Training for 0.0 epochs, 500 steps, 5.3311 loss, 2.7874564459930317e-08 learning rate.

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

3_MixtureEmbeddingsModel/MixSentenceTransformer_config.json

@@ -0,0 +1,15 @@
+{
+  "expert_model_names": [
+    "infgrad/stella-base-en-v2",
+    "thenlper/gte-base",
+    "sentence-transformers/gtr-t5-base"
+  ],
+  "encoder_dim": 384,
+  "topk": 2,
+  "freeze_experts": false,
+  "normalize_experts": false,
+  "has_blender": false,
+  "has_noise": false,
+  "use_encoder_expert": false,
+  "use_gate_norm_last": false
+}

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/README.md

@@ -0,0 +1,94 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+
+---
+
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+
+## Full Model Architecture
+```
+EncoderSentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/infgrad_stella-base-en-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.36.2",
+    "pytorch": "2.1.2+cu121"
+  }
+}

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:caaac1823f5ad9ab016e335d7e5b7aaa2c8ffbf169581465f141a188155f848b
+size 437951328

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/modules.json

@@ -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"
+  }
+]

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

3_MixtureEmbeddingsModel/expert_00_infgrad_stella-base-en-v2/tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

3_MixtureEmbeddingsModel/expert_01_thenlper_gte-base/1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

