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README.md

@@ -1,411 +1,177 @@
 ---
-base_model: sentence-transformers/all-MiniLM-L6-v2
-datasets: []
-language: []
+language: en
+license: apache-2.0
 library_name: sentence-transformers
-pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- sentence-similarity
 - feature-extraction
-- generated_from_trainer
-- dataset_size:68874
-- loss:MultipleNegativesRankingLoss
-widget:
-- source_sentence: two extracts utilities, steal email. to .pst APT1 and uses MAPIGET,
-    GETMAIL emails GETMAIL Outlook from
-  sentences:
-  - April wild using takes Security the vulnerabilities attack , , 360 the lead new
-    in group’s the APT-C-06 2018 (CVE-2018-8174) the Core . 0-day In capturing
-  - in have and used scheduled droppers to persistence payloads tasks
-  - two extracts utilities, steal email. to .pst APT1 and uses MAPIGET, GETMAIL emails
-    GETMAIL Outlook from
-- source_sentence: screen victim's remote has obtain machines. management tool monitoring
-    from and to captures ConnectWise SOUTHFIELD the
-  sentences:
-  - months any last intense evolve over team faster gathering 24 than capabilities
-    Operation . the the observed technical of effort During observed we , Iranian
-    intelligence rapidly the
-  - screen victim's remote has obtain machines. management tool monitoring from and
-    to captures ConnectWise SOUTHFIELD the
-  - C :\Windows\system32\cmd.exe /C shell whoami /all
-- source_sentence: values database registry passwords from
-  sentences:
-  - BARIUM , a Chinese state player that also goes by APT17 , Axiom and Deputy Dog
-    , was previously linked to the ShadowPad and CCleaner incidents , which were also
-    supply-chain attacks that used software updates to sneak onto machines .
-  - It makes direct system calls using the “syscall” instruction.
-  - values database registry passwords from
-- source_sentence: Bazar can inject code through calling <code>VirtualAllocExNuma</code>.
-  sentences:
-  - Bazar can inject code through calling <code>VirtualAllocExNuma</code>.
-  - input redirected and (cmd.exe) output. a process MCMD launch with can
-  - and status</code> uses located determine is System files searches <code>/Library/Preferences/</code>
-    enabled. Integrity firewall in XCSSET to Protection configuration
-- source_sentence: to communications. Shark use ability in C2 HTTP
-  sentences:
-  - altered change adding hashes. four samples apparently a random bytes likely the
-    to Patchwork file attempt letters of in
-  - malware. to a download of specific execute KONNI used PowerShell 64-bit and
-  - to communications. Shark use ability in C2 HTTP
+- sentence-similarity
+- transformers
+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
+pipeline_tag: sentence-similarity
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+# 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.
 
-## Model Details
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
-- **Maximum Sequence Length:** 256 tokens
-- **Output Dimensionality:** 128 tokens
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+```
+pip install -U sentence-transformers
+```
 
-### Model Sources
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
 
-### Full Model Architecture
+## 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.
 
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Dense({'in_features': 384, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
-  (3): Dropout(
-    (dropout_layer): Dropout(p=0.3, inplace=False)
-  )
-)
-```
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
 
-## Usage
+#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)
 
-### Direct Usage (Sentence Transformers)
 
-First install the Sentence Transformers library:
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
 
-```bash
-pip install -U sentence-transformers
-```
+# 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')
 
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'to communications. Shark use ability in C2 HTTP',
-    'to communications. Shark use ability in C2 HTTP',
-    'altered change adding hashes. four samples apparently a random bytes likely the to Patchwork file attempt letters of in',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 128]
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
 
