finetuned-bge-base-en

SentenceTransformer based on BAAI/bge-base-en

This is a sentence-transformers model finetuned from BAAI/bge-base-en. It maps sentences & paragraphs to a 768-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: BAAI/bge-base-en <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 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': True, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("aaa961/finetuned-bge-base-en")
# Run inference
sentences = [
    'Occurence highlighting highlights wrong part of the code <!-- Please search existing issues to avoid creating duplicates. -->\r\n\r\n## Environment data\r\n\r\n-   VS Code version: 1.58.0-insider 062e6519f8973fede2ca736e80682bd19007460a \r\n-   Jupyter Extension version (available under the Extensions sidebar):  v2021.8.1000539794\r\n-   Python Extension version (available under the Extensions sidebar): v2021.6.944021595\r\n-   OS (Windows | Mac | Linux distro) and version: Ubuntu 18.04\r\n-   Python and/or Anaconda version: 3.9.2 Anaconda\r\n-   Type of virtual environment used (N/A | venv | virtualenv | conda | ...): conda\r\n-   Jupyter server running: Remote \r\n\r\nIt seems that issues https://github.com/microsoft/vscode/issues/120148 and https://github.com/microsoft/vscode-jupyter/issues/5451 have been closed but the problem still exists in the last versions. I have not seen any similar issues on the repo',
    'File explorer is expanding all root folders in a MR workspace Steps to Reproduce:\r\n\r\n1.  Create a MR workspace file with more than one folder\r\n2. Open the MR workspace\r\n\r\n🐛 All top level folders are expanded. This is very slow if there are lot of root folders and also if the MR workspace is in remote\r\n',
    'Quick input reset scroll position * use latest from master\r\n* f1 > insert snippet\r\n* scroll down to an extension snippet and hide it (press 👁️ icon)\r\n* :bug: the scroll position resets\r\n\r\nThis is happening when reassigning the items (since the press changed the label) here: https://github.com/microsoft/vscode/blob/92314d61a55f466c125fa9d1f9fe8da633a82423/src/vs/workbench/contrib/snippets/browser/insertSnippet.ts#L213',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5572, 0.5031],
#         [0.5572, 1.0000, 0.5477],
#         [0.5031, 0.5477, 1.0000]])

<!-- ### 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

Triplet

• Dataset: bge-base-en-train
• Evaluated with <code>TripletEvaluator</code>

Triplet

• Dataset: bge-base-en-train
• Evaluated with <code>TripletEvaluator</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: 445 training samples
• Columns: <code>sentence</code> and <code>label</code>
• Approximate statistics based on the first 445 samples:

  	sentence	label
  type	string	float
  details	<ul><li>min: 20 tokens</li><li>mean: 338.03 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 1.0</li><li>mean: 149.53</li><li>max: 299.0</li></ul>

• Samples:

  sentence	label
  <code>Branch list is sometimes out of order
  <br>Type: <b>Bug</b>

  <br> <br>1. Open a workspace <br>2. Quickly open the branch picker and type `main` <br> <br>**Bug** <br>The first time you do this, sometimes you end up with an unordered list: <br> <br> <br> <br> <br>The correct order shows up when you keep start typing or try doing this again: <br> <br> <br> <br> <br> <br> <br> <br> <br>VS Code version: Code - Insiders 1.91.0-insider (Universal) (0354163c1c66b950b0762364f5b4cd37937b624a, 2024-06-26T10:12:33.304Z) <br>OS version: Darwin arm64 23.5.0 <br>Modes: <br> <br><details> <br><summary>System Info</summary> <br> <br>\|Item\|Value\| <br>\|---\|---\| <br>\|CPUs\|Apple M2 Max (12 x 2400)\| <br>\|GPU Status\|2d_canvas: unavailable_software<br>canvas_oop_rasterization: disabled_off<br>direct_rendering_display_compositor: disabled_off_ok<br>gpu_compositing: disabled_software<br>multiple_raster_threads: enabled_on<br>ope...</code> | <code>299.0</code> | | <code>Git Branch Picker Race Condition If I paste the branch too quickly and then press enter, it does not switch to it, but creates a new branch. <br>This breaks muscle memory, as it works when you do it slowly. <br> <br> <br> <br> <br>Once loading completes, it should select the branch again.</code> | <code>299.0</code> | | <code>Ctrl+I stopped working after first hold+talk+release Testing #213355 <br> <br>Screencast shows that it seems to be in the wrong context and is trying to stop the session? <br> <br> <br> <br>Repro was just asking "Testing testing" and then trying to ask something else</code> | <code>298.0</code> |
• Loss: <code>BatchSemiHardTripletLoss</code>