3_MixtureEmbeddingsModel/expert_01_thenlper_gte-base/README.md

@@ -0,0 +1,2702 @@
+---
+tags:
+- mteb
+- sentence-similarity
+- sentence-transformers
+- Sentence Transformers
+model-index:
+- name: gte-base 
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 74.17910447761193
+    - type: ap
+      value: 36.827146398068926
+    - type: f1
+      value: 68.11292888046363
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 91.77345000000001
+    - type: ap
+      value: 88.33530426691347
+    - type: f1
+      value: 91.76549906404642
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.964
+    - type: f1
+      value: 48.22995586184998
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.147999999999996
+    - type: map_at_10
+      value: 48.253
+    - type: map_at_100
+      value: 49.038
+    - type: map_at_1000
+      value: 49.042
+    - type: map_at_3
+      value: 43.433
+    - type: map_at_5
+      value: 46.182
+    - type: mrr_at_1
+      value: 32.717
+    - type: mrr_at_10
+      value: 48.467
+    - type: mrr_at_100
+      value: 49.252
+    - type: mrr_at_1000
+      value: 49.254999999999995
+    - type: mrr_at_3
+      value: 43.599
+    - type: mrr_at_5
+      value: 46.408
+    - type: ndcg_at_1
+      value: 32.147999999999996
+    - type: ndcg_at_10
+      value: 57.12199999999999
+    - type: ndcg_at_100
+      value: 60.316
+    - type: ndcg_at_1000
+      value: 60.402
+    - type: ndcg_at_3
+      value: 47.178
+    - type: ndcg_at_5
+      value: 52.146
+    - type: precision_at_1
+      value: 32.147999999999996
+    - type: precision_at_10
+      value: 8.542
+    - type: precision_at_100
+      value: 0.9900000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 19.346
+    - type: precision_at_5
+      value: 14.026
+    - type: recall_at_1
+      value: 32.147999999999996
+    - type: recall_at_10
+      value: 85.42
+    - type: recall_at_100
+      value: 99.004
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 58.037000000000006
+    - type: recall_at_5
+      value: 70.128
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 48.59706013699614
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 43.01463593002057
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 61.80250355752458
+    - type: mrr
+      value: 74.79455216989844
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.87448576082345
+    - type: cos_sim_spearman
+      value: 87.64235843637468
+    - type: euclidean_pearson
+      value: 88.4901825511062
+    - type: euclidean_spearman
+      value: 87.74537283182033
+    - type: manhattan_pearson
+      value: 88.39040638362911
+    - type: manhattan_spearman
+      value: 87.62669542888003
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.06818181818183
+    - type: f1
+      value: 85.02524460098233
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 38.20471092679967
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 36.58967592147641
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.411
+    - type: map_at_10
+      value: 45.162
+    - type: map_at_100
+      value: 46.717
+    - type: map_at_1000
+      value: 46.836
+    - type: map_at_3
+      value: 41.428
+    - type: map_at_5
+      value: 43.54
+    - type: mrr_at_1
+      value: 39.914
+    - type: mrr_at_10
+      value: 51.534
+    - type: mrr_at_100
+      value: 52.185
+    - type: mrr_at_1000
+      value: 52.22
+    - type: mrr_at_3
+      value: 49.046
+    - type: mrr_at_5
+      value: 50.548
+    - type: ndcg_at_1
+      value: 39.914
+    - type: ndcg_at_10
+      value: 52.235
+    - type: ndcg_at_100
+      value: 57.4
+    - type: ndcg_at_1000
+      value: 58.982
+    - type: ndcg_at_3
+      value: 47.332
+    - type: ndcg_at_5
+      value: 49.62
+    - type: precision_at_1
+      value: 39.914
+    - type: precision_at_10
+      value: 10.258000000000001
+    - type: precision_at_100
+      value: 1.6219999999999999
+    - type: precision_at_1000
+      value: 0.20500000000000002
+    - type: precision_at_3
+      value: 23.462
+    - type: precision_at_5
+      value: 16.71
+    - type: recall_at_1
+      value: 32.411
+    - type: recall_at_10
+      value: 65.408
+    - type: recall_at_100
+      value: 87.248
+    - type: recall_at_1000
+      value: 96.951
+    - type: recall_at_3
+      value: 50.349999999999994
+    - type: recall_at_5
+      value: 57.431
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.911
+    - type: map_at_10
+      value: 42.608000000000004
+    - type: map_at_100
+      value: 43.948
+    - type: map_at_1000
+      value: 44.089
+    - type: map_at_3
+      value: 39.652
+    - type: map_at_5
+      value: 41.236
+    - type: mrr_at_1
+      value: 40.064
+    - type: mrr_at_10
+      value: 48.916
+    - type: mrr_at_100
+      value: 49.539
+    - type: mrr_at_1000
+      value: 49.583
+    - type: mrr_at_3
+      value: 46.741
+    - type: mrr_at_5
+      value: 48.037
+    - type: ndcg_at_1
+      value: 40.064
+    - type: ndcg_at_10
+      value: 48.442
+    - type: ndcg_at_100
+      value: 52.798
+    - type: ndcg_at_1000
+      value: 54.871
+    - type: ndcg_at_3
+      value: 44.528
+    - type: ndcg_at_5
+      value: 46.211
+    - type: precision_at_1
+      value: 40.064
+    - type: precision_at_10
+      value: 9.178
+    - type: precision_at_100
+      value: 1.452
+    - type: precision_at_1000
+      value: 0.193
+    - type: precision_at_3
+      value: 21.614
+    - type: precision_at_5
+      value: 15.185
+    - type: recall_at_1
+      value: 31.911
+    - type: recall_at_10
+      value: 58.155
+    - type: recall_at_100
+      value: 76.46300000000001
+    - type: recall_at_1000
+      value: 89.622
+    - type: recall_at_3
+      value: 46.195
+    - type: recall_at_5
+      value: 51.288999999999994
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.597
+    - type: map_at_10
+      value: 54.290000000000006
+    - type: map_at_100
+      value: 55.340999999999994
+    - type: map_at_1000
+      value: 55.388999999999996
+    - type: map_at_3
+      value: 50.931000000000004
+    - type: map_at_5
+      value: 52.839999999999996
+    - type: mrr_at_1
+      value: 46.646
+    - type: mrr_at_10
+      value: 57.524
+    - type: mrr_at_100
+      value: 58.225
+    - type: mrr_at_1000
+      value: 58.245999999999995
+    - type: mrr_at_3
+      value: 55.235
+    - type: mrr_at_5
+      value: 56.589
+    - type: ndcg_at_1
+      value: 46.646
+    - type: ndcg_at_10
+      value: 60.324999999999996
+    - type: ndcg_at_100
+      value: 64.30900000000001
+    - type: ndcg_at_1000
+      value: 65.19
+    - type: ndcg_at_3
+      value: 54.983000000000004
+    - type: ndcg_at_5
+      value: 57.621
+    - type: precision_at_1
+      value: 46.646
+    - type: precision_at_10
+      value: 9.774
+    - type: precision_at_100
+      value: 1.265
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 24.911
+    - type: precision_at_5
+      value: 16.977999999999998
+    - type: recall_at_1
+      value: 40.597
+    - type: recall_at_10
+      value: 74.773
+    - type: recall_at_100
+      value: 91.61200000000001
+    - type: recall_at_1000
+      value: 97.726
+    - type: recall_at_3
+      value: 60.458
+    - type: recall_at_5
+      value: 66.956
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.122
+    - type: map_at_10
+      value: 36.711
+    - type: map_at_100
+      value: 37.775
+    - type: map_at_1000
+      value: 37.842999999999996
+    - type: map_at_3
+      value: 33.693
+    - type: map_at_5
+      value: 35.607
+    - type: mrr_at_1
+      value: 29.153000000000002
+    - type: mrr_at_10
+      value: 38.873999999999995
+    - type: mrr_at_100
+      value: 39.739000000000004
+    - type: mrr_at_1000
+      value: 39.794000000000004
+    - type: mrr_at_3
+      value: 36.102000000000004
+    - type: mrr_at_5
+      value: 37.876
+    - type: ndcg_at_1
+      value: 29.153000000000002
+    - type: ndcg_at_10
+      value: 42.048
+    - type: ndcg_at_100
+      value: 47.144999999999996
+    - type: ndcg_at_1000
+      value: 48.901
+    - type: ndcg_at_3
+      value: 36.402
+    - type: ndcg_at_5
+      value: 39.562999999999995
+    - type: precision_at_1
+      value: 29.153000000000002
+    - type: precision_at_10
+      value: 6.4750000000000005
+    - type: precision_at_100
+      value: 0.951
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 15.479999999999999
+    - type: precision_at_5
+      value: 11.028
+    - type: recall_at_1
+      value: 27.122
+    - type: recall_at_10
+      value: 56.279999999999994
+    - type: recall_at_100
+      value: 79.597
+    - type: recall_at_1000
+      value: 92.804
+    - type: recall_at_3
+      value: 41.437000000000005
+    - type: recall_at_5
+      value: 49.019
