Upload checkpoint-23100

checkpoints/checkpoint-23100/1_Pooling/config.json

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+{
+    "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,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

checkpoints/checkpoint-23100/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:985664
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: QuangDuy/bert-base-stage2-hf
+widget:
+- source_sentence: gần là gì
+  sentences:
+  - Thủ thuật tạo dòng chảy gần động mạch là một phương pháp thay thế phù hợp cho
+    kỹ thuật DRIL để điều trị bệnh nhân ARI. Kỹ thuật được mô tả thích hợp hơn cho
+    các bác sĩ phẫu thuật không muốn nối động mạch trục.
+  - Trang thảo luận có thể chứa các đề xuất. (Tháng 9 năm 2010). Polyurethane nhiệt
+    dẻo (TPU) là bất kỳ loại nhựa polyurethane nào với nhiều đặc tính, bao gồm độ
+    đàn hồi, độ trong suốt và khả năng chống dầu, mỡ và mài mòn. PU có nhiều ứng dụng
+    bao gồm tấm thiết bị ô tô, bánh xe đẩy, dụng cụ điện, đồ thể thao, thiết bị y
+    tế, dây đai truyền động, giày dép, bè bơm hơi và nhiều ứng dụng màng, tấm và hồ
+    sơ ép đùn.
+  - Bởi vì cách tiếp cận như vậy, về nguyên tắc, có vẻ không được mong muốn, chúng
+    tôi đã phát triển và áp dụng một kỹ thuật thay thế được gọi là phương pháp tiếp
+    cận gần dòng vào động mạch (PAI). Quy trình này chuyển đổi nguồn cung cấp động
+    mạch của đường tiếp cận động mạch đến động mạch gần hơn có công suất cao hơn bằng
+    cách sử dụng mảnh ghép polytetrafluoroethylen cỡ nhỏ làm bộ phận trung chuyển.
+- source_sentence: npat là gì
+  sentences:
+  - 'là gì: zantac, dùng để làm gì; và cái gì ... 59. cái gì là: pepcid, dùng để;
+    và là gì ... 60. là gì: bi-citra, dùng để làm gì; và cái gì ... 61. là gì: glucagon,
+    dùng để làm gì; và cái gì ... 62. là gì: humulin, dùng để làm gì; và cái gì ...'
+  - NMIMS-NPAT (Bài kiểm tra quốc gia cho các chương trình sau 12 tuổi) là bài kiểm
+    tra đầu vào chính thức để tuyển sinh vào các Chương trình cấp bằng đại học và
+    bằng cấp tích hợp được cung cấp bởi các trường thành viên của Viện Nghiên cứu
+    Quản lý Narsee Monjee của SVKM (Được coi là Đại học) tại Cơ sở Mumbai & Shirpur.
+    andidates có thể đăng ký trực tuyến trước ngày 30 tháng 4 năm 2015 cho NMIMS-NPAT
+    (Các chương trình sau ngày 12) dự kiến vào ngày 9 và 10 tháng 5 năm 2015 tại các
+    trung tâm trên toàn quốc.
+  - Kali là một nguyên tố tuyệt vời được sử dụng trong vườn vì kali là chìa khóa để
+    giúp cây ra trái trong khi nitơ n tăng tán lá và sự phát triển của cây và p phốt
+    pho giúp cấu trúc rễ.
+- source_sentence: mtr là gì
+  sentences:
+  - Cài đặt mtr trong hệ điều hành yêu thích của bạn. mtr có thể được cài đặt trong
+    Ubuntu linux bằng cách cài đặt gói MTR. Một mtr cài đặt apt-get đơn giản sẽ làm
+    được điều đó, hoặc nếu bạn thích sử dụng trình quản lý gói để cài đặt chương trình.
+  - Cập nhật ngày 09 tháng 8 năm 2016. Phát triển Nguồn nhân lực (HRD) là khuôn khổ
+    để giúp nhân viên phát triển các kỹ năng, kiến thức và khả năng cá nhân và tổ
+    chức của họ.
