snowflake-arctic-embed-s-ft-pstuts

SentenceTransformer based on Snowflake/snowflake-arctic-embed-s

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-s. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Snowflake/snowflake-arctic-embed-s <!-- at revision e596f507467533e48a2e17c007f0e1dacc837b33 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("mbudisic/snowflake-arctic-embed-s-ft-pstuts")
# Run inference
sentences = [
    'How I use Brush tool and zoom in same time, I dont wanna keep switchin tools?',
    "Zooming and panning are ways to navigate around an image that you'll use often as you work on images in Photoshop CC. To practice working with the zoom and pan controls, open this image from the tutorial practice files, or open a large image of your own. Zooming means changing the magnification of the image, as you might do if you were looking at the sky through a telescope. You may want to zoom in for a closer view of part of an image, or you may want to zoom out to see more of an image on your screen. The most straightforward way to zoom is to select the Zoom tool, toward the bottom of the Tools panel here. Then go up to the Options bar for the Zoom tool, where you'll find a plus icon for zooming in, and a minus icon for zooming out. Let's start with the plus icon activated which is the default. Then to zoom in, move into the image and click. And each time you click, you'll zoom in a little further. To zoom back out to see more of the image again, go back to the Options bar, and this time select the minus icon, and then click several times in the image to zoom back out. If you want to zoom in again, you have to go back to the Options bar, click the plus icon, and click in the image to zoom in again. Now you may get tired of going up to the Options bar every time you want to switch between zooming in and zooming out. So, here's a shortcut that will help you. When the zoom in option is active, as it is now, you can switch to zooming out by holding the Option key on your keyboard if you're on a Mac, or the ALT key on Windows. Hold down that key and then click in the image. And that will automatically switch you back to zooming out. Then release your finger from the Option or ALT key, and you're switched back to zooming in. And so, you can click in the image to zoom in again. The Zoom tool has a couple of options in its Options bar, that you can use to move quickly to zoom levels that you use often. The Fit Screen option, here in the Options bar, comes in handy when you're zoomed in like this and you want to get back to a view of the entire image. Just click the Fit Screen option, and the entire image fits itself into your document window. Another useful option is this 100% option. Clicking this, zooms you into 100% view of the image, which is the best way to view an image when you're checking it for sharpness. Now, I'm working on a small screen and this image is pretty large, so when I zoom in to 100%, I can't see the whole image on my screen. Although you may not experience the same thing if you're working on a large monitor. So, if I want to see a different part of this image at this zoom level, I'm going to need to move the image around in my document window. That's called panning. And it's done with another tool, the Hand tool. So, I'm going to go back to the Tools panel, and I'm going to select the Hand tool there, which is just above the Zoom tool. Then I'll move into the image, and notice that my cursor is now changed to a hand icon. I'll click, drag, and move the image in the document window, to a place that I want to see, and then I'll release my mouse. When I'm done checking the sharpness here and I want to go back to view the entire image on screen, I'll go up to the Options bar for the Hand tool, and there I'll see the same Fit Screen option that we had for the Zoom tool. So, I can just click Fit Screen in the Hand tool Options bar, and that takes me back to see the entire image in my document window. Let me show you another way to zoom. Instead of clicking, you can do continuous zoom by holding your mouse down on the image. I'll go back and get the Zoom tool in the Tools panel. And then I'm going to click and hold in the image. And the image zooms in continuously. If you zoom in really far like this, you can see the pixels, that are the building blocks of an image in Photoshop CC. By the way, the size of these pixels can affect the image quality of a print, which is why image resolution is an important topic, especially for printing. Something we'll talk more about when we cover resizing an image later in this series. I'm going to go up to the Options bar and click Fit Screen, so I can see the entire image on my screen again. One more thing, let's say that you're working with another tool, maybe the Brush tool, and you're painting in a small area and you don't want to switch out of the Brush tool over to the Zoom tool just to zoom. Well, there's a shortcut that you can use instead of the Zoom tool. And that is to hold the Command key on a Mac, or the Ctrl key on a PC, as you press the plus key on your keyboard. And every time you do that, that will zoom you in. If you want to zoom back out, hold the Command key on a Mac or the Ctrl key on a PC, and press the minus key on your keyboard. And that will zoom you back out. So, that's an introduction to zooming and panning, that I hope will help you to navigate your images as you're working on them in Photoshop CC. To finish up with this lesson, you can",
    '<2-hop>\n\nbit and make that bird look like it was in the original image. So as you can see, refining images is easier than ever using the Content Aware Fill, the Patch Tool, and the Content Aware Move Tool in Photoshop CC.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### 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.* -->

