init

Dockerfile

@@ -0,0 +1,33 @@
+FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 as base
+
+# Install system dependencies in a single RUN command to reduce layers
+# Combine apt-get update, upgrade, and installation of packages. Clean up in the same layer to reduce image size.
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y python3.10 python3-pip git wget curl build-essential && \
+    apt-get autoremove -y && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# install ffmpeg
+RUN wget https://johnvansickle.com/ffmpeg/builds/ffmpeg-git-amd64-static.tar.xz &&\
+    wget https://johnvansickle.com/ffmpeg/builds/ffmpeg-git-amd64-static.tar.xz.md5 &&\
+    md5sum -c ffmpeg-git-amd64-static.tar.xz.md5 &&\
+    tar xvf ffmpeg-git-amd64-static.tar.xz &&\
+    mv ffmpeg-git-*-static/ffprobe ffmpeg-git-*-static/ffmpeg /usr/local/bin/ &&\
+    rm -rf ffmpeg-git-*
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir packaging wheel torch
+RUN pip install --no-cache-dir audiocraft  # HACK: installation fails within the requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir --upgrade torch torchaudio
+
+COPY . .
+
+RUN pip install --no-cache-dir -e .
+
+ENTRYPOINT ["python3.10", "serving.py"]

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

all_emo_dirs.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:beadd1f3c7eada0fa99dbdecc5c370036c1c044955a02f019f879bdc6f5fefcb
+size 20343

app.py

@@ -0,0 +1,364 @@
+import gradio as gr
+import os
+
+
+is_prod = True
+if os.environ.get('PROD_MODE') == 'local':
+    is_prod = False
+
+import pickle
+
+if not is_prod:
+    import os
+    os.environ['HF_HOME'] = '/proj/afosr/metavoice/cache'
+    os.environ['TRANSFORMERS_CACHE'] = '/proj/afosr/metavoice/cache'
+    os.environ['HF_DATASETS_CACHE'] = '/proj/afosr/metavoice/cache'
+    os.environ['HF_METRICS_CACHE'] = '/proj/afosr/metavoice/cache'
+    os.environ['HF_MODULES_CACHE'] = '/proj/afosr/metavoice/cache'
+    ffmpeg_path = '/home/hc3295/ffmpeg_build/bin'
+    os.environ['PATH'] += os.pathsep + ffmpeg_path
+
+
+import shutil
+import tempfile
+import time
+from pathlib import Path
+
+import librosa
+import torch
+from huggingface_hub import snapshot_download
+
+from fam.llm.adapters import FlattenedInterleavedEncodec2Codebook
+from fam.llm.decoders import EncodecDecoder
+from fam.llm.fast_inference_utils import build_model, main
+from fam.llm.inference import (
+    EncodecDecoder,
+    InferenceConfig,
+    Model,
+    TiltedEncodec,
+    TrainedBPETokeniser,
+    get_cached_embedding,
+    get_cached_file,    
+    get_enhancer,
+)
+from fam.llm.utils import (
+    check_audio_file,
+    get_default_dtype,
+    get_device,
+    normalize_text,
+)
+
+debug = False
+if not debug:
+    model_name = "metavoiceio/metavoice-1B-v0.1"
+    seed = 1337
+    output_dir = "outputs"
+    _dtype = get_default_dtype()
+    _device = 'cuda:0'
+    _model_dir = snapshot_download(repo_id=model_name)
+    first_stage_adapter = FlattenedInterleavedEncodec2Codebook(end_of_audio_token=1024)
+    output_dir = output_dir
+    os.makedirs(output_dir, exist_ok=True)
+
+    second_stage_ckpt_path = f"{_model_dir}/second_stage.pt"
+    config_second_stage = InferenceConfig(
+        ckpt_path=second_stage_ckpt_path,
+        num_samples=1,
+        seed=seed,
+        device=_device,
+        dtype=_dtype,
+        compile=False,
+        init_from="resume",
+        output_dir=output_dir,
+    )
+    data_adapter_second_stage = TiltedEncodec(end_of_audio_token=1024)
+    llm_second_stage = Model(
+        config_second_stage, TrainedBPETokeniser, EncodecDecoder, data_adapter_fn=data_adapter_second_stage.decode
+    )
+    enhancer = get_enhancer("df")
+
+    precision = {"float16": torch.float16, "bfloat16": torch.bfloat16}[_dtype]
+    model, tokenizer, smodel, model_size = build_model(
+        precision=precision,
+        checkpoint_path=Path(f"{_model_dir}/first_stage.pt"),
+        spk_emb_ckpt_path=Path(f"{_model_dir}/speaker_encoder.pt"),
+        device=_device,
+        compile=True,
+        compile_prefill=True,
+    )
+
+
+def generate_sample(text, emo_dir = None, source_path = None, emo_path = None, neutral_path = None, strength = 0.1, top_p = 0.95, guidance_scale = 3.0, preset_dropdown = None, toggle = None):
+
+    print('text', text)
+    print('emo_dir', emo_dir)
+    print('source_path', source_path)
+    print('emo_path', emo_path)
+    print('neutral_path', neutral_path)
+    print('strength', strength)
+    print('top_p', top_p)
+    print('guidance_scale', guidance_scale)
+
+    if toggle == RADIO_CHOICES[0]:
+        source_path = PRESET_VOICES[preset_dropdown]
+    source_path = get_cached_file(source_path)
+    check_audio_file(source_path)
+    source_emb = get_cached_embedding(source_path, smodel).to(device=_device, dtype=precision)
+
+    if emo_dir == EMO_NAMES[0]:
+        emo_path = get_cached_file(emo_path)
+        check_audio_file(emo_path)
+        emo_emb = get_cached_embedding(emo_path, smodel).to(device=_device, dtype=precision)
+
+        neutral_path = get_cached_file(neutral_path)
+        check_audio_file(neutral_path)
+        neutral_emb = get_cached_embedding(neutral_path, smodel).to(device=_device, dtype=precision)
+
+        emo_dir = emo_emb - neutral_emb
+        emo_dir = emo_dir / torch.norm(emo_dir, p=2)
+    else:
+        emo_dir = torch.tensor(ALL_EMO_DIRS[emo_dir], device=_device, dtype=precision)
+    
+    
+    edited_emb = source_emb + strength * emo_dir
+    edited_emb = edited_emb.to(device=_device, dtype=precision)
+
+    temperature=1.0
+    text = normalize_text(text)
+
+    start = time.time()
+    # first stage LLM
+    tokens = main(
+        model=model,
+        tokenizer=tokenizer,
+        model_size=model_size,
+        prompt=text,
+        spk_emb=edited_emb,
+        top_p=torch.tensor(top_p, device=_device, dtype=precision),
+        guidance_scale=torch.tensor(guidance_scale, device=_device, dtype=precision),
+        temperature=torch.tensor(temperature, device=_device, dtype=precision),
+    )
+    text_ids, extracted_audio_ids = first_stage_adapter.decode([tokens])
+
+    b_speaker_embs = edited_emb.unsqueeze(0)
+
+    # second stage LLM + multi-band diffusion model
+    wav_files = llm_second_stage(
+        texts=[text],
+        encodec_tokens=[torch.tensor(extracted_audio_ids, dtype=torch.int32, device=_device).unsqueeze(0)],
+        speaker_embs=b_speaker_embs,
+        batch_size=1,
+        guidance_scale=None,
+        top_p=None,
+        top_k=200,
+        temperature=1.0,
+        max_new_tokens=None,
+    )
+
+    wav_file = wav_files[0]
+    with tempfile.NamedTemporaryFile(suffix=".wav") as enhanced_tmp:
+        enhancer(str(wav_file) + ".wav", enhanced_tmp.name)
+        shutil.copy2(enhanced_tmp.name, str(wav_file) + ".wav")
+        print(f"\nSaved audio to {wav_file}.wav")
+    
+    output_path = str(wav_file) + ".wav"
+    return output_path
+
+
+ALL_EMO_DIRS = pickle.load(open('all_emo_dirs.pkl', 'rb'))
+EMO_NAMES = ['Upload your own sample'] + list(ALL_EMO_DIRS.keys())
+
+RADIO_CHOICES = ["Preset voices", "Upload your voice"]
+MAX_CHARS = 220
+PRESET_VOICES = {
+    # female
+    "Bria": "https://cdn.themetavoice.xyz/speakers%2Fbria.mp3",
+    # male
+    "Alex": "https://cdn.themetavoice.xyz/speakers/alex.mp3",
+    "Jacob": "https://cdn.themetavoice.xyz/speakers/jacob.wav",
+}
+
+
+def denormalise_top_p(top_p):
+    # returns top_p in the range [0.9, 1.0]
+    return round(0.9 + top_p / 100, 2)
+
+
+def denormalise_guidance(guidance):
+    # returns guidance in the range [1.0, 3.0]
+    return 1 + ((guidance - 1) * (3 - 1)) / (5 - 1)
+
+
+def _check_file_size(path):
+    if not path:
+        return
+    filesize = os.path.getsize(path)
+    filesize_mb = filesize / 1024 / 1024
+    if filesize_mb >= 50:
+        raise gr.Error(f"Please upload a sample less than 20MB for voice cloning. Provided: {round(filesize_mb)} MB")
+
+
+def _handle_edge_cases(to_say, upload_target):
+    if not to_say:
+        raise gr.Error("Please provide text to synthesise")
+
+    if len(to_say) > MAX_CHARS:
+        gr.Warning(
+            f"Max {MAX_CHARS} characters allowed. Provided: {len(to_say)} characters. Truncating and generating speech...Result at the end can be unstable as a result."
+        )
+
+    if not upload_target:
+        return
+
+    check_audio_file(upload_target)  # check file duration to be atleast 30s
+    _check_file_size(upload_target)
+
+
+def tts(to_say, top_p, guidance, toggle, preset_dropdown, upload_target):
+    try:
+        d_top_p = denormalise_top_p(top_p)
+        d_guidance = denormalise_guidance(guidance)
+
+        _handle_edge_cases(to_say, upload_target)
+
+        to_say = to_say if len(to_say) < MAX_CHARS else to_say[:MAX_CHARS]
+
+        return TTS_MODEL.synthesise(
+            text=to_say,
+            spk_ref_path=PRESET_VOICES[preset_dropdown] if toggle == RADIO_CHOICES[0] else upload_target,
+            top_p=d_top_p,
+            guidance_scale=d_guidance,
+        )
+    except Exception as e:
+        raise gr.Error(f"Something went wrong. Reason: {str(e)}")
+
+
+def change_voice_selection_layout(choice):
+    if choice == RADIO_CHOICES[0]:
+        return [gr.update(visible=True), gr.update(visible=False)]
+
+    return [gr.update(visible=False), gr.update(visible=True)]
+
+def change_emotion_selection_layout(choice):
+    if choice == EMO_NAMES[0]:
+        return [gr.update(visible=True)]
+
+    return [gr.update(visible=False)]
+
+title = """
+</style>
+<h1 style="margin-top: 10px;" class="page-title">Demo for <span style="margin-left: 10px;background-color: #E0FEE4;padding: 15px;border-radius: 10px;">🎛️ EmoKnob</span></h1>
+"""
+
+description = """
+- While existing TTS services do not allow fine-grained control over emotions, EmoKnob allows users to control emotion in speech with few-shot samples.
+- In this demo, you can select from a few preset voices and upload your own emotional samples to clone.
+- You can then use preset emotion or upload your own emotional-neutral sample pair to control emotions.
+- You can adjust the strength of the emotion by using the slider.
+
+
+EmoKnob is uses [MetaVoice](https://github.com/metavoiceio/metavoice-src) as voice cloning backbone.
+"""
+
+with gr.Blocks(title="EmoKnob Demo") as demo:
+    gr.Markdown(title)
+    gr.Image("emo-knob-teaser-1.svg", show_label=False, container=False)
+
+    with gr.Row():
+        gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column():
+            to_say = gr.TextArea(
+                label=f"What should I say!? (max {MAX_CHARS} characters).",
+                lines=4,
+                value="To be or not to be, that is the question.",
+            )
+
+            
+
+            with gr.Row(), gr.Column():
+                # voice settings
+                top_p = gr.Slider(
+                    value=0.95,
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=1.0,
+                    label="Speech Stability - improves text following for a challenging speaker",
+                )
+                guidance = gr.Slider(
+                    value=3.0,
+                    minimum=1.0,
+                    maximum=5.0,
+                    step=1.0,
+                    label="Speaker similarity - How closely to match speaker identity and speech style.",
+                )
+
+                strength = gr.Slider(
+                    value=0.1,
+                    minimum=0.0,
+                    maximum=5.0,
+                    step=0.01,
+                    label="Strength - how strong the emotion is. Setting it to too large a value may result in unstable output.",
+                )
+
+                
+
+                # voice select
+                toggle = gr.Radio(choices=RADIO_CHOICES, label="Choose voice", value=RADIO_CHOICES[0])
+
+            with gr.Row(visible=True) as row_1:
+                preset_dropdown = gr.Dropdown(
+                    PRESET_VOICES.keys(), label="Preset voices", value=list(PRESET_VOICES.keys())[0]
+                )
+                with gr.Accordion("Preview: Preset voices", open=False):
+                    for label, path in PRESET_VOICES.items():
+                        gr.Audio(value=path, label=label)
+
+            with gr.Row(visible=False) as row_2:
+                upload_target = gr.Audio(
+                    sources=["upload"],
+                    type="filepath",
+                    label="Upload a clean sample to clone.",
+                )
+            with gr.Row():
+                emotion_name = gr.Radio(choices=EMO_NAMES, label="Emotion", value=EMO_NAMES[0])
+            with gr.Row(visible=True) as row_3:
+                upload_neutral = gr.Audio(
+                    sources=["upload"],
+                    type="filepath",
+                    label="Upload a neutral sample to compute the emotion direction. Should be same speaker as the emotional sample.",
+                )
+
+                upload_emo = gr.Audio(
+                    sources=["upload"],
+                    type="filepath",
+                    label="Upload an emotional sample to compute the emotion direction. Should be same speaker as the neutral sample.",
+                )
+
+            toggle.change(
+                change_voice_selection_layout,
+                inputs=toggle,
+                outputs=[row_1, row_2],
+            )
+
+            # emotion_name.change(
+            #     change_emotion_selection_layout,
+            #     inputs=emotion_name,
+            #     outputs=[row_3],
+            # )
+
+        with gr.Column():
+            speech = gr.Audio(
+                type="filepath",
+                label="Model says...",
+            )
+
+    submit = gr.Button("Generate Speech")
+    submit.click(
+        fn=generate_sample,
+        inputs=[to_say, emotion_name, upload_target, upload_emo, upload_neutral, strength, top_p, guidance, preset_dropdown, toggle],
+        outputs=speech,
+    )
+
+demo.launch()