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.757
+    - type: map_at_10
+      value: 26.739
+    - type: map_at_100
+      value: 28.015
+    - type: map_at_1000
+      value: 28.127999999999997
+    - type: map_at_3
+      value: 23.986
+    - type: map_at_5
+      value: 25.514
+    - type: mrr_at_1
+      value: 22.015
+    - type: mrr_at_10
+      value: 31.325999999999997
+    - type: mrr_at_100
+      value: 32.368
+    - type: mrr_at_1000
+      value: 32.426
+    - type: mrr_at_3
+      value: 28.897000000000002
+    - type: mrr_at_5
+      value: 30.147000000000002
+    - type: ndcg_at_1
+      value: 22.015
+    - type: ndcg_at_10
+      value: 32.225
+    - type: ndcg_at_100
+      value: 38.405
+    - type: ndcg_at_1000
+      value: 40.932
+    - type: ndcg_at_3
+      value: 27.403
+    - type: ndcg_at_5
+      value: 29.587000000000003
+    - type: precision_at_1
+      value: 22.015
+    - type: precision_at_10
+      value: 5.9830000000000005
+    - type: precision_at_100
+      value: 1.051
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 13.391
+    - type: precision_at_5
+      value: 9.602
+    - type: recall_at_1
+      value: 17.757
+    - type: recall_at_10
+      value: 44.467
+    - type: recall_at_100
+      value: 71.53699999999999
+    - type: recall_at_1000
+      value: 89.281
+    - type: recall_at_3
+      value: 31.095
+    - type: recall_at_5
+      value: 36.818
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.354
+    - type: map_at_10
+      value: 42.134
+    - type: map_at_100
+      value: 43.429
+    - type: map_at_1000
+      value: 43.532
+    - type: map_at_3
+      value: 38.491
+    - type: map_at_5
+      value: 40.736
+    - type: mrr_at_1
+      value: 37.247
+    - type: mrr_at_10
+      value: 47.775
+    - type: mrr_at_100
+      value: 48.522999999999996
+    - type: mrr_at_1000
+      value: 48.567
+    - type: mrr_at_3
+      value: 45.059
+    - type: mrr_at_5
+      value: 46.811
+    - type: ndcg_at_1
+      value: 37.247
+    - type: ndcg_at_10
+      value: 48.609
+    - type: ndcg_at_100
+      value: 53.782
+    - type: ndcg_at_1000
+      value: 55.666000000000004
+    - type: ndcg_at_3
+      value: 42.866
+    - type: ndcg_at_5
+      value: 46.001
+    - type: precision_at_1
+      value: 37.247
+    - type: precision_at_10
+      value: 8.892999999999999
+    - type: precision_at_100
+      value: 1.341
+    - type: precision_at_1000
+      value: 0.168
+    - type: precision_at_3
+      value: 20.5
+    - type: precision_at_5
+      value: 14.976
+    - type: recall_at_1
+      value: 30.354
+    - type: recall_at_10
+      value: 62.273
+    - type: recall_at_100
+      value: 83.65599999999999
+    - type: recall_at_1000
+      value: 95.82000000000001
+    - type: recall_at_3
+      value: 46.464
+    - type: recall_at_5
+      value: 54.225
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.949
+    - type: map_at_10
+      value: 37.230000000000004
+    - type: map_at_100
+      value: 38.644
+    - type: map_at_1000
+      value: 38.751999999999995
+    - type: map_at_3
+      value: 33.816
+    - type: map_at_5
+      value: 35.817
+    - type: mrr_at_1
+      value: 33.446999999999996
+    - type: mrr_at_10
+      value: 42.970000000000006
+    - type: mrr_at_100
+      value: 43.873
+    - type: mrr_at_1000
+      value: 43.922
+    - type: mrr_at_3
+      value: 40.467999999999996
+    - type: mrr_at_5
+      value: 41.861
+    - type: ndcg_at_1
+      value: 33.446999999999996
+    - type: ndcg_at_10
+      value: 43.403000000000006
+    - type: ndcg_at_100
+      value: 49.247
+    - type: ndcg_at_1000
+      value: 51.361999999999995
+    - type: ndcg_at_3
+      value: 38.155
+    - type: ndcg_at_5
+      value: 40.643
+    - type: precision_at_1
+      value: 33.446999999999996
+    - type: precision_at_10
+      value: 8.128
+    - type: precision_at_100
+      value: 1.274
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 18.493000000000002
+    - type: precision_at_5
+      value: 13.333
+    - type: recall_at_1
+      value: 26.949
+    - type: recall_at_10
+      value: 56.006
+    - type: recall_at_100
+      value: 80.99199999999999
+    - type: recall_at_1000
+      value: 95.074
+    - type: recall_at_3
+      value: 40.809
+    - type: recall_at_5
+      value: 47.57
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.243583333333333
+    - type: map_at_10
+      value: 37.193250000000006
+    - type: map_at_100
+      value: 38.44833333333334
+    - type: map_at_1000
+      value: 38.56083333333333
+    - type: map_at_3
+      value: 34.06633333333333
+    - type: map_at_5
+      value: 35.87858333333334
+    - type: mrr_at_1
+      value: 32.291583333333335
+    - type: mrr_at_10
+      value: 41.482749999999996
+    - type: mrr_at_100
+      value: 42.33583333333333
+    - type: mrr_at_1000
+      value: 42.38683333333333
+    - type: mrr_at_3
+      value: 38.952999999999996
+    - type: mrr_at_5
+      value: 40.45333333333333
+    - type: ndcg_at_1
+      value: 32.291583333333335
+    - type: ndcg_at_10
+      value: 42.90533333333334
+    - type: ndcg_at_100
+      value: 48.138666666666666
+    - type: ndcg_at_1000
+      value: 50.229083333333335
+    - type: ndcg_at_3
+      value: 37.76133333333334
+    - type: ndcg_at_5
+      value: 40.31033333333334
+    - type: precision_at_1
+      value: 32.291583333333335
+    - type: precision_at_10
+      value: 7.585583333333333
+    - type: precision_at_100
+      value: 1.2045000000000001
+    - type: precision_at_1000
+      value: 0.15733333333333335
+    - type: precision_at_3
+      value: 17.485416666666666
+    - type: precision_at_5
+      value: 12.5145
+    - type: recall_at_1
+      value: 27.243583333333333
+    - type: recall_at_10
+      value: 55.45108333333334
+    - type: recall_at_100
+      value: 78.25858333333335
+    - type: recall_at_1000
+      value: 92.61716666666665
+    - type: recall_at_3
+      value: 41.130583333333334
+    - type: recall_at_5
+      value: 47.73133333333334
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.325
+    - type: map_at_10
+      value: 32.795
+    - type: map_at_100
+      value: 33.96
+    - type: map_at_1000
+      value: 34.054
+    - type: map_at_3
+      value: 30.64
+    - type: map_at_5
+      value: 31.771
+    - type: mrr_at_1
+      value: 29.908
+    - type: mrr_at_10
+      value: 35.83
+    - type: mrr_at_100
+      value: 36.868
+    - type: mrr_at_1000
+      value: 36.928
+    - type: mrr_at_3
+      value: 33.896
+    - type: mrr_at_5
+      value: 34.893
+    - type: ndcg_at_1
+      value: 29.908
+    - type: ndcg_at_10
+      value: 36.746
+    - type: ndcg_at_100
+      value: 42.225
+    - type: ndcg_at_1000
+      value: 44.523
+    - type: ndcg_at_3
+      value: 32.82
+    - type: ndcg_at_5
+      value: 34.583000000000006
+    - type: precision_at_1
+      value: 29.908
+    - type: precision_at_10
+      value: 5.6129999999999995
+    - type: precision_at_100
+      value: 0.9079999999999999
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 13.753000000000002
+    - type: precision_at_5
+      value: 9.417
+    - type: recall_at_1
+      value: 26.325
+    - type: recall_at_10
+      value: 45.975
+    - type: recall_at_100
+      value: 70.393
+    - type: recall_at_1000
+      value: 87.217
+    - type: recall_at_3
+      value: 35.195
+    - type: recall_at_5
+      value: 39.69
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.828
+    - type: map_at_10
+      value: 25.759
+    - type: map_at_100
+      value: 26.961000000000002
+    - type: map_at_1000
+      value: 27.094
+    - type: map_at_3
+      value: 23.166999999999998
+    - type: map_at_5
+      value: 24.610000000000003
+    - type: mrr_at_1
+      value: 21.61
+    - type: mrr_at_10
+      value: 29.605999999999998
+    - type: mrr_at_100
+      value: 30.586000000000002
+    - type: mrr_at_1000
+      value: 30.664
+    - type: mrr_at_3
+      value: 27.214
+    - type: mrr_at_5
+      value: 28.571
+    - type: ndcg_at_1
+      value: 21.61
+    - type: ndcg_at_10
+      value: 30.740000000000002
+    - type: ndcg_at_100
+      value: 36.332
+    - type: ndcg_at_1000
+      value: 39.296
+    - type: ndcg_at_3
+      value: 26.11
+    - type: ndcg_at_5
+      value: 28.297
+    - type: precision_at_1
+      value: 21.61
+    - type: precision_at_10
+      value: 5.643
+    - type: precision_at_100
+      value: 1.0
+    - type: precision_at_1000
+      value: 0.14400000000000002
+    - type: precision_at_3
+      value: 12.4
+    - type: precision_at_5
+      value: 9.119
+    - type: recall_at_1
+      value: 17.828
+    - type: recall_at_10
+      value: 41.876000000000005
+    - type: recall_at_100
+      value: 66.648