+  - Từ Wikipedia, bách khoa toàn thư miễn phí. Đường sắt vận chuyển hàng loạt hoặc
+    MTR (bằng tiếng Trung, é¦Ââ „¢港éµ路æœÂâ €°éÂâ" ¢ÂॠÂâ € ¦¬Ã ¥ ¸, nghĩa đen
+    là Công ty Đường sắt Hồng Kông; hoặc 港éµ) là hệ thống đường sắt vận chuyển nhanh
+    chính ở Hồng Kông. Kể từ khi dịch vụ MTR lần đầu tiên được khai trương vào năm
+    1979, mạng lưới đã phát triển lên 150 trạm. Được xây dựng và điều hành bởi MTR
+    Corporation Limited, hệ thống MTR là phương tiện giao thông công cộng rất phổ
+    biến ở Hồng Kông, với khoảng 2,46 triệu hành khách đi lại mỗi ngày.
+- source_sentence: snp là gì?
+  sentences:
+  - Grid là một phần lớn của hệ thống Tron, được lập trình bởi Kevin Flynn. Thường
+    được Flynn gọi là biên giới kỹ thuật số của mình, Grid được tạo ra để cung cấp
+    một nền tảng thử nghiệm nơi tất cả các dạng ...
+  - Đa hình nucleotide đơn hay SNP (phát âm là đoạn cắt) là một biến thể trình tự
+    DNA xảy ra khi một nucleotide đơn - A, T, C hoặc G - trong bộ gen (hoặc trình
+    tự dùng chung khác) khác nhau giữa các thành viên của một loài (hoặc giữa các
+    nhiễm sắc thể được ghép đôi trong một cá nhân).
+  - 'Trước khi có thể mở tệp SNP, bạn sẽ cần tìm ra loại tệp mà phần mở rộng tệp SNP
+    đề cập đến. Mẹo: Lỗi liên kết tệp SNP không chính xác có thể là triệu chứng của
+    các sự cố cơ bản khác trong hệ điều hành Windows của bạn.'
+- source_sentence: pcb là gì
+  sentences:
+  - thiết bị lưu trữ ảo (VSA) Thiết bị lưu trữ ảo (VSA) là một bộ điều khiển lưu trữ
+    chạy trên máy ảo (VM) để tạo lưu trữ dùng chung mà không cần thêm phần cứng. Tải
+    xuống hướng dẫn miễn phí này.
+  - PCB là từ viết tắt của bảng mạch in. Nó là một bảng có các đường và miếng đệm
+    kết nối các điểm khác nhau với nhau. Trong hình trên, có những dấu vết kết nối
+    điện của các đầu nối và thành phần khác nhau với nhau. PCB cho phép chuyển tín
+    hiệu và nguồn điện giữa các thiết bị vật lý.
+  - Thuốc hàn là một dạng chất hàn được sử dụng trong lắp ráp PCB, và bao gồm cả lắp
+    ráp PCB nguyên mẫu, đặc biệt là khi sử dụng kỹ thuật hàn nóng chảy lại. Thuốc
+    hàn là hỗn hợp giữa các khối cầu hàn và một dạng chất trợ dung chuyên dụng. hướng
+    dẫn, thông tin, bài viết về keo hàn là gì, cách sử dụng keo hàn và đạt được nhiều
+    nhất từ quá trình lắp ráp PCB. Hướng dẫn Quy trình hàn Bao gồm.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on QuangDuy/bert-base-stage2-hf
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [QuangDuy/bert-base-stage2-hf](https://huggingface.co/QuangDuy/bert-base-stage2-hf). 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:** [QuangDuy/bert-base-stage2-hf](https://huggingface.co/QuangDuy/bert-base-stage2-hf) <!-- at revision 6a6ac1ff59259c4fe29b121488afa79d0bfe3e6a -->
+- **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](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): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (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, '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:
+
+```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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'pcb là gì',
+    'PCB là từ viết tắt của bảng mạch in. Nó là một bảng có các đường và miếng đệm kết nối các điểm khác nhau với nhau. Trong hình trên, có những dấu vết kết nối điện của các đầu nối và thành phần khác nhau với nhau. PCB cho phép chuyển tín hiệu và nguồn điện giữa các thiết bị vật lý.',
+    'Thuốc hàn là một dạng chất hàn được sử dụng trong lắp ráp PCB, và bao gồm cả lắp ráp PCB nguyên mẫu, đặc biệt là khi sử dụng kỹ thuật hàn nóng chảy lại. Thuốc hàn là hỗn hợp giữa các khối cầu hàn và một dạng chất trợ dung chuyên dụng. hướng dẫn, thông tin, bài viết về keo hàn là gì, cách sử dụng keo hàn và đạt được nhiều nhất từ quá trình lắp ráp PCB. Hướng dẫn Quy trình hàn Bao gồm.',
