init - 初始化

.gitignore

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+*.pyc
+model_infos.json
+space
+.venv
+results
+mteb
+**/.DS_Store
+
+# FastAPI
+**/__pycache__/
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Gradio
+flagged/
+gradio_cached_examples/
+tmp/
+logs/
+
+# IDE
+.idea/
+.vscode/
+
+# 环境相关
+.env
+.env.*

DESCRIPTION.md

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+Massive Text Embedding Benchmark (MTEB) Leaderboard.

LICENSE

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+MIT License
+
+Copyright (c) 2025 笑尿伊人
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 16:59
+# @Author   : q275343119
+# @File     : app.py
+# @Description: Main entry point for the application
+from app.ui.home import init_home
+from app.ui.about import init_about
+
+import gradio as gr
+
+
+def main():
+    home = init_home()
+    about = init_about()
+
+    demo = gr.TabbedInterface(
+        [home, about],
+        ["Home", "About"],
+        css="footer {display: none}"
+    )
+    demo.launch()
+
+
+if __name__ == '__main__':
+    main()

app/__init__.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:13
+# @Author   : q275343119
+# @File     : __init__.py.py
+# @Description:

app/backend/__init__.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:16
+# @Author   : q275343119
+# @File     : __init__.py.py
+# @Description: Backend module initialization

app/backend/constant.py

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+from enum import Enum
+
+
+class Navigation(Enum):
+    TEXT_LEADERBOARD = "Text Leaderboard"
+    MULTIMODAL_LEADERBOARD = "Multimodal Leaderboard"
+
+
+class TaskCategory(Enum):
+    LAW = "Law"
+    CODE = "Code"
+    CONVERSATIONAL = "Conversational"
+    TECH = "Tech"
+    LONG_CONTEXT = "Long-context"
+    MULTILINGUAL = "Multilingual"
+
+
+class ModelProvider(Enum):
+    OPENAI = "OpenAI"
+    VOYAGEAI = "VoyageAI"
+    COHERE = "Cohere"
+    OTHERS = "Others"
+
+
+class EvaluationMetric(Enum):
+    NDCG_1 = "NDCG@1"
+    NDCG_3 = "NDCG@3"
+    NDCG_5 = "NDCG@5"
+    NDCG_10 = "NDCG@10"
+    NDCG_20 = "NDCG@20"
+    NDCG_50 = "NDCG@50"
+    NDCG_100 = "NDCG@100"
+    RECALL_1 = "RECALL@1"
+    RECALL_3 = "RECALL@3"
+    RECALL_5 = "RECALL@5"
+    RECALL_10 = "RECALL@10"
+    RECALL_20 = "RECALL@20"
+    RECALL_50 = "RECALL@50"
+    RECALL_100 = "RECALL@100"
+    PRECISION_1 = "PRECISION@1"
+    PRECISION_3 = "PRECISION@3"
+    PRECISION_5 = "PRECISION@5"
+    PRECISION_10 = "PRECISION@10"
+    PRECISION_20 = "PRECISION@20"
+    PRECISION_50 = "PRECISION@50"
+    PRECISION_100 = "PRECISION@100"

