--- language: - en license: mit tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:219902 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: '

Is your feature request related to a problem? Please describe.
scipy.cluster.hierarchy.linkage uses double (float64) to store and do its computation for hierarchical clustering. However, I have a very large dataset (292000x292000) that I would like to perform hclust on but my computer is RAM limited. I have 252GB RAM and I think the clustering algorithm should be able to work on my dataset when all values are stored and computed using float16s instead.

For large datasets on machines with insufficient RAM to store and compute on Arrays of float64s, it would be awesome if computation could be done on a different precision float to reduce the memory footprint.

Additionally, adding choices for datatypes could be very useful for almost all scipy functions.

Describe the solution you''d like
Allow for an argument to specify what datatype you''d like to use (e.g. np.float64, np.float32, np.float16)

The argument could be like dtype=''np.double'' by default but changable to whatever datatype is chosen.

' sentences: - "

One representative error:

\n
torch/csrc/autograd/functions/init.cpp:220:37: error:\
    \ address of overloaded function 'getTupleAttr' does not match required type '_object\
    \ *(_object *, void *)'\n  {(char*)\"output_padding\", (getter)getTupleAttr<ConvBackwardBackward,\
    \ std::vector<int>, ConvParams,\n                                    ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\
    torch/csrc/autograd/functions/init.cpp:82:11: note: candidate template ignored:\
    \ invalid explicitly-specified argument for template parameter 'Convert'\nPyObject*\
    \ getTupleAttr(PyObject* obj, void* _unused)\n
\n

The cause of the problem is aa91193:

\n
   {(char*)\"\
    output_padding\", (getter)getTupleAttr<ConvForward, std::vector<int>,\
    \ ConvParams,\n-                                         &ConvParams::output_padding,\
    \ long, PyInt_FromLong>, NULL, NULL, NULL},\n+                            \
    \             &ConvParams::output_padding, int64_t, PyInt_FromLong>, NULL,\
    \ NULL, NULL},\n
\n

It seems that on clang, changing\ \ the type parameter here is sufficient to cause template instantiation to fail.

\n\

Maybe the easiest way to fix this is to write a more portable\ \ version of PyInt_FromLong (and friends) which always returns int64_t.

" - "

I try the scipy ward clustering, when calculating linkage, it\ \ report follow error:

\n
ward_h = linkage(X,\
    \ method='ward', metric='euclidean')\nPython(2557,0x7fff732cc310) malloc: ***\
    \ mach_vm_map(size=18446744067627675648) failed (error code=3)\n*** error: can't\
    \ allocate region\n*** set a breakpoint in malloc_error_break to debug\n---------------------------------------------------------------------------\n\
    MemoryError                               Traceback (most recent call last)\n\
    <ipython-input-10-769ae7c53f7c> in <module>()\n----> 1 ward_h =\
    \ linkage(X, method='ward', metric='euclidean')\n\n/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/cluster/hierarchy.pyc\
    \ in linkage(y, method, metric)\n    652             Z = np.zeros((n - 1, 4))\n\
    \    653             _hierarchy_wrap.linkage_euclid_wrap(dm, Z, X, m, n,\n-->\
    \ 654                                               int(_cpy_euclid_methods[method]))\n\
    \    655     return Z\n    656 \n\nMemoryError: out of memory while computing\
    \ linkage\n
\n

How can I solve this？

\nThe data set I use is here: https://dl.dropboxusercontent.com/u/68126956/df.csv.

\n\

Thanks.

" - '

Make sure these boxes are checked before submitting your issue - thank you!

Superset version

0.19.1

Expected results

I try to draw mapbox in superset. I have dataset with column Latitude and Longitude and use it in respective field.

Actual results

TypeError: <superset.connectors.druid.models.DruidMetric object at 0xefbea90> is not JSON serializable

Steps to reproduce

togithub

Anyone already have the same problem?
Thanks

' - source_sentence: '

Challenge Waypoint: Global Scope and Functions has an issue.
User Agent is: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36.
Please describe how to reproduce this issue, and include links to screenshots if possible.

The test case has a typo in it:
Do not decalre oopsGlobal using the var keyword

' sentences: - '

Challenge Waypoint: Local Scope and Functions has an issue.
User Agent is: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.48 Safari/537.36.

The test asks you to remove the second console.log() to pass. When you remove the second console.log() it still fails. You have to remove both console.log() calls to pass(including the one inside myFunction().)

Reproducible every time by removing the console.log() outside of myFunction(), as asked by test. I''m assuming this is a bug in the test and not the text as there''s no reason to remove the console.log() call within myFunction().

' - "\n

Issue 1:

\n

In the following\ \ code (using C++ syntax highlighting), the string literal(s) are not highlighted\ \ at all.
\nThis issue is exclusive to C++ syntax highlighting and does not\ \ occur with C syntax highlighting.

