init

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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 3840,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": true,
+  "include_prompt": true
+}

LICENSE.txt

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+Tencent is pleased to support the community by making KaLM-Embedding available.
+
+Copyright (C)  2025 Tencent.  All rights reserved. 
+
+The open-source software and/or models included in this distribution may have been modified by Tencent (“Tencent Modifications”). All Tencent Modifications are Copyright (C) Tencent.
+
+KaLM-Embedding is licensed under License Term of KaLM-Embedding, except for the third-party components listed in the NOTICE file, which remain licensed under their respective original terms. KaLM-Embedding does not impose any additional restrictions beyond those specified in the original licenses of these third-party components. Users are required to comply with all applicable terms and conditions of the original licenses and to ensure that the use of these third-party components conforms to all relevant laws and regulations.
+
+For the avoidance of doubt, KaLM-Embedding refers solely to training datasets, parameters, and weights made publicly available by Tencent in accordance with License Term of KaLM-Embedding. 
+
+Terms of License Term of KaLM-Embedding:
+--------------------------------------------------------------------
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+
+1. KaLM-Embedding IS NOT INTENDED FOR USE WITHIN THE EUROPEAN UNION.
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+Dependencies and Licenses:
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+This open-source project, KaLM-Embedding, builds upon the following open-source models and/or software components, each of which remains licensed under its original license. Certain models or software may include modifications made by Tencent (“Tencent Modifications”), which are Copyright (C) Tencent.
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README.md

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+---
+datasets:
+- KaLM-Embedding/KaLM-embedding-finetuning-data
+base_model:
+- google/gemma-3-12b-pt
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+tags:
+- Retrieval
+- STS
+- Classification
+- Clustering
+- Reranking
+- vllm
+license: other
+license_name: tencent-kalm-embedding-community
+extra_gated_eu_disallowed: true
+---
+
+
+<h1 align="center">KaLM-Embedding-Gemma3-12B-2511</h1>
+
+<p align="center">
+  <a href="https://huggingface.co/tencent/KaLM-Embedding-Gemma3-12B-2511">
+    <img src="https://img.shields.io/badge/%F0%9F%A4%97_HuggingFace-Model-ffbd45.svg" alt="HuggingFace">
+  </a>
+  <a href="https://kalm-embedding.github.io/">
+    <img src="https://img.shields.io/badge/Home-Page-purple.svg?logo=github&" alt="Homepage">
+  </a>
+  <a href="https://github.com/Tencent/KaLM-Embedding-Gemma3-12B-2511">
+    <img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub">
+  </a>
+  <a href="https://arxiv.org/abs/2506.20923">
+    <img src="https://img.shields.io/badge/Paper-KaLM--Embedding-d4333f?logo=arxiv&logoColor=white&colorA=cccccc&colorB=d4333f&style=flat" alt="Paper">
+  </a>
+</p>
+
+
+## Short Description
+
+**KaLM-Embedding-Gemma3-12B-2511** is a versatile and compact embedding model, which achieves SOTA performance in MMTEB (due to 11-2025).