app/backend/data_engine.py

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+"""
+Data service provider
+"""
+import json
+from typing import List
+
+import pandas as pd
+
+from app.backend.constant import ModelProvider
+
+
+class DataEngine:
+
+    def __init__(self):
+        self.df = self.init_dataframe()
+
+    @property
+    def leaderboards(self):
+        """
+        Get leaderboard data
+        """
+        with open('./mock_data/leaderboard.json', 'r') as f:
+            return json.load(f)
+
+    @property
+    def models(self):
+        """
+        Get models data
+        """
+        with open('./mock_data/models.json', 'r') as f:
+            return json.load(f)
+
+    @property
+    def tasks(self):
+        """
+        Get tasks data
+        """
+        with open('./mock_data/tasks.json', 'r') as f:
+            return json.load(f)
+
+    @property
+    def results(self):
+        """
+        Get results data
+        """
+        with open('./mock_data/results.json', 'r') as f:
+            return json.load(f)
+
+    def init_dataframe(self):
+        """
+        Initialize DataFrame
+        """
+        d = {"hello": [123], "world": [456]}
+        return pd.DataFrame(d)
+
+    def _check_providers(self, organization: str, providers: List):
+        if not providers:
+            return True
+        if "Others" in providers:
+            if organization not in (
+                    ModelProvider.OPENAI.value, ModelProvider.COHERE.value, ModelProvider.VOYAGEAI.value):
+                return True
+        return organization in providers
+
+    def filter_df(self, leaderboard: str, task: str, providers: List, sort_key: str):
+
+        tasks = []
+        for lb in self.leaderboards:
+            if lb["name"] == leaderboard:
+                tasks = lb["tasks"]
+                break
+        df_list = []
+        for t in (filter(lambda x: x.upper() == task.upper(), tasks)):
+            datasets = []
+            for ta in self.tasks:
+                if ta["slug"].upper() == t.upper():
+                    datasets = ta["datasets"]
+                    break
+            for model in self.models:
+                if t in model["tasks"] and self._check_providers(model["organization"], providers):
+
+                    for dataset in datasets:
+                        results = self.results[dataset]
+                        for result in results:
+                            if result['model_name'] == model["model_name"]:
+                                d = result["results"]
+                                d["class"] = result["class"]
+                                d["organization"] = result["organization"]
+                                d["model_name"] = result["model_name"]
+                                df = pd.DataFrame([d])
+                                df = df[["class", "organization", "model_name", "ndcg_at_1", "ndcg_at_3", "ndcg_at_5",
+                                         "ndcg_at_10",
+                                         "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+                                         "recall_at_5", "recall_at_10",
+                                         "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+                                         "precision_at_3", "precision_at_5",
+                                         "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]]
+                                df_list.append(df)
+            if df_list:
+                return pd.concat(df_list).sort_values(by=sort_key.replace("@", '_at_').lower())
+            return pd.DataFrame(columns=["class", "organization", "model_name", "ndcg_at_1", "ndcg_at_3", "ndcg_at_5",
+                                         "ndcg_at_10",
+                                         "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+                                         "recall_at_5", "recall_at_10",
+                                         "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+                                         "precision_at_3", "precision_at_5",
+                                         "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"])
+
+    def get_model_result(self, model: dict, task_datasets_map: dict, results: dict):
+        """
+        get_model_result
+        """
+        model_class = model["class"]
+        model_organization = model["organization"]
+        model_model_name = model["model_name"]
+        for leaderboard in model["leaderboards"]:
+            for task in model["tasks"]:
+                for dateset in task_datasets_map.get(task, []):
+                    for result in results[dateset]:
+                        if result["model_name"] == model_model_name:
+                            d_result = result["results"]
+                            d_result["class"] = model_class
+                            d_result["organization"] = model_organization
+                            d_result["model_name"] = model_model_name
+                            d_result["leaderboard"] = leaderboard
+                            d_result["dateset"] = dateset
+                            d_result["task"] = task
+                            yield d_result
+
+    def jsons_to_df(self):
+
+        # change leaderboards to task_leaderboard_map
+        task_leaderboard_map = {}
+        leaderboards = self.leaderboards
+        for leaderboard in leaderboards:
+            for task in leaderboard["tasks"]:
+                task_leaderboard_map[task] = leaderboard["name"]
+
+        # change tasks to task_datasets_map
+        task_datasets_map = {}
+        for task in self.tasks:
+            task_datasets_map[task["slug"]] = task["datasets"]
+
+        df_results_list = []
+        results = self.results
+        for model in self.models:
+            for d_result in self.get_model_result(model, task_datasets_map, results):
+                if d_result:
+                    df_results_list.append(pd.DataFrame([d_result]))
+
+        if df_results_list:
+            df_result = pd.concat(df_results_list)
+            return df_result[
+                ["leaderboard", "task", "class", "organization", "model_name", "dateset", "ndcg_at_1", "ndcg_at_3",
+                 "ndcg_at_5",
+                 "ndcg_at_10",
+                 "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+                 "recall_at_5", "recall_at_10",
+                 "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+                 "precision_at_3", "precision_at_5",
+                 "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]], leaderboards
+        return pd.DataFrame(
+            columns=["leaderboard", "task", "class", "organization", "model_name", "dateset", "ndcg_at_1", "ndcg_at_3",
+                     "ndcg_at_5",
+                     "ndcg_at_10",
+                     "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+                     "recall_at_5", "recall_at_10",
+                     "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+                     "precision_at_3", "precision_at_5",
+                     "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]), leaderboards
+
+    def filter_by_providers(self, df_result: pd.DataFrame, providers: List):
+        """
+        filter_by_providers
+        """
+        return df_result[df_result['organization'].apply(lambda x: self._check_providers(x, providers))]
+
+    def summarize_dataframe(self):
+        """
+        Summarize data statistics
+        """

app/ui/__init__.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:16
+# @Author   : q275343119
+# @File     : __init__.py.py
+# @Description:

app/ui/about.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:20
+# @Author   : q275343119
+# @File     : about.py
+# @Description: About page implementation
+
+import gradio as gr
+
+
+def init_about():
+    """Initialize the about page"""
+    with gr.Blocks() as block:
+        with gr.Column():
+            # Motivation section
+            gr.Markdown("## Motivation")
+            gr.Markdown("""
+            [Placeholder] This section explains why we created this embedding benchmark:
+            - To standardize embedding evaluation
+            - To promote transparency in model performance
+            - To accelerate progress in embedding research
+            """)
+
+            # Organizers section
+            gr.Markdown("## Organizers")
+            gr.Markdown("""
+            [Placeholder] The MTEB benchmark is organized by:
+            - Person A - Organization X
+            - Person B - Organization Y
+            - Person C - Organization Z
+            """)
+
+            # FAQ section
+            gr.Markdown("## Frequently Asked Questions")
+            gr.Markdown("""
+            [Placeholder] Common questions about MTEB:
+
+            **Q: How can I submit my model?**  
+            A: Follow the submission guidelines in our GitHub repository.
+
+            **Q: What metrics are used?**  
+            A: We use various metrics depending on the task type.
+
+            **Q: How often is the leaderboard updated?**  
+            A: The leaderboard is updated daily.
+            """)
+
+    block.queue(max_size=10)
+    return block

app/ui/component/__init__.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:25
+# @Author   : q275343119
+# @File     : __init__.py.py
+# @Description:

app/ui/component/df_component.py

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+import pandas as pd
+import gradio as gr
+
+from app.backend.data_engine import DataEngine
+
+
+class DataFrameComponent:
+
+    def __init__(self, data_engine: DataEngine):
+        self.data_engine = data_engine
+
+    def show(self, df=None):
+        with gr.Row():
+            # Create DataFrame display area
+            if df is None:
+                df = self.data_engine.init_dataframe()
+            print(df)
+            df_display = gr.DataFrame(df, col_count=(len(df.columns), 'fixed'))
+
+        return df_display

app/ui/component/filter_component.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:26
+# @Author   : q275343119
+# @File     : filter_component.py
+# @Description: Filter component implementation
+"""
+Filter component for data filtering
+"""
+from typing import List
+import gradio as gr
+
+from app.backend.data_engine import DataEngine
+
+
+class FilterComponent:
+
+    def __init__(self, data_engine: DataEngine, navigations: List, model_provides: List,
+                 evaluation_metrics: List):
+        self.data_engine = data_engine
+        self.navigations = navigations
+        self.task_categories = []
+        self.model_provides = model_provides
+        self.evaluation_metrics = evaluation_metrics
+
+    def show(self):
+        with gr.Row():
+            navigations = gr.Radio(
+                label="Navigation",
+                choices=self.navigations,
+                value=self.navigations[0],
+                interactive=True,
+                elem_classes=["filter-checkbox-group"],
+                scale=2,
+            )
+
+            model_provides = gr.CheckboxGroup(
+                label="Model Provides",
+                choices=self.model_provides,
+                value=self.model_provides,
+                interactive=True,
+                elem_classes=["filter-checkbox-group"],
+                scale=2,
+            )
+            evaluation_metrics = gr.Dropdown(
+                label="Evaluation Metrics",
+                choices=self.evaluation_metrics,
+                value="NDCG@10",
+                interactive=True,
+                elem_classes=["filter-checkbox-group"],
+                scale=2,
+            )
+
+        return navigations, model_provides, evaluation_metrics

app/ui/component/subtabs_component.py

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+from typing import List
+import gradio as gr
+import pandas as pd
+
+from app.backend.data_engine import DataEngine
+
+
+class SubtabsComponent:
+
+    def __init__(self, data_engine: DataEngine):
+        self.data_engine = data_engine
+
+    def show(self, navigations: str = None, model_provides: List = None, evaluation_metrics: str = None):
+        df_result, leaderboards = self.data_engine.jsons_to_df()
+
+        navigations = "Text" if navigations is None else navigations.split(" ", maxsplit=1)[0]
+        model_provides = [] if model_provides is None else model_provides
+        evaluation_metrics = "NDCG@10" if evaluation_metrics is None else evaluation_metrics
+
+        df_result = self.data_engine.filter_by_providers(df_result, model_provides).sort_values(
+            by=evaluation_metrics.replace("@", '_at_').lower())
+
+        items = []
+        for leaderboard in leaderboards:
+
+            with gr.Column(visible=leaderboard["name"] == navigations) as column:
+                with gr.Tabs():
+                    with gr.TabItem("overall"):
+                        df_leaderboard = df_result[df_result["leaderboard"] == leaderboard["slug"]]
+
+                        gr_df = gr.Dataframe(df_leaderboard)
+                        items.append(gr_df)
+
+                    for task in leaderboard["tasks"]:
+                        with gr.TabItem(task):
+                            df = df_leaderboard[df_leaderboard["task"] == task]
+
+                            gr_df = gr.Dataframe(df)
+                            items.append(gr_df)
+            items.append(column)
+        return items