+]
+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, 0.6234, 0.1118],
+#         [0.6234, 1.0000, 0.3754],
+#         [0.1118, 0.3754, 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.*
+-->
+
+<!--
+## 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: 985,664 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                      | sentence_1                                                                          | sentence_2                                                                         |
+  |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                          | string                                                                              | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 5.64 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 89.52 tokens</li><li>max: 252 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 92.4 tokens</li><li>max: 314 tokens</li></ul> |
+* Samples:
+  | sentence_0              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                        | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>afm là gì</code>  | <code>AFM là gì? Kính hiển vi lực nguyên tử (AFM) là một loại kính hiển vi thăm dò quét (SPM). SPM được thiết kế để đo các đặc tính cục bộ, chẳng hạn như chiều cao, ma sát, từ tính, bằng một đầu dò. Để thu được hình ảnh, SPM raster quét đầu dò trên một khu vực nhỏ của mẫu, đo đặc tính cục bộ đồng thời.</code>                                                                                                                                            | <code>Mica trần là một chất nền tốt và đặc trưng có thể được sử dụng để cung cấp hình ảnh rõ ràng, sắc nét của DNA. DNA có thể được chụp bằng AFM trên mica khô trong điều kiện môi trường xung quanh hoặc trong bộ đệm sinh lý. Hình ảnh AFM Hình 3.FM AFM là kỹ thuật quét không phá hủy trong đó đầu của đầu dò AFM được sử dụng để quét bề mặt của mẫu. Các đầu d�� AFM cực kỳ sắc nét (thường theo thứ tự hàng chục nanomet), vì vậy AFM thường xuyên có thể hình ảnh các tính năng cực kỳ nhỏ trên bề mặt mẫu.</code> |
+  | <code>xốp là gì</code>  | <code>Thuật ngữ "Styrofoam" thực sự là một dạng cách nhiệt bọt polystyrene đã được đăng ký nhãn hiệu của Dow Chemical Co. Còn được gọi là xốp Polystrene mở rộng (EPS), về cơ bản, Styrofoam là một dạng của nhựa polystyrene. Đổi lại, nhựa polystyrene thường được mã hóa là nhựa # 6. Xốp được sử dụng rộng rãi trên toàn thế giới cho nhiều mục đích khác nhau bao gồm đóng gói, tách cà phê, đĩa, khay đựng thức ăn, chế tạo các bộ phận xe hơi, v.v.</code> | <code>Liên quan: tấm xốp máy cắt xốp tấm xốp xốp lớn xốp xốp khối xốp polystyrene khối xốp dây nóng máy cắt xốp quả bóng xốp polystyrene. Đây là giá trung bình dựa trên doanh số bán sản phẩm này trong cùng tình trạng từ tất cả các danh sách trên ebay.com trong 14 qua ngày hoặc nếu không có đủ số lượng danh sách cho một phép tính có ý nghĩa, trong 90 ngày qua.</code>                                                                                                                                           |