cache/models--Salesforce--SFR-Embedding-Mistral/blobs/42dcdfcaf9e42a488d4be06500dd771d7aa11e83

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 4096,
+  "do_lower_case": false
+}

cache/models--Salesforce--SFR-Embedding-Mistral/blobs/c19160bba3c1267f959caf6d13fb07f9ea232e04

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "intfloat/e5-mistral-7b-instruct",
+  "architectures": [
+    "MistralModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.37.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

cache/models--Salesforce--SFR-Embedding-Mistral/blobs/ef62bf21fb2396937098b86ae80c68813b229c18

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.37.2",
+    "pytorch": "2.1.0+cu121"
+  }
+}

cache/models--Salesforce--SFR-Embedding-Mistral/blobs/f7640f94e81bb7f4f04daf1668850b38763a13d9

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

cache/models--Salesforce--SFR-Embedding-Mistral/blobs/f8194e4e9432d287bf257d4a7d4a0f2446c32da8

@@ -0,0 +1,297 @@
+{
+  "metadata": {
+    "total_size": 14221320192
+  },
+  "weight_map": {
+    "embed_tokens.weight": "model-00001-of-00003.safetensors",
+    "layers.0.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.0.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.0.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.0.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.0.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.0.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.0.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.0.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.0.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.1.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.1.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.1.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.10.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.10.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.10.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.10.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.10.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.10.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.10.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.10.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.10.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.11.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.11.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.11.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.11.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.11.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.11.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.11.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.11.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.11.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.12.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.12.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.12.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.13.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.13.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.13.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.14.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.14.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.14.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.15.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.15.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.15.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.16.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.16.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.16.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.17.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.17.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.17.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.18.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.18.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.18.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.19.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.19.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.19.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.2.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.2.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.2.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.2.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.2.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.2.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.2.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.2.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.2.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.20.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.20.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.20.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.20.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.20.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.20.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.20.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.20.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.20.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.21.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "layers.21.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.21.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.22.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.22.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.22.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.22.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.22.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.22.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.22.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.22.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.22.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "layers.23.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.23.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.23.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.23.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.23.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.23.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.23.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.23.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.23.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.24.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.24.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.24.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.25.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.25.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.25.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.26.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.26.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.26.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.27.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.27.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.27.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.28.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.28.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.28.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.29.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.29.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.29.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.3.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.3.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.3.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.3.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.3.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.3.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.3.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.3.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.3.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.30.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.30.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.30.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.30.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.30.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.30.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.30.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.30.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.30.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.31.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "layers.31.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.31.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "layers.4.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.4.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.4.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.4.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.4.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.4.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.4.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.4.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.4.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.5.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.5.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.5.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.6.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.6.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.6.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.7.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.7.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.7.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.8.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.8.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.9.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "norm.weight": "model-00003-of-00003.safetensors"
+  }
+}

cache/models--Salesforce--SFR-Embedding-Mistral/blobs/feb95adc7e79e878999ba5a1d3ddfe9f16eff0f1