\n

Code:

\n
int main()\n{\n printf(\"\ %s\\n\", \"a string\");\n}\n
\n

Result

\n\

\"Bug

\n

Issue 2:

\n

In custom tmLanguage based colorizers that reference\ \ source.c from within rules that begin with certain patterns, the inlined C highlighting\ \ is incorrect (this issue also applies to rules using source.c++ but does not\ \ occur in rules using source.js).

\n

Code

\n

(See https://github.com/SE2Dev/VSCode-BugExample)

\n\

Result

\n

\"Bug

" - '

I''m not sure why the comments in the variables.less got changed to this style //**, but it my opinion it was a brave move.

For me at least it prevents compilation when using the nodejs compiler lessc.

At also breaks the syntax highlighting in my IDE, Visual Studio 2013

' - source_sentence: He described his ambition as “a relentless pursuit of excellence.” sentences: - His goal is characterized by an unwavering drive toward high standards in every endeavor - Sidechain liquidity pools facilitate token swaps without mainnet fees - The harvest from her backyard was exceptionally crisp and vibrant - source_sentence: The honeybee's hum echoed over rows of flowering lavender sentences: - This cheese aged for months, developing deep amber tones - Between thunderclaps a lover's promise rang clear - Thoughtful acts, no matter how modest, have the power to lift spirits and create positive ripple effects - source_sentence: Two men working on their knees in front of a building. sentences: - There are people working. - A man is in a kayak. - There are people not working. datasets: - aisuko/quora_duplicate_questions pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SimpleEmbed This is a [sentence-transformers](https://www.SBERT.net) model trained on the hanzceo/sts-en-en, jinaai/negation-dataset, andersonbcdefg/jina_negation_v2, WhereIsAI/github-issue-similarity and [aisuko/quora_duplicate_questions](https://huggingface.co/datasets/aisuko/quora_duplicate_questions) datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text - **Training Datasets:** - hanzceo/sts-en-en - jinaai/negation-dataset - andersonbcdefg/jina_negation_v2 - WhereIsAI/github-issue-similarity - [aisuko/quora_duplicate_questions](https://huggingface.co/datasets/aisuko/quora_duplicate_questions) - **Language:** en - **License:** mit ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding({}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("hanzceo/SimpleEmbed-dev1") # Run inference queries = [ 'Two men working on their knees in front of a building.', ] documents = [ 'There are people working.', 'There are people not working.', 'A man is in a kayak.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 1024] [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[ 0.4950, 0.1923, -0.0330]]) ``` ## Training Details ### Training Datasets
hanzceo/sts-en-en #### hanzceo/sts-en-en * Dataset: hanzceo/sts-en-en * Size: 1,182 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 100 samples: | | anchor | positive | negative | |:---------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------| | type | string | string | string | | modality | text | text | text | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------| | The intricate framework elucidates previously uncharted dimensions of cellular plasticity | Researchers have unveiled an innovative approach that captures dynamic fluctuations within cellular architecture | The old wooden bridge creaked under the weight of the passing tractor | | Observers note a pronounced correlation between epigenetic markers and metabolic fluxes | Analysts discovered subtle shifts in transcriptional patterns across divergent cell populations | The stock market experienced a slight dip following the unexpected jobs report | | The novel assay integrates multi-omics datasets to reconstruct regulatory networks | Investigators applied machine learning to synthesize heterogeneous biological information | Apples float in water because they are made up of 25 percent air | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ```
jinaai/negation-dataset #### jinaai/negation-dataset * Dataset: jinaai/negation-dataset * Size: 10,000 training samples * Columns: anchor, entailment, and negative * Approximate statistics based on the first 100 samples: | | anchor | entailment | negative | |:---------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| | type | string | string | string | | modality | text | text | text | | details | | | | * Samples: | anchor | entailment | negative | |:-------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|:---------------------------------------------------------| | Two young girls are playing outside in a non-urban environment. | Two girls are playing outside. | Two girls are not playing outside. | | A man with a red shirt is watching another man who is standing on top of a attached cart filled to the top. | A man is standing on top of a cart. | A man is not standing on top of a cart. | | A man in a blue shirt driving a Segway type vehicle. | A person is riding a motorized vehicle. | A person is not riding a motorized vehicle. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ```
andersonbcdefg/jina_negation_v2 #### andersonbcdefg/jina_negation_v2 * Dataset: andersonbcdefg/jina_negation_v2 * Size: 50,000 training samples * Columns: query, pos, and neg * Approximate statistics based on the first 100 samples: | | query | pos | neg | |:---------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | string | string | string | | modality | text | text | text | | details | | | | * Samples: | query | pos | neg | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:------------------------------------------------------------------------------| | A dog happily looking onward in the back seat of a car. | A dog sitting down inside of a vehicle. | A dog standing up outside of a vehicle. | | Alas, madame, said Poirot, "I thought you had come to honour me with a visit!" | Poirot said that he thought the woman had come to visit him. | Poirot said that he thought the woman had not come to visit him. | | yeah i i i agree i the thing that scares me uh though about where i would i would want definitely want some sort of legislation and coming from the north east i'm just not used to seeing um these and i i know this may sound kind of stereo typical but the cowboys with the gun racks in the back of their trucks | I would want the law to cover guns. | I would not want the law to cover guns. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ```
WhereIsAI/github-issue-similarity #### WhereIsAI/github-issue-similarity * Dataset: WhereIsAI/github-issue-similarity * Size: 9,457 training samples * Columns: anchor and positive * Approximate statistics based on the first 100 samples: | | anchor | positive | |:---------|:----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |

Bug summary


I put a torch.Tensor in matplotlib.pyplot.hist() , but it draw a wrong graphic and take a long time.