+  | <code>ugc là gì?</code> | <code>Nội dung do người dùng tạo. Nội dung do người dùng tạo (UGC), còn được gọi là nội dung do người dùng tạo (UCC), là bất kỳ dạng nội dung nào được tạo bởi người dùng của một hệ thống hoặc dịch vụ và được cung cấp công khai trên hệ thống đó.</code>                                                                                                                                                                                                       | <code>Tìm kiếm định nghĩa của UGC? Tìm hiểu ý nghĩa đầy đủ của UGC trên Abbreviations.com là gì! 'Ủy ban Tài trợ Đại học' là một lựa chọn - hãy truy cập để xem thêm @ Tài nguyên từ viết tắt và từ viết tắt lớn nhất và có thẩm quyền nhất của Web.</code>                                                                                                                                                                                                                                                                |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `warmup_steps`: 7701
+- `bf16`: True
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: {'use_reentrant': False}
+- `batch_sampler`: no_duplicates
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 0.01
+- `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`: None
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 7701
+- `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
+- `bf16`: True
+- `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`: True
+- `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
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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`: True
+- `gradient_checkpointing_kwargs`: {'use_reentrant': False}
+- `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`: no
+- `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`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | validation loss |
+|:------:|:-----:|:-------------:|:---------------:|
+| 0.0065 | 50    | 15.9471       | -               |
+| 0.0130 | 100   | 15.1591       | -               |
+| 0.0195 | 150   | 14.0548       | -               |
+| 0.0260 | 200   | 12.6234       | -               |
+| 0.0325 | 250   | 11.0546       | -               |
+| 0.0390 | 300   | 9.1498        | -               |
+| 0.0455 | 350   | 7.5429        | -               |
+| 0.0519 | 400   | 6.4324        | -               |
+| 0.0584 | 450   | 5.9025        | -               |
+| 0.0649 | 500   | 5.4181        | -               |
+| 0.0714 | 550   | 5.0231        | -               |
+| 0.0779 | 600   | 4.7729        | -               |
+| 0.0844 | 650   | 4.6554        | -               |
+| 0.0909 | 700   | 4.4429        | -               |
+| 0.0974 | 750   | 4.2589        | -               |
+| 0.1039 | 800   | 3.9485        | -               |
+| 0.1104 | 850   | 3.8368        | -               |
+| 0.1169 | 900   | 3.8319        | -               |
+| 0.1234 | 950   | 3.6684        | -               |
+| 0.1299 | 1000  | 3.5635        | -               |
+| 0.1364 | 1050  | 3.518         | -               |
+| 0.1429 | 1100  | 3.4108        | -               |
+| 0.1494 | 1150  | 3.4189        | -               |
+| 0.1558 | 1200  | 3.3312        | -               |
+| 0.1623 | 1250  | 3.3368        | -               |
+| 0.1688 | 1300  | 3.1654        | -               |
+| 0.1753 | 1350  | 3.1133        | -               |
+| 0.1818 | 1400  | 2.9981        | -               |
+| 0.1883 | 1450  | 2.9279        | -               |
+| 0.1948 | 1500  | 3.0855        | -               |
+| 0.2013 | 1550  | 2.8648        | -               |
+| 0.2078 | 1600  | 2.933         | -               |
+| 0.2143 | 1650  | 2.9586        | -               |
+| 0.2208 | 1700  | 2.8735        | -               |
+| 0.2273 | 1750  | 2.8262        | -               |
+| 0.2338 | 1800  | 2.8138        | -               |
+| 0.2403 | 1850  | 2.7759        | -               |
+| 0.2468 | 1900  | 2.7806        | -               |
+| 0.2532 | 1950  | 2.6917        | -               |