@@ -0,0 +1,3398 @@
+---
+tags:
+- mteb
+- sentence-transformers
+- transformers
+model-index:
+- name: SFR-Embedding-Mistral
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 77.92537313432834
+    - type: ap
+      value: 40.86767661556651
+    - type: f1
+      value: 71.65758897929837
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 95.967
+    - type: ap
+      value: 94.46300829592593
+    - type: f1
+      value: 95.96507173189292
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 54.352000000000004
+    - type: f1
+      value: 53.636682615380174
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 43.314
+    - type: ndcg_at_2
+      value: 54.757
+    - type: ndcg_at_3
+      value: 58.84700000000001
+    - type: ndcg_at_5
+      value: 63.634
+    - type: ndcg_at_7
+      value: 65.741
+    - type: ndcg_at_10
+      value: 67.171
+    - type: ndcg_at_20
+      value: 68.585
+    - type: ndcg_at_30
+      value: 68.81
+    - type: ndcg_at_50
+      value: 68.932
+    - type: ndcg_at_70
+      value: 68.992
+    - type: ndcg_at_100
+      value: 69.014
+    - type: ndcg_at_200
+      value: 69.014
+    - type: ndcg_at_300
+      value: 69.014
+    - type: ndcg_at_500
+      value: 69.014
+    - type: ndcg_at_700
+      value: 69.014
+    - type: ndcg_at_1000
+      value: 69.014
+    - type: map_at_1
+      value: 43.314
+    - type: map_at_2
+      value: 52.383
+    - type: map_at_3
+      value: 55.108999999999995
+    - type: map_at_5
+      value: 57.772999999999996
+    - type: map_at_7
+      value: 58.718
+    - type: map_at_10
+      value: 59.256
+    - type: map_at_20
+      value: 59.668
+    - type: map_at_30
+      value: 59.709999999999994
+    - type: map_at_50
+      value: 59.727
+    - type: map_at_70
+      value: 59.733999999999995
+    - type: map_at_100
+      value: 59.73500000000001
+    - type: map_at_200
+      value: 59.73500000000001
+    - type: map_at_300
+      value: 59.73500000000001
+    - type: map_at_500
+      value: 59.73500000000001
+    - type: map_at_700
+      value: 59.73500000000001
+    - type: map_at_1000
+      value: 59.73500000000001
+    - type: recall_at_1
+      value: 43.314
+    - type: recall_at_2
+      value: 61.451
+    - type: recall_at_3
+      value: 69.63000000000001
+    - type: recall_at_5
+      value: 81.223
+    - type: recall_at_7
+      value: 87.33999999999999
+    - type: recall_at_10
+      value: 92.034
+    - type: recall_at_20
+      value: 97.44
+    - type: recall_at_30
+      value: 98.506
+    - type: recall_at_50
+      value: 99.14699999999999
+    - type: recall_at_70
+      value: 99.502
+    - type: recall_at_100
+      value: 99.644
+    - type: recall_at_200
+      value: 99.644
+    - type: recall_at_300
+      value: 99.644
+    - type: recall_at_500
+      value: 99.644
+    - type: recall_at_700
+      value: 99.644
+    - type: recall_at_1000
+      value: 99.644
+    - type: precision_at_1
+      value: 43.314
+    - type: precision_at_2
+      value: 30.725
+    - type: precision_at_3
+      value: 23.21
+    - type: precision_at_5
+      value: 16.245
+    - type: precision_at_7
+      value: 12.477
+    - type: precision_at_10
+      value: 9.203
+    - type: precision_at_20
+      value: 4.872
+    - type: precision_at_30
+      value: 3.2840000000000003
+    - type: precision_at_50
+      value: 1.983
+    - type: precision_at_70
+      value: 1.421
+    - type: precision_at_100
+      value: 0.996
+    - type: precision_at_200
+      value: 0.498
+    - type: precision_at_300
+      value: 0.332
+    - type: precision_at_500
+      value: 0.199
+    - type: precision_at_700
+      value: 0.14200000000000002
+    - type: precision_at_1000
+      value: 0.1
+    - type: mrr_at_1
+      value: 44.666
+    - type: mrr_at_2
+      value: 52.418
+    - type: mrr_at_3
+      value: 55.595000000000006
+    - type: mrr_at_5
+      value: 58.205
+    - type: mrr_at_7
+      value: 59.202999999999996
+    - type: mrr_at_10
+      value: 59.727
+    - type: mrr_at_20
+      value: 60.133
+    - type: mrr_at_30
+      value: 60.178
+    - type: mrr_at_50
+      value: 60.192
+    - type: mrr_at_70
+      value: 60.19799999999999
+    - type: mrr_at_100
+      value: 60.199999999999996
+    - type: mrr_at_200
+      value: 60.199999999999996
+    - type: mrr_at_300
+      value: 60.199999999999996
+    - type: mrr_at_500
+      value: 60.199999999999996
+    - type: mrr_at_700
+      value: 60.199999999999996
+    - type: mrr_at_1000
+      value: 60.199999999999996
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 52.07508593014336
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 47.381339333240675
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 67.58376647859171
+    - type: mrr
+      value: 80.56885635140483
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.40107280274783
+    - type: cos_sim_spearman
+      value: 86.07003345325681
+    - type: euclidean_pearson
+      value: 87.1726034325395
+    - type: euclidean_spearman
+      value: 86.07003345325681
+    - type: manhattan_pearson
+      value: 87.25660625029772
+    - type: manhattan_spearman
+      value: 86.3808839096893
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 88.81168831168831
+    - type: f1
+      value: 88.76514496560141
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 43.9382520874344
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 41.14351847240913
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 34.51166666666667
+    - type: ndcg_at_2
+      value: 38.51591666666667
+    - type: ndcg_at_3
+      value: 40.95083333333333
+    - type: ndcg_at_5
+      value: 43.580666666666666
+    - type: ndcg_at_7
+      value: 45.0625
+    - type: ndcg_at_10
+      value: 46.49083333333333
+    - type: ndcg_at_20
+      value: 48.731333333333325
+    - type: ndcg_at_30
+      value: 49.78666666666667
+    - type: ndcg_at_50
+      value: 50.84049999999999
+    - type: ndcg_at_70
+      value: 51.393750000000004
+    - type: ndcg_at_100
+      value: 51.883333333333326
+    - type: ndcg_at_200
+      value: 52.65225
+    - type: ndcg_at_300
+      value: 52.98241666666669
+    - type: ndcg_at_500
+      value: 53.28541666666668
+    - type: ndcg_at_700
+      value: 53.49241666666668
+    - type: ndcg_at_1000
+      value: 53.63758333333334
+    - type: map_at_1
+      value: 29.10075
+    - type: map_at_2
+      value: 34.636500000000005
+    - type: map_at_3
+      value: 36.92033333333333
+    - type: map_at_5
+      value: 38.81641666666666
+    - type: map_at_7
+      value: 39.635416666666664
+    - type: map_at_10
+      value: 40.294583333333335
+    - type: map_at_20
+      value: 41.07574999999999
+    - type: map_at_30
+      value: 41.333
+    - type: map_at_50
+      value: 41.529333333333334
+    - type: map_at_70
+      value: 41.606833333333334
+    - type: map_at_100
+      value: 41.66224999999999
+    - type: map_at_200
+      value: 41.72691666666666
+    - type: map_at_300
+      value: 41.746583333333334
+    - type: map_at_500
+      value: 41.75983333333333
+    - type: map_at_700
+      value: 41.76558333333333
+    - type: map_at_1000
+      value: 41.769000000000005
+    - type: recall_at_1
+      value: 29.10075
+    - type: recall_at_2
+      value: 39.07658333333333
+    - type: recall_at_3
+      value: 44.93591666666667
+    - type: recall_at_5
+      value: 51.66883333333333
+    - type: recall_at_7
+      value: 55.881000000000014
+    - type: recall_at_10
+      value: 60.34691666666667
+    - type: recall_at_20
+      value: 68.44016666666667
+    - type: recall_at_30
+      value: 72.90766666666667
+    - type: recall_at_50
+      value: 77.843
+    - type: recall_at_70
+      value: 80.70366666666668
+    - type: recall_at_100
+      value: 83.42866666666667
+    - type: recall_at_200
+      value: 88.06816666666668
+    - type: recall_at_300
+      value: 90.249
+    - type: recall_at_500
+      value: 92.37616666666668
+    - type: recall_at_700
+      value: 93.978
+    - type: recall_at_1000
+      value: 95.12791666666666
+    - type: precision_at_1
+      value: 34.51166666666667
+    - type: precision_at_2
+      value: 24.326333333333327
+    - type: precision_at_3
+      value: 19.099249999999998
+    - type: precision_at_5
+      value: 13.672666666666666
+    - type: precision_at_7
+      value: 10.772
+    - type: precision_at_10
+      value: 8.302166666666668
+    - type: precision_at_20
+      value: 4.8960833333333325
+    - type: precision_at_30
+      value: 3.551083333333333
+    - type: precision_at_50
+      value: 2.3386666666666662
+    - type: precision_at_70
+      value: 1.7605833333333334
+    - type: precision_at_100
+      value: 1.2965
+    - type: precision_at_200
+      value: 0.7106666666666668
+    - type: precision_at_300
+      value: 0.4955
+    - type: precision_at_500
+      value: 0.3106666666666667
+    - type: precision_at_700
+      value: 0.22791666666666668
+    - type: precision_at_1000
+      value: 0.1635833333333333
+    - type: mrr_at_1
+      value: 34.51166666666667
+    - type: mrr_at_2
+      value: 39.954249999999995
+    - type: mrr_at_3
+      value: 41.93741666666668
+    - type: mrr_at_5
+      value: 43.487166666666674
+    - type: mrr_at_7
+      value: 44.14983333333333
+    - type: mrr_at_10
+      value: 44.62766666666666
+    - type: mrr_at_20
+      value: 45.15291666666668
+    - type: mrr_at_30
+      value: 45.317
+    - type: mrr_at_50
+      value: 45.42875
+    - type: mrr_at_70
+      value: 45.46966666666667
+    - type: mrr_at_100
+      value: 45.49716666666667
+    - type: mrr_at_200
+      value: 45.525166666666664
+    - type: mrr_at_300
+      value: 45.53233333333335
+    - type: mrr_at_500
+      value: 45.5365
+    - type: mrr_at_700
+      value: 45.538583333333335
+    - type: mrr_at_1000
+      value: 45.539583333333326
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 35.179
+    - type: ndcg_at_2
+      value: 31.243
+    - type: ndcg_at_3
+      value: 30.562
+    - type: ndcg_at_5
+      value: 32.409
+    - type: ndcg_at_7
+      value: 34.525
+    - type: ndcg_at_10
+      value: 36.415
+    - type: ndcg_at_20
+      value: 39.443
+    - type: ndcg_at_30
+      value: 40.796
+    - type: ndcg_at_50
+      value: 42.16
+    - type: ndcg_at_70
+      value: 42.971
+    - type: ndcg_at_100
+      value: 43.691
+    - type: ndcg_at_200
+      value: 45.004
+    - type: ndcg_at_300
+      value: 45.527
+    - type: ndcg_at_500
+      value: 46.072
+    - type: ndcg_at_700
+      value: 46.387
+    - type: ndcg_at_1000
+      value: 46.663
+    - type: map_at_1
+      value: 15.692
+    - type: map_at_2
+      value: 20.116
+    - type: map_at_3
+      value: 22.6
+    - type: map_at_5
+      value: 24.701
+    - type: map_at_7
+      value: 25.934
+    - type: map_at_10
+      value: 26.843
+    - type: map_at_20
+      value: 27.975
+    - type: map_at_30
+      value: 28.372000000000003
+    - type: map_at_50
+      value: 28.671000000000003
+    - type: map_at_70
+      value: 28.803
+    - type: map_at_100
+      value: 28.895
+    - type: map_at_200
+      value: 29.011
+    - type: map_at_300
+      value: 29.042
+    - type: map_at_500
+      value: 29.065
+    - type: map_at_700
+      value: 29.075
+    - type: map_at_1000
+      value: 29.081000000000003
+    - type: recall_at_1
+      value: 15.692
+    - type: recall_at_2
+      value: 22.602
+    - type: recall_at_3
+      value: 27.814
+    - type: recall_at_5
+      value: 33.756
+    - type: recall_at_7
+      value: 38.073
+    - type: recall_at_10
+      value: 42.553000000000004
+    - type: recall_at_20
+      value: 51.121
+    - type: recall_at_30
+      value: 55.523999999999994
+    - type: recall_at_50
+      value: 60.586
+    - type: recall_at_70
+      value: 63.94
+    - type: recall_at_100
+      value: 67.134
+    - type: recall_at_200
+      value: 73.543
+    - type: recall_at_300
+      value: 76.372
+    - type: recall_at_500
+      value: 79.60199999999999
+    - type: recall_at_700
+      value: 81.536
+    - type: recall_at_1000
+      value: 83.37400000000001
+    - type: precision_at_1
+      value: 35.179
+    - type: precision_at_2
+      value: 27.199
+    - type: precision_at_3
+      value: 22.953000000000003
+    - type: precision_at_5
+      value: 17.224999999999998
+    - type: precision_at_7
+      value: 14.238999999999999
+    - type: precision_at_10
+      value: 11.303
+    - type: precision_at_20
+      value: 6.954000000000001
+    - type: precision_at_30
+      value: 5.116
+    - type: precision_at_50
+      value: 3.395
+    - type: precision_at_70
+      value: 2.579
+    - type: precision_at_100
+      value: 1.9109999999999998
+    - type: precision_at_200
+      value: 1.065
+    - type: precision_at_300
+      value: 0.743
+    - type: precision_at_500
+      value: 0.46699999999999997
+    - type: precision_at_700
+      value: 0.344
+    - type: precision_at_1000
+      value: 0.247
+    - type: mrr_at_1
+      value: 35.179
+    - type: mrr_at_2
+      value: 41.792
+    - type: mrr_at_3
+      value: 44.484
+    - type: mrr_at_5
+      value: 46.39
+    - type: mrr_at_7
+      value: 47.125
+    - type: mrr_at_10
+      value: 47.711999999999996
+    - type: mrr_at_20
+      value: 48.214
+    - type: mrr_at_30
+      value: 48.325
+    - type: mrr_at_50
+      value: 48.392
+    - type: mrr_at_70
+      value: 48.418
+    - type: mrr_at_100
+      value: 48.44
+    - type: mrr_at_200
+      value: 48.46
+    - type: mrr_at_300
+      value: 48.461999999999996
+    - type: mrr_at_500
+      value: 48.466
+    - type: mrr_at_700
+      value: 48.466
+    - type: mrr_at_1000
+      value: 48.467
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 62.375
+    - type: ndcg_at_2
+      value: 56.286
+    - type: ndcg_at_3
+      value: 53.665
+    - type: ndcg_at_5
+      value: 51.139
+    - type: ndcg_at_7
+      value: 49.873
+    - type: ndcg_at_10
+      value: 49.056
+    - type: ndcg_at_20
+      value: 48.783
+    - type: ndcg_at_30
+      value: 49.166
+    - type: ndcg_at_50
+      value: 51.141999999999996
+    - type: ndcg_at_70
+      value: 52.774
+    - type: ndcg_at_100
+      value: 54.403
+    - type: ndcg_at_200
+      value: 57.419
+    - type: ndcg_at_300
+      value: 58.778
+    - type: ndcg_at_500
+      value: 60.228
+    - type: ndcg_at_700
+      value: 61.07599999999999
+    - type: ndcg_at_1000
+      value: 61.846000000000004
+    - type: map_at_1
+      value: 10.359