+    - type: recall_at_1000
+      value: 87.763
+    - type: recall_at_3
+      value: 28.957
+    - type: recall_at_5
+      value: 34.494
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.921000000000003
+    - type: map_at_10
+      value: 37.156
+    - type: map_at_100
+      value: 38.399
+    - type: map_at_1000
+      value: 38.498
+    - type: map_at_3
+      value: 34.134
+    - type: map_at_5
+      value: 35.936
+    - type: mrr_at_1
+      value: 32.649
+    - type: mrr_at_10
+      value: 41.19
+    - type: mrr_at_100
+      value: 42.102000000000004
+    - type: mrr_at_1000
+      value: 42.157
+    - type: mrr_at_3
+      value: 38.464
+    - type: mrr_at_5
+      value: 40.148
+    - type: ndcg_at_1
+      value: 32.649
+    - type: ndcg_at_10
+      value: 42.679
+    - type: ndcg_at_100
+      value: 48.27
+    - type: ndcg_at_1000
+      value: 50.312
+    - type: ndcg_at_3
+      value: 37.269000000000005
+    - type: ndcg_at_5
+      value: 40.055
+    - type: precision_at_1
+      value: 32.649
+    - type: precision_at_10
+      value: 7.155
+    - type: precision_at_100
+      value: 1.124
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_3
+      value: 16.791
+    - type: precision_at_5
+      value: 12.015
+    - type: recall_at_1
+      value: 27.921000000000003
+    - type: recall_at_10
+      value: 55.357
+    - type: recall_at_100
+      value: 79.476
+    - type: recall_at_1000
+      value: 93.314
+    - type: recall_at_3
+      value: 40.891
+    - type: recall_at_5
+      value: 47.851
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.524
+    - type: map_at_10
+      value: 35.135
+    - type: map_at_100
+      value: 36.665
+    - type: map_at_1000
+      value: 36.886
+    - type: map_at_3
+      value: 31.367
+    - type: map_at_5
+      value: 33.724
+    - type: mrr_at_1
+      value: 30.631999999999998
+    - type: mrr_at_10
+      value: 39.616
+    - type: mrr_at_100
+      value: 40.54
+    - type: mrr_at_1000
+      value: 40.585
+    - type: mrr_at_3
+      value: 36.462
+    - type: mrr_at_5
+      value: 38.507999999999996
+    - type: ndcg_at_1
+      value: 30.631999999999998
+    - type: ndcg_at_10
+      value: 41.61
+    - type: ndcg_at_100
+      value: 47.249
+    - type: ndcg_at_1000
+      value: 49.662
+    - type: ndcg_at_3
+      value: 35.421
+    - type: ndcg_at_5
+      value: 38.811
+    - type: precision_at_1
+      value: 30.631999999999998
+    - type: precision_at_10
+      value: 8.123
+    - type: precision_at_100
+      value: 1.5810000000000002
+    - type: precision_at_1000
+      value: 0.245
+    - type: precision_at_3
+      value: 16.337
+    - type: precision_at_5
+      value: 12.568999999999999
+    - type: recall_at_1
+      value: 25.524
+    - type: recall_at_10
+      value: 54.994
+    - type: recall_at_100
+      value: 80.03099999999999
+    - type: recall_at_1000
+      value: 95.25099999999999
+    - type: recall_at_3
+      value: 37.563
+    - type: recall_at_5
+      value: 46.428999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.224
+    - type: map_at_10
+      value: 30.599999999999998
+    - type: map_at_100
+      value: 31.526
+    - type: map_at_1000
+      value: 31.629
+    - type: map_at_3
+      value: 27.491
+    - type: map_at_5
+      value: 29.212
+    - type: mrr_at_1
+      value: 24.214
+    - type: mrr_at_10
+      value: 32.632
+    - type: mrr_at_100
+      value: 33.482
+    - type: mrr_at_1000
+      value: 33.550000000000004
+    - type: mrr_at_3
+      value: 29.852
+    - type: mrr_at_5
+      value: 31.451
+    - type: ndcg_at_1
+      value: 24.214
+    - type: ndcg_at_10
+      value: 35.802
+    - type: ndcg_at_100
+      value: 40.502
+    - type: ndcg_at_1000
+      value: 43.052
+    - type: ndcg_at_3
+      value: 29.847
+    - type: ndcg_at_5
+      value: 32.732
+    - type: precision_at_1
+      value: 24.214
+    - type: precision_at_10
+      value: 5.804
+    - type: precision_at_100
+      value: 0.885
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 12.692999999999998
+    - type: precision_at_5
+      value: 9.242
+    - type: recall_at_1
+      value: 22.224
+    - type: recall_at_10
+      value: 49.849
+    - type: recall_at_100
+      value: 71.45
+    - type: recall_at_1000
+      value: 90.583
+    - type: recall_at_3
+      value: 34.153
+    - type: recall_at_5
+      value: 41.004000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 12.386999999999999
+    - type: map_at_10
+      value: 20.182
+    - type: map_at_100
+      value: 21.86
+    - type: map_at_1000
+      value: 22.054000000000002
+    - type: map_at_3
+      value: 17.165
+    - type: map_at_5
+      value: 18.643
+    - type: mrr_at_1
+      value: 26.906000000000002
+    - type: mrr_at_10
+      value: 37.907999999999994
+    - type: mrr_at_100
+      value: 38.868
+    - type: mrr_at_1000
+      value: 38.913
+    - type: mrr_at_3
+      value: 34.853
+    - type: mrr_at_5
+      value: 36.567
+    - type: ndcg_at_1
+      value: 26.906000000000002
+    - type: ndcg_at_10
+      value: 28.103
+    - type: ndcg_at_100
+      value: 35.073
+    - type: ndcg_at_1000
+      value: 38.653
+    - type: ndcg_at_3
+      value: 23.345
+    - type: ndcg_at_5
+      value: 24.828
+    - type: precision_at_1
+      value: 26.906000000000002
+    - type: precision_at_10
+      value: 8.547
+    - type: precision_at_100
+      value: 1.617
+    - type: precision_at_1000
+      value: 0.22799999999999998
+    - type: precision_at_3
+      value: 17.025000000000002
+    - type: precision_at_5
+      value: 12.834000000000001
+    - type: recall_at_1
+      value: 12.386999999999999
+    - type: recall_at_10
+      value: 33.306999999999995
+    - type: recall_at_100
+      value: 57.516
+    - type: recall_at_1000
+      value: 77.74799999999999
+    - type: recall_at_3
+      value: 21.433
+    - type: recall_at_5
+      value: 25.915
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.322
+    - type: map_at_10
+      value: 20.469
+    - type: map_at_100
+      value: 28.638
+    - type: map_at_1000
+      value: 30.433
+    - type: map_at_3
+      value: 14.802000000000001
+    - type: map_at_5
+      value: 17.297
+    - type: mrr_at_1
+      value: 68.75
+    - type: mrr_at_10
+      value: 76.29599999999999
+    - type: mrr_at_100
+      value: 76.62400000000001
+    - type: mrr_at_1000
+      value: 76.633
+    - type: mrr_at_3
+      value: 75.083
+    - type: mrr_at_5
+      value: 75.771
+    - type: ndcg_at_1
+      value: 54.87499999999999
+    - type: ndcg_at_10
+      value: 41.185
+    - type: ndcg_at_100
+      value: 46.400000000000006
+    - type: ndcg_at_1000
+      value: 54.223
+    - type: ndcg_at_3
+      value: 45.489000000000004
+    - type: ndcg_at_5
+      value: 43.161
+    - type: precision_at_1
+      value: 68.75
+    - type: precision_at_10
+      value: 32.300000000000004
+    - type: precision_at_100
+      value: 10.607999999999999
+    - type: precision_at_1000
+      value: 2.237
+    - type: precision_at_3
+      value: 49.083
+    - type: precision_at_5
+      value: 41.6
+    - type: recall_at_1
+      value: 9.322
+    - type: recall_at_10
+      value: 25.696
+    - type: recall_at_100
+      value: 52.898
+    - type: recall_at_1000
+      value: 77.281
+    - type: recall_at_3
+      value: 15.943
+    - type: recall_at_5
+      value: 19.836000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 48.650000000000006
+    - type: f1
+      value: 43.528467245539396
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 66.56
+    - type: map_at_10
+      value: 76.767
+    - type: map_at_100
+      value: 77.054
+    - type: map_at_1000
+      value: 77.068
+    - type: map_at_3
+      value: 75.29299999999999
+    - type: map_at_5
+      value: 76.24
+    - type: mrr_at_1
+      value: 71.842
+    - type: mrr_at_10
+      value: 81.459
+    - type: mrr_at_100
+      value: 81.58800000000001
+    - type: mrr_at_1000
+      value: 81.59100000000001
+    - type: mrr_at_3
+      value: 80.188
+    - type: mrr_at_5
+      value: 81.038
+    - type: ndcg_at_1
+      value: 71.842
+    - type: ndcg_at_10
+      value: 81.51899999999999
+    - type: ndcg_at_100
+      value: 82.544
+    - type: ndcg_at_1000
+      value: 82.829
+    - type: ndcg_at_3
+      value: 78.92
+    - type: ndcg_at_5
+      value: 80.406
+    - type: precision_at_1
+      value: 71.842
+    - type: precision_at_10
+      value: 10.066
+    - type: precision_at_100
+      value: 1.076
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 30.703000000000003
+    - type: precision_at_5
+      value: 19.301
+    - type: recall_at_1
+      value: 66.56
+    - type: recall_at_10
+      value: 91.55
+    - type: recall_at_100
+      value: 95.67099999999999
