+    - type: precision_at_10
+      value: 7.135166666666666
+    - type: precision_at_100
+      value: 1.1535833333333334
+    - type: precision_at_1000
+      value: 0.15391666666666665
+    - type: precision_at_3
+      value: 16.713
+    - type: precision_at_5
+      value: 11.828416666666666
+    - type: recall_at_1
+      value: 26.791749999999997
+    - type: recall_at_10
+      value: 51.98625
+    - type: recall_at_100
+      value: 75.30358333333334
+    - type: recall_at_1000
+      value: 91.05433333333333
+    - type: recall_at_3
+      value: 39.39583333333333
+    - type: recall_at_5
+      value: 45.05925
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.219
+    - type: map_at_10
+      value: 29.162
+    - type: map_at_100
+      value: 30.049999999999997
+    - type: map_at_1000
+      value: 30.144
+    - type: map_at_3
+      value: 27.204
+    - type: map_at_5
+      value: 28.351
+    - type: mrr_at_1
+      value: 25.153
+    - type: mrr_at_10
+      value: 31.814999999999998
+    - type: mrr_at_100
+      value: 32.573
+    - type: mrr_at_1000
+      value: 32.645
+    - type: mrr_at_3
+      value: 29.934
+    - type: mrr_at_5
+      value: 30.946
+    - type: ndcg_at_1
+      value: 25.153
+    - type: ndcg_at_10
+      value: 33.099000000000004
+    - type: ndcg_at_100
+      value: 37.768
+    - type: ndcg_at_1000
+      value: 40.331
+    - type: ndcg_at_3
+      value: 29.473
+    - type: ndcg_at_5
+      value: 31.206
+    - type: precision_at_1
+      value: 25.153
+    - type: precision_at_10
+      value: 5.183999999999999
+    - type: precision_at_100
+      value: 0.8170000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 12.831999999999999
+    - type: precision_at_5
+      value: 8.895999999999999
+    - type: recall_at_1
+      value: 22.219
+    - type: recall_at_10
+      value: 42.637
+    - type: recall_at_100
+      value: 64.704
+    - type: recall_at_1000
+      value: 83.963
+    - type: recall_at_3
+      value: 32.444
+    - type: recall_at_5
+      value: 36.802
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.427999999999997
+    - type: map_at_10
+      value: 24.029
+    - type: map_at_100
+      value: 25.119999999999997
+    - type: map_at_1000
+      value: 25.257
+    - type: map_at_3
+      value: 22.016
+    - type: map_at_5
+      value: 23.143
+    - type: mrr_at_1
+      value: 21.129
+    - type: mrr_at_10
+      value: 27.750000000000004
+    - type: mrr_at_100
+      value: 28.666999999999998
+    - type: mrr_at_1000
+      value: 28.754999999999995
+    - type: mrr_at_3
+      value: 25.849
+    - type: mrr_at_5
+      value: 26.939999999999998
+    - type: ndcg_at_1
+      value: 21.129
+    - type: ndcg_at_10
+      value: 28.203
+    - type: ndcg_at_100
+      value: 33.44
+    - type: ndcg_at_1000
+      value: 36.61
+    - type: ndcg_at_3
+      value: 24.648999999999997
+    - type: ndcg_at_5
+      value: 26.316
+    - type: precision_at_1
+      value: 21.129
+    - type: precision_at_10
+      value: 5.055
+    - type: precision_at_100
+      value: 0.909
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 11.666
+    - type: precision_at_5
+      value: 8.3
+    - type: recall_at_1
+      value: 17.427999999999997
+    - type: recall_at_10
+      value: 36.923
+    - type: recall_at_100
+      value: 60.606
+    - type: recall_at_1000
+      value: 83.19
+    - type: recall_at_3
+      value: 26.845000000000002
+    - type: recall_at_5
+      value: 31.247000000000003
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.457000000000004
+    - type: map_at_10
+      value: 35.228
+    - type: map_at_100
+      value: 36.475
+    - type: map_at_1000
+      value: 36.585
+    - type: map_at_3
+      value: 32.444
+    - type: map_at_5
+      value: 34.046
+    - type: mrr_at_1
+      value: 30.784
+    - type: mrr_at_10
+      value: 39.133
+    - type: mrr_at_100
+      value: 40.11
+    - type: mrr_at_1000
+      value: 40.169
+    - type: mrr_at_3
+      value: 36.692
+    - type: mrr_at_5
+      value: 38.17
+    - type: ndcg_at_1
+      value: 30.784
+    - type: ndcg_at_10
+      value: 40.358
+    - type: ndcg_at_100
+      value: 46.119
+    - type: ndcg_at_1000
+      value: 48.428
+    - type: ndcg_at_3
+      value: 35.504000000000005
+    - type: ndcg_at_5
+      value: 37.864
+    - type: precision_at_1
+      value: 30.784
+    - type: precision_at_10
+      value: 6.800000000000001
+    - type: precision_at_100
+      value: 1.083
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 15.920000000000002
+    - type: precision_at_5
+      value: 11.437
+    - type: recall_at_1
+      value: 26.457000000000004
+    - type: recall_at_10
+      value: 51.845
+    - type: recall_at_100
+      value: 77.046