+    - type: map_at_2
+      value: 14.446
+    - type: map_at_3
+      value: 16.689
+    - type: map_at_5
+      value: 20.096
+    - type: map_at_7
+      value: 22.247
+    - type: map_at_10
+      value: 24.468999999999998
+    - type: map_at_20
+      value: 28.938000000000002
+    - type: map_at_30
+      value: 31.134
+    - type: map_at_50
+      value: 33.403
+    - type: map_at_70
+      value: 34.486
+    - type: map_at_100
+      value: 35.337
+    - type: map_at_200
+      value: 36.364999999999995
+    - type: map_at_300
+      value: 36.735
+    - type: map_at_500
+      value: 37.057
+    - type: map_at_700
+      value: 37.225
+    - type: map_at_1000
+      value: 37.379
+    - type: recall_at_1
+      value: 10.359
+    - type: recall_at_2
+      value: 14.945
+    - type: recall_at_3
+      value: 17.694
+    - type: recall_at_5
+      value: 22.677
+    - type: recall_at_7
+      value: 26.131
+    - type: recall_at_10
+      value: 30.053
+    - type: recall_at_20
+      value: 39.518
+    - type: recall_at_30
+      value: 44.925
+    - type: recall_at_50
+      value: 52.154
+    - type: recall_at_70
+      value: 56.729
+    - type: recall_at_100
+      value: 61.18900000000001
+    - type: recall_at_200
+      value: 70.407
+    - type: recall_at_300
+      value: 74.412
+    - type: recall_at_500
+      value: 78.891
+    - type: recall_at_700
+      value: 81.74
+    - type: recall_at_1000
+      value: 84.253
+    - type: precision_at_1
+      value: 75
+    - type: precision_at_2
+      value: 64.125
+    - type: precision_at_3
+      value: 57.833
+    - type: precision_at_5
+      value: 50.24999999999999
+    - type: precision_at_7
+      value: 44.75
+    - type: precision_at_10
+      value: 39.75
+    - type: precision_at_20
+      value: 30.412
+    - type: precision_at_30
+      value: 25.141999999999996
+    - type: precision_at_50
+      value: 19.2
+    - type: precision_at_70
+      value: 15.729000000000001
+    - type: precision_at_100
+      value: 12.552
+    - type: precision_at_200
+      value: 7.866
+    - type: precision_at_300
+      value: 5.9270000000000005
+    - type: precision_at_500
+      value: 4.1129999999999995
+    - type: precision_at_700
+      value: 3.2460000000000004
+    - type: precision_at_1000
+      value: 2.5260000000000002
+    - type: mrr_at_1
+      value: 75
+    - type: mrr_at_2
+      value: 78.625
+    - type: mrr_at_3
+      value: 79.708
+    - type: mrr_at_5
+      value: 80.446
+    - type: mrr_at_7
+      value: 80.862
+    - type: mrr_at_10
+      value: 81.161
+    - type: mrr_at_20
+      value: 81.3
+    - type: mrr_at_30
+      value: 81.348
+    - type: mrr_at_50
+      value: 81.361
+    - type: mrr_at_70
+      value: 81.361
+    - type: mrr_at_100
+      value: 81.361
+    - type: mrr_at_200
+      value: 81.367
+    - type: mrr_at_300
+      value: 81.367
+    - type: mrr_at_500
+      value: 81.368
+    - type: mrr_at_700
+      value: 81.368
+    - type: mrr_at_1000
+      value: 81.368
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 50.239999999999995
+    - type: f1
+      value: 46.42361822342044
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 83.723
+    - type: ndcg_at_2
+      value: 86.777
+    - type: ndcg_at_3
+      value: 87.997
+    - type: ndcg_at_5
+      value: 88.864
+    - type: ndcg_at_7
+      value: 89.143
+    - type: ndcg_at_10
+      value: 89.349
+    - type: ndcg_at_20
+      value: 89.709
+    - type: ndcg_at_30
+      value: 89.82900000000001
+    - type: ndcg_at_50
+      value: 89.923
+    - type: ndcg_at_70
+      value: 89.982
+    - type: ndcg_at_100
+      value: 90.026
+    - type: ndcg_at_200
+      value: 90.10000000000001
+    - type: ndcg_at_300
+      value: 90.12599999999999
+    - type: ndcg_at_500
+      value: 90.17399999999999
+    - type: ndcg_at_700
+      value: 90.19
+    - type: ndcg_at_1000
+      value: 90.208
+    - type: map_at_1
+      value: 77.64999999999999
+    - type: map_at_2
+      value: 83.769
+    - type: map_at_3
+      value: 85.041
+    - type: map_at_5
+      value: 85.736
+    - type: map_at_7
+      value: 85.924
+    - type: map_at_10
+      value: 86.032
+    - type: map_at_20
+      value: 86.177
+    - type: map_at_30
+      value: 86.213
+    - type: map_at_50
+      value: 86.233
+    - type: map_at_70
+      value: 86.24300000000001
+    - type: map_at_100
+      value: 86.249
+    - type: map_at_200
+      value: 86.256
+    - type: map_at_300
+      value: 86.258
+    - type: map_at_500
+      value: 86.26
+    - type: map_at_700
+      value: 86.26
+    - type: map_at_1000
+      value: 86.261
+    - type: recall_at_1
+      value: 77.64999999999999
+    - type: recall_at_2
+      value: 88.53999999999999
+    - type: recall_at_3
+      value: 91.696
+    - type: recall_at_5
+      value: 93.916
+    - type: recall_at_7
+      value: 94.731
+    - type: recall_at_10
+      value: 95.318
+    - type: recall_at_20
+      value: 96.507
+    - type: recall_at_30
+      value: 96.956
+    - type: recall_at_50
+      value: 97.34899999999999
+    - type: recall_at_70
+      value: 97.61
+    - type: recall_at_100
+      value: 97.83
+    - type: recall_at_200
+      value: 98.223
+    - type: recall_at_300
+      value: 98.374
+    - type: recall_at_500
+      value: 98.67899999999999
+    - type: recall_at_700
+      value: 98.787
+    - type: recall_at_1000
+      value: 98.919
+    - type: precision_at_1
+      value: 83.723
+    - type: precision_at_2
+      value: 48.425000000000004
+    - type: precision_at_3
+      value: 33.638
+    - type: precision_at_5
+      value: 20.843
+    - type: precision_at_7
+      value: 15.079
+    - type: precision_at_10
+      value: 10.674999999999999
+    - type: precision_at_20
+      value: 5.457999999999999
+    - type: precision_at_30
+      value: 3.6740000000000004
+    - type: precision_at_50
+      value: 2.2239999999999998
+    - type: precision_at_70
+      value: 1.599
+    - type: precision_at_100
+      value: 1.125
+    - type: precision_at_200
+      value: 0.5680000000000001
+    - type: precision_at_300
+      value: 0.38
+    - type: precision_at_500
+      value: 0.22999999999999998
+    - type: precision_at_700
+      value: 0.165
+    - type: precision_at_1000
+      value: 0.116
+    - type: mrr_at_1
+      value: 83.723
+    - type: mrr_at_2
+      value: 88.794
+    - type: mrr_at_3
+      value: 89.679
+    - type: mrr_at_5
+      value: 90.049
+    - type: mrr_at_7
+      value: 90.129
+    - type: mrr_at_10
+      value: 90.167
+    - type: mrr_at_20
+      value: 90.208
+    - type: mrr_at_30
+      value: 90.214
+    - type: mrr_at_50
+      value: 90.217
+    - type: mrr_at_70
+      value: 90.218
+    - type: mrr_at_100
+      value: 90.21900000000001
+    - type: mrr_at_200
+      value: 90.21900000000001
+    - type: mrr_at_300
+      value: 90.21900000000001
+    - type: mrr_at_500
+      value: 90.21900000000001
+    - type: mrr_at_700
+      value: 90.21900000000001
+    - type: mrr_at_1000
+      value: 90.21900000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 59.721999999999994
+    - type: ndcg_at_2
+      value: 56.85
+    - type: ndcg_at_3
+      value: 56.462999999999994
+    - type: ndcg_at_5
+      value: 57.75599999999999
+    - type: ndcg_at_7
+      value: 59.109
+    - type: ndcg_at_10
+      value: 60.402
+    - type: ndcg_at_20
+      value: 63.071999999999996
+    - type: ndcg_at_30
+      value: 64.302
+    - type: ndcg_at_50
+      value: 65.619
+    - type: ndcg_at_70
+      value: 66.161
+    - type: ndcg_at_100
+      value: 66.645
+    - type: ndcg_at_200
+      value: 67.353
+    - type: ndcg_at_300
+      value: 67.646
+    - type: ndcg_at_500
+      value: 67.852
+    - type: ndcg_at_700
+      value: 67.974
+    - type: ndcg_at_1000
+      value: 68.084
+    - type: map_at_1
+      value: 31.56
+    - type: map_at_2
+      value: 42.093
+    - type: map_at_3
+      value: 46.177
+    - type: map_at_5
+      value: 49.78
+    - type: map_at_7
+      value: 51.410999999999994
+    - type: map_at_10
+      value: 52.524
+    - type: map_at_20
+      value: 53.815000000000005
+    - type: map_at_30
+      value: 54.201
+    - type: map_at_50
+      value: 54.531
+    - type: map_at_70
+      value: 54.625
+    - type: map_at_100
+      value: 54.686
+    - type: map_at_200
+      value: 54.757999999999996
+    - type: map_at_300
+      value: 54.776
+    - type: map_at_500
+      value: 54.786
+    - type: map_at_700
+      value: 54.790000000000006
+    - type: map_at_1000
+      value: 54.793000000000006
+    - type: recall_at_1
+      value: 31.56
+    - type: recall_at_2
+      value: 44.858
+    - type: recall_at_3
+      value: 51.11
+    - type: recall_at_5
+      value: 58.394
+    - type: recall_at_7
+      value: 63.001
+    - type: recall_at_10
+      value: 66.81200000000001
+    - type: recall_at_20
+      value: 74.901
+    - type: recall_at_30
+      value: 79.218
+    - type: recall_at_50
+      value: 84.49
+    - type: recall_at_70
+      value: 87.003
+    - type: recall_at_100
+      value: 89.345
+    - type: recall_at_200
+      value: 93.173
+    - type: recall_at_300
+      value: 94.906
+    - type: recall_at_500
+      value: 96.223
+    - type: recall_at_700
+      value: 97.043
+    - type: recall_at_1000
+      value: 97.785
+    - type: precision_at_1
+      value: 59.721999999999994
+    - type: precision_at_2
+      value: 46.682
+    - type: precision_at_3
+      value: 37.602999999999994
+    - type: precision_at_5
+      value: 27.500000000000004
+    - type: precision_at_7
+      value: 21.847
+    - type: precision_at_10
+      value: 16.667
+    - type: precision_at_20
+      value: 9.545
+    - type: precision_at_30
+      value: 6.795
+    - type: precision_at_50
+      value: 4.38
+    - type: precision_at_70
+      value: 3.221
+    - type: precision_at_100
+      value: 2.319
+    - type: precision_at_200
+      value: 1.2149999999999999
+    - type: precision_at_300
+      value: 0.827
+    - type: precision_at_500
+      value: 0.504
+    - type: precision_at_700
+      value: 0.364
+    - type: precision_at_1000
+      value: 0.257
+    - type: mrr_at_1
+      value: 59.721999999999994
+    - type: mrr_at_2
+      value: 64.506
+    - type: mrr_at_3
+      value: 65.792
+    - type: mrr_at_5
+      value: 66.965
+    - type: mrr_at_7
+      value: 67.34700000000001
+    - type: mrr_at_10
+      value: 67.57
+    - type: mrr_at_20
+      value: 67.896
+    - type: mrr_at_30
+      value: 68.008
+    - type: mrr_at_50
+      value: 68.083
+    - type: mrr_at_70
+      value: 68.105
+    - type: mrr_at_100
+      value: 68.116
+    - type: mrr_at_200
+      value: 68.12700000000001
+    - type: mrr_at_300
+      value: 68.13
+    - type: mrr_at_500
+      value: 68.132
+    - type: mrr_at_700
+      value: 68.133
+    - type: mrr_at_1000
+      value: 68.133
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 81.796
+    - type: ndcg_at_2
+      value: 67.999
+    - type: ndcg_at_3
+      value: 72.15599999999999
+    - type: ndcg_at_5
+      value: 74.99900000000001
+    - type: ndcg_at_7
+      value: 76.179
+    - type: ndcg_at_10
+      value: 77.022
+    - type: ndcg_at_20
+      value: 78.173
+    - type: ndcg_at_30
+      value: 78.648
+    - type: ndcg_at_50
+      value: 79.104
+    - type: ndcg_at_70
+      value: 79.335
+    - type: ndcg_at_100
+      value: 79.56
+    - type: ndcg_at_200
+      value: 79.911
+    - type: ndcg_at_300
+      value: 80.045
+    - type: ndcg_at_500
+      value: 80.19500000000001
+    - type: ndcg_at_700
+      value: 80.281
+    - type: ndcg_at_1000
+      value: 80.35
+    - type: map_at_1
+      value: 40.898
+    - type: map_at_2
+      value: 62.016000000000005
+    - type: map_at_3
+      value: 66.121
+    - type: map_at_5
+      value: 68.471
+    - type: map_at_7
+      value: 69.261
+    - type: map_at_10
+      value: 69.738
+    - type: map_at_20
+      value: 70.208
+    - type: map_at_30
+      value: 70.343
+    - type: map_at_50
+      value: 70.43700000000001
+    - type: map_at_70
+      value: 70.47099999999999
+    - type: map_at_100
+      value: 70.498
+    - type: map_at_200
+      value: 70.526
+    - type: map_at_300
+      value: 70.533
+    - type: map_at_500
+      value: 70.538
+    - type: map_at_700
+      value: 70.541
+    - type: map_at_1000
+      value: 70.542
+    - type: recall_at_1
+      value: 40.898
+    - type: recall_at_2
+      value: 63.964
+    - type: recall_at_3
+      value: 70.743
+    - type: recall_at_5
+      value: 76.36699999999999
+    - type: recall_at_7
+      value: 79.142
+    - type: recall_at_10
+      value: 81.404
+    - type: recall_at_20
+      value: 85.111
+    - type: recall_at_30
+      value: 86.92800000000001
+    - type: recall_at_50
+      value: 88.899
+    - type: recall_at_70
+      value: 90.01400000000001
+    - type: recall_at_100
+      value: 91.19500000000001
+    - type: recall_at_200
+      value: 93.234
+    - type: recall_at_300
+      value: 94.105
+    - type: recall_at_500
+      value: 95.159
+    - type: recall_at_700
+      value: 95.8
+    - type: recall_at_1000
+      value: 96.34700000000001
+    - type: precision_at_1
+      value: 81.796
+    - type: precision_at_2
+      value: 63.964
+    - type: precision_at_3
+      value: 47.162
+    - type: precision_at_5
+      value: 30.547
+    - type: precision_at_7
+      value: 22.612
+    - type: precision_at_10
+      value: 16.281000000000002
+    - type: precision_at_20
+      value: 8.511000000000001
+    - type: precision_at_30
+      value: 5.795
+    - type: precision_at_50
+      value: 3.556
+    - type: precision_at_70
+      value: 2.572
+    - type: precision_at_100
+      value: 1.8239999999999998
+    - type: precision_at_200
+      value: 0.932
+    - type: precision_at_300
+      value: 0.627
+    - type: precision_at_500
+      value: 0.381
+    - type: precision_at_700
+      value: 0.27399999999999997
+    - type: precision_at_1000
+      value: 0.193
+    - type: mrr_at_1
+      value: 81.796
+    - type: mrr_at_2
+      value: 85.69200000000001
+    - type: mrr_at_3
+      value: 86.52
+    - type: mrr_at_5
+      value: 86.973
+    - type: mrr_at_7
+      value: 87.13300000000001
+    - type: mrr_at_10
+      value: 87.208
+    - type: mrr_at_20
+      value: 87.303
+    - type: mrr_at_30
+      value: 87.32799999999999
+    - type: mrr_at_50
+      value: 87.347
+    - type: mrr_at_70
+      value: 87.35199999999999