+    - type: recall_at_1000
+      value: 97.539
+    - type: recall_at_3
+      value: 84.46900000000001
+    - type: recall_at_5
+      value: 88.201
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.087
+    - type: map_at_10
+      value: 32.830999999999996
+    - type: map_at_100
+      value: 34.814
+    - type: map_at_1000
+      value: 34.999
+    - type: map_at_3
+      value: 28.198
+    - type: map_at_5
+      value: 30.779
+    - type: mrr_at_1
+      value: 38.889
+    - type: mrr_at_10
+      value: 48.415
+    - type: mrr_at_100
+      value: 49.187
+    - type: mrr_at_1000
+      value: 49.226
+    - type: mrr_at_3
+      value: 45.705
+    - type: mrr_at_5
+      value: 47.225
+    - type: ndcg_at_1
+      value: 38.889
+    - type: ndcg_at_10
+      value: 40.758
+    - type: ndcg_at_100
+      value: 47.671
+    - type: ndcg_at_1000
+      value: 50.744
+    - type: ndcg_at_3
+      value: 36.296
+    - type: ndcg_at_5
+      value: 37.852999999999994
+    - type: precision_at_1
+      value: 38.889
+    - type: precision_at_10
+      value: 11.466
+    - type: precision_at_100
+      value: 1.8499999999999999
+    - type: precision_at_1000
+      value: 0.24
+    - type: precision_at_3
+      value: 24.126
+    - type: precision_at_5
+      value: 18.21
+    - type: recall_at_1
+      value: 20.087
+    - type: recall_at_10
+      value: 48.042
+    - type: recall_at_100
+      value: 73.493
+    - type: recall_at_1000
+      value: 91.851
+    - type: recall_at_3
+      value: 32.694
+    - type: recall_at_5
+      value: 39.099000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.096000000000004
+    - type: map_at_10
+      value: 56.99999999999999
+    - type: map_at_100
+      value: 57.914
+    - type: map_at_1000
+      value: 57.984
+    - type: map_at_3
+      value: 53.900999999999996
+    - type: map_at_5
+      value: 55.827000000000005
+    - type: mrr_at_1
+      value: 76.19200000000001
+    - type: mrr_at_10
+      value: 81.955
+    - type: mrr_at_100
+      value: 82.164
+    - type: mrr_at_1000
+      value: 82.173
+    - type: mrr_at_3
+      value: 80.963
+    - type: mrr_at_5
+      value: 81.574
+    - type: ndcg_at_1
+      value: 76.19200000000001
+    - type: ndcg_at_10
+      value: 65.75
+    - type: ndcg_at_100
+      value: 68.949
+    - type: ndcg_at_1000
+      value: 70.342
+    - type: ndcg_at_3
+      value: 61.29
+    - type: ndcg_at_5
+      value: 63.747
+    - type: precision_at_1
+      value: 76.19200000000001
+    - type: precision_at_10
+      value: 13.571
+    - type: precision_at_100
+      value: 1.6070000000000002
+    - type: precision_at_1000
+      value: 0.179
+    - type: precision_at_3
+      value: 38.663
+    - type: precision_at_5
+      value: 25.136999999999997
+    - type: recall_at_1
+      value: 38.096000000000004
+    - type: recall_at_10
+      value: 67.853
+    - type: recall_at_100
+      value: 80.365
+    - type: recall_at_1000
+      value: 89.629
+    - type: recall_at_3
+      value: 57.995
+    - type: recall_at_5
+      value: 62.843
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 85.95200000000001
+    - type: ap
+      value: 80.73847277002109
+    - type: f1
+      value: 85.92406135678594
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.916999999999998
+    - type: map_at_10
+      value: 33.23
+    - type: map_at_100
+      value: 34.427
+    - type: map_at_1000
+      value: 34.477000000000004
+    - type: map_at_3
+      value: 29.292
+    - type: map_at_5
+      value: 31.6
+    - type: mrr_at_1
+      value: 21.547
+    - type: mrr_at_10
+      value: 33.839999999999996
+    - type: mrr_at_100
+      value: 34.979
+    - type: mrr_at_1000
+      value: 35.022999999999996
+    - type: mrr_at_3
+      value: 29.988
+    - type: mrr_at_5
+      value: 32.259
+    - type: ndcg_at_1
+      value: 21.519
+    - type: ndcg_at_10
+      value: 40.209
+    - type: ndcg_at_100
+      value: 45.954
+    - type: ndcg_at_1000
+      value: 47.187
+    - type: ndcg_at_3
+      value: 32.227
+    - type: ndcg_at_5
+      value: 36.347
+    - type: precision_at_1
+      value: 21.519
+    - type: precision_at_10
+      value: 6.447
+    - type: precision_at_100
+      value: 0.932
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 13.877999999999998
+    - type: precision_at_5
+      value: 10.404
+    - type: recall_at_1
+      value: 20.916999999999998
+    - type: recall_at_10
+      value: 61.7
+    - type: recall_at_100
+      value: 88.202
+    - type: recall_at_1000
+      value: 97.588
+    - type: recall_at_3
+      value: 40.044999999999995
+    - type: recall_at_5
+      value: 49.964999999999996
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.02781577747379
+    - type: f1
+      value: 92.83653922768306
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 72.04286365709075
+    - type: f1
+      value: 53.43867658525793
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 71.47276395427035
+    - type: f1
+      value: 69.77017399597342
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 76.3819771351715
+    - type: f1
+      value: 76.8484533435409
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.16515993299593
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.77145323314774
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.53637706586391
+    - type: mrr
+      value: 33.7312926288863
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 7.063999999999999
+    - type: map_at_10
+      value: 15.046999999999999
+    - type: map_at_100
+      value: 19.116
+    - type: map_at_1000
+      value: 20.702
+    - type: map_at_3
+      value: 10.932
+    - type: map_at_5
+      value: 12.751999999999999
+    - type: mrr_at_1
+      value: 50.464
+    - type: mrr_at_10
+      value: 58.189
+    - type: mrr_at_100
+      value: 58.733999999999995
+    - type: mrr_at_1000
+      value: 58.769000000000005
+    - type: mrr_at_3
+      value: 56.24400000000001
+    - type: mrr_at_5
+      value: 57.68299999999999
+    - type: ndcg_at_1
+      value: 48.142
+    - type: ndcg_at_10
+      value: 37.897
+    - type: ndcg_at_100
+      value: 35.264
+    - type: ndcg_at_1000
+      value: 44.033
+    - type: ndcg_at_3
+      value: 42.967
+    - type: ndcg_at_5
+      value: 40.815
+    - type: precision_at_1
+      value: 50.15500000000001
+    - type: precision_at_10
+      value: 28.235
+    - type: precision_at_100
+      value: 8.994
+    - type: precision_at_1000
+      value: 2.218
+    - type: precision_at_3
+      value: 40.041
+    - type: precision_at_5
+      value: 35.046
+    - type: recall_at_1
+      value: 7.063999999999999
+    - type: recall_at_10
+      value: 18.598
+    - type: recall_at_100
+      value: 35.577999999999996
+    - type: recall_at_1000
+      value: 67.43
+    - type: recall_at_3
+      value: 11.562999999999999
+    - type: recall_at_5
+      value: 14.771
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.046
+    - type: map_at_10
+      value: 44.808
+    - type: map_at_100
+      value: 45.898
+    - type: map_at_1000
+      value: 45.927
+    - type: map_at_3
+      value: 40.19
+    - type: map_at_5
+      value: 42.897
+    - type: mrr_at_1
+      value: 32.706
+    - type: mrr_at_10
+      value: 47.275
+    - type: mrr_at_100
+      value: 48.075
+    - type: mrr_at_1000
+      value: 48.095
+    - type: mrr_at_3
+      value: 43.463
+    - type: mrr_at_5
+      value: 45.741
+    - type: ndcg_at_1
+      value: 32.706
+    - type: ndcg_at_10
+      value: 52.835
+    - type: ndcg_at_100
+      value: 57.345
+    - type: ndcg_at_1000
+      value: 57.985
+    - type: ndcg_at_3
+      value: 44.171
+    - type: ndcg_at_5
+      value: 48.661
+    - type: precision_at_1
+      value: 32.706
+    - type: precision_at_10
+      value: 8.895999999999999
+    - type: precision_at_100
+      value: 1.143
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 20.238999999999997
+    - type: precision_at_5
+      value: 14.728
+    - type: recall_at_1
+      value: 29.046
+    - type: recall_at_10
+      value: 74.831
+    - type: recall_at_100
+      value: 94.192
+    - type: recall_at_1000
+      value: 98.897
+    - type: recall_at_3
+      value: 52.37500000000001
+    - type: recall_at_5
+      value: 62.732
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.38799999999999
+    - type: map_at_10
+      value: 84.315
+    - type: map_at_100
+      value: 84.955
+    - type: map_at_1000
+      value: 84.971
+    - type: map_at_3
+      value: 81.33399999999999
+    - type: map_at_5
+      value: 83.21300000000001
+    - type: mrr_at_1
+      value: 81.03
+    - type: mrr_at_10
+      value: 87.395
+    - type: mrr_at_100
+      value: 87.488
+    - type: mrr_at_1000
+      value: 87.48899999999999