Evaluation

Metrics

Information Retrieval

• Evaluated with <code>InformationRetrievalEvaluator</code>

<!-- ## 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: 90 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 90 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 34.76 tokens</li><li>max: 56 tokens</li></ul>	<ul><li>min: 49 tokens</li><li>mean: 374.26 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 0.5</li><li>mean: 0.83</li><li>max: 1.0</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>How can a beginner Photoshop user utilize the Lasso Tool to remove distracting elements from an image?</code>	<code>>> Removing distracting elements is easier than ever with the 2014 release of Photoshop CC. Not only has the Content Aware technology improved in terms of quality, but the speed has also been improved significantly. Let's go ahead take a look at a few examples. Now in this image of the cactus, I'm going to select the Lasso Tool and I simply want to get rid of this cactus here in the background. The easiest way to do this would be to select Edit and then Fill. I'll use Content Aware, but when I click OK, you'll notice there's a little bit of a seam here where the colors aren't blended very well. And that's because, in the past, the Content Aware technology really focused on the texture in the image. So let's undo that Command + Z or Ctrl + Z. And this time when I select Edit, Fill, I'm going to use the new Color Adaptation option. This time when I click OK, you can see that Photoshop does a much better job in blending those colors. Now let's go ahead and try to remove the other two cact...</code>	<code>1.0</code>
  <code>How can a beginner use the Perspective Warp feature in Adobe Photoshop to manipulate perspective, such as straightening buildings and changing the viewpoint, and what steps and controls are involved in this process according to the provided guide?</code>	<code><1-hop><br><br>>> What I want to show you in this video is something that is absolutely amazing. It's a brand new feature in Adobe Photoshop Creative Cloud called Perspective Warp. Now I have a photograph open. I didn't take this photo. It was taken by a company called PhotoSpin. And don't forget if you want to follow along, you can download the assets for this video. What I want to do first though is make a copy of it. I'm going to drag it down- this is one way to do it-make a copy. That is not necessary, but this way we get to see kind of a before and an after. Now it will work with just about any image, but your first test is to go up to the word Edit on the pull-down menu and go down, and you better see Perspective Warp. If you don't, no big deal. Just go out to the cloud, and download the latest version of Photoshop. Now what does it do? What does Perspective Warp do? It literally allows me to re-enter a three-dimensional world to change the perspective of the image as if, as the photog...</code>	<code>1.0</code>
  <code>Wht is the funtion of Ctrl + Z in Photoshp when you make a mistake while using Content Aware or Patch Tool, and how can beginners use it to undo their last action step by step?</code>	<code>>> Removing distracting elements is easier than ever with the 2014 release of Photoshop CC. Not only has the Content Aware technology improved in terms of quality, but the speed has also been improved significantly. Let's go ahead take a look at a few examples. Now in this image of the cactus, I'm going to select the Lasso Tool and I simply want to get rid of this cactus here in the background. The easiest way to do this would be to select Edit and then Fill. I'll use Content Aware, but when I click OK, you'll notice there's a little bit of a seam here where the colors aren't blended very well. And that's because, in the past, the Content Aware technology really focused on the texture in the image. So let's undo that Command + Z or Ctrl + Z. And this time when I select Edit, Fill, I'm going to use the new Color Adaptation option. This time when I click OK, you can see that Photoshop does a much better job in blending those colors. Now let's go ahead and try to remove the other two cact...</code>	<code>1.0</code>

• Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• num_train_epochs: 50
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8
• 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: 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: 50
• 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}
• tp_size: 0
• 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: None
• hub_always_push: False
• 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.7.0+cu126
• Accelerate: 1.7.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}

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