+| 0.2597 | 2000  | 2.781         | -               |
+| 0.2662 | 2050  | 2.6924        | -               |
+| 0.2727 | 2100  | 2.6548        | -               |
+| 0.2792 | 2150  | 2.5892        | -               |
+| 0.2857 | 2200  | 2.4805        | -               |
+| 0.2922 | 2250  | 2.5601        | -               |
+| 0.2987 | 2300  | 2.4803        | -               |
+| 0.3052 | 2350  | 2.4615        | -               |
+| 0.3117 | 2400  | 2.497         | -               |
+| 0.3182 | 2450  | 2.5528        | -               |
+| 0.3247 | 2500  | 2.3179        | -               |
+| 0.3312 | 2550  | 2.4226        | -               |
+| 0.3377 | 2600  | 2.3794        | -               |
+| 0.3442 | 2650  | 2.4812        | -               |
+| 0.3506 | 2700  | 2.3969        | -               |
+| 0.3571 | 2750  | 2.3512        | -               |
+| 0.3636 | 2800  | 2.3615        | -               |
+| 0.3701 | 2850  | 2.3175        | -               |
+| 0.3766 | 2900  | 2.3293        | -               |
+| 0.3831 | 2950  | 2.353         | -               |
+| 0.3896 | 3000  | 2.2407        | -               |
+| 0.3961 | 3050  | 2.3554        | -               |
+| 0.4026 | 3100  | 2.2079        | -               |
+| 0.4091 | 3150  | 2.3148        | -               |
+| 0.4156 | 3200  | 2.3595        | -               |
+| 0.4221 | 3250  | 2.3108        | -               |
+| 0.4286 | 3300  | 2.3236        | -               |
+| 0.4351 | 3350  | 2.1848        | -               |
+| 0.4416 | 3400  | 2.2364        | -               |
+| 0.4481 | 3450  | 2.2219        | -               |
+| 0.4545 | 3500  | 2.1346        | -               |
+| 0.4610 | 3550  | 2.2422        | -               |
+| 0.4675 | 3600  | 2.2226        | -               |
+| 0.4740 | 3650  | 2.1422        | -               |
+| 0.4805 | 3700  | 2.0838        | -               |
+| 0.4870 | 3750  | 2.1076        | -               |
+| 0.4935 | 3800  | 2.1266        | -               |
+| 0.5    | 3850  | 2.1255        | 5.7896          |
+| 0.5065 | 3900  | 2.1261        | -               |
+| 0.5130 | 3950  | 2.1352        | -               |
+| 0.5195 | 4000  | 2.0632        | -               |
+| 0.5260 | 4050  | 2.1564        | -               |
+| 0.5325 | 4100  | 2.0435        | -               |
+| 0.5390 | 4150  | 2.0099        | -               |
+| 0.5455 | 4200  | 2.0602        | -               |
+| 0.5519 | 4250  | 2.0414        | -               |
+| 0.5584 | 4300  | 2.0636        | -               |
+| 0.5649 | 4350  | 2.0575        | -               |
+| 0.5714 | 4400  | 2.0827        | -               |
+| 0.5779 | 4450  | 1.9545        | -               |
+| 0.5844 | 4500  | 2.0735        | -               |
+| 0.5909 | 4550  | 1.9039        | -               |
+| 0.5974 | 4600  | 1.9605        | -               |
+| 0.6039 | 4650  | 2.0449        | -               |
+| 0.6104 | 4700  | 1.9651        | -               |
+| 0.6169 | 4750  | 1.9632        | -               |
+| 0.6234 | 4800  | 2.0401        | -               |
+| 0.6299 | 4850  | 2.0056        | -               |
+| 0.6364 | 4900  | 1.9399        | -               |
+| 0.6429 | 4950  | 1.8899        | -               |
+| 0.6494 | 5000  | 2.0154        | -               |
+| 0.6558 | 5050  | 1.9857        | -               |
+| 0.6623 | 5100  | 1.9239        | -               |
+| 0.6688 | 5150  | 1.9741        | -               |
+| 0.6753 | 5200  | 1.9258        | -               |
+| 0.6818 | 5250  | 1.9545        | -               |
+| 0.6883 | 5300  | 1.9496        | -               |
+| 0.6948 | 5350  | 1.9479        | -               |