+    - type: mrr_at_100
+      value: 87.355
+    - type: mrr_at_200
+      value: 87.357
+    - type: mrr_at_300
+      value: 87.357
+    - type: mrr_at_500
+      value: 87.358
+    - type: mrr_at_700
+      value: 87.358
+    - type: mrr_at_1000
+      value: 87.358
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 94.79200000000002
+    - type: ap
+      value: 92.54484356773553
+    - type: f1
+      value: 94.78965313682525
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 24.398
+    - type: ndcg_at_2
+      value: 31.336000000000002
+    - type: ndcg_at_3
+      value: 35.266999999999996
+    - type: ndcg_at_5
+      value: 39.356
+    - type: ndcg_at_7
+      value: 41.562
+    - type: ndcg_at_10
+      value: 43.408
+    - type: ndcg_at_20
+      value: 46.107
+    - type: ndcg_at_30
+      value: 47.164
+    - type: ndcg_at_50
+      value: 48.126000000000005
+    - type: ndcg_at_70
+      value: 48.626999999999995
+    - type: ndcg_at_100
+      value: 49.043
+    - type: ndcg_at_200
+      value: 49.575
+    - type: ndcg_at_300
+      value: 49.794
+    - type: ndcg_at_500
+      value: 49.942
+    - type: ndcg_at_700
+      value: 50.014
+    - type: ndcg_at_1000
+      value: 50.077000000000005
+    - type: map_at_1
+      value: 23.723
+    - type: map_at_2
+      value: 29.593000000000004
+    - type: map_at_3
+      value: 32.273
+    - type: map_at_5
+      value: 34.587
+    - type: map_at_7
+      value: 35.589999999999996
+    - type: map_at_10
+      value: 36.296
+    - type: map_at_20
+      value: 37.059999999999995
+    - type: map_at_30
+      value: 37.265
+    - type: map_at_50
+      value: 37.402
+    - type: map_at_70
+      value: 37.454
+    - type: map_at_100
+      value: 37.486999999999995
+    - type: map_at_200
+      value: 37.516
+    - type: map_at_300
+      value: 37.524
+    - type: map_at_500
+      value: 37.528
+    - type: map_at_700
+      value: 37.529
+    - type: map_at_1000
+      value: 37.53
+    - type: recall_at_1
+      value: 23.723
+    - type: recall_at_2
+      value: 35.355
+    - type: recall_at_3
+      value: 43.22
+    - type: recall_at_5
+      value: 53.025
+    - type: recall_at_7
+      value: 59.327
+    - type: recall_at_10
+      value: 65.302
+    - type: recall_at_20
+      value: 75.765
+    - type: recall_at_30
+      value: 80.632
+    - type: recall_at_50
+      value: 85.63499999999999
+    - type: recall_at_70
+      value: 88.554
+    - type: recall_at_100
+      value: 91.16300000000001
+    - type: recall_at_200
+      value: 94.85
+    - type: recall_at_300
+      value: 96.532
+    - type: recall_at_500
+      value: 97.751
+    - type: recall_at_700
+      value: 98.383
+    - type: recall_at_1000
+      value: 98.97
+    - type: precision_at_1
+      value: 24.398
+    - type: precision_at_2
+      value: 18.274
+    - type: precision_at_3
+      value: 14.951999999999998
+    - type: precision_at_5
+      value: 11.052
+    - type: precision_at_7
+      value: 8.84
+    - type: precision_at_10
+      value: 6.8309999999999995
+    - type: precision_at_20
+      value: 3.978
+    - type: precision_at_30
+      value: 2.827
+    - type: precision_at_50
+      value: 1.807
+    - type: precision_at_70
+      value: 1.336
+    - type: precision_at_100
+      value: 0.964
+    - type: precision_at_200
+      value: 0.502
+    - type: precision_at_300
+      value: 0.34099999999999997
+    - type: precision_at_500
+      value: 0.208
+    - type: precision_at_700
+      value: 0.15
+    - type: precision_at_1000
+      value: 0.105
+    - type: mrr_at_1
+      value: 24.398
+    - type: mrr_at_2
+      value: 30.351
+    - type: mrr_at_3
+      value: 33.001000000000005
+    - type: mrr_at_5
+      value: 35.228
+    - type: mrr_at_7
+      value: 36.223
+    - type: mrr_at_10
+      value: 36.903999999999996
+    - type: mrr_at_20
+      value: 37.631
+    - type: mrr_at_30
+      value: 37.830000000000005
+    - type: mrr_at_50
+      value: 37.955
+    - type: mrr_at_70
+      value: 38.003
+    - type: mrr_at_100
+      value: 38.033
+    - type: mrr_at_200
+      value: 38.059
+    - type: mrr_at_300
+      value: 38.066
+    - type: mrr_at_500
+      value: 38.068999999999996
+    - type: mrr_at_700
+      value: 38.07
+    - type: mrr_at_1000
+      value: 38.07
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 96.35658914728683
+    - type: f1
+      value: 96.15039630903114
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 86.29730962152303
+    - type: f1
+      value: 71.12166316567485
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 79.98991257565568
+    - type: f1
+      value: 77.41680115095276
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 82.1990585070612
+    - type: f1
+      value: 82.23719179179362
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 40.03019554933584
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 38.999760551497815
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.72383151953079
+    - type: mrr
+      value: 33.93989699030721
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 51.858000000000004
+    - type: ndcg_at_2
+      value: 49.675999999999995
+    - type: ndcg_at_3
+      value: 47.519
+    - type: ndcg_at_5
+      value: 45.198
+    - type: ndcg_at_7
+      value: 43.504
+    - type: ndcg_at_10
+      value: 41.88
+    - type: ndcg_at_20
+      value: 39.122
+    - type: ndcg_at_30
+      value: 37.95
+    - type: ndcg_at_50
+      value: 37.602999999999994
+    - type: ndcg_at_70
+      value: 37.836
+    - type: ndcg_at_100
+      value: 38.493
+    - type: ndcg_at_200
+      value: 40.187
+    - type: ndcg_at_300
+      value: 41.524
+    - type: ndcg_at_500
+      value: 43.657000000000004
+    - type: ndcg_at_700
+      value: 45.234
+    - type: ndcg_at_1000
+      value: 47.047
+    - type: map_at_1
+      value: 6.392
+    - type: map_at_2
+      value: 10.113
+    - type: map_at_3
+      value: 11.543000000000001
+    - type: map_at_5
+      value: 13.729
+    - type: map_at_7
+      value: 14.985000000000001
+    - type: map_at_10
+      value: 16.217000000000002
+    - type: map_at_20
+      value: 18.106
+    - type: map_at_30
+      value: 18.878
+    - type: map_at_50
+      value: 19.822
+    - type: map_at_70
+      value: 20.352999999999998
+    - type: map_at_100
+      value: 20.827
+    - type: map_at_200
+      value: 21.512
+    - type: map_at_300
+      value: 21.826
+    - type: map_at_500
+      value: 22.155
+    - type: map_at_700
+      value: 22.349
+    - type: map_at_1000
+      value: 22.531000000000002
+    - type: recall_at_1
+      value: 6.392
+    - type: recall_at_2
+      value: 11.215
+    - type: recall_at_3
+      value: 13.231000000000002
+    - type: recall_at_5
+      value: 16.66
+    - type: recall_at_7
+      value: 18.802
+    - type: recall_at_10
+      value: 21.185000000000002
+    - type: recall_at_20
+      value: 25.35
+    - type: recall_at_30
+      value: 27.91
+    - type: recall_at_50
+      value: 32.845
+    - type: recall_at_70
+      value: 35.789
+    - type: recall_at_100
+      value: 39.247
+    - type: recall_at_200
+      value: 46.655
+    - type: recall_at_300
+      value: 51.43299999999999
+    - type: recall_at_500
+      value: 59.472
+    - type: recall_at_700
+      value: 64.742
+    - type: recall_at_1000
+      value: 70.97099999999999
+    - type: precision_at_1
+      value: 53.559999999999995
+    - type: precision_at_2
+      value: 48.762
+    - type: precision_at_3
+      value: 44.169000000000004
+    - type: precision_at_5
+      value: 39.071
+    - type: precision_at_7
+      value: 35.161
+    - type: precision_at_10
+      value: 31.238
+    - type: precision_at_20
+      value: 23.064999999999998
+    - type: precision_at_30
+      value: 18.844
+    - type: precision_at_50
+      value: 14.601
+    - type: precision_at_70
+      value: 12.088000000000001
+    - type: precision_at_100
+      value: 9.844999999999999
+    - type: precision_at_200
+      value: 6.358
+    - type: precision_at_300
+      value: 4.915
+    - type: precision_at_500
+      value: 3.531
+    - type: precision_at_700
+      value: 2.8649999999999998
+    - type: precision_at_1000
+      value: 2.289
+    - type: mrr_at_1
+      value: 54.17999999999999
+    - type: mrr_at_2
+      value: 59.288
+    - type: mrr_at_3
+      value: 60.836
+    - type: mrr_at_5
+      value: 62.275999999999996
+    - type: mrr_at_7
+      value: 62.688
+    - type: mrr_at_10
+      value: 62.865
+    - type: mrr_at_20
+      value: 63.11
+    - type: mrr_at_30
+      value: 63.193999999999996
+    - type: mrr_at_50
+      value: 63.258
+    - type: mrr_at_70
+      value: 63.278
+    - type: mrr_at_100
+      value: 63.297000000000004
+    - type: mrr_at_200
+      value: 63.315999999999995
+    - type: mrr_at_300
+      value: 63.318
+    - type: mrr_at_500
+      value: 63.32299999999999
+    - type: mrr_at_700
+      value: 63.324000000000005
+    - type: mrr_at_1000
+      value: 63.324999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 50.897999999999996
+    - type: ndcg_at_2
+      value: 59.126
+    - type: ndcg_at_3
+      value: 63.093999999999994
+    - type: ndcg_at_5
+      value: 67.197
+    - type: ndcg_at_7
+      value: 68.719
+    - type: ndcg_at_10
+      value: 69.915
+    - type: ndcg_at_20
+      value: 71.229
+    - type: ndcg_at_30
+      value: 71.667
+    - type: ndcg_at_50
+      value: 71.98
+    - type: ndcg_at_70
+      value: 72.127
+    - type: ndcg_at_100
+      value: 72.217
+    - type: ndcg_at_200
+      value: 72.319
+    - type: ndcg_at_300
+      value: 72.347
+    - type: ndcg_at_500
+      value: 72.37
+    - type: ndcg_at_700
+      value: 72.379
+    - type: ndcg_at_1000
+      value: 72.381
+    - type: map_at_1
+      value: 45.297
+    - type: map_at_2
+      value: 55.596000000000004
+    - type: map_at_3
+      value: 58.724
+    - type: map_at_5
+      value: 61.387
+    - type: map_at_7
+      value: 62.173
+    - type: map_at_10
+      value: 62.69
+    - type: map_at_20
+      value: 63.125
+    - type: map_at_30
+      value: 63.223
+    - type: map_at_50
+      value: 63.27700000000001
+    - type: map_at_70
+      value: 63.295
+    - type: map_at_100
+      value: 63.303
+    - type: map_at_200
+      value: 63.31
+    - type: map_at_300
+      value: 63.31099999999999
+    - type: map_at_500
+      value: 63.312000000000005
+    - type: map_at_700
+      value: 63.312000000000005
+    - type: map_at_1000
+      value: 63.312000000000005
+    - type: recall_at_1
+      value: 45.297
+    - type: recall_at_2
+      value: 63.866
+    - type: recall_at_3
+      value: 71.898
+    - type: recall_at_5
+      value: 81.16600000000001
+    - type: recall_at_7
+      value: 85.301
+    - type: recall_at_10
+      value: 88.94800000000001
+    - type: recall_at_20
+      value: 93.719
+    - type: recall_at_30
+      value: 95.628
+    - type: recall_at_50
+      value: 97.14699999999999
+    - type: recall_at_70
+      value: 97.955
+    - type: recall_at_100
+      value: 98.48599999999999
+    - type: recall_at_200
+      value: 99.157
+    - type: recall_at_300
+      value: 99.355
+    - type: recall_at_500
+      value: 99.53699999999999
+    - type: recall_at_700
+      value: 99.62299999999999
+    - type: recall_at_1000
+      value: 99.638
+    - type: precision_at_1
+      value: 50.897999999999996
+    - type: precision_at_2
+      value: 36.703
+    - type: precision_at_3
+      value: 27.926000000000002
+    - type: precision_at_5
+      value: 19.276
+    - type: precision_at_7
+      value: 14.533999999999999
+    - type: precision_at_10
+      value: 10.678
+    - type: precision_at_20
+      value: 5.663
+    - type: precision_at_30
+      value: 3.8600000000000003
+    - type: precision_at_50
+      value: 2.358
+    - type: precision_at_70
+      value: 1.7000000000000002
+    - type: precision_at_100
+      value: 1.198
+    - type: precision_at_200
+      value: 0.603
+    - type: precision_at_300
+      value: 0.40299999999999997
+    - type: precision_at_500
+      value: 0.242
+    - type: precision_at_700
+      value: 0.173
+    - type: precision_at_1000
+      value: 0.121
+    - type: mrr_at_1
+      value: 50.897999999999996
+    - type: mrr_at_2
+      value: 59.994
+    - type: mrr_at_3
+      value: 62.553000000000004
+    - type: mrr_at_5
+      value: 64.307
+    - type: mrr_at_7
+      value: 64.864
+    - type: mrr_at_10
+      value: 65.22200000000001
+    - type: mrr_at_20
+      value: 65.499
+    - type: mrr_at_30
+      value: 65.561
+    - type: mrr_at_50
+      value: 65.592
+    - type: mrr_at_70
+      value: 65.602
+    - type: mrr_at_100
+      value: 65.607
+    - type: mrr_at_200
+      value: 65.61099999999999
+    - type: mrr_at_300
+      value: 65.61200000000001
+    - type: mrr_at_500
+      value: 65.61200000000001
+    - type: mrr_at_700
+      value: 65.61200000000001
+    - type: mrr_at_1000
+      value: 65.61200000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 82.96
+    - type: ndcg_at_2
+      value: 85.614
+    - type: ndcg_at_3
+      value: 87.19
+    - type: ndcg_at_5
+      value: 88.654
+    - type: ndcg_at_7
+      value: 89.287
+    - type: ndcg_at_10
+      value: 89.785
+    - type: ndcg_at_20
+      value: 90.384
+    - type: ndcg_at_30
+      value: 90.589
+    - type: ndcg_at_50
+      value: 90.738
+    - type: ndcg_at_70
+      value: 90.789
+    - type: ndcg_at_100
+      value: 90.824
+    - type: ndcg_at_200
+      value: 90.869
+    - type: ndcg_at_300
+      value: 90.881
+    - type: ndcg_at_500
+      value: 90.886
+    - type: ndcg_at_700
+      value: 90.889
+    - type: ndcg_at_1000
+      value: 90.889
+    - type: map_at_1
+      value: 72.152
+    - type: map_at_2
+      value: 80.818
+    - type: map_at_3
+      value: 83.462
+    - type: map_at_5
+      value: 85.286
+    - type: map_at_7
+      value: 85.921
+    - type: map_at_10
+      value: 86.334
+    - type: map_at_20
+      value: 86.737
+    - type: map_at_30
+      value: 86.847
+    - type: map_at_50
+      value: 86.911
+    - type: map_at_70
+      value: 86.932
+    - type: map_at_100
+      value: 86.943
+    - type: map_at_200
+      value: 86.953
+    - type: map_at_300
+      value: 86.955
+    - type: map_at_500
+      value: 86.956
+    - type: map_at_700
+      value: 86.956
+    - type: map_at_1000
+      value: 86.956
+    - type: recall_at_1
+      value: 72.152
+    - type: recall_at_2
+      value: 84.129