+    - type: mrr_at_3
+      value: 86.41499999999999
+    - type: mrr_at_5
+      value: 87.074
+    - type: ndcg_at_1
+      value: 81.04
+    - type: ndcg_at_10
+      value: 88.151
+    - type: ndcg_at_100
+      value: 89.38199999999999
+    - type: ndcg_at_1000
+      value: 89.479
+    - type: ndcg_at_3
+      value: 85.24000000000001
+    - type: ndcg_at_5
+      value: 86.856
+    - type: precision_at_1
+      value: 81.04
+    - type: precision_at_10
+      value: 13.372
+    - type: precision_at_100
+      value: 1.526
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.217
+    - type: precision_at_5
+      value: 24.502
+    - type: recall_at_1
+      value: 70.38799999999999
+    - type: recall_at_10
+      value: 95.452
+    - type: recall_at_100
+      value: 99.59700000000001
+    - type: recall_at_1000
+      value: 99.988
+    - type: recall_at_3
+      value: 87.11
+    - type: recall_at_5
+      value: 91.662
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 59.334991029213235
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 62.586500854616666
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.153
+    - type: map_at_10
+      value: 14.277000000000001
+    - type: map_at_100
+      value: 16.922
+    - type: map_at_1000
+      value: 17.302999999999997
+    - type: map_at_3
+      value: 9.961
+    - type: map_at_5
+      value: 12.257
+    - type: mrr_at_1
+      value: 25.4
+    - type: mrr_at_10
+      value: 37.458000000000006
+    - type: mrr_at_100
+      value: 38.681
+    - type: mrr_at_1000
+      value: 38.722
+    - type: mrr_at_3
+      value: 34.1
+    - type: mrr_at_5
+      value: 36.17
+    - type: ndcg_at_1
+      value: 25.4
+    - type: ndcg_at_10
+      value: 23.132
+    - type: ndcg_at_100
+      value: 32.908
+    - type: ndcg_at_1000
+      value: 38.754
+    - type: ndcg_at_3
+      value: 21.82
+    - type: ndcg_at_5
+      value: 19.353
+    - type: precision_at_1
+      value: 25.4
+    - type: precision_at_10
+      value: 12.1
+    - type: precision_at_100
+      value: 2.628
+    - type: precision_at_1000
+      value: 0.402
+    - type: precision_at_3
+      value: 20.732999999999997
+    - type: precision_at_5
+      value: 17.34
+    - type: recall_at_1
+      value: 5.153
+    - type: recall_at_10
+      value: 24.54
+    - type: recall_at_100
+      value: 53.293
+    - type: recall_at_1000
+      value: 81.57
+    - type: recall_at_3
+      value: 12.613
+    - type: recall_at_5
+      value: 17.577
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.86284404925333
+    - type: cos_sim_spearman
+      value: 78.85870555294795
+    - type: euclidean_pearson
+      value: 82.20105295276093
+    - type: euclidean_spearman
+      value: 78.92125617009592
+    - type: manhattan_pearson
+      value: 82.15840025289069
+    - type: manhattan_spearman
+      value: 78.85955732900803
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.98747423389027
+    - type: cos_sim_spearman
+      value: 75.71298531799367
+    - type: euclidean_pearson
+      value: 81.59709559192291
+    - type: euclidean_spearman
+      value: 75.40622749225653
+    - type: manhattan_pearson
+      value: 81.55553547608804
+    - type: manhattan_spearman
+      value: 75.39380235424899
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.76861330695503
+    - type: cos_sim_spearman
+      value: 85.72991921531624
+    - type: euclidean_pearson
+      value: 84.84504307397536
+    - type: euclidean_spearman
+      value: 86.02679162824732
+    - type: manhattan_pearson
+      value: 84.79969439220142
+    - type: manhattan_spearman
+      value: 85.99238837291625
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.31929747511796
+    - type: cos_sim_spearman
+      value: 81.50806522502528
+    - type: euclidean_pearson
+      value: 82.93936686512777
+    - type: euclidean_spearman
+      value: 81.54403447993224
+    - type: manhattan_pearson
+      value: 82.89696981900828
+    - type: manhattan_spearman
+      value: 81.52817825470865
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.14413295332908
+    - type: cos_sim_spearman
+      value: 88.81032027008195
+    - type: euclidean_pearson
+      value: 88.19205563407645
+    - type: euclidean_spearman
+      value: 88.89738339479216
+    - type: manhattan_pearson
+      value: 88.11075942004189
+    - type: manhattan_spearman
+      value: 88.8297061675564
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.15980075557017
+    - type: cos_sim_spearman
+      value: 83.81896308594801
+    - type: euclidean_pearson
+      value: 83.11195254311338
+    - type: euclidean_spearman
+      value: 84.10479481755407
+    - type: manhattan_pearson
+      value: 83.13915225100556
+    - type: manhattan_spearman
+      value: 84.09895591027859
+  - 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: 87.93669480147919
+    - type: cos_sim_spearman
+      value: 87.89861394614361
+    - type: euclidean_pearson
+      value: 88.37316413202339
+    - type: euclidean_spearman
+      value: 88.18033817842569
+    - type: manhattan_pearson
+      value: 88.39427578879469
+    - type: manhattan_spearman
+      value: 88.09185009236847
+  - 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: 66.62215083348255
+    - type: cos_sim_spearman
+      value: 67.33243665716736
+    - type: euclidean_pearson
+      value: 67.60871701996284
+    - type: euclidean_spearman
+      value: 66.75929225238659
+    - type: manhattan_pearson
+      value: 67.63907838970992
+    - type: manhattan_spearman
+      value: 66.79313656754846
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.65549191934764
+    - type: cos_sim_spearman
+      value: 85.73266847750143
+    - type: euclidean_pearson
+      value: 85.75609932254318
+    - type: euclidean_spearman
+      value: 85.9452287759371
+    - type: manhattan_pearson
+      value: 85.69717413063573
+    - type: manhattan_spearman
+      value: 85.86546318377046
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 87.08164129085783
+    - type: mrr
+      value: 96.2877273416489
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 62.09400000000001
+    - type: map_at_10
+      value: 71.712
+    - type: map_at_100
+      value: 72.128
+    - type: map_at_1000
+      value: 72.14399999999999
+    - type: map_at_3
+      value: 68.93
+    - type: map_at_5
+      value: 70.694
+    - type: mrr_at_1
+      value: 65.0
+    - type: mrr_at_10
+      value: 72.572
+    - type: mrr_at_100
+      value: 72.842
+    - type: mrr_at_1000
+      value: 72.856
+    - type: mrr_at_3
+      value: 70.44399999999999
+    - type: mrr_at_5
+      value: 71.744
+    - type: ndcg_at_1
+      value: 65.0
+    - type: ndcg_at_10
+      value: 76.178
+    - type: ndcg_at_100
+      value: 77.887
+    - type: ndcg_at_1000
+      value: 78.227
+    - type: ndcg_at_3
+      value: 71.367
+    - type: ndcg_at_5
+      value: 73.938
+    - type: precision_at_1
+      value: 65.0
+    - type: precision_at_10
+      value: 10.033
+    - type: precision_at_100
+      value: 1.097
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 27.667
+    - type: precision_at_5
+      value: 18.4
+    - type: recall_at_1
+      value: 62.09400000000001
+    - type: recall_at_10
+      value: 89.022
+    - type: recall_at_100
+      value: 96.833
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 75.922
+    - type: recall_at_5
+      value: 82.428
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.82178217821782
+    - type: cos_sim_ap
+      value: 95.71282508220798
+    - type: cos_sim_f1
+      value: 90.73120494335737
+    - type: cos_sim_precision
+      value: 93.52441613588111
+    - type: cos_sim_recall
+      value: 88.1
+    - type: dot_accuracy
+      value: 99.73960396039604
+    - type: dot_ap
+      value: 92.98534606529098
+    - type: dot_f1
+      value: 86.83024536805209
+    - type: dot_precision
+      value: 86.96088264794383
+    - type: dot_recall
+      value: 86.7
+    - type: euclidean_accuracy
+      value: 99.82475247524752
+    - type: euclidean_ap
+      value: 95.72927039014849
+    - type: euclidean_f1
+      value: 90.89974293059126
+    - type: euclidean_precision
+      value: 93.54497354497354
+    - type: euclidean_recall
+      value: 88.4
+    - type: manhattan_accuracy
+      value: 99.82574257425742
+    - type: manhattan_ap
+      value: 95.72142177390405
+    - type: manhattan_f1
+      value: 91.00152516522625
+    - type: manhattan_precision
+      value: 92.55429162357808
+    - type: manhattan_recall
+      value: 89.5
+    - type: max_accuracy
+      value: 99.82574257425742
+    - type: max_ap
+      value: 95.72927039014849
+    - type: max_f1
+      value: 91.00152516522625
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 66.63957663468679