+
+
+## MMTEB Evaluation Results
+
+| Rank (Borda) | Model | Mean (Task) | Mean (TaskType) | Bitext Mining | Classification | Clustering | Instruction Reranking | Multilabel Classification | Pair Classification | Reranking | Retrieval | STS |
+| :--- | :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| **1** | **KaLM-Embedding-Gemma3-12B-2511** | **72.32** | **62.51** | **83.76** | **77.88** | 55.77 | 5.49 | **33.03** | 84.73 | 67.27 | **75.66** | 79.02 |
+| 2 | llama-embed-nemotron-8b | 69.46 | 61.09 | 81.72 | 73.21 | 54.35 | 10.82 | 29.86 | 83.97 | **67.78** | 68.69 | 79.41 |
+| 3 | Qwen3-Embedding-8B | 70.58 | 61.69 | 80.89 | 74.00 | **57.65** | 10.06 | 28.66 | **86.40** | 65.63 | 70.88 | **81.08** |
+| 4 | gemini-embedding-001 | 68.37 | 59.59 | 79.28 | 71.82 | 54.59 | 5.18 | 29.16 | 83.63 | 65.58 | 67.71 | 79.40 |
+| 5 | Qwen3-Embedding-4B | 69.45 | 60.86 | 79.36 | 72.33 | 57.15 | **11.56** | 26.77 | 85.05 | 65.08 | 69.60 | 80.86 |
+| 6 | Qwen3-Embedding-0.6B | 64.34 | 56.01 | 72.23 | 66.83 | 52.33 | 5.09 | 24.59 | 80.83 | 61.41 | 64.65 | 76.17 |
+| 7 | gte-Qwen2-7B-instruct | 62.51 | 55.93 | 73.92 | 61.55 | 52.77 | 4.94 | 25.48 | 85.13 | 65.55 | 60.08 | 73.98 |
+| 8 | Linq-Embed-Mistral | 61.47 | 54.14 | 70.34 | 62.24 | 50.60 | 0.94 | 24.77 | 80.43 | 64.37 | 58.69 | 74.86 |
+| 9 | multilingual-e5-large-instruct | 63.22 | 55.08 | 80.13 | 64.94 | 50.75 | -0.40 | 22.91 | 80.86 | 62.61 | 57.12 | 76.81 |
+| 10 | embeddinggemma-300m | 61.15 | 54.31 | 64.40 | 60.90 | 51.17 | 5.61 | 24.82 | 81.40 | 63.25 | 62.49 | 74.73 |
+
+
+## Model Details
+- Model Size: 11.76B
+- Embedding Dimension: 3840
+- Max Input Tokens: 32k
+- MRL dimensions: 3840, 2048, 1024, 512, 256, 128, and 64
+- Pooling: lasttoken pooling
+
+
+## Training Recipe
+- High-quality supervised finetuning
+
+
+## 📑 Open-source Plan
+
+- [x] Model Checkpoint 
+    - [x] [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v2](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v2.5](https://huggingface.co/KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5)
+    - [x] [KaLM-Embedding-Gemma3-12B-2511](https://huggingface.co/tencent/KaLM-Embedding-Gemma3-12B-2511)
+- [x] Training and Evaluation Code: [HITsz-TMG/KaLM-Embedding](https://github.com/HITsz-TMG/KaLM-Embedding)
+- [x] Technical Report: [KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model](https://arxiv.org/abs/2506.20923v4)
+- [x] Pre-training Data: [Pre-training Data](https://huggingface.co/datasets/HIT-TMG/KaLM-embedding-pretrain-data)
+- [x] Fine-tuning Data: [Fine-tuning Data](https://huggingface.co/datasets/KaLM-Embedding/KaLM-embedding-finetuning-data)
+
+
+## Usage
+### sentence-transformers support
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+You can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+import torch
+
+model = SentenceTransformer(
+    "tencent/KaLM-Embedding-Gemma3-12B-2511",
+    trust_remote_code=True,
+    model_kwargs={
+        "torch_dtype": torch.bfloat16,
+        "attn_implementation": "flash_attention_2",  # Optional
+    },
+)
+model.max_seq_length = 512
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+prompt = "Instruct: Classifying the category of french news.\nQuery:"
+embeddings = model.encode(
+    sentences,
+    prompt=prompt,
+    normalize_embeddings=True,
+    batch_size=256,
+    show_progress_bar=True,
+)
+print(embeddings)
+'''
+[[-0.01867676  0.02319336  0.00280762 ... -0.02075195  0.00196838
+  -0.0703125 ]
+ [-0.0067749   0.03491211  0.01434326 ... -0.0043335   0.00509644
+  -0.04174805]]
+'''
+```
+
+Or you can use `encode_query` and `encode_document` to automatically add the default prompt for queries (`"Instruct: Given a query, retrieve documents that answer the query \n Query: "`) and documents (`""`), respectively.