Although transform to numpy, the function work well. But all the others function I used are work well on tensor. So I think its a bug.


Code for reproduction


import matplotlib.pyplot as plt
import torch

plt.hist(torch.randn... | 
Bug report

Bug summary

Generating np.random.randn(1000) values, visualizing them with plt.hist(). Works fine with Numpy.

When I replace Numpy with tensorflow.experimental.numpy, Matplotlib 3.3.4 fails to display the histogram correctly. Matplotlib 3.2.2 works fine.

Code for reproduction

import matplotlib. |
  | 
https://github.com/kubernetes/kubernetes/blob/master/pkg/kubelet/container_bridge.go#L122-L143

container_bridge.go assumes that the virtual IP of services & pods will be in the 10. space.

I propose there is no reason to make this assumption.

As outlined in #15932, cluster admins may need to deploy to hosts in which 10. is reserved for the nodes. In such a case, Kubelets must support an alternative range.
                                      | 
Today kubelet sets up an iptables MASQUERADE rule for any traffic destined for anything except 10.0.0.0/8.  This is close, but not even correct on GCE, and certainly not right elsewhere.

First GCE.  We probably want something like:

iptables -t nat -N KUBE-IPMASQ
iptables -t nat -A KUBE-IPMASQ -d 10.0.0.0/8 -j RETURN
iptables -t nat -A KUBE-IPMASQ -d 172.16.0.0/12 -j RETURN
iptables -t nat -A KUBE-IPMASQ -d 192.168.0.0/16 -j RETURN
iptables -t nat -A KUBE-IPMASQ -j MASQUERADE
iptables -t nat -I...             |
  | 
Is there an existing issue for this?


  •  I have searched the existing issues


This issue exists in the latest npm version


  •  I am using the latest npm


Current Behavior

Currently, my package.json specifies "typescript": "^5.0.2". When I change it to say "typescript": "^5.0.3", npm 9 spins for 4:28 before deciding it doesn't exist. For comparison, npm 8 installs it with no problem in 0:44.

Ironically, I can't upgrade npm to 9.6 due to this issue: npm 9.5.1 times out when I run npm i -g npm.

Expec... | 
Is there an existing issue for this?


  •  I have searched the existing issues


This issue exists in the latest npm version


  •  I am using the latest npm


Current Behavior

When running npm install it will sometimes hang at a random point.  When it does this, it is stuck forever.  CTRL+C will do nothing the first time that combination is pressed when this has occurred.  Pressing that key combination the second time will make the current line (the one showing the little progress bar) disappear but that's it.  No further responses to that key combination are observed.

The C...                   |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```
aisuko/quora_duplicate_questions #### aisuko/quora_duplicate_questions * Dataset: [aisuko/quora_duplicate_questions](https://huggingface.co/datasets/aisuko/quora_duplicate_questions) at [a14d279](https://huggingface.co/datasets/aisuko/quora_duplicate_questions/tree/a14d27956a816dbb441515c1b732465fe5064092) * Size: 149,263 training samples * Columns: question1 and question2 * Approximate statistics based on the first 100 samples: | | question1 | question2 | |:---------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details |
  • min: 16 characters
  • mean: 54.75 characters
  • max: 139 characters
|
  • min: 21 characters
  • mean: 54.34 characters
  • max: 127 characters
| * Samples: | question1 | question2 | |:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me? | I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me? | | How can I be a good geologist? | What should I do to be a great geologist? | | How do I read and find my YouTube comments? | How can I see all my Youtube comments? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ```
### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 256 - `learning_rate`: 0.002 - `weight_decay`: 0.01 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `seed`: 69 - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 8 - `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`: 0.002 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: 69 - `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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `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 - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0012 | 1 | 7.9156 | | 1.1601 | 1000 | 11.1122 | | 2.3202 | 2000 | 6.1974 | | 3.4803 | 3000 | 4.1201 | | 4.6404 | 4000 | 3.2187 | | 5.8005 | 5000 | 2.6037 | | 6.9606 | 6000 | 2.2084 | | 8.1206 | 7000 | 1.887 | | 9.2807 | 8000 | 1.8408 | ### Training Time - **Training**: 37.3 minutes ### Framework Versions - Python: 3.12.6 - Sentence Transformers: 5.6.0 - Transformers: 4.56.0 - PyTorch: 2.8.0+cu129 - Accelerate: 1.10.1 - Datasets: 5.0.0 - Tokenizers: 0.22.0 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```