+    - type: recall_at_1000
+      value: 92.892
+    - type: recall_at_3
+      value: 38.89
+    - type: recall_at_5
+      value: 44.688
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.378999999999998
+    - type: map_at_10
+      value: 37.373
+    - type: map_at_100
+      value: 39.107
+    - type: map_at_1000
+      value: 39.317
+    - type: map_at_3
+      value: 34.563
+    - type: map_at_5
+      value: 36.173
+    - type: mrr_at_1
+      value: 35.178
+    - type: mrr_at_10
+      value: 42.44
+    - type: mrr_at_100
+      value: 43.434
+    - type: mrr_at_1000
+      value: 43.482
+    - type: mrr_at_3
+      value: 39.987
+    - type: mrr_at_5
+      value: 41.370000000000005
+    - type: ndcg_at_1
+      value: 35.178
+    - type: ndcg_at_10
+      value: 42.82
+    - type: ndcg_at_100
+      value: 48.935
+    - type: ndcg_at_1000
+      value: 51.28
+    - type: ndcg_at_3
+      value: 38.562999999999995
+    - type: ndcg_at_5
+      value: 40.687
+    - type: precision_at_1
+      value: 35.178
+    - type: precision_at_10
+      value: 7.945
+    - type: precision_at_100
+      value: 1.524
+    - type: precision_at_1000
+      value: 0.242
+    - type: precision_at_3
+      value: 17.721
+    - type: precision_at_5
+      value: 12.925
+    - type: recall_at_1
+      value: 29.378999999999998
+    - type: recall_at_10
+      value: 52.141999999999996
+    - type: recall_at_100
+      value: 79.49000000000001
+    - type: recall_at_1000
+      value: 93.782
+    - type: recall_at_3
+      value: 39.579
+    - type: recall_at_5
+      value: 45.462
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.814999999999998
+    - type: map_at_10
+      value: 27.383999999999997
+    - type: map_at_100
+      value: 28.483999999999998
+    - type: map_at_1000
+      value: 28.585
+    - type: map_at_3
+      value: 24.807000000000002
+    - type: map_at_5
+      value: 26.485999999999997
+    - type: mrr_at_1
+      value: 21.996
+    - type: mrr_at_10
+      value: 29.584
+    - type: mrr_at_100
+      value: 30.611
+    - type: mrr_at_1000
+      value: 30.684
+    - type: mrr_at_3
+      value: 27.11
+    - type: mrr_at_5
+      value: 28.746
+    - type: ndcg_at_1
+      value: 21.996
+    - type: ndcg_at_10
+      value: 32.024
+    - type: ndcg_at_100
+      value: 37.528
+    - type: ndcg_at_1000
+      value: 40.150999999999996
+    - type: ndcg_at_3
+      value: 27.016000000000002
+    - type: ndcg_at_5
+      value: 29.927999999999997
+    - type: precision_at_1
+      value: 21.996
+    - type: precision_at_10
+      value: 5.102
+    - type: precision_at_100
+      value: 0.856
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 11.583
+    - type: precision_at_5
+      value: 8.577
+    - type: recall_at_1
+      value: 19.814999999999998
+    - type: recall_at_10
+      value: 44.239
+    - type: recall_at_100
+      value: 69.269
+    - type: recall_at_1000
+      value: 89.216
+    - type: recall_at_3
+      value: 31.102999999999998
+    - type: recall_at_5
+      value: 38.078
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 11.349
+    - type: map_at_10
+      value: 19.436
+    - type: map_at_100
+      value: 21.282999999999998
+    - type: map_at_1000
+      value: 21.479
+    - type: map_at_3
+      value: 15.841
+    - type: map_at_5
+      value: 17.558
+    - type: mrr_at_1
+      value: 25.863000000000003
+    - type: mrr_at_10
+      value: 37.218
+    - type: mrr_at_100
+      value: 38.198
+    - type: mrr_at_1000
+      value: 38.236
+    - type: mrr_at_3
+      value: 33.409
+    - type: mrr_at_5
+      value: 35.602000000000004
+    - type: ndcg_at_1
+      value: 25.863000000000003
+    - type: ndcg_at_10
+      value: 27.953
+    - type: ndcg_at_100
+      value: 35.327
+    - type: ndcg_at_1000
+      value: 38.708999999999996
+    - type: ndcg_at_3
+      value: 21.985
+    - type: ndcg_at_5
+      value: 23.957
+    - type: precision_at_1
+      value: 25.863000000000003
+    - type: precision_at_10
+      value: 8.99
+    - type: precision_at_100
+      value: 1.6889999999999998
+    - type: precision_at_1000
+      value: 0.232
+    - type: precision_at_3
+      value: 16.308
+    - type: precision_at_5
+      value: 12.912
+    - type: recall_at_1
+      value: 11.349
+    - type: recall_at_10
+      value: 34.581
+    - type: recall_at_100
+      value: 60.178
+    - type: recall_at_1000
+      value: 78.88199999999999
+    - type: recall_at_3
+      value: 20.041999999999998
+    - type: recall_at_5
+      value: 25.458
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 7.893
+    - type: map_at_10
+      value: 15.457
+    - type: map_at_100
+      value: 20.905
+    - type: map_at_1000
+      value: 22.116
+    - type: map_at_3