app/ui/home.py

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+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:20
+# @Author   : q275343119
+# @File     : home.py
+# @Description: Home page implementation and UI components
+import pandas as pd
+
+from app.backend.constant import Navigation, TaskCategory, ModelProvider, EvaluationMetric
+from app.backend.data_engine import DataEngine
+from app.ui.component.df_component import DataFrameComponent
+from app.ui.component.filter_component import FilterComponent
+from app.ui.component.subtabs_component import SubtabsComponent
+from app.ui.static import HOME_CSS
+
+import gradio as gr
+
+NUM_DATASETS = 1
+NUM_SCORES = 2
+NUM_MODELS = 3
+
+
+def init_home():
+    """
+    Initialize the home page
+    """
+    data_engine = DataEngine()
+
+    with gr.Blocks(css=HOME_CSS) as block:
+        gr.Markdown(f"""
+            [Voyageai] Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models. Also check out [MTEB Arena](https://huggingface.co/spaces/mteb/arena) ⚔️
+            """)
+
+        filter_area = FilterComponent(
+            data_engine,
+            [element.value for element in Navigation],
+            [element.value for element in ModelProvider],
+            [element.value for element in EvaluationMetric],
+        )
+
+        navigations, model_provides, evaluation_metrics = filter_area.show()
+
+        sub_tabs = SubtabsComponent(data_engine)
+        columns = sub_tabs.show()
+
+        # df_area = DataFrameComponent(data_engine)
+        # df_display = df_area.show(pd.DataFrame(columns=[element.value for element in Navigation]))
+
+
+        gr.Markdown(f"""
+        - **Total Datasets**: {NUM_DATASETS}
+        - **Total Languages**: 113
+        - **Total Scores**: {NUM_SCORES}
+        - **Total Models**: {NUM_MODELS}
+        """ + r"""
+        Made with ❤️ for NLP. If this work is useful to you, please consider citing:
+
+        ```bibtex
+        @article{muennighoff2022mteb,
+            doi = {10.48550/ARXIV.2210.07316},
+            url = {https://arxiv.org/abs/2210.07316},
+            author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+            title = {MTEB: Massive Text Embedding Benchmark},
+            publisher = {arXiv},
+            journal={arXiv preprint arXiv:2210.07316},  
+            year = {2022}
+        }
+        ```
+        """)
+        # all_outputs = list[sub_tab_map.values()]
+        block.load(sub_tabs.show,
+                   inputs=[navigations, model_provides, evaluation_metrics], outputs=columns)
+
+        gr.on(triggers=[navigations.change,
+                        model_provides.change,
+                        evaluation_metrics.change],
+              inputs=[navigations, model_provides, evaluation_metrics],
+              fn=sub_tabs.show,
+              outputs=columns
+              )
+
+
+    block.queue(max_size=10)
+    return block

app/ui/static.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+# @Date     : 2024/12/24 17:16
+# @Author   : q275343119
+# @File     : static.py
+# @Description:
+
+HOME_CSS = """
+table > thead {
+    white-space: normal
+}
+
+table {
+    --cell-width-1: 250px
+}
+
+table > tbody > tr > td:nth-child(2) > div {
+    overflow-x: auto
+}
+
+.filter-checkbox-group {
+    max-width: max-content;
+}
+"""

mock_data/leaderboard.json

@@ -0,0 +1,26 @@
+[
+  {
+    "slug": "text",
+    "name": "Text",
+    "tasks": [
+      "law",
+      "long-context",
+      "finance",
+      "conversational",
+      "tech",
+      "multilingual",
+      "code",
+      "healthcare"
+    ]
+  },
+  {
+    "slug": "multimodal",
+    "name": "Multimodal",
+    "tasks": [
+      "text-to-photo",
+      "document-screenshot",
+      "figures-and-tables",
+      "text-to-text"
+    ]
+  }
+]