+    - type: recall_at_3
+      value: 88.87
+    - type: recall_at_5
+      value: 93.067
+    - type: recall_at_7
+      value: 94.882
+    - type: recall_at_10
+      value: 96.353
+    - type: recall_at_20
+      value: 98.26700000000001
+    - type: recall_at_30
+      value: 98.92999999999999
+    - type: recall_at_50
+      value: 99.441
+    - type: recall_at_70
+      value: 99.619
+    - type: recall_at_100
+      value: 99.748
+    - type: recall_at_200
+      value: 99.911
+    - type: recall_at_300
+      value: 99.956
+    - type: recall_at_500
+      value: 99.98
+    - type: recall_at_700
+      value: 99.991
+    - type: recall_at_1000
+      value: 99.996
+    - type: precision_at_1
+      value: 82.96
+    - type: precision_at_2
+      value: 52.175000000000004
+    - type: precision_at_3
+      value: 38.223
+    - type: precision_at_5
+      value: 25.056
+    - type: precision_at_7
+      value: 18.717
+    - type: precision_at_10
+      value: 13.614999999999998
+    - type: precision_at_20
+      value: 7.208
+    - type: precision_at_30
+      value: 4.928
+    - type: precision_at_50
+      value: 3.024
+    - type: precision_at_70
+      value: 2.183
+    - type: precision_at_100
+      value: 1.54
+    - type: precision_at_200
+      value: 0.779
+    - type: precision_at_300
+      value: 0.521
+    - type: precision_at_500
+      value: 0.313
+    - type: precision_at_700
+      value: 0.22399999999999998
+    - type: precision_at_1000
+      value: 0.157
+    - type: mrr_at_1
+      value: 82.96
+    - type: mrr_at_2
+      value: 87.005
+    - type: mrr_at_3
+      value: 88.07199999999999
+    - type: mrr_at_5
+      value: 88.634
+    - type: mrr_at_7
+      value: 88.793
+    - type: mrr_at_10
+      value: 88.87899999999999
+    - type: mrr_at_20
+      value: 88.94999999999999
+    - type: mrr_at_30
+      value: 88.96
+    - type: mrr_at_50
+      value: 88.965
+    - type: mrr_at_70
+      value: 88.966
+    - type: mrr_at_100
+      value: 88.967
+    - type: mrr_at_200
+      value: 88.967
+    - type: mrr_at_300
+      value: 88.967
+    - type: mrr_at_500
+      value: 88.967
+    - type: mrr_at_700
+      value: 88.967
+    - type: mrr_at_1000
+      value: 88.967
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 59.90388554491155
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 67.64232539036783
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 22.6
+    - type: ndcg_at_2
+      value: 20.355999999999998
+    - type: ndcg_at_3
+      value: 18.536
+    - type: ndcg_at_5
+      value: 16.523
+    - type: ndcg_at_7
+      value: 17.979
+    - type: ndcg_at_10
+      value: 19.908
+    - type: ndcg_at_20
+      value: 22.887
+    - type: ndcg_at_30
+      value: 24.43
+    - type: ndcg_at_50
+      value: 25.959
+    - type: ndcg_at_70
+      value: 26.989
+    - type: ndcg_at_100
+      value: 27.977
+    - type: ndcg_at_200
+      value: 29.831000000000003
+    - type: ndcg_at_300
+      value: 30.787
+    - type: ndcg_at_500
+      value: 31.974999999999998
+    - type: ndcg_at_700
+      value: 32.554
+    - type: ndcg_at_1000
+      value: 33.277
+    - type: map_at_1
+      value: 4.593
+    - type: map_at_2
+      value: 6.923
+    - type: map_at_3
+      value: 8.3
+    - type: map_at_5
+      value: 10.072000000000001
+    - type: map_at_7
+      value: 10.782
+    - type: map_at_10
+      value: 11.72
+    - type: map_at_20
+      value: 12.838
+    - type: map_at_30
+      value: 13.257
+    - type: map_at_50
+      value: 13.569
+    - type: map_at_70
+      value: 13.733
+    - type: map_at_100
+      value: 13.858999999999998
+    - type: map_at_200
+      value: 14.018
+    - type: map_at_300
+      value: 14.072999999999999
+    - type: map_at_500
+      value: 14.126
+    - type: map_at_700
+      value: 14.145
+    - type: map_at_1000
+      value: 14.161999999999999
+    - type: recall_at_1
+      value: 4.593
+    - type: recall_at_2
+      value: 7.997999999999999
+    - type: recall_at_3
+      value: 10.563
+    - type: recall_at_5
+      value: 14.907
+    - type: recall_at_7
+      value: 17.4
+    - type: recall_at_10
+      value: 21.18
+    - type: recall_at_20
+      value: 28.144999999999996
+    - type: recall_at_30
+      value: 32.462
+    - type: recall_at_50
+      value: 37.267
+    - type: recall_at_70
+      value: 40.875
+    - type: recall_at_100
+      value: 44.641999999999996
+    - type: recall_at_200
+      value: 52.573
+    - type: recall_at_300
+      value: 57.089999999999996
+    - type: recall_at_500
+      value: 63.14300000000001
+    - type: recall_at_700
+      value: 66.313
+    - type: recall_at_1000
+      value: 70.458
+    - type: precision_at_1
+      value: 22.6
+    - type: precision_at_2
+      value: 19.7
+    - type: precision_at_3
+      value: 17.333000000000002
+    - type: precision_at_5
+      value: 14.680000000000001
+    - type: precision_at_7
+      value: 12.243
+    - type: precision_at_10
+      value: 10.440000000000001
+    - type: precision_at_20
+      value: 6.944999999999999
+    - type: precision_at_30
+      value: 5.333
+    - type: precision_at_50
+      value: 3.678
+    - type: precision_at_70
+      value: 2.881
+    - type: precision_at_100
+      value: 2.2030000000000003
+    - type: precision_at_200
+      value: 1.295
+    - type: precision_at_300
+      value: 0.9369999999999999
+    - type: precision_at_500
+      value: 0.622
+    - type: precision_at_700
+      value: 0.466
+    - type: precision_at_1000
+      value: 0.347
+    - type: mrr_at_1
+      value: 22.6
+    - type: mrr_at_2
+      value: 27.900000000000002
+    - type: mrr_at_3
+      value: 30.067
+    - type: mrr_at_5
+      value: 32.207
+    - type: mrr_at_7
+      value: 33.004
+    - type: mrr_at_10
+      value: 33.596
+    - type: mrr_at_20
+      value: 34.268
+    - type: mrr_at_30
+      value: 34.492
+    - type: mrr_at_50
+      value: 34.628
+    - type: mrr_at_70
+      value: 34.681
+    - type: mrr_at_100
+      value: 34.717
+    - type: mrr_at_200
+      value: 34.757
+    - type: mrr_at_300
+      value: 34.768
+    - type: mrr_at_500
+      value: 34.772
+    - type: mrr_at_700
+      value: 34.774
+    - type: mrr_at_1000
+      value: 34.775
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.90122745229677
+    - type: cos_sim_spearman
+      value: 82.92294737327579
+    - type: euclidean_pearson
+      value: 84.08979655773187
+    - type: euclidean_spearman
+      value: 82.92294657285412
+    - type: manhattan_pearson
+      value: 84.09347480531832
+    - type: manhattan_spearman
+      value: 82.91564613948087
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.01218713698583
+    - type: cos_sim_spearman
+      value: 79.46865215168464
+    - type: euclidean_pearson
+      value: 83.22621889891909
+    - type: euclidean_spearman
+      value: 79.46853821709514
+    - type: manhattan_pearson
+      value: 83.69962580788805
+    - type: manhattan_spearman
+      value: 79.9561593356932
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.98438696342964
+    - type: cos_sim_spearman
+      value: 89.15419511870839
+    - type: euclidean_pearson
+      value: 88.49646141802894
+    - type: euclidean_spearman
+      value: 89.15419503946019
+    - type: manhattan_pearson
+      value: 88.6420585616327
+    - type: manhattan_spearman
+      value: 89.42648950757743
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.30772547759544
+    - type: cos_sim_spearman
+      value: 84.93199878424691
+    - type: euclidean_pearson
+      value: 86.16266630395455
+    - type: euclidean_spearman
+      value: 84.93198798543634
+    - type: manhattan_pearson
+      value: 86.14285723189803
+    - type: manhattan_spearman
+      value: 85.0361672522687
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.21342071197127
+    - type: cos_sim_spearman
+      value: 90.7407512744838
+    - type: euclidean_pearson
+      value: 90.1517933113061
+    - type: euclidean_spearman
+      value: 90.74075125431919
+    - type: manhattan_pearson
+      value: 90.17963034676193
+    - type: manhattan_spearman
+      value: 90.88999275865135
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.82518054100498
+    - type: cos_sim_spearman
+      value: 87.81570533154735
+    - type: euclidean_pearson
+      value: 86.91684561573618
+    - type: euclidean_spearman
+      value: 87.81570533154735
+    - type: manhattan_pearson
+      value: 86.98311935744032
+    - type: manhattan_spearman
+      value: 87.9594667151966
+  - 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_pearson
+      value: 92.09578436612053
+    - type: cos_sim_spearman
+      value: 92.01519349090438
+    - type: euclidean_pearson
+      value: 92.07113635890894
+    - type: euclidean_spearman
+      value: 92.01519349090438
+    - type: manhattan_pearson
+      value: 91.89343820765625
+    - type: manhattan_spearman
+      value: 91.7443476810177
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 69.29997751464549
+    - type: cos_sim_spearman
+      value: 68.36425436812782
+    - type: euclidean_pearson
+      value: 69.81381677661783
+    - type: euclidean_spearman
+      value: 68.36425436812782
+    - type: manhattan_pearson
+      value: 69.92823397008026
+    - type: manhattan_spearman
+      value: 68.35770640039254
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.39126315452359
+    - type: cos_sim_spearman
+      value: 88.99708463265337
+    - type: euclidean_pearson
+      value: 88.60793820038607
+    - type: euclidean_spearman
+      value: 88.99708463265337
+    - type: manhattan_pearson
+      value: 88.69860633571047
+    - type: manhattan_spearman
+      value: 89.20094593888012
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 86.58028062818582
+    - type: mrr
+      value: 96.53586790841693
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 66.333
+    - type: ndcg_at_2
+      value: 70.655
+    - type: ndcg_at_3
+      value: 72.801
+    - type: ndcg_at_5
+      value: 75.793
+    - type: ndcg_at_7
+      value: 76.946
+    - type: ndcg_at_10
+      value: 77.66199999999999
+    - type: ndcg_at_20
+      value: 78.786
+    - type: ndcg_at_30
+      value: 79.066
+    - type: ndcg_at_50
+      value: 79.255
+    - type: ndcg_at_70
+      value: 79.423
+    - type: ndcg_at_100
+      value: 79.476
+    - type: ndcg_at_200
+      value: 79.65299999999999
+    - type: ndcg_at_300
+      value: 79.696
+    - type: ndcg_at_500
+      value: 79.73599999999999
+    - type: ndcg_at_700
+      value: 79.77199999999999
+    - type: ndcg_at_1000
+      value: 79.77199999999999
+    - type: map_at_1
+      value: 63.383
+    - type: map_at_2
+      value: 68.144
+    - type: map_at_3
+      value: 70.19800000000001
+    - type: map_at_5
+      value: 72.38
+    - type: map_at_7
+      value: 72.955
+    - type: map_at_10
+      value: 73.312
+    - type: map_at_20
+      value: 73.678
+    - type: map_at_30
+      value: 73.72800000000001
+    - type: map_at_50
+      value: 73.75500000000001
+    - type: map_at_70
+      value: 73.771
+    - type: map_at_100
+      value: 73.776
+    - type: map_at_200
+      value: 73.783
+    - type: map_at_300
+      value: 73.784
+    - type: map_at_500
+      value: 73.785
+    - type: map_at_700
+      value: 73.786
+    - type: map_at_1000
+      value: 73.786
+    - type: recall_at_1
+      value: 63.383
+    - type: recall_at_2
+      value: 72.283
+    - type: recall_at_3
+      value: 77.183
+    - type: recall_at_5
+      value: 84.56099999999999
+    - type: recall_at_7
+      value: 87.67200000000001
+    - type: recall_at_10
+      value: 89.822
+    - type: recall_at_20
+      value: 94
+    - type: recall_at_30
+      value: 95.333
+    - type: recall_at_50
+      value: 96.333
+    - type: recall_at_70
+      value: 97.333
+    - type: recall_at_100
+      value: 97.667
+    - type: recall_at_200
+      value: 99
+    - type: recall_at_300
+      value: 99.333
+    - type: recall_at_500
+      value: 99.667
+    - type: recall_at_700
+      value: 100
+    - type: recall_at_1000
+      value: 100
+    - type: precision_at_1
+      value: 66.333
+    - type: precision_at_2
+      value: 38.667
+    - type: precision_at_3
+      value: 28.111000000000004
+    - type: precision_at_5
+      value: 18.933
+    - type: precision_at_7
+      value: 14.094999999999999
+    - type: precision_at_10
+      value: 10.167
+    - type: precision_at_20
+      value: 5.35
+    - type: precision_at_30
+      value: 3.611
+    - type: precision_at_50
+      value: 2.1870000000000003
+    - type: precision_at_70
+      value: 1.576
+    - type: precision_at_100
+      value: 1.107
+    - type: precision_at_200
+      value: 0.5599999999999999
+    - type: precision_at_300
+      value: 0.374
+    - type: precision_at_500
+      value: 0.22499999999999998
+    - type: precision_at_700
+      value: 0.161
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: mrr_at_1
+      value: 66.333
+    - type: mrr_at_2
+      value: 70.833
+    - type: mrr_at_3
+      value: 72.167
+    - type: mrr_at_5
+      value: 73.6
+    - type: mrr_at_7
+      value: 74.084
+    - type: mrr_at_10
+      value: 74.283
+    - type: mrr_at_20
+      value: 74.54499999999999
+    - type: mrr_at_30
+      value: 74.59599999999999
+    - type: mrr_at_50
+      value: 74.622
+    - type: mrr_at_70
+      value: 74.639
+    - type: mrr_at_100
+      value: 74.643
+    - type: mrr_at_200
+      value: 74.65
+    - type: mrr_at_300
+      value: 74.652
+    - type: mrr_at_500
+      value: 74.653
+    - type: mrr_at_700
+      value: 74.653
+    - type: mrr_at_1000
+      value: 74.653
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.84554455445544
+    - type: cos_sim_ap
+      value: 96.31178339136798
+    - type: cos_sim_f1
+      value: 92.1921921921922
+    - type: cos_sim_precision
+      value: 92.28456913827655
+    - type: cos_sim_recall
+      value: 92.10000000000001
+    - type: dot_accuracy
+      value: 99.84554455445544
+    - type: dot_ap
+      value: 96.31178339136797
+    - type: dot_f1
+      value: 92.1921921921922
+    - type: dot_precision
+      value: 92.28456913827655
+    - type: dot_recall
+      value: 92.10000000000001
+    - type: euclidean_accuracy
+      value: 99.84554455445544
+    - type: euclidean_ap
+      value: 96.31178339136798
+    - type: euclidean_f1
+      value: 92.1921921921922
+    - type: euclidean_precision
+      value: 92.28456913827655
+    - type: euclidean_recall