+
+```python
+from sentence_transformers import SentenceTransformer
+import torch
+
+model = SentenceTransformer(
+    "tencent/KaLM-Embedding-Gemma3-12B-2511",
+    trust_remote_code=True,
+    model_kwargs={
+        "torch_dtype": torch.bfloat16,
+        "attn_implementation": "flash_attention_2",  # Optional
+    },
+)
+model.max_seq_length = 512
+
+queries = [
+    "What is the capital of China?",
+    "Explain gravity",
+]
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
+]
+
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+'''
+tensor([[0.9034, 0.2563],
+        [0.3153, 0.7396]])
+'''
+```
+
+
+## Citation
+If you find this model useful, please consider giving a star and citation.
+```
+@misc{zhao2025kalmembeddingv2,
+      title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model}, 
+      author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
+      year={2025},
+      eprint={2506.20923},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2506.20923}, 
+}
+
+@misc{hu2025kalmembedding,
+      title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model}, 
+      author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
+      year={2025},
+      eprint={2501.01028},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.01028}, 
+}
+```
+
+
+## Contact
+If you encounter any issue, feel free to contact us via the email: <[email protected]>, <[email protected]>

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3TextModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 3840,
+  "initializer_range": 0.02,
+  "intermediate_size": 15360,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": {
+    "factor": 8.0,
+    "rope_type": "linear"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 1024,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_cache": true,
+  "vocab_size": 262208
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.55.0",
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modules.json

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+    "name": "2",
+    "path": "2_Normalize",
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sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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task_prompts.json

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+    "AmazonCounterfactualClassification": "Classify a given Amazon customer review text as either counterfactual or not-counterfactual",
+    "AmazonPolarityClassification": "Classify Amazon reviews into positive or negative sentiment",
+    "AmazonReviewsClassification": "Classify the given Amazon review into its appropriate rating category",
+    "Banking77Classification": "Given a online banking query, find the corresponding intents",
+    "EmotionClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise",
+    "ImdbClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset",
+    "MassiveIntentClassification": "Given a user utterance as query, find the user intents",
+    "MassiveScenarioClassification": "Given a user utterance as query, find the user scenarios",
+    "MTOPDomainClassification": "Classify the intent domain of the given utterance in task-oriented conversation",
+    "MTOPIntentClassification": "Classify the intent of the given utterance in task-oriented conversation",
+    "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic",
+    "TweetSentimentExtractionClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral",
+    "TNews": "Classify the fine-grained category of the given news title",
+    "IFlyTek": "Given an App description text, find the appropriate fine-grained category",
+    "MultilingualSentiment": "Classify sentiment of the customer review into positive, neutral, or negative",
+    "JDReview": "Classify the customer review for iPhone on e-commerce platform into positive or negative",
+    "OnlineShopping": "Classify the customer review for online shopping into positive or negative",
+    "Waimai": "Classify the customer review from a food takeaway platform into positive or negative",
+    "ArxivClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
+    "ArxivClusteringS2S": "Identify the main and secondary category of Arxiv papers based on the titles",
+    "BiorxivClusteringP2P": "Identify the main category of Biorxiv papers based on the titles and abstracts",
+    "BiorxivClusteringP2P.v2": "Identify the main category of Biorxiv papers based on the titles and abstracts",