+      value: 11.593
+    - type: map_at_5
+      value: 13.134
+    - type: mrr_at_1
+      value: 57.49999999999999
+    - type: mrr_at_10
+      value: 65.467
+    - type: mrr_at_100
+      value: 66.022
+    - type: mrr_at_1000
+      value: 66.039
+    - type: mrr_at_3
+      value: 63.458000000000006
+    - type: mrr_at_5
+      value: 64.546
+    - type: ndcg_at_1
+      value: 45.875
+    - type: ndcg_at_10
+      value: 33.344
+    - type: ndcg_at_100
+      value: 36.849
+    - type: ndcg_at_1000
+      value: 44.03
+    - type: ndcg_at_3
+      value: 37.504
+    - type: ndcg_at_5
+      value: 34.892
+    - type: precision_at_1
+      value: 57.49999999999999
+    - type: precision_at_10
+      value: 25.95
+    - type: precision_at_100
+      value: 7.89
+    - type: precision_at_1000
+      value: 1.669
+    - type: precision_at_3
+      value: 40.333000000000006
+    - type: precision_at_5
+      value: 33.050000000000004
+    - type: recall_at_1
+      value: 7.893
+    - type: recall_at_10
+      value: 20.724999999999998
+    - type: recall_at_100
+      value: 42.516
+    - type: recall_at_1000
+      value: 65.822
+    - type: recall_at_3
+      value: 12.615000000000002
+    - type: recall_at_5
+      value: 15.482000000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 51.760000000000005
+    - type: f1
+      value: 45.51690565701713
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 53.882
+    - type: map_at_10
+      value: 65.902
+    - type: map_at_100
+      value: 66.33
+    - type: map_at_1000
+      value: 66.348
+    - type: map_at_3
+      value: 63.75999999999999
+    - type: map_at_5
+      value: 65.181
+    - type: mrr_at_1
+      value: 58.041
+    - type: mrr_at_10
+      value: 70.133
+    - type: mrr_at_100
+      value: 70.463
+    - type: mrr_at_1000
+      value: 70.47
+    - type: mrr_at_3
+      value: 68.164
+    - type: mrr_at_5
+      value: 69.465
+    - type: ndcg_at_1
+      value: 58.041
+    - type: ndcg_at_10
+      value: 71.84700000000001
+    - type: ndcg_at_100
+      value: 73.699
+    - type: ndcg_at_1000
+      value: 74.06700000000001
+    - type: ndcg_at_3
+      value: 67.855
+    - type: ndcg_at_5
+      value: 70.203
+    - type: precision_at_1
+      value: 58.041
+    - type: precision_at_10
+      value: 9.427000000000001
+    - type: precision_at_100
+      value: 1.049
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 27.278000000000002
+    - type: precision_at_5
+      value: 17.693
+    - type: recall_at_1
+      value: 53.882
+    - type: recall_at_10
+      value: 85.99
+    - type: recall_at_100
+      value: 94.09100000000001
+    - type: recall_at_1000
+      value: 96.612
+    - type: recall_at_3
+      value: 75.25
+    - type: recall_at_5
+      value: 80.997
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.165
+    - type: map_at_10
+      value: 31.845000000000002
+    - type: map_at_100
+      value: 33.678999999999995
+    - type: map_at_1000
+      value: 33.878
+    - type: map_at_3
+      value: 27.881
+    - type: map_at_5
+      value: 30.049999999999997
+    - type: mrr_at_1
+      value: 38.272
+    - type: mrr_at_10
+      value: 47.04
+    - type: mrr_at_100
+      value: 47.923
+    - type: mrr_at_1000
+      value: 47.973
+    - type: mrr_at_3
+      value: 44.985
+    - type: mrr_at_5
+      value: 46.150000000000006
+    - type: ndcg_at_1
+      value: 38.272
+    - type: ndcg_at_10
+      value: 39.177
+    - type: ndcg_at_100
+      value: 45.995000000000005
+    - type: ndcg_at_1000
+      value: 49.312
+    - type: ndcg_at_3
+      value: 36.135
+    - type: ndcg_at_5
+      value: 36.936
+    - type: precision_at_1
+      value: 38.272
+    - type: precision_at_10
+      value: 10.926
+    - type: precision_at_100
+      value: 1.809
+    - type: precision_at_1000
+      value: 0.23700000000000002
+    - type: precision_at_3
+      value: 24.331
+    - type: precision_at_5
+      value: 17.747
+    - type: recall_at_1
+      value: 19.165
+    - type: recall_at_10
+      value: 45.103
+    - type: recall_at_100
+      value: 70.295
+    - type: recall_at_1000
+      value: 90.592
+    - type: recall_at_3
+      value: 32.832
+    - type: recall_at_5
+      value: 37.905
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.397
+    - type: map_at_10
+      value: 44.83
+    - type: map_at_100
+      value: 45.716
+    - type: map_at_1000
+      value: 45.797
+    - type: map_at_3
+      value: 41.955999999999996
+    - type: map_at_5
+      value: 43.736999999999995
+    - type: mrr_at_1
+      value: 64.794
+    - type: mrr_at_10
+      value: 71.866
+    - type: mrr_at_100
+      value: 72.22
+    - type: mrr_at_1000
+      value: 72.238
+    - type: mrr_at_3
+      value: 70.416