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Dataset
-
-#### Unnamed Dataset
-
-
-* Size: 68,874 training samples
-* Columns: <code>sentence_0</code> and <code>sentence_1</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                         | sentence_1                                                                         |
-  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                             |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 24.91 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 24.91 tokens</li><li>max: 111 tokens</li></ul> |
-* Samples:
-  | sentence_0                                                                                                                                                                                                                                                                                                                  | sentence_1                                                                                                                                                                                                                                                                                                                  |
-  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>RDAT can upload a file via HTTP POST response to the C2 split into 102,400-byte portions. RDAT can also download data from the C2 which is split into 81,920-byte portions.</code>                                                                                                                                    | <code>RDAT can upload a file via HTTP POST response to the C2 split into 102,400-byte portions. RDAT can also download data from the C2 which is split into 81,920-byte portions.</code>                                                                                                                                    |
-  | <code>The threat actor attempted to compromise critical assets , such as database servers , billing servers , and the active directory .</code>                                                                                                                                                                             | <code>The threat actor attempted to compromise critical assets , such as database servers , billing servers , and the active directory .</code>                                                                                                                                                                             |
-  | <code>computers leaked in two WannaCry , exploit Windows a any unpatched into turn EternalBlue computers used vulnerabilities to incorporated CVE-2017-0145 ransomware the vulnerable to the the . to victim's the that spreading connected and also capable other itself of network CVE-2017-0144 to worm and known</code> | <code>computers leaked in two WannaCry , exploit Windows a any unpatched into turn EternalBlue computers used vulnerabilities to incorporated CVE-2017-0145 ransomware the vulnerable to the the . to victim's the that spreading connected and also capable other itself of network CVE-2017-0144 to worm and known</code> |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 5
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1
-- `num_train_epochs`: 5
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `save_on_each_node`: False
-- `save_only_model`: False
-- `restore_callback_states_from_checkpoint`: False
-- `no_cuda`: False
-- `use_cpu`: False
-- `use_mps_device`: False
-- `seed`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: False
-- `fp16`: False
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `local_rank`: 0
-- `ddp_backend`: None
-- `tpu_num_cores`: None
-- `tpu_metrics_debug`: False
-- `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
-- `past_index`: -1
-- `disable_tqdm`: False
-- `remove_unused_columns`: True
-- `label_names`: None
-- `load_best_model_at_end`: False
-- `ignore_data_skip`: False
-- `fsdp`: []
-- `fsdp_min_num_params`: 0
-- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
-- `fsdp_transformer_layer_cls_to_wrap`: None
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `skip_memory_metrics`: True
-- `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
-- `resume_from_checkpoint`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: False
-- `hub_always_push`: False
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `eval_do_concat_batches`: True
-- `fp16_backend`: auto
-- `push_to_hub_model_id`: None
-- `push_to_hub_organization`: None
-- `mp_parameters`: 
-- `auto_find_batch_size`: False
-- `full_determinism`: False
-- `torchdynamo`: None
-- `ray_scope`: last
-- `ddp_timeout`: 1800
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `dispatch_batches`: None
-- `split_batches`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `eval_use_gather_object`: False
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-
-</details>
-
-### Training Logs
-| Epoch  | Step  | Training Loss |
-|:------:|:-----:|:-------------:|
-| 0.1161 | 500   | 0.0029        |
-| 0.2323 | 1000  | 0.0017        |
-| 0.3484 | 1500  | 0.0015        |
-| 0.4646 | 2000  | 0.0015        |
-| 0.5807 | 2500  | 0.0023        |
-| 0.6969 | 3000  | 0.0016        |
-| 0.8130 | 3500  | 0.0017        |
-| 0.9292 | 4000  | 0.0013        |
-| 1.0453 | 4500  | 0.0011        |
-| 1.1614 | 5000  | 0.001         |
-| 1.2776 | 5500  | 0.0009        |
-| 1.3937 | 6000  | 0.0009        |
-| 1.5099 | 6500  | 0.0012        |
-| 1.6260 | 7000  | 0.0013        |
-| 1.7422 | 7500  | 0.0013        |
-| 1.8583 | 8000  | 0.0013        |
-| 1.9744 | 8500  | 0.0008        |
-| 2.0906 | 9000  | 0.0007        |
-| 2.2067 | 9500  | 0.0007        |
-| 2.3229 | 10000 | 0.0009        |
-| 2.4390 | 10500 | 0.0007        |
-| 2.5552 | 11000 | 0.0011        |
-| 2.6713 | 11500 | 0.0009        |
-| 2.7875 | 12000 | 0.0008        |
-| 2.9036 | 12500 | 0.0006        |
-| 3.0197 | 13000 | 0.0006        |
-| 3.1359 | 13500 | 0.0007        |
-| 3.2520 | 14000 | 0.0006        |
-| 3.3682 | 14500 | 0.0007        |
-| 3.4843 | 15000 | 0.0006        |
-| 3.6005 | 15500 | 0.0013        |
-| 3.7166 | 16000 | 0.0008        |
-| 3.8328 | 16500 | 0.0008        |
-| 3.9489 | 17000 | 0.0006        |
-| 4.0650 | 17500 | 0.0006        |
-| 4.1812 | 18000 | 0.0006        |
-| 4.2973 | 18500 | 0.0005        |
-| 4.4135 | 19000 | 0.0006        |
-| 4.5296 | 19500 | 0.0008        |
-| 4.6458 | 20000 | 0.0006        |
-| 4.7619 | 20500 | 0.0006        |
-| 4.8780 | 21000 | 0.0005        |
-| 4.9942 | 21500 | 0.0005        |
-
-
-### Framework Versions
-- Python: 3.11.7
-- Sentence Transformers: 3.0.1
-- Transformers: 4.43.3
-- PyTorch: 2.4.0+cu118
-- Accelerate: 0.33.0
-- Datasets: 2.20.0
-- Tokenizers: 0.19.1
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
 
-#### MultipleNegativesRankingLoss
-```bibtex
-@misc{henderson2017efficient,
-    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
-    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
-    year={2017},
-    eprint={1705.00652},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
+print("Sentence embeddings:")
+print(sentence_embeddings)
 ```
 
-<!--
-## Glossary
+## 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 developed 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 developed 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 intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs 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
 
-*Clearly define terms in order to be accessible across audiences.*
--->
+We trained our 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`.
 
-<!--
-## Model Card Authors
+#### Training data
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
+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.
 
-<!--
-## Model Card Contact
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+| 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** |

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:1377e9af0ca0b016a9f2aa584d6fc71ab3ea6804fae21ef9fb1416e2944057ac
 size 90864192

modules.json

@@ -14,13 +14,7 @@
   {
     "idx": 2,
     "name": "2",
-    "path": "2_Dense",
-    "type": "sentence_transformers.models.Dense"
-  },
-  {
-    "idx": 3,
-    "name": "3",
-    "path": "3_Dropout",
-    "type": "sentence_transformers.models.Dropout"
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
   }
 ]