+    "BiorxivClusteringS2S": "Identify the main category of Biorxiv papers based on the titles",
+    "BiorxivClusteringS2S.v2": "Identify the main category of Biorxiv papers based on the titles",
+    "MedrxivClusteringP2P": "Identify the main category of Medrxiv papers based on the titles and abstracts",
+    "MedrxivClusteringP2P.v2": "Identify the main category of Medrxiv papers based on the titles and abstracts",
+    "MedrxivClusteringS2S": "Identify the main category of Medrxiv papers based on the titles",
+    "MedrxivClusteringS2S.v2": "Identify the main category of Medrxiv papers based on the titles",
+    "RedditClustering": "Identify the topic or theme of Reddit posts based on the titles",
+    "RedditClusteringP2P": "Identify the topic or theme of Reddit posts based on the titles and posts",
+    "StackExchangeClustering": "Identify the topic or theme of StackExchange posts based on the titles",
+    "StackExchangeClustering.v2": "Identify the topic or theme of StackExchange posts based on the titles",
+    "StackExchangeClusteringP2P": "Identify the topic or theme of StackExchange posts based on the given paragraphs",
+    "StackExchangeClusteringP2P.v2": "Identify the topic or theme of StackExchange posts based on the given paragraphs",
+    "TwentyNewsgroupsClustering": "Identify the topic or theme of the given news articles",
+    "CLSClusteringS2S": "Identify the main category of scholar papers based on the titles",
+    "CLSClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts",
+    "CLSClusteringP2P.v2": "Identify the main category of scholar papers based on the titles and abstracts",
+    "ThuNewsClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
+    "ThuNewsClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
+    "AskUbuntuDupQuestions": "Retrieve duplicate questions from AskUbuntu forum",
+    "MindSmallReranking": "Retrieve relevant news articles based on user browsing history",
+    "SciDocsRR": "Given a title of a scientific paper, retrieve the titles of other relevant papers",
+    "StackOverflowDupQuestions": "Retrieve duplicate questions from StackOverflow forum",
+    "SprintDuplicateQuestions": "Retrieve semantically duplicate questions",
+    "TwitterSemEval2015": "Retrieve tweets that are semantically similar to the given tweet",
+    "TwitterURLCorpus": "Retrieve tweets that are semantically similar to the given tweet",
+    "T2Reranking": "Given a Chinese search query, retrieve web passages that answer the question",
+    "MMarcoReranking": "Given a Chinese search query, retrieve web passages that answer the question",
+    "CMedQAv1-reranking": "Given a Chinese community medical question, retrieve replies that best answer the question",
+    "CMedQAv2-reranking": "Given a Chinese community medical question, retrieve replies that best answer the question",
+    "Ocnli": "Retrieve semantically similar text.",
+    "Cmnli": "Retrieve semantically similar text.",
+    "ArguAna": {"query": "Given a claim, find documents that refute the claim", "passage": "Given a claim, find documents that refute the claim"},
+    "ClimateFEVER": "Given a claim about climate change, retrieve documents that support or refute the claim",
+    "ClimateFEVERHardNegatives": "Given a claim about climate change, retrieve documents that support or refute the claim",
+    "DBPedia": "Given a query, retrieve relevant entity descriptions from DBPedia",
+    "FEVER": "Given a claim, retrieve documents that support or refute the claim",
+    "FEVERHardNegatives": "Given a claim, retrieve documents that support or refute the claim",
+    "FiQA2018": "Given a financial question, retrieve user replies that best answer the question",
+    "HotpotQA": "Given a multi-hop question, retrieve documents that can help answer the question",
+    "HotpotQAHardNegatives": "Given a multi-hop question, retrieve documents that can help answer the question",
+    "MSMARCO": "Given a web search query, retrieve relevant passages that answer the query",
+    "NFCorpus": "Given a question, retrieve relevant documents that best answer the question",
+    "NQ": "Given a question, retrieve Wikipedia passages that answer the question",
+    "QuoraRetrieval": "Given a question, retrieve questions that are semantically equivalent to the given question",
+    "SCIDOCS": "Given a title of a scientific paper, retrieve the titles of other relevant papers",
+    "SciFact": "Given a scientific claim, retrieve documents that support or refute the claim",