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 36.003307257923964
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.005825525863905
+    - type: mrr
+      value: 53.854683919022165
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.503611569974098
+    - type: cos_sim_spearman
+      value: 31.17155564248449
+    - type: dot_pearson
+      value: 26.740428413981306
+    - type: dot_spearman
+      value: 26.55727635469746
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.23600000000000002
+    - type: map_at_10
+      value: 1.7670000000000001
+    - type: map_at_100
+      value: 10.208
+    - type: map_at_1000
+      value: 25.997999999999998
+    - type: map_at_3
+      value: 0.605
+    - type: map_at_5
+      value: 0.9560000000000001
+    - type: mrr_at_1
+      value: 84.0
+    - type: mrr_at_10
+      value: 90.167
+    - type: mrr_at_100
+      value: 90.167
+    - type: mrr_at_1000
+      value: 90.167
+    - type: mrr_at_3
+      value: 89.667
+    - type: mrr_at_5
+      value: 90.167
+    - type: ndcg_at_1
+      value: 77.0
+    - type: ndcg_at_10
+      value: 68.783
+    - type: ndcg_at_100
+      value: 54.196
+    - type: ndcg_at_1000
+      value: 52.077
+    - type: ndcg_at_3
+      value: 71.642
+    - type: ndcg_at_5
+      value: 70.45700000000001
+    - type: precision_at_1
+      value: 84.0
+    - type: precision_at_10
+      value: 73.0
+    - type: precision_at_100
+      value: 55.48
+    - type: precision_at_1000
+      value: 23.102
+    - type: precision_at_3
+      value: 76.0
+    - type: precision_at_5
+      value: 74.8
+    - type: recall_at_1
+      value: 0.23600000000000002
+    - type: recall_at_10
+      value: 1.9869999999999999
+    - type: recall_at_100
+      value: 13.749
+    - type: recall_at_1000
+      value: 50.157
+    - type: recall_at_3
+      value: 0.633
+    - type: recall_at_5
+      value: 1.0290000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.437
+    - type: map_at_10
+      value: 8.791
+    - type: map_at_100
+      value: 15.001999999999999
+    - type: map_at_1000
+      value: 16.549
+    - type: map_at_3
+      value: 3.8080000000000003
+    - type: map_at_5
+      value: 5.632000000000001
+    - type: mrr_at_1
+      value: 20.408
+    - type: mrr_at_10
+      value: 36.96
+    - type: mrr_at_100
+      value: 37.912
+    - type: mrr_at_1000
+      value: 37.912
+    - type: mrr_at_3
+      value: 29.592000000000002
+    - type: mrr_at_5
+      value: 34.489999999999995
+    - type: ndcg_at_1
+      value: 19.387999999999998
+    - type: ndcg_at_10
+      value: 22.554
+    - type: ndcg_at_100
+      value: 35.197
+    - type: ndcg_at_1000
+      value: 46.58
+    - type: ndcg_at_3
+      value: 20.285
+    - type: ndcg_at_5
+      value: 21.924
+    - type: precision_at_1
+      value: 20.408
+    - type: precision_at_10
+      value: 21.837
+    - type: precision_at_100
+      value: 7.754999999999999
+    - type: precision_at_1000
+      value: 1.537
+    - type: precision_at_3
+      value: 21.769
+    - type: precision_at_5
+      value: 23.673
+    - type: recall_at_1
+      value: 1.437
+    - type: recall_at_10
+      value: 16.314999999999998
+    - type: recall_at_100
+      value: 47.635
+    - type: recall_at_1000
+      value: 82.963
+    - type: recall_at_3
+      value: 4.955
+    - type: recall_at_5
+      value: 8.805
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.6128
+    - type: ap
+      value: 14.279639861175664
+    - type: f1
+      value: 54.922292491204274
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 57.01188455008489
+    - type: f1
+      value: 57.377953019225515
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 52.306769136544254
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.64701674912082
+    - type: cos_sim_ap
+      value: 72.46600945328552
+    - type: cos_sim_f1
+      value: 67.96572367648784
+    - type: cos_sim_precision
+      value: 61.21801649397336
+    - type: cos_sim_recall
+      value: 76.38522427440633
+    - type: dot_accuracy
+      value: 82.33295583238957
+    - type: dot_ap
+      value: 62.54843443071716
+    - type: dot_f1
+      value: 60.38378562507096
+    - type: dot_precision
+      value: 52.99980067769583
+    - type: dot_recall
+      value: 70.15831134564644
+    - type: euclidean_accuracy
+      value: 85.7423854085951
+    - type: euclidean_ap
+      value: 72.76873850945174
+    - type: euclidean_f1
+      value: 68.23556960543262
+    - type: euclidean_precision
+      value: 61.3344559040202
+    - type: euclidean_recall
+      value: 76.88654353562005
+    - type: manhattan_accuracy
+      value: 85.74834594981225
+    - type: manhattan_ap
+      value: 72.66825372446462
+    - type: manhattan_f1
+      value: 68.21539194662853
+    - type: manhattan_precision
+      value: 62.185056472632496
+    - type: manhattan_recall
+      value: 75.54089709762533
+    - type: max_accuracy
+      value: 85.74834594981225
+    - type: max_ap
+      value: 72.76873850945174
+    - type: max_f1
+      value: 68.23556960543262
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.73171110334924
+    - type: cos_sim_ap
+      value: 85.51855542063649
+    - type: cos_sim_f1
+      value: 77.95706775700934
+    - type: cos_sim_precision
+      value: 74.12524298805887
+    - type: cos_sim_recall
+      value: 82.20665229442562
+    - type: dot_accuracy
+      value: 86.94842240074514
+    - type: dot_ap
+      value: 80.90995345771762
+    - type: dot_f1
+      value: 74.20765027322403
+    - type: dot_precision
+      value: 70.42594385285575
+    - type: dot_recall
+      value: 78.41854019094548
+    - type: euclidean_accuracy
+      value: 88.73753250281368
+    - type: euclidean_ap
+      value: 85.54712254033734
+    - type: euclidean_f1
+      value: 78.07565728654365
+    - type: euclidean_precision
+      value: 75.1120597652081
+    - type: euclidean_recall
+      value: 81.282722513089
+    - type: manhattan_accuracy
+      value: 88.72588970388482
+    - type: manhattan_ap
+      value: 85.52118291594071
+    - type: manhattan_f1
+      value: 78.04428724070593
+    - type: manhattan_precision
+      value: 74.83219105490002
+    - type: manhattan_recall
+      value: 81.54450261780106
+    - type: max_accuracy
+      value: 88.73753250281368
+    - type: max_ap
+      value: 85.54712254033734
+    - type: max_f1
+      value: 78.07565728654365
+language:
+- en
+license: mit
+---
+
+# gte-base
+
+General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
+
+The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
+
+## Metrics
+
+We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+
+
+
+| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
+| [**gte-base**](https://huggingface.co/thenlper/gte-base) 	| 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
+| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	| 0.44 | 768 | 514 	| 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	| 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
+| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 	| 0.13 | 384 | 512 	| 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
+| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 	| 0.09 | 384 | 512 	| 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
+| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) 	| 0.44 | 768 | 512 	| 56.00 | 41.1 	| 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
+| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	| 0.22 | 768 | 512 	| 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
+
+
+## Usage
+
+Code example
+
+```python
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+input_texts = [
+    "what is the capital of China?",
+    "how to implement quick sort in python?",
+    "Beijing",
+    "sorting algorithms"
+]
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base")
+model = AutoModel.from_pretrained("thenlper/gte-base")
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+outputs = model(**batch_dict)
+embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# (Optionally) normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+```
+
+Use with sentence-transformers:
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+sentences = ['That is a happy person', 'That is a very happy person']
+
+model = SentenceTransformer('thenlper/gte-base')
+embeddings = model.encode(sentences)
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+### Limitation
+
+This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{li2023general,
+      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
+      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
+      year={2023},
+      eprint={2308.03281},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