+| 0.7013 | 5400  | 1.9068        | -               |
+| 0.7078 | 5450  | 1.847         | -               |
+| 0.7143 | 5500  | 1.8001        | -               |
+| 0.7208 | 5550  | 1.9119        | -               |
+| 0.7273 | 5600  | 1.8347        | -               |
+| 0.7338 | 5650  | 1.944         | -               |
+| 0.7403 | 5700  | 1.8523        | -               |
+| 0.7468 | 5750  | 1.7925        | -               |
+| 0.7532 | 5800  | 1.8803        | -               |
+| 0.7597 | 5850  | 1.8662        | -               |
+| 0.7662 | 5900  | 1.8841        | -               |
+| 0.7727 | 5950  | 1.7907        | -               |
+| 0.7792 | 6000  | 1.7876        | -               |
+| 0.7857 | 6050  | 1.8248        | -               |
+| 0.7922 | 6100  | 1.7473        | -               |
+| 0.7987 | 6150  | 1.8196        | -               |
+| 0.8052 | 6200  | 1.7241        | -               |
+| 0.8117 | 6250  | 1.8118        | -               |
+| 0.8182 | 6300  | 1.8037        | -               |
+| 0.8247 | 6350  | 1.7531        | -               |
+| 0.8312 | 6400  | 1.8088        | -               |
+| 0.8377 | 6450  | 1.7525        | -               |
+| 0.8442 | 6500  | 1.7287        | -               |
+| 0.8506 | 6550  | 1.756         | -               |
+| 0.8571 | 6600  | 1.7888        | -               |
+| 0.8636 | 6650  | 1.709         | -               |
+| 0.8701 | 6700  | 1.722         | -               |
+| 0.8766 | 6750  | 1.6935        | -               |
+| 0.8831 | 6800  | 1.7679        | -               |
+| 0.8896 | 6850  | 1.7438        | -               |
+| 0.8961 | 6900  | 1.6815        | -               |
+| 0.9026 | 6950  | 1.7269        | -               |
+| 0.9091 | 7000  | 1.7285        | -               |
+| 0.9156 | 7050  | 1.711         | -               |
+| 0.9221 | 7100  | 1.6137        | -               |
+| 0.9286 | 7150  | 1.6785        | -               |
+| 0.9351 | 7200  | 1.7142        | -               |
+| 0.9416 | 7250  | 1.7585        | -               |
+| 0.9481 | 7300  | 1.7268        | -               |
+| 0.9545 | 7350  | 1.6739        | -               |
+| 0.9610 | 7400  | 1.6462        | -               |
+| 0.9675 | 7450  | 1.7591        | -               |
+| 0.9740 | 7500  | 1.6058        | -               |
+| 0.9805 | 7550  | 1.6984        | -               |
+| 0.9870 | 7600  | 1.6609        | -               |
+| 0.9935 | 7650  | 1.6488        | -               |
+| 1.0    | 7700  | 1.7626        | 5.6307          |
+| 1.0065 | 7750  | 1.6603        | -               |
+| 1.0130 | 7800  | 1.6979        | -               |
+| 1.0195 | 7850  | 1.6138        | -               |
+| 1.0260 | 7900  | 1.7008        | -               |
+| 1.0325 | 7950  | 1.6281        | -               |
+| 1.0390 | 8000  | 1.6029        | -               |
+| 1.0455 | 8050  | 1.6984        | -               |
+| 1.0519 | 8100  | 1.5865        | -               |
+| 1.0584 | 8150  | 1.6686        | -               |
+| 1.0649 | 8200  | 1.5427        | -               |
+| 1.0714 | 8250  | 1.5441        | -               |
+| 1.0779 | 8300  | 1.5524        | -               |
+| 1.0844 | 8350  | 1.5644        | -               |
+| 1.0909 | 8400  | 1.5964        | -               |
+| 1.0974 | 8450  | 1.5653        | -               |
+| 1.1039 | 8500  | 1.4443        | -               |
+| 1.1104 | 8550  | 1.5609        | -               |
+| 1.1169 | 8600  | 1.5097        | -               |
+| 1.1234 | 8650  | 1.5169        | -               |
+| 1.1299 | 8700  | 1.4992        | -               |
+| 1.1364 | 8750  | 1.5337        | -               |
+| 1.1429 | 8800  | 1.4971        | -               |