+    "Touche2020": "Given a question, retrieve detailed and persuasive arguments that answer the question",
+    "Touche2020Retrieval.v3": "Given a question, retrieve detailed and persuasive arguments that answer the question",
+    "TRECCOVID": "Given a medical query, retrieve documents that answer the query",
+    "T2Retrieval": "Given a Chinese search query, retrieve web passages that answer the question",
+    "MMarcoRetrieval": "Given a web search query, retrieve relevant passages that answer the query",
+    "VoyageMMarcoReranking": "Given a Japanese search query, retrieve web passages that answer the question",
+    "DuRetrieval": "Given a Chinese search query, retrieve web passages that answer the question",
+    "CovidRetrieval": "Given a question on COVID-19, retrieve news articles that answer the question",
+    "CmedqaRetrieval": "Given a Chinese community medical question, retrieve replies that best answer the question",
+    "EcomRetrieval": "Given a user query from an e-commerce website, retrieve description sentences of relevant products",
+    "MedicalRetrieval": "Given a medical question, retrieve user replies that best answer the question",
+    "VideoRetrieval": "Given a video search query, retrieve the titles of relevant videos",
+    "STSBenchmarkMultilingualSTS": "Retrieve semantically similar text",
+    "SICKFr": "Retrieve semantically similar text",
+    "SummEvalFr": "Given a news summary, retrieve other semantically similar summaries",
+    "MasakhaNEWSClassification":  "Classify the News in the given texts into one of the seven category: politics,sports,health,business,entertainment,technology,religion ",
+    "OpusparcusPC":"Retrieve semantically similar text",
+    "PAWSX":"Retrieve semantically similar text",
+    "HALClusteringS2S": "Identify the main category of academic passage based on the titles and contents",
+    "MasakhaNEWSClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
+    "MasakhaNEWSClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
+    "MLSUMClusteringP2P": "Identify the topic or theme of the given articles based on the titles and contents",
+    "MLSUMClusteringS2S":  "Identify the topic or theme of the given articles based on the titles",
+    "SyntecReranking": "Given a question, retrieve passages that answer the question",
+    "AlloprofReranking": "Given a question, retrieve passages that answer the question",
+    "AlloprofRetrieval": "Given a question, retrieve passages that answer the question",
+    "BSARDRetrieval": "Given a question, retrieve passages that answer the question",
+    "SyntecRetrieval": "Given a question, retrieve passages that answer the question",
+    "XPQARetrieval": "Given a question, retrieve passages that answer the question",
+    "MintakaRetrieval": "Given a question, retrieve passages that answer the question",
+    "CBD":"Classify the sentiment of polish tweet reviews",
+    "PolEmo2.0-IN": "Classify the sentiment of medicine and hotels online reviews",
+    "PolEmo2.0-OUT":"Classify the sentiment of products and school online reviews",
+    "AllegroReviews": "Classify the sentiment of reviews from e-commerce marketplace Allegro",
+    "PAC": "Classify Polish contract clauses into one of the following two types: \"Safe Contract Clauses\" and \"Unfair Contract Clauses\".",
+    "SICK-E-PL": "Retrieve semantically similar text",
+    "SICK-R-PL": "Retrieve semantically similar text",
+    "STS22": "Retrieve semantically similar text",
+    "AFQMC": "Retrieve semantically similar text",
+    "AFQMC": "Retrieve semantically similar text",
+    "BQ": "Retrieve semantically similar text",
+    "LCQMC": "Retrieve semantically similar text",
+    "PAWSX": "Retrieve semantically similar text",
+    "QBQTC": "Retrieve semantically similar text",
+    "STS12": "Retrieve semantically similar text",
+    "PpcPC": "Retrieve semantically similar text",
+    "CDSC-E": "Retrieve semantically similar text",
+    "BornholmBitextMining": "Retrieve parallel sentences",
+    "NorwegianCourtsBitextMining": "Retrieve parallel sentences",
+    "PSC": "Retrieve semantically similar text",
+    "EightTagsClustering": "Identify of headlines from social media posts in Polish  into 8 categories: film, history, food, medicine, motorization, work, sport and technology",
+    "ArguAna-PL": "Given a claim, find documents that refute the claim",
+    "DBPedia-PL": "Given a query, retrieve relevant entity descriptions from DBPedia",