+    - type: mrr_at_5
+      value: 71.304
+    - type: ndcg_at_1
+      value: 64.794
+    - type: ndcg_at_10
+      value: 54.186
+    - type: ndcg_at_100
+      value: 57.623000000000005
+    - type: ndcg_at_1000
+      value: 59.302
+    - type: ndcg_at_3
+      value: 49.703
+    - type: ndcg_at_5
+      value: 52.154999999999994
+    - type: precision_at_1
+      value: 64.794
+    - type: precision_at_10
+      value: 11.219
+    - type: precision_at_100
+      value: 1.394
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 30.767
+    - type: precision_at_5
+      value: 20.397000000000002
+    - type: recall_at_1
+      value: 32.397
+    - type: recall_at_10
+      value: 56.096999999999994
+    - type: recall_at_100
+      value: 69.696
+    - type: recall_at_1000
+      value: 80.88499999999999
+    - type: recall_at_3
+      value: 46.150999999999996
+    - type: recall_at_5
+      value: 50.993
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 81.1744
+    - type: ap
+      value: 75.44973697032414
+    - type: f1
+      value: 81.09901117955782
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.519000000000002
+    - type: map_at_10
+      value: 31.025000000000002
+    - type: map_at_100
+      value: 32.275999999999996
+    - type: map_at_1000
+      value: 32.329
+    - type: map_at_3
+      value: 27.132
+    - type: map_at_5
+      value: 29.415999999999997
+    - type: mrr_at_1
+      value: 20.115
+    - type: mrr_at_10
+      value: 31.569000000000003
+    - type: mrr_at_100
+      value: 32.768
+    - type: mrr_at_1000
+      value: 32.816
+    - type: mrr_at_3
+      value: 27.748
+    - type: mrr_at_5
+      value: 29.956
+    - type: ndcg_at_1
+      value: 20.115
+    - type: ndcg_at_10
+      value: 37.756
+    - type: ndcg_at_100
+      value: 43.858000000000004
+    - type: ndcg_at_1000
+      value: 45.199
+    - type: ndcg_at_3
+      value: 29.818
+    - type: ndcg_at_5
+      value: 33.875
+    - type: precision_at_1
+      value: 20.115
+    - type: precision_at_10
+      value: 6.122
+    - type: precision_at_100
+      value: 0.919
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 12.794
+    - type: precision_at_5
+      value: 9.731
+    - type: recall_at_1
+      value: 19.519000000000002
+    - type: recall_at_10
+      value: 58.62500000000001
+    - type: recall_at_100
+      value: 86.99
+    - type: recall_at_1000
+      value: 97.268
+    - type: recall_at_3
+      value: 37.002
+    - type: recall_at_5
+      value: 46.778
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.71865025079799
+    - type: f1
+      value: 93.38906173610519
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 70.2576379388965
+    - type: f1
+      value: 49.20405830249464
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 67.48486886348351
+    - type: f1
+      value: 64.92199176095157
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 72.59246805648958
+    - type: f1
+      value: 72.1222026389164
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 30.887642595096825
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.3764418784054
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.81544126336991
+    - type: mrr
+      value: 32.82666576268031
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.185
+    - type: map_at_10
+      value: 11.158
+    - type: map_at_100
+      value: 14.041
+    - type: map_at_1000
+      value: 15.360999999999999
+    - type: map_at_3
+      value: 8.417
+    - type: map_at_5
+      value: 9.378
+    - type: mrr_at_1
+      value: 44.582
+    - type: mrr_at_10
+      value: 53.083999999999996
+    - type: mrr_at_100
+      value: 53.787
+    - type: mrr_at_1000
+      value: 53.824000000000005
+    - type: mrr_at_3
+      value: 51.187000000000005
+    - type: mrr_at_5
+      value: 52.379
+    - type: ndcg_at_1
+      value: 42.57
+    - type: ndcg_at_10
+      value: 31.593
+    - type: ndcg_at_100
+      value: 29.093999999999998
+    - type: ndcg_at_1000
+      value: 37.909
+    - type: ndcg_at_3
+      value: 37.083
+    - type: ndcg_at_5
+      value: 34.397
+    - type: precision_at_1
+      value: 43.963
+    - type: precision_at_10
+      value: 23.498
+    - type: precision_at_100
+      value: 7.6160000000000005
+    - type: precision_at_1000
+      value: 2.032
+    - type: precision_at_3
+      value: 34.572
+    - type: precision_at_5
+      value: 29.412
+    - type: recall_at_1
+      value: 5.185
+    - type: recall_at_10
+      value: 15.234
+    - type: recall_at_100
+      value: 29.49
+    - type: recall_at_1000
+      value: 62.273999999999994
+    - type: recall_at_3
+      value: 9.55
+    - type: recall_at_5
+      value: 11.103
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.803