+| 1.1494 | 8850  | 1.5218        | -               |
+| 1.1558 | 8900  | 1.526         | -               |
+| 1.1623 | 8950  | 1.5531        | -               |
+| 1.1688 | 9000  | 1.5353        | -               |
+| 1.1753 | 9050  | 1.4596        | -               |
+| 1.1818 | 9100  | 1.434         | -               |
+| 1.1883 | 9150  | 1.4644        | -               |
+| 1.1948 | 9200  | 1.5245        | -               |
+| 1.2013 | 9250  | 1.3869        | -               |
+| 1.2078 | 9300  | 1.4361        | -               |
+| 1.2143 | 9350  | 1.4296        | -               |
+| 1.2208 | 9400  | 1.4338        | -               |
+| 1.2273 | 9450  | 1.383         | -               |
+| 1.2338 | 9500  | 1.4061        | -               |
+| 1.2403 | 9550  | 1.3497        | -               |
+| 1.2468 | 9600  | 1.4223        | -               |
+| 1.2532 | 9650  | 1.3842        | -               |
+| 1.2597 | 9700  | 1.3321        | -               |
+| 1.2662 | 9750  | 1.3396        | -               |
+| 1.2727 | 9800  | 1.3623        | -               |
+| 1.2792 | 9850  | 1.2811        | -               |
+| 1.2857 | 9900  | 1.2448        | -               |
+| 1.2922 | 9950  | 1.297         | -               |
+| 1.2987 | 10000 | 1.2812        | -               |
+| 1.3052 | 10050 | 1.2745        | -               |
+| 1.3117 | 10100 | 1.3114        | -               |
+| 1.3182 | 10150 | 1.3039        | -               |
+| 1.3247 | 10200 | 1.2243        | -               |
+| 1.3312 | 10250 | 1.2523        | -               |
+| 1.3377 | 10300 | 1.2213        | -               |
+| 1.3442 | 10350 | 1.3041        | -               |
+| 1.3506 | 10400 | 1.2164        | -               |
+| 1.3571 | 10450 | 1.2241        | -               |
+| 1.3636 | 10500 | 1.211         | -               |
+| 1.3701 | 10550 | 1.1839        | -               |
+| 1.3766 | 10600 | 1.1748        | -               |
+| 1.3831 | 10650 | 1.1886        | -               |
+| 1.3896 | 10700 | 1.1752        | -               |
+| 1.3961 | 10750 | 1.1968        | -               |
+| 1.4026 | 10800 | 1.1345        | -               |
+| 1.4091 | 10850 | 1.1882        | -               |
+| 1.4156 | 10900 | 1.2257        | -               |
+| 1.4221 | 10950 | 1.2079        | -               |
+| 1.4286 | 11000 | 1.1455        | -               |
+| 1.4351 | 11050 | 1.0684        | -               |
+| 1.4416 | 11100 | 1.0872        | -               |
+| 1.4481 | 11150 | 1.0888        | -               |
+| 1.4545 | 11200 | 1.0119        | -               |
+| 1.4610 | 11250 | 1.0735        | -               |
+| 1.4675 | 11300 | 1.0829        | -               |
+| 1.4740 | 11350 | 1.0539        | -               |
+| 1.4805 | 11400 | 1.0158        | -               |
+| 1.4870 | 11450 | 1.0087        | -               |
+| 1.4935 | 11500 | 1.0371        | -               |
+| 1.5    | 11550 | 1.0171        | 5.6201          |
+| 1.5065 | 11600 | 1.0076        | -               |
+| 1.5130 | 11650 | 1.0063        | -               |
+| 1.5195 | 11700 | 1.0176        | -               |
+| 1.5260 | 11750 | 1.0075        | -               |
+| 1.5325 | 11800 | 0.9626        | -               |
+| 1.5390 | 11850 | 0.92          | -               |
+| 1.5455 | 11900 | 0.9672        | -               |
+| 1.5519 | 11950 | 0.9586        | -               |
+| 1.5584 | 12000 | 0.9745        | -               |
+| 1.5649 | 12050 | 0.9643        | -               |
+| 1.5714 | 12100 | 0.9866        | -               |
+| 1.5779 | 12150 | 0.9212        | -               |
+| 1.5844 | 12200 | 0.9023        | -               |