+    "FiQA-PL": "Given a financial question, retrieve user replies that best answer the question",
+    "HotpotQA-PL": "Given a multi-hop question, retrieve documents that can help answer the question",
+    "MSMARCO-PL": "Given a web search query, retrieve relevant passages that answer the query",
+    "NFCorpus-PL": "Given a question, retrieve relevant documents that best answer the question",
+    "NQ-PL": "Given a question, retrieve Wikipedia passages that answer the question",
+    "Quora-PL": "Given a question, retrieve questions that are semantically equivalent to the given question",
+    "SCIDOCS-PL": "Given a title of a scientific paper, retrieve the titles of other relevant papers",
+    "SciFact-PL": "Given a scientific claim, retrieve documents that support or refute the claim",
+    "TRECCOVID-PL": "Given a medical query, retrieve documents that answer the query",
+    "GeoreviewClassification": "Classify the organization rating based on the reviews",
+    "HeadlineClassification": "Classify the topic or theme of the given news headline",
+    "InappropriatenessClassification": "Classify the given message as either sensitive topic or not",
+    "KinopoiskClassification": "Classify the sentiment expressed in the given movie review text",
+    "RuReviewsClassification": "Classify product reviews into positive, negative or neutral sentiment",
+    "RuSciBenchGRNTIClassification": "Classify the category of scientific papers based on the titles and abstracts",
+    "RuSciBenchOECDClassification": "Classify the category of scientific papers based on the titles and abstracts",
+    "GeoreviewClusteringP2P": "Identify the organization category based on the reviews",
+    "RuSciBenchGRNTIClusteringP2P": "Identify the category of scientific papers based on the titles and abstracts",
+    "RuSciBenchOECDClusteringP2P": "Identify the category of scientific papers based on the titles and abstracts",
+    "TERRa": "Given a premise, retrieve a hypothesis that is entailed by the premise",
+    "RuBQReranking": "Given a question, retrieve Wikipedia passages that answer the question",
+    "RiaNewsRetrieval": "Given a headline, retrieval relevant articles",
+    "RuBQRetrieval": "Given a question, retrieve Wikipedia passages that answer the question",
+    "RUParaPhraserSTS": "Retrieve semantically similar text",
+    "RuSTSBenchmarkSTS": "Retrieve semantically similar text",
+    "AppsRetrieval": "Given a question about code problem, retrieval code that can solve user's problem", 
+    "COIRCodeSearchNetRetrieval": "Given a code snippet, retrieve the comment corresponding to that code.",
+    "CodeEditSearchRetrieval": "Given a piece of code, retrieval code that in the ",
+    "CodeFeedbackMT": "Given a question about coding, retrieval code or passage that can solve user's question",
+    "CodeFeedbackST": "Given a question about coding, retrieval code or passage that can solve user's question",
+    "CodeSearchNetCCRetrieval": "Given a code comment, retrieve the code snippet corresponding to that comment.",
+    "CodeSearchNetRetrieval": "Given a code snippet, retrieve the comment corresponding to that code.",
+    "CodeTransOceanContest": "Given a piece for code, retrieval semantically similar code",
+    "CodeTransOceanDL": "Given a piece for code, retrieval semantically similar code",
+    "CosQA": "Given a question about coding, retrieval code or passage that can solve user's question",
+    "StackOverflowQA": "Given a question about coding, retrieval code or passage that can solve user's question",
+    "SyntheticText2SQL": "Given a user's question, retrieve SQL queries that are appropriate responses to the question",
+    "BibleNLPBitextMining": "Retrieve parallel sentences",
+    "BUCC.v2": "Retrieve parallel sentences",
+    "DiaBlaBitextMining": "Retrieve parallel sentences",
+    "FloresBitextMining": "Retrieve parallel sentences",
+    "IN22GenBitextMining": "Retrieve parallel sentences",
+    "IndicGenBenchFloresBitextMining": "Retrieve parallel sentences",
+    "NollySentiBitextMining": "Retrieve parallel sentences",
+    "NTREXBitextMining": "Retrieve parallel sentences",
+    "NusaTranslationBitextMining": "Retrieve parallel sentences",
+    "NusaXBitextMining": "Retrieve parallel sentences",
+    "Tatoeba": "Retrieve parallel sentences",
+    "BulgarianStoreReviewSentimentClassfication": "Classify user reviews into positive, negative or mixed sentiment",