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+  }
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3_MixtureEmbeddingsModel/expert_02_sentence-transformers_gtr-t5-base/README.md

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+---
+pipeline_tag: sentence-similarity
+language: en
+license: apache-2.0
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# sentence-transformers/gtr-t5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
+
+This model was converted from the Tensorflow model [gtr-base-1](https://tfhub.dev/google/gtr/gtr-base/1) to PyTorch. When using this model, have a look at the publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
+
+The model uses only the encoder from a T5-base model. The weights are stored in FP16.  
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/gtr-t5-base')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+The model requires sentence-transformers version 2.2.0 or newer.
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-base)
+
+
+
+## Citing & Authors
+
+If you find this model helpful, please cite the respective publication:
+[Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899)

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+  "vocab_size": 32128
+}

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3_MixtureEmbeddingsModel/expert_02_sentence-transformers_gtr-t5-base/tokenizer_config.json

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README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+language: en
+license: apache-2.0
+datasets:
+- s2orc
+- flax-sentence-embeddings/stackexchange_xml
+- ms_marco
+- gooaq
+- yahoo_answers_topics
+- code_search_net
+- search_qa
+- eli5
+- snli
+- multi_nli
+- wikihow
+- natural_questions
+- trivia_qa
+- embedding-data/sentence-compression
+- embedding-data/flickr30k-captions
+- embedding-data/altlex
+- embedding-data/simple-wiki
+- embedding-data/QQP
+- embedding-data/SPECTER
+- embedding-data/PAQ_pairs
+- embedding-data/WikiAnswers
+
+---
+
+
+# all-MiniLM-L6-v2
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
+
+------
+
+## Background
+
+The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
+contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 
+1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
+
+We developped this model during the 
+[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
+organized by Hugging Face. We developped this model as part of the project:
+[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
+We then apply the cross entropy loss by comparing with true pairs.
+
+#### Hyper parameters
+
+We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
+We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
+a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
+
+#### Training data
+
+We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
+We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
+
+
+| Dataset                                                  | Paper                                    | Number of training tuples  |
+|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
+| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
+| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
+| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
+| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
+| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
+| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
+| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
+| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
+| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
+| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
+| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
+| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
+| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
+| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
+| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
+| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
+| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
+| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
+| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
+| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
+| **Total** | | **1,170,060,424** |

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