+| 1.5909 | 12250 | 0.8912        | -               |
+| 1.5974 | 12300 | 0.8844        | -               |
+| 1.6039 | 12350 | 0.9322        | -               |
+| 1.6104 | 12400 | 0.9373        | -               |
+| 1.6169 | 12450 | 0.892         | -               |
+| 1.6234 | 12500 | 0.9218        | -               |
+| 1.6299 | 12550 | 0.8738        | -               |
+| 1.6364 | 12600 | 0.8698        | -               |
+| 1.6429 | 12650 | 0.8722        | -               |
+| 1.6494 | 12700 | 0.8918        | -               |
+| 1.6558 | 12750 | 0.9164        | -               |
+| 1.6623 | 12800 | 0.8194        | -               |
+| 1.6688 | 12850 | 0.8228        | -               |
+| 1.6753 | 12900 | 0.8091        | -               |
+| 1.6818 | 12950 | 0.8162        | -               |
+| 1.6883 | 13000 | 0.8185        | -               |
+| 1.6948 | 13050 | 0.8196        | -               |
+| 1.7013 | 13100 | 0.7892        | -               |
+| 1.7078 | 13150 | 0.7315        | -               |
+| 1.7143 | 13200 | 0.7381        | -               |
+| 1.7208 | 13250 | 0.7465        | -               |
+| 1.7273 | 13300 | 0.7168        | -               |
+| 1.7338 | 13350 | 0.7449        | -               |
+| 1.7403 | 13400 | 0.7447        | -               |
+| 1.7468 | 13450 | 0.6669        | -               |
+| 1.7532 | 13500 | 0.7496        | -               |
+| 1.7597 | 13550 | 0.6783        | -               |
+| 1.7662 | 13600 | 0.7175        | -               |
+| 1.7727 | 13650 | 0.6728        | -               |
+| 1.7792 | 13700 | 0.6659        | -               |
+| 1.7857 | 13750 | 0.6861        | -               |
+| 1.7922 | 13800 | 0.6372        | -               |
+| 1.7987 | 13850 | 0.6679        | -               |
+| 1.8052 | 13900 | 0.6307        | -               |
+| 1.8117 | 13950 | 0.6307        | -               |
+| 1.8182 | 14000 | 0.622         | -               |
+| 1.8247 | 14050 | 0.6362        | -               |
+| 1.8312 | 14100 | 0.62          | -               |
+| 1.8377 | 14150 | 0.617         | -               |
+| 1.8442 | 14200 | 0.6069        | -               |
+| 1.8506 | 14250 | 0.5839        | -               |
+| 1.8571 | 14300 | 0.5974        | -               |
+| 1.8636 | 14350 | 0.5727        | -               |
+| 1.8701 | 14400 | 0.5629        | -               |
+| 1.8766 | 14450 | 0.56          | -               |
+| 1.8831 | 14500 | 0.5891        | -               |
+| 1.8896 | 14550 | 0.5899        | -               |
+| 1.8961 | 14600 | 0.5348        | -               |
+| 1.9026 | 14650 | 0.5753        | -               |
+| 1.9091 | 14700 | 0.5668        | -               |
+| 1.9156 | 14750 | 0.5778        | -               |
+| 1.9221 | 14800 | 0.5127        | -               |
+| 1.9286 | 14850 | 0.5291        | -               |
+| 1.9351 | 14900 | 0.5512        | -               |
+| 1.9416 | 14950 | 0.533         | -               |
+| 1.9481 | 15000 | 0.5455        | -               |
+| 1.9545 | 15050 | 0.511         | -               |
+| 1.9610 | 15100 | 0.4827        | -               |
+| 1.9675 | 15150 | 0.5358        | -               |
+| 1.9740 | 15200 | 0.4733        | -               |
+| 1.9805 | 15250 | 0.4979        | -               |
+| 1.9870 | 15300 | 0.4809        | -               |
+| 1.9935 | 15350 | 0.4783        | -               |
+| 2.0    | 15400 | 0.5226        | 5.8563          |
+| 2.0065 | 15450 | 0.4851        | -               |
+| 2.0130 | 15500 | 0.5236        | -               |
+| 2.0195 | 15550 | 0.487         | -               |
+| 2.0260 | 15600 | 0.4984        | -               |
+| 2.0325 | 15