+    - type: map_at_10
+      value: 38.183
+    - type: map_at_100
+      value: 39.421
+    - type: map_at_1000
+      value: 39.464
+    - type: map_at_3
+      value: 33.835
+    - type: map_at_5
+      value: 36.327
+    - type: mrr_at_1
+      value: 26.68
+    - type: mrr_at_10
+      value: 40.439
+    - type: mrr_at_100
+      value: 41.415
+    - type: mrr_at_1000
+      value: 41.443999999999996
+    - type: mrr_at_3
+      value: 36.612
+    - type: mrr_at_5
+      value: 38.877
+    - type: ndcg_at_1
+      value: 26.68
+    - type: ndcg_at_10
+      value: 45.882
+    - type: ndcg_at_100
+      value: 51.227999999999994
+    - type: ndcg_at_1000
+      value: 52.207
+    - type: ndcg_at_3
+      value: 37.511
+    - type: ndcg_at_5
+      value: 41.749
+    - type: precision_at_1
+      value: 26.68
+    - type: precision_at_10
+      value: 7.9750000000000005
+    - type: precision_at_100
+      value: 1.0959999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 17.449
+    - type: precision_at_5
+      value: 12.897
+    - type: recall_at_1
+      value: 23.803
+    - type: recall_at_10
+      value: 67.152
+    - type: recall_at_100
+      value: 90.522
+    - type: recall_at_1000
+      value: 97.743
+    - type: recall_at_3
+      value: 45.338
+    - type: recall_at_5
+      value: 55.106
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.473
+    - type: map_at_10
+      value: 84.452
+    - type: map_at_100
+      value: 85.101
+    - type: map_at_1000
+      value: 85.115
+    - type: map_at_3
+      value: 81.435
+    - type: map_at_5
+      value: 83.338
+    - type: mrr_at_1
+      value: 81.19
+    - type: mrr_at_10
+      value: 87.324
+    - type: mrr_at_100
+      value: 87.434
+    - type: mrr_at_1000
+      value: 87.435
+    - type: mrr_at_3
+      value: 86.31
+    - type: mrr_at_5
+      value: 87.002
+    - type: ndcg_at_1
+      value: 81.21000000000001
+    - type: ndcg_at_10
+      value: 88.19
+    - type: ndcg_at_100
+      value: 89.44
+    - type: ndcg_at_1000
+      value: 89.526
+    - type: ndcg_at_3
+      value: 85.237
+    - type: ndcg_at_5
+      value: 86.892
+    - type: precision_at_1
+      value: 81.21000000000001
+    - type: precision_at_10
+      value: 13.417000000000002
+    - type: precision_at_100
+      value: 1.537
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.31
+    - type: precision_at_5
+      value: 24.59
+    - type: recall_at_1
+      value: 70.473
+    - type: recall_at_10
+      value: 95.367
+    - type: recall_at_100
+      value: 99.616
+    - type: recall_at_1000
+      value: 99.996
+    - type: recall_at_3
+      value: 86.936
+    - type: recall_at_5
+      value: 91.557
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 59.25776525253911
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 63.22135271663078
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.003
+    - type: map_at_10
+      value: 10.062999999999999
+    - type: map_at_100
+      value: 11.854000000000001
+    - type: map_at_1000
+      value: 12.145999999999999
+    - type: map_at_3
+      value: 7.242
+    - type: map_at_5
+      value: 8.652999999999999
+    - type: mrr_at_1
+      value: 19.7
+    - type: mrr_at_10
+      value: 29.721999999999998
+    - type: mrr_at_100
+      value: 30.867
+    - type: mrr_at_1000
+      value: 30.944
+    - type: mrr_at_3
+      value: 26.683
+    - type: mrr_at_5
+      value: 28.498
+    - type: ndcg_at_1
+      value: 19.7
+    - type: ndcg_at_10
+      value: 17.095
+    - type: ndcg_at_100
+      value: 24.375
+    - type: ndcg_at_1000
+      value: 29.831000000000003
+    - type: ndcg_at_3
+      value: 16.305
+    - type: ndcg_at_5
+      value: 14.291
+    - type: precision_at_1
+      value: 19.7
+    - type: precision_at_10
+      value: 8.799999999999999
+    - type: precision_at_100
+      value: 1.9349999999999998
+    - type: precision_at_1000
+      value: 0.32399999999999995
+    - type: precision_at_3
+      value: 15.2
+    - type: precision_at_5
+      value: 12.540000000000001
+    - type: recall_at_1
+      value: 4.003
+    - type: recall_at_10
+      value: 17.877000000000002
+    - type: recall_at_100
+      value: 39.217
+    - type: recall_at_1000
+      value: 65.862
+    - type: recall_at_3
+      value: 9.242
+    - type: recall_at_5
+      value: 12.715000000000002
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_spearman
+      value: 80.25888668589654
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_spearman
+      value: 77.02037527837669
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_spearman
+      value: 86.58432681008449
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_spearman
+      value: 81.31697756099051