+      value: 92.10000000000001
+    - type: manhattan_accuracy
+      value: 99.84752475247525
+    - type: manhattan_ap
+      value: 96.4591954606088
+    - type: manhattan_f1
+      value: 92.25352112676056
+    - type: manhattan_precision
+      value: 92.81376518218623
+    - type: manhattan_recall
+      value: 91.7
+    - type: max_accuracy
+      value: 99.84752475247525
+    - type: max_ap
+      value: 96.4591954606088
+    - type: max_f1
+      value: 92.25352112676056
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 74.24659759283294
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 46.77690051260451
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 55.68436757803185
+    - type: mrr
+      value: 56.82157711569475
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.652482405629843
+    - type: cos_sim_spearman
+      value: 31.16341822347735
+    - type: dot_pearson
+      value: 31.652479892699837
+    - type: dot_spearman
+      value: 31.16341822347735
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 92
+    - type: ndcg_at_2
+      value: 90.839
+    - type: ndcg_at_3
+      value: 90.642
+    - type: ndcg_at_5
+      value: 90.348
+    - type: ndcg_at_7
+      value: 89.015
+    - type: ndcg_at_10
+      value: 87.599
+    - type: ndcg_at_20
+      value: 84.434
+    - type: ndcg_at_30
+      value: 81.655
+    - type: ndcg_at_50
+      value: 77.278
+    - type: ndcg_at_70
+      value: 73.957
+    - type: ndcg_at_100
+      value: 69.56
+    - type: ndcg_at_200
+      value: 60.724000000000004
+    - type: ndcg_at_300
+      value: 57.245000000000005
+    - type: ndcg_at_500
+      value: 56.316
+    - type: ndcg_at_700
+      value: 58.399
+    - type: ndcg_at_1000
+      value: 62.21600000000001
+    - type: map_at_1
+      value: 0.247
+    - type: map_at_2
+      value: 0.488
+    - type: map_at_3
+      value: 0.7230000000000001
+    - type: map_at_5
+      value: 1.204
+    - type: map_at_7
+      value: 1.6500000000000001
+    - type: map_at_10
+      value: 2.292
+    - type: map_at_20
+      value: 4.274
+    - type: map_at_30
+      value: 6.027
+    - type: map_at_50
+      value: 9.083
+    - type: map_at_70
+      value: 11.751000000000001
+    - type: map_at_100
+      value: 14.912
+    - type: map_at_200
+      value: 22.213
+    - type: map_at_300
+      value: 26.667999999999996
+    - type: map_at_500
+      value: 31.556
+    - type: map_at_700
+      value: 34.221000000000004
+    - type: map_at_1000
+      value: 36.443999999999996
+    - type: recall_at_1
+      value: 0.247
+    - type: recall_at_2
+      value: 0.49899999999999994
+    - type: recall_at_3
+      value: 0.742
+    - type: recall_at_5
+      value: 1.247
+    - type: recall_at_7
+      value: 1.722
+    - type: recall_at_10
+      value: 2.405
+    - type: recall_at_20
+      value: 4.583
+    - type: recall_at_30
+      value: 6.587999999999999
+    - type: recall_at_50
+      value: 10.188
+    - type: recall_at_70
+      value: 13.496
+    - type: recall_at_100
+      value: 17.578
+    - type: recall_at_200
+      value: 28.158
+    - type: recall_at_300
+      value: 35.532000000000004
+    - type: recall_at_500
+      value: 45.31
+    - type: recall_at_700
+      value: 51.822
+    - type: recall_at_1000
+      value: 58.53
+    - type: precision_at_1
+      value: 96
+    - type: precision_at_2
+      value: 96
+    - type: precision_at_3
+      value: 95.333
+    - type: precision_at_5
+      value: 94.8
+    - type: precision_at_7
+      value: 93.429
+    - type: precision_at_10
+      value: 91.4
+    - type: precision_at_20
+      value: 87.7
+    - type: precision_at_30
+      value: 84.867
+    - type: precision_at_50
+      value: 80.24
+    - type: precision_at_70
+      value: 76.371
+    - type: precision_at_100
+      value: 71.08
+    - type: precision_at_200
+      value: 59.4
+    - type: precision_at_300
+      value: 51.459999999999994
+    - type: precision_at_500
+      value: 40.644000000000005
+    - type: precision_at_700
+      value: 33.889
+    - type: precision_at_1000
+      value: 27.250000000000004
+    - type: mrr_at_1
+      value: 96
+    - type: mrr_at_2
+      value: 98
+    - type: mrr_at_3
+      value: 98
+    - type: mrr_at_5
+      value: 98
+    - type: mrr_at_7
+      value: 98
+    - type: mrr_at_10
+      value: 98
+    - type: mrr_at_20
+      value: 98
+    - type: mrr_at_30
+      value: 98
+    - type: mrr_at_50
+      value: 98
+    - type: mrr_at_70
+      value: 98
+    - type: mrr_at_100
+      value: 98
+    - type: mrr_at_200
+      value: 98
+    - type: mrr_at_300
+      value: 98
+    - type: mrr_at_500
+      value: 98
+    - type: mrr_at_700
+      value: 98
+    - type: mrr_at_1000
+      value: 98
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_1
+      value: 43.878
+    - type: ndcg_at_2
+      value: 37.956
+    - type: ndcg_at_3
+      value: 35.053
+    - type: ndcg_at_5
+      value: 32.59
+    - type: ndcg_at_7
+      value: 30.226
+    - type: ndcg_at_10
+      value: 29.005
+    - type: ndcg_at_20
+      value: 30.11
+    - type: ndcg_at_30
+      value: 32.019999999999996
+    - type: ndcg_at_50
+      value: 34.354
+    - type: ndcg_at_70
+      value: 36.665
+    - type: ndcg_at_100
+      value: 38.888
+    - type: ndcg_at_200
+      value: 43.435
+    - type: ndcg_at_300
+      value: 45.795
+    - type: ndcg_at_500
+      value: 48.699999999999996
+    - type: ndcg_at_700
+      value: 50.242
+    - type: ndcg_at_1000
+      value: 51.529
+    - type: map_at_1
+      value: 3.521
+    - type: map_at_2
+      value: 5.309
+    - type: map_at_3
+      value: 6.576
+    - type: map_at_5
+      value: 8.97
+    - type: map_at_7
+      value: 10.194
+    - type: map_at_10
+      value: 11.949
+    - type: map_at_20
+      value: 14.686
+    - type: map_at_30
+      value: 15.8
+    - type: map_at_50
+      value: 16.59
+    - type: map_at_70
+      value: 17.2
+    - type: map_at_100
+      value: 17.765
+    - type: map_at_200
+      value: 18.636
+    - type: map_at_300
+      value: 18.972
+    - type: map_at_500
+      value: 19.301
+    - type: map_at_700
+      value: 19.445
+    - type: map_at_1000
+      value: 19.546
+    - type: recall_at_1
+      value: 3.521
+    - type: recall_at_2
+      value: 5.848
+    - type: recall_at_3
+      value: 7.657
+    - type: recall_at_5
+      value: 11.368
+    - type: recall_at_7
+      value: 13.748
+    - type: recall_at_10
+      value: 18.061
+    - type: recall_at_20
+      value: 26.844
+    - type: recall_at_30
+      value: 31.186000000000003
+    - type: recall_at_50
+      value: 35.951
+    - type: recall_at_70
+      value: 40.961999999999996
+    - type: recall_at_100
+      value: 46.743
+    - type: recall_at_200
+      value: 58.483
+    - type: recall_at_300
+      value: 65.973
+    - type: recall_at_500
+      value: 75.233
+    - type: recall_at_700
+      value: 80.472
+    - type: recall_at_1000
+      value: 85.02
+    - type: precision_at_1
+      value: 46.939
+    - type: precision_at_2
+      value: 38.775999999999996
+    - type: precision_at_3
+      value: 34.694
+    - type: precision_at_5
+      value: 31.429000000000002
+    - type: precision_at_7
+      value: 27.697
+    - type: precision_at_10
+      value: 24.490000000000002
+    - type: precision_at_20
+      value: 18.776
+    - type: precision_at_30
+      value: 15.034
+    - type: precision_at_50
+      value: 10.857
+    - type: precision_at_70
+      value: 9.096
+    - type: precision_at_100
+      value: 7.51
+    - type: precision_at_200
+      value: 4.929
+    - type: precision_at_300
+      value: 3.7760000000000002
+    - type: precision_at_500
+      value: 2.6780000000000004
+    - type: precision_at_700
+      value: 2.085
+    - type: precision_at_1000
+      value: 1.5709999999999997
+    - type: mrr_at_1
+      value: 46.939
+    - type: mrr_at_2
+      value: 55.102
+    - type: mrr_at_3
+      value: 57.823
+    - type: mrr_at_5
+      value: 60.68
+    - type: mrr_at_7
+      value: 60.972
+    - type: mrr_at_10
+      value: 61.199000000000005
+    - type: mrr_at_20
+      value: 61.831
+    - type: mrr_at_30
+      value: 61.831
+    - type: mrr_at_50
+      value: 61.873
+    - type: mrr_at_70
+      value: 61.873
+    - type: mrr_at_100
+      value: 61.873
+    - type: mrr_at_200
+      value: 61.873
+    - type: mrr_at_300
+      value: 61.873
+    - type: mrr_at_500
+      value: 61.873
+    - type: mrr_at_700
+      value: 61.873
+    - type: mrr_at_1000
+      value: 61.873
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 69.3294
+    - type: ap
+      value: 14.561333393364736
+    - type: f1
+      value: 53.992309820496466
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 63.63893604980192
+    - type: f1
+      value: 63.92959380489434
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 56.270879258659775
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.71073493473207
+    - type: cos_sim_ap
+      value: 81.52392540284202
+    - type: cos_sim_f1
+      value: 74.71162377994676
+    - type: cos_sim_precision
+      value: 71.89558428885094
+    - type: cos_sim_recall
+      value: 77.75725593667546
+    - type: dot_accuracy
+      value: 88.71073493473207
+    - type: dot_ap
+      value: 81.52394754041109
+    - type: dot_f1
+      value: 74.71162377994676
+    - type: dot_precision
+      value: 71.89558428885094
+    - type: dot_recall
+      value: 77.75725593667546
+    - type: euclidean_accuracy
+      value: 88.71073493473207
+    - type: euclidean_ap
+      value: 81.52392035435321
+    - type: euclidean_f1
+      value: 74.71162377994676
+    - type: euclidean_precision
+      value: 71.89558428885094
+    - type: euclidean_recall
+      value: 77.75725593667546
+    - type: manhattan_accuracy
+      value: 88.47231328604637
+    - type: manhattan_ap
+      value: 81.22907439267321
+    - type: manhattan_f1
+      value: 74.3351571446749
+    - type: manhattan_precision
+      value: 71.78667977390022
+    - type: manhattan_recall
+      value: 77.0712401055409
+    - type: max_accuracy
+      value: 88.71073493473207
+    - type: max_ap
+      value: 81.52394754041109
+    - type: max_f1
+      value: 74.71162377994676
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.85136026700819
+    - type: cos_sim_ap
+      value: 87.7768002924216
+    - type: cos_sim_f1
+      value: 80.358908624794
+    - type: cos_sim_precision
+      value: 76.62918209122023
+    - type: cos_sim_recall
+      value: 84.47028025870034
+    - type: dot_accuracy
+      value: 89.85136026700819
+    - type: dot_ap
+      value: 87.77680027889778
+    - type: dot_f1
+      value: 80.358908624794
+    - type: dot_precision
+      value: 76.62918209122023
+    - type: dot_recall
+      value: 84.47028025870034
+    - type: euclidean_accuracy
+      value: 89.85136026700819
+    - type: euclidean_ap
+      value: 87.77680174697751
+    - type: euclidean_f1
+      value: 80.358908624794
+    - type: euclidean_precision
+      value: 76.62918209122023
+    - type: euclidean_recall
+      value: 84.47028025870034
+    - type: manhattan_accuracy
+      value: 89.86300306593705
+    - type: manhattan_ap
+      value: 87.78613271895861
+    - type: manhattan_f1
+      value: 80.31831016905645
+    - type: manhattan_precision
+      value: 76.68230516070304
+    - type: manhattan_recall
+      value: 84.3162919618109
+    - type: max_accuracy
+      value: 89.86300306593705
+    - type: max_ap
+      value: 87.78613271895861
+    - type: max_f1
+      value: 80.358908624794
+language:
+- en
+license: cc-by-nc-4.0
+---
+
+<h1 align="center">Salesforce/SFR-Embedding-Mistral</h1>
+
+**SFR-Embedding by Salesforce Research.**
+
+The model is trained on top of [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
+
+This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
+- [MTEB benchmark](https://arxiv.org/abs/2210.07316)
+- [Mistral](https://arxiv.org/abs/2310.06825)
+- [E5-mistral-7b-instruct](https://arxiv.org/pdf/2401.00368.pdf)
+
+More technical details will be updated later.
+
+## How to run
+
+### Transformers
+The models can be used as follows:
+```python
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+def last_token_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery: {query}'
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+queries = [
+    get_detailed_instruct(task, 'How to bake a chocolate cake'),
+    get_detailed_instruct(task, 'Symptoms of the flu')
+]
+# No need to add instruction for retrieval documents
+passages = [
+    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
+    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
+]
+
+# load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
+model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
+
+# get the embeddings
+max_length = 4096
+input_texts = queries + passages
+batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
+outputs = model(**batch_dict)
+embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+# [[86.7153549194336, 36.64569091796875], [35.00493621826172, 82.0738525390625]]
+```
+
+### Sentence Transformers
+```python
+
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer("Salesforce/SFR-Embedding-Mistral")
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery: {query}'
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+queries = [
+    get_detailed_instruct(task, 'How to bake a chocolate cake'),
+    get_detailed_instruct(task, 'Symptoms of the flu')
+]
+# No need to add instruction for retrieval documents
+passages = [
+    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
+    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
+]
+
+embeddings = model.encode(queries + passages)
+scores = util.cos_sim(embeddings[:2], embeddings[2:]) * 100
+print(scores.tolist())
+# [[86.71537780761719, 36.645721435546875], [35.00497055053711, 82.07388305664062]]
+```
+
+### MTEB Benchmark Evaluation
+Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB](https://arxiv.org/abs/2210.07316) benchmark.
+
+
+SFR-Embedding Team (∗indicates lead contributors).
+* Rui Meng*
+* Ye Liu*
+* Shafiq Rayhan Joty
+* Caiming Xiong
+* Yingbo Zhou
+* Semih Yavuz
+
+### Citation
+```bibtex
+@misc{SFRAIResearch2024,
+  title={SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning},
+  author={Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},
+  howpublished={Salesforce AI Research Blog},
+  year={2024},
+  url={https://blog.salesforceairesearch.com/sfr-embedded-mistral/}
+}
+```
+
+
+
+
+