Evaluation Dataset

Unnamed Dataset

• Size: 95 evaluation samples
• Columns: <code>sentence</code> and <code>label</code>
• Approximate statistics based on the first 95 samples:

  	sentence	label
  type	string	float
  details	<ul><li>min: 38 tokens</li><li>mean: 348.78 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 3.0</li><li>mean: 149.72</li><li>max: 296.0</li></ul>

• Samples:

  sentence	label
  <code>VS Code does not delete old extension versions even after restart <!-- ⚠️⚠️ Do Not Delete This! bug_report_template ⚠️⚠️ -->
  <br><!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ -->
  <br><!-- 🕮 Read our guide about submitting issues: https://github.com/microsoft/vscode/wiki/Submitting-Bugs-and-Suggestions -->
  <br><!-- 🔎 Search existing issues to avoid creating duplicates. -->
  <br><!-- 🧪 Test using the latest Insiders build to see if your issue has already been fixed: https://code.visualstudio.com/insiders/ -->
  <br><!-- 💡 Instead of creating your report here, use 'Report Issue' from the 'Help' menu in VS Code to pre-fill useful information. -->
  <br><!-- 🔧 Launch with `code --disable-extensions` to check. -->
  <br>Does this issue occur when all extensions are disabled?: Yes

  <br> <br><!-- 🪓 If you answered No above, use 'Help: Start Extension Bisect' from Command Palette to try to identify the cause. --> <br><!-- 📣 Issues caused by an extension need to be reported directly to the ex...</code> | <code>291.0</code> | | <code>Enhanced Branch Switching removes focus from Terminal <br>Type: <b>Bug</b> <br> <br>I love the new Enhanced Branch Switching feature that restores the open editors when you switch branches. One thing I noticed is that if I switch branches from the integrated terminal using git commants, the terminal looses focus while the open editors for the new branch are being restored. This causes a command I'm typing to be interrupted in the middle of my typing. <br> <br>It would be awesome if the terminal did not loose focus while the editors for the new branch are being opened. <br> <br>Thanks! <br> <br> <br>VS Code version: Code 1.89.0 (b58957e67ee1e712cebf466b995adf4c5307b2bd, 2024-05-01T02:10:13.496Z) <br>OS version: Darwin arm64 23.4.0 <br>Modes: <br> <br><details> <br><summary>System Info</summary> <br> <br>\|Item\|Value\| <br>\|---\|---\| <br>\|CPUs\|Apple M1 Max (10 x 24)\| <br>\|GPU Status\|2d_canvas: enabled<br>canvas_oop_rasterization: enabled_on<br>direct_rendering_display_compositor: disabled_off_ok<br>gpu_compositing: enabled<br>multiple_raste...</code> | <code>296.0</code> | | <code>scm.workingSets.enabled steals focus from integrated terminal Type: <b>Bug</b> <br> <br>I changed a test-projects user-settings to: <br> <br>```js <br>{ <br> "scm.workingSets.enabled": true, <br> "scm.workingSets.default": "empty" <br>} <br>``` <br> <br>And then opened the integrated terminal. <br> <br>When I ran the command: <br> <br>```sh <br>git switch -c tmp <br>``` <br> <br>I expected input-focus to remain in the integrated terminal. Instead, focus was removed (I can't tell where focus went, though). <br> <br>If I then click into the integrated terminal and run: <br> <br>```sh <br>git switch - <br>``` <br> <br>Focus is again removed from the integrated console. This time it lands into the editor window I had open in the previous branch. <br> <br>When I'm performing branch-switch operations in the terminal I frequently execute additional scm-operations (like creating and pushing a commit). <br> <br>VS Code version: Code 1.89.0 (b58957e67ee1e712cebf466b995adf4c5307b2bd, 2024-05-01T02:08:25.066Z) <br>OS version: Linux x64 6.8.7-arch1-1 <br>Modes: <br> <br><details> <br><summary>Syste...</code> | <code>296.0</code> |
• Loss: <code>BatchSemiHardTripletLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 5
• warmup_ratio: 0.1
• batch_sampler: group_by_label

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: 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: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 5
• 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: 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: 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: group_by_label
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.10.10
• Sentence Transformers: 5.1.0
• Transformers: 4.55.3
• PyTorch: 2.7.1+cu128
• Accelerate: 1.10.0
• Datasets: 4.0.0
• Tokenizers: 0.21.4

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",
}

BatchSemiHardTripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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