+    "CzechProductReviewSentimentClassification": "Classify product reviews into positive, neutral, or negative sentiment",
+    "GreekLegalCodeClassification": "Given a greek legal text, classify its topic",
+    "DBpediaClassification": "Given a Wikipedia articles, categorized it into classes based on its DBpedia ontology",
+    "FinancialPhrasebankClassification": "Given financial news, categorized by sentiment into positive, negative, or neutral",
+    "PoemSentimentClassification": "Gvien a poem, categorized by sentiment into positive, no_impact, negative or mixed",
+    "TweetTopicSingleClassification": "Gvien a twitter, classify its topic",
+    "EstonianValenceClassification": "Given a news article, categorized by sentiment into negatiivne, positiivne, neutraalne or vastuolulin",
+    "FilipinoShopeeReviewsClassification": "Given a shop review, classify its rating on a scale from 1 to 5",
+    "GujaratiNewsClassification": "Given a Gujarati news articles, classify ist topic",
+    "SentimentAnalysisHindi": "Given a hindi text, categorized by sentiment into positive, negative or neutral",
+    "IndonesianIdClickbaitClassification": "Given an Indonesian news headlines, classify its into clickbait or non-clickbait",
+    "ItaCaseholdClassification": "Given a judgments, classify its topic",
+    "KorSarcasmClassification": "Given a twitter, categorized it into sarcasm or not_sarcasm",
+    "KurdishSentimentClassification": "Given a text, categorized by sentiment into positive or negative",
+    "MacedonianTweetSentimentClassification": "Given a Macedonian tweet, categorized by sentiment into positive, negative, or neutral",
+    "AfriSentiClassification": "Given a text, categorized by sentiment into positive, negative, or neutral",
+    "CataloniaTweetClassification": "Given a tweet, categorized by sentiment into AGAINST, FAVOR or NEUTRAL",
+    "CyrillicTurkicLangClassification": "Given a text, classify its language",
+    "IndicLangClassification": "Given a text, classify its language",
+    "MultiHateClassification": "Given a text, categorized by sentiment into hate or non-hate",
+    "NusaParagraphEmotionClassification": "Given a paragraph, classify its emotion",
+    "NusaX-senti": "Given a text, categorized by sentiment into positive or negative",
+    "SwissJudgementClassification": "Given a news article, categorized it into approval or dismissal",
+    "NepaliNewsClassification": "Given a news article, categorized it into business, entertainment or sports",
+    "OdiaNewsClassification": "Given a news article, categorized it into business, entertainment or sports",
+    "PunjabiNewsClassification": "Given a news article, categorized it into two-classes",
+    "SinhalaNewsClassification": "Given a news article, categorized it into political, business, technology, sports and Entertainment",
+    "CSFDSKMovieReviewSentimentClassification": "Given a movie review, classify its rating on a scale from 0 to 5",
+    "SiswatiNewsClassification": "Given a news article in Siswati, classify its topic",
+    "SlovakMovieReviewSentimentClassification": "Given a movie review, categorized it into positive or negative",
+    "SwahiliNewsClassification": "Given a news article, classify its domain",
+    "TswanaNewsClassification": "Given a news article, classify its topic",
+    "IsiZuluNewsClassification": "Given a news article, classify its topic",
+    "WikiCitiesClustering": "Identify of Wikipedia articles of cities by country",
+    "RomaniBibleClustering": "Identify verses from the Bible in Kalderash Romani by book.",
+    "ArXivHierarchicalClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
+    "ArXivHierarchicalClusteringS2S":  "Identify the main and secondary category of Arxiv papers based on the titles",
+    "BigPatentClustering.v2": "Identify the category of documents from the Big Patent dataset",
+    "AlloProfClusteringS2S": "Identify the topic of document titles from Allo Prof dataset",
+    "AlloProfClusteringS2S.v2": "Identify the topic of document titles from Allo Prof dataset",
+    "AlloProfClusteringP2P": "Identify the topic of document titles and descriptions from Allo Prof dataset",
+    "HALClusteringS2S.v2": "Identify the topic of titles from HAL",
+    "SIB200ClusteringS2S": "Identify the category of documents",
+    "WikiClusteringP2P.v2": "Identify the category of wiki passages",
+    "PlscClusteringP2P.v2": "Identify the category of titles+abstracts from Library of Science",