mock_data/models.json

@@ -0,0 +1,400 @@
+[
+
+  {
+    "class": "sentence-transformers",
+    "organization": "BAAI",
+    "model_name": "bge-law-transformer",
+    "leaderboards": [
+      "text"
+    ],
+    "tasks": [
+      "law"
+    ]
+  },
+  {
+    "class": "custom",
+    "organization": "OpenAI",
+    "model_name": "openai-law-embed-v2",
+    "leaderboards": [
+      "text"
+    ],
+    "tasks": [
+      "law"
+    ]
+  },
+  {
+    "class": "proprietary",
+    "organization": "Google",
+    "model_name": "google-law-model-v1",
+    "leaderboards": [
+      "text"
+    ],
+    "tasks": [
+      "law"
+    ]
+  },
+  {
+    "class": "sentence-transformers",
+    "organization": "HuggingFace",
+    "model_name": "hf-long-context-transformer",
+    "leaderboards": [
+      "text"
+    ],
+    "tasks": [
+      "long-context"
+    ]
+  },
+  {
+    "class": "custom",
+    "organization": "Apple",
+    "model_name": "apple-long-context-embed",
+    "leaderboards": [
+      "text"
+    ],
+    "tasks": [
+      "long-context"
+    ]
+  },
+  {
+    "class": "proprietary",
+    "organization": "Microsoft",
+    "model_name": "microsoft-long-context-v1",
+    "leaderboards": [
+      "text"
+    ],
+    "tasks": [
+      "long-context"
+    ]
+  },
+  {
+    "class": "sentence-transformers",
+    "organization": "HuggingFace",
+    "model_name": "hf-finance-transformer",
+    "leaderboards": [
+      "text"
+    ],
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+]

mock_data/results.json

@@ -0,0 +1,3098 @@
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+        "precision_at_100": 0.03843
+      }
+    }
+  ]
+}

mock_data/tasks.json

@@ -0,0 +1,110 @@
+[
+  {
+    "slug": "law",
+    "name": "Law",
+    "datasets": [
+      "ALIA_casedocs",
+      "LegalTexts",
+      "CaseLaw"
+    ]
+  },
+  {
+    "slug": "finance",
+    "name": "Finance",
+    "datasets": [
+      "FinancialStatements",
+      "StockMarketData",
+      "BankingRecords"
+    ]
+  },
+  {
+    "slug": "healthcare",
+    "name": "Healthcare",
+    "datasets": [
+      "PatientRecords",
+      "MedicalImages",
+      "ClinicalTrials"
+    ]
+  },
+  {
+    "slug": "tech",
+    "name": "Technology",
+    "datasets": [
+      "TechBlogs",
+      "DeviceSpecifications",
+      "SoftwareDocumentation"
+    ]
+  },
+  {
+    "slug": "conversational",
+    "name": "Conversational",
+    "datasets": [
+      "ChatLogs",
+      "CustomerServiceInteractions",
+      "ForumPosts"
+    ]
+  },
+  {
+    "slug": "code",
+    "name": "Code",
+    "datasets": [
+      "GitHubRepositories",
+      "CodeSnippets",
+      "ProgrammingQuestions"
+    ]
+  },
+  {
+    "slug": "multilingual",
+    "name": "Multilingual",
+    "datasets": [
+      "ParallelCorpora",
+      "TranslationDatasets",
+      "MultilingualText"
+    ]
+  },
+  {
+    "slug": "long-context",
+    "name": "Long Context",
+    "datasets": [
+      "ResearchPapers",
+      "Books",
+      "LegalDocuments"
+    ]
+  },
+  {
+    "slug": "text-to-photo",
+    "name": "Text to Photo",
+    "datasets": [
+      "TextPhotoPairs",
+      "ImageCaptions",
+      "VisualDescriptions"
+    ]
+  },
+  {
+    "slug": "document-screenshot",
+    "name": "Document Screenshot",
+    "datasets": [
+      "ScannedDocuments",
+      "PDFScreenshots",
+      "DigitalForms"
+    ]
+  },
+  {
+    "slug": "figures-and-tables",
+    "name": "Figures and Tables",
+    "datasets": [
+      "ScientificFigures",
+      "DataTables",
+      "ChartsAndGraphs"
+    ]
+  },
+  {
+    "slug": "text-to-text",
+    "name": "Text to Text",
+    "datasets": [
+      "ParaphraseDatasets",
+      "SummarizationDatasets",
+      "TextRewriting"
+    ]
+  }
+]

requirements.txt

@@ -0,0 +1,3 @@
+gradio==4.20.0
+uvicorn>=0.14.0
+fastapi<0.113.0