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_spearman
+      value: 88.18867599667057
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_spearman
+      value: 84.87853941747623
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_spearman
+      value: 89.46479925383916
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_spearman
+      value: 66.45272113649146
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_spearman
+      value: 86.43357313527851
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 78.82761687254882
+    - type: mrr
+      value: 93.46223674655047
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 44.583
+    - type: map_at_10
+      value: 52.978
+    - type: map_at_100
+      value: 53.803
+    - type: map_at_1000
+      value: 53.839999999999996
+    - type: map_at_3
+      value: 50.03300000000001
+    - type: map_at_5
+      value: 51.939
+    - type: mrr_at_1
+      value: 47.0
+    - type: mrr_at_10
+      value: 54.730000000000004
+    - type: mrr_at_100
+      value: 55.31399999999999
+    - type: mrr_at_1000
+      value: 55.346
+    - type: mrr_at_3
+      value: 52.0
+    - type: mrr_at_5
+      value: 53.783
+    - type: ndcg_at_1
+      value: 47.0
+    - type: ndcg_at_10
+      value: 57.82899999999999
+    - type: ndcg_at_100
+      value: 61.49400000000001
+    - type: ndcg_at_1000
+      value: 62.676
+    - type: ndcg_at_3
+      value: 52.373000000000005
+    - type: ndcg_at_5
+      value: 55.481
+    - type: precision_at_1
+      value: 47.0
+    - type: precision_at_10
+      value: 7.867
+    - type: precision_at_100
+      value: 0.997
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 20.556
+    - type: precision_at_5
+      value: 14.066999999999998
+    - type: recall_at_1
+      value: 44.583
+    - type: recall_at_10
+      value: 71.172
+    - type: recall_at_100
+      value: 87.7
+    - type: recall_at_1000
+      value: 97.333
+    - type: recall_at_3
+      value: 56.511
+    - type: recall_at_5
+      value: 64.206
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.66237623762376
+    - type: cos_sim_ap
+      value: 90.35465126226322
+    - type: cos_sim_f1
+      value: 82.44575936883628
+    - type: cos_sim_precision
+      value: 81.32295719844358
+    - type: cos_sim_recall
+      value: 83.6
+    - type: dot_accuracy
+      value: 99.66237623762376
+    - type: dot_ap
+      value: 90.35464287920453
+    - type: dot_f1
+      value: 82.44575936883628
+    - type: dot_precision
+      value: 81.32295719844358
+    - type: dot_recall
+      value: 83.6
+    - type: euclidean_accuracy
+      value: 99.66237623762376
+    - type: euclidean_ap
+      value: 90.3546512622632
+    - type: euclidean_f1
+      value: 82.44575936883628
+    - type: euclidean_precision
+      value: 81.32295719844358
+    - type: euclidean_recall
+      value: 83.6
+    - type: manhattan_accuracy
+      value: 99.65940594059406
+    - type: manhattan_ap
+      value: 90.29220174849843
+    - type: manhattan_f1
+      value: 82.4987605354487
+    - type: manhattan_precision
+      value: 81.80924287118977
+    - type: manhattan_recall
+      value: 83.2
+    - type: max_accuracy
+      value: 99.66237623762376
+    - type: max_ap
+      value: 90.35465126226322
+    - type: max_f1
+      value: 82.4987605354487
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 65.0394225901397
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.27954189859326
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.99055979974896
+    - type: mrr
+      value: 51.82745257193787
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.21655465344237
+    - type: cos_sim_spearman
+      value: 29.853205339630172
+    - type: dot_pearson
+      value: 30.216540628083564
+    - type: dot_spearman
+      value: 29.868978894753027
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.2
+    - type: map_at_10
+      value: 1.398
+    - type: map_at_100
+      value: 7.406
+    - type: map_at_1000
+      value: 18.401
+    - type: map_at_3
+      value: 0.479
+    - type: map_at_5
+      value: 0.772
+    - type: mrr_at_1
+      value: 70.0
+    - type: mrr_at_10
+      value: 79.25999999999999
+    - type: mrr_at_100
+      value: 79.25999999999999
+    - type: mrr_at_1000
+      value: 79.25999999999999
+    - type: mrr_at_3
+      value: 77.333
+    - type: mrr_at_5
+      value: 78.133
+    - type: ndcg_at_1
+      value: 63.0
+    - type: ndcg_at_10
+      value: 58.548
+    - type: ndcg_at_100
+      value: 45.216
+    - type: ndcg_at_1000
+      value: 41.149
+    - type: ndcg_at_3
+      value: 60.641999999999996
+    - type: ndcg_at_5
+      value: 61.135
+    - type: precision_at_1
+      value: 70.0
+    - type: precision_at_10
+      value: 64.0
+    - type: precision_at_100