+    "KorHateSpeechMLClassification": "Given a Korean online news comments, classify its fine-grained hate speech classes",
+    "MalteseNewsClassification": "Given a maltese new, classify its topic",
+    "MultiEURLEXMultilabelClassification": "Given a text, classify its topic",
+    "BrazilianToxicTweetsClassification": "Classify the toxic tweets in Brazilian Portuguese into one of the six categories: LGBTQ+phobia, Xenophobia, Obscene, Insult, Misogyny and Racism.",
+    "CTKFactsNLI": "Retrieve semantically similar text",
+    "indonli": "Retrieve semantically similar text",
+    "ArmenianParaphrasePC": "Retrieve semantically similar text",
+    "PawsXPairClassification": "Retrieve semantically similar text",
+    "RTE3": "Retrieve semantically similar text",
+    "XNLI": "Retrieve semantically similar text",
+    "PpcPC": "Retrieve semantically similar text",
+    "GermanSTSBenchmark": "Retrieve semantically similar text",
+    "SICK-R": "Retrieve semantically similar text",
+    "STS13": "Retrieve semantically similar text",
+    "STS14": "Retrieve semantically similar text",
+    "STSBenchmark": "Retrieve semantically similar text",
+    "FaroeseSTS": "Retrieve semantically similar text",
+    "FinParaSTS": "Retrieve semantically similar text",
+    "JSICK": "Retrieve semantically similar text",
+    "IndicCrosslingualSTS": "Retrieve parallel sentences",
+    "SemRel24STS": "Retrieve semantically similar text",
+    "STS17": "Retrieve semantically similar text",
+    "STS22.v2": "Retrieve semantically similar text",
+    "STSES": "Retrieve semantically similar text",
+    "STSB": "Retrieve semantically similar text",
+    "AILAStatutes": "Identifying the most relevant statutes for a given situation",
+    "HagridRetrieval": "Given an information-seeking question, retrieve the best replies to answer the question",
+    "LegalBenchCorporateLobbying": "Given a query, retrieve relevant legal bill summaries",
+    "LEMBPasskeyRetrieval": "Retrieval the relevant passage for the given query",
+    "BelebeleRetrieval": "Retrieval the relevant passage for the given query",
+    "MLQARetrieval": "Retrieval the relevant passage for the given query",
+    "StatcanDialogueDatasetRetrieval": "Retrieval the relevant passage for the given query",
+    "WikipediaRetrievalMultilingual": "Retrieval the relevant passage for the given query",
+    "Core17InstructionRetrieval": "Retrieval the relevant passage for the given query with conditions",
+    "News21InstructionRetrieval": "Retrieval the relevant passage for the given query with conditions",
+    "Robust04InstructionRetrieval": "Retrieval the relevant passage for the given query with conditions",
+    "WebLINXCandidatesReranking": "Retrieval the relevant passage for the given query",
+    "WikipediaRerankingMultilingual": "Retrieval the relevant passage for the given query",
+    "STS15": "Retrieve semantically similar text",
+    "MIRACLRetrievalHardNegatives": "Retrieval relevant passage for the given query",
+    "BIOSSES": "Retrieve semantically similar text",
+    "CQADupstackRetrieval": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackGamingRetrieval": {"query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question"},
+    "CQADupstackUnixRetrieval": {"query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question"},
+    "STS16": "Retrieve semantically similar text",
+    "SummEval": "Retrieve semantically similar text",
+    "ATEC": "Retrieve semantically similar text",
+    "ScalaClassification": "Classify passages into correct or correct in Scandinavian Languages based on linguistic acceptability",
+    "SpartQA": "Given the following spatial reasoning question, retrieve the right answer.",
+    "CEDRClassification": "Given a comment as query, classify expressed emotions into joy, sadness, surprise, fear, and anger",
+    "DalajClassification": "Classify texts based on linguistic acceptability in Swedish",
+    "TempReasonL1": "Given the following question about time, retrieve the correct answer.",
+    "WinoGrande": "Given the following sentence, retrieve an appropriate answer to fill in the missing underscored part.",
+    "NordicLangClassification": "Classify texts based on language",
+    "TwitterHjerneRetrieval": "Retrieve answers to questions asked in Danish tweets",
+    "SwednClusteringP2P": "Identify news categories in Swedish passages"
+}

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