cache/models--Salesforce--SFR-Embedding-Mistral/refs/main

@@ -0,0 +1 @@
+938c560d1c236aa563b2dbdf084f28ab28bccb11

cache/models--Salesforce--SFR-Embedding-Mistral/snapshots/938c560d1c236aa563b2dbdf084f28ab28bccb11/README.md

@@ -0,0 +1 @@
+../../blobs/feb95adc7e79e878999ba5a1d3ddfe9f16eff0f1

cache/models--Salesforce--SFR-Embedding-Mistral/snapshots/938c560d1c236aa563b2dbdf084f28ab28bccb11/config.json

@@ -0,0 +1 @@
+../../blobs/c19160bba3c1267f959caf6d13fb07f9ea232e04

cache/models--Salesforce--SFR-Embedding-Mistral/snapshots/938c560d1c236aa563b2dbdf084f28ab28bccb11/config_sentence_transformers.json

@@ -0,0 +1 @@
+../../blobs/ef62bf21fb2396937098b86ae80c68813b229c18

cache/models--Salesforce--SFR-Embedding-Mistral/snapshots/938c560d1c236aa563b2dbdf084f28ab28bccb11/model.safetensors.index.json

@@ -0,0 +1 @@
+../../blobs/f8194e4e9432d287bf257d4a7d4a0f2446c32da8

cache/models--Salesforce--SFR-Embedding-Mistral/snapshots/938c560d1c236aa563b2dbdf084f28ab28bccb11/modules.json

@@ -0,0 +1 @@
+../../blobs/f7640f94e81bb7f4f04daf1668850b38763a13d9

cache/models--Salesforce--SFR-Embedding-Mistral/snapshots/938c560d1c236aa563b2dbdf084f28ab28bccb11/sentence_bert_config.json

@@ -0,0 +1 @@
+../../blobs/42dcdfcaf9e42a488d4be06500dd771d7aa11e83

docker-compose.yml

@@ -0,0 +1,61 @@
+version: "3.5"
+
+networks:
+  metavoice-net:
+    driver: bridge
+
+volumes:
+  hf-cache:
+    driver: local
+
+x-common-settings: &common-settings
+  volumes:
+    - hf-cache:/.hf-cache
+    - ./assets:/app/assets
+  deploy:
+    replicas: 1
+    resources:
+      reservations:
+        devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [ gpu ]
+  runtime: nvidia
+  ipc: host
+  tty: true # enable colorized logs
+  build:
+    context: .
+  image: metavoice-server:latest
+  networks:
+    - metavoice-net
+  environment:
+    - NVIDIA_VISIBLE_DEVICES=all
+    - HF_HOME=/.hf-cache
+  logging:
+    options:
+      max-size: "100m"
+      max-file: "10"
+
+services:
+  server:
+    <<: *common-settings
+    container_name: metavoice-server
+    command: [ "--port=58004" ]
+    ports:
+      - 58004:58004
+    healthcheck:
+      test: [ "CMD", "curl", "http://metavoice-server:58004/health" ]
+      interval: 1m
+      timeout: 10s
+      retries: 20
+  ui:
+    <<: *common-settings
+    container_name: metavoice-ui
+    entrypoint: [ "python3.10", "app.py" ]
+    ports:
+      - 7861:7861
+    healthcheck:
+      test: [ "CMD", "curl", "http://localhost:7861" ]
+      interval: 1m
+      timeout: 10s
+      retries: 1