+      value: 46.92
+    - type: precision_at_1000
+      value: 18.642
+    - type: precision_at_3
+      value: 64.667
+    - type: precision_at_5
+      value: 66.4
+    - type: recall_at_1
+      value: 0.2
+    - type: recall_at_10
+      value: 1.6729999999999998
+    - type: recall_at_100
+      value: 10.856
+    - type: recall_at_1000
+      value: 38.964999999999996
+    - type: recall_at_3
+      value: 0.504
+    - type: recall_at_5
+      value: 0.852
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.6629999999999998
+    - type: map_at_10
+      value: 8.601
+    - type: map_at_100
+      value: 14.354
+    - type: map_at_1000
+      value: 15.927
+    - type: map_at_3
+      value: 4.1930000000000005
+    - type: map_at_5
+      value: 5.655
+    - type: mrr_at_1
+      value: 18.367
+    - type: mrr_at_10
+      value: 34.466
+    - type: mrr_at_100
+      value: 35.235
+    - type: mrr_at_1000
+      value: 35.27
+    - type: mrr_at_3
+      value: 28.571
+    - type: mrr_at_5
+      value: 31.531
+    - type: ndcg_at_1
+      value: 14.285999999999998
+    - type: ndcg_at_10
+      value: 20.374
+    - type: ndcg_at_100
+      value: 33.532000000000004
+    - type: ndcg_at_1000
+      value: 45.561
+    - type: ndcg_at_3
+      value: 18.442
+    - type: ndcg_at_5
+      value: 18.076
+    - type: precision_at_1
+      value: 18.367
+    - type: precision_at_10
+      value: 20.204
+    - type: precision_at_100
+      value: 7.489999999999999
+    - type: precision_at_1000
+      value: 1.5630000000000002
+    - type: precision_at_3
+      value: 21.769
+    - type: precision_at_5
+      value: 20.408
+    - type: recall_at_1
+      value: 1.6629999999999998
+    - type: recall_at_10
+      value: 15.549
+    - type: recall_at_100
+      value: 47.497
+    - type: recall_at_1000
+      value: 84.524
+    - type: recall_at_3
+      value: 5.289
+    - type: recall_at_5
+      value: 8.035
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.8194
+    - type: ap
+      value: 14.447702451658554
+    - type: f1
+      value: 55.13659412856185
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 63.310696095076416
+    - type: f1
+      value: 63.360434851097814
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 51.30677907335145
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.12386004649221
+    - type: cos_sim_ap
+      value: 73.99096426215495
+    - type: cos_sim_f1
+      value: 68.18416968442834
+    - type: cos_sim_precision
+      value: 66.86960933536275
+    - type: cos_sim_recall
+      value: 69.55145118733509
+    - type: dot_accuracy
+      value: 86.12386004649221
+    - type: dot_ap
+      value: 73.99096813038672
+    - type: dot_f1
+      value: 68.18416968442834
+    - type: dot_precision
+      value: 66.86960933536275
+    - type: dot_recall
+      value: 69.55145118733509
+    - type: euclidean_accuracy
+      value: 86.12386004649221
+    - type: euclidean_ap
+      value: 73.99095984980165
+    - type: euclidean_f1
+      value: 68.18416968442834
+    - type: euclidean_precision
+      value: 66.86960933536275
+    - type: euclidean_recall
+      value: 69.55145118733509
+    - type: manhattan_accuracy
+      value: 86.09405734040651
+    - type: manhattan_ap
+      value: 73.96825745608601
+    - type: manhattan_f1
+      value: 68.13888179729383
+    - type: manhattan_precision
+      value: 65.99901088031652
+    - type: manhattan_recall
+      value: 70.42216358839049
+    - type: max_accuracy
+      value: 86.12386004649221
+    - type: max_ap
+      value: 73.99096813038672
+    - type: max_f1
+      value: 68.18416968442834
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.99367407924865
+    - type: cos_sim_ap
+      value: 86.19720829843081
+    - type: cos_sim_f1
+      value: 78.39889075384951
+    - type: cos_sim_precision
+      value: 74.5110278818144
+    - type: cos_sim_recall
+      value: 82.71481367416075
+    - type: dot_accuracy
+      value: 88.99367407924865
+    - type: dot_ap
+      value: 86.19718471454047
+    - type: dot_f1
+      value: 78.39889075384951
+    - type: dot_precision
+      value: 74.5110278818144
+    - type: dot_recall
+      value: 82.71481367416075
+    - type: euclidean_accuracy
+      value: 88.99367407924865
+    - type: euclidean_ap
+      value: 86.1972021422436
+    - type: euclidean_f1
+      value: 78.39889075384951
+    - type: euclidean_precision
+      value: 74.5110278818144
+    - type: euclidean_recall
+      value: 82.71481367416075
+    - type: manhattan_accuracy
+      value: 88.95680521597392
+    - type: manhattan_ap
+      value: 86.16659921351506
+    - type: manhattan_f1
+      value: 78.39125971550081
+    - type: manhattan_precision
+      value: 74.82502799552073
+    - type: manhattan_recall
+      value: 82.31444410224823
+    - type: max_accuracy
+      value: 88.99367407924865
+    - type: max_ap
+      value: 86.19720829843081
+    - type: max_f1
+      value: 78.39889075384951
+---
+
+# hkunlp/instructor-base
+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!
+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/)! 
+
+**************************** **Updates** ****************************
+
+* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-base) trained with hard negatives, which gives better performance.
+* 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-base) and [project page](https://instructor-embedding.github.io/)! Check them out!
+
+## Quick start
+<hr />
+
+## Installation
+```bash
+pip install InstructorEmbedding
+```
+
+## Compute your customized embeddings
+Then you can use the model like this to calculate domain-specific and task-aware embeddings:
+```python
+from InstructorEmbedding import INSTRUCTOR
+model = INSTRUCTOR('hkunlp/instructor-base')
+sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+instruction = "Represent the Science title:"
+embeddings = model.encode([[instruction,sentence]])
+print(embeddings)
+```
+
+## Use cases
+<hr />
+
+## Calculate embeddings for your customized texts
+If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: 
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`:
+* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
+* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
+* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
+
+## Calculate Sentence similarities
+You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
+```python
+from sklearn.metrics.pairwise import cosine_similarity
+sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], 
+               ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
+sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
+               ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
+embeddings_a = model.encode(sentences_a)
+embeddings_b = model.encode(sentences_b)
+similarities = cosine_similarity(embeddings_a,embeddings_b)
+print(similarities)
+```
+
+## Information Retrieval
+You can also use **customized embeddings** for information retrieval.
+```python
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+query  = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
+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.'],
+          ['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"],
+          ['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.']]
+query_embeddings = model.encode(query)
+corpus_embeddings = model.encode(corpus)
+similarities = cosine_similarity(query_embeddings,corpus_embeddings)
+retrieved_doc_id = np.argmax(similarities)
+print(retrieved_doc_id)
+```
+
+## Clustering
+Use **customized embeddings** for clustering texts in groups.
+```python
+import sklearn.cluster
+sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
+             ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
+             ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
+             ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
+             ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher,  Vector States of Charmonium']]
+embeddings = model.encode(sentences)
+clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
+clustering_model.fit(embeddings)
+cluster_assignment = clustering_model.labels_
+print(cluster_assignment)
+```

models/hkunlp_instructor-base/config.json

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+{
+  "_name_or_path": "/scratch/acd14245px/metatrain_models/enhanced_large/0103_base_fever_40000/checkpoint-200/",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
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+      "prefix": "summarize: "
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+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
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+      "prefix": "translate English to Romanian: "
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+  "transformers_version": "4.20.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

models/hkunlp_instructor-base/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

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+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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+}

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+}

requirements.txt

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+streamlit
+numpy
+pandas
+seaborn
+plotly
+ipykernel
+jupyterlab
+jupyter
+tqdm
+llama-index
+InstructorEmbedding
+pypdf
+langchain
+transformers
+huggingface
+sentence-transformers
+llama-cpp-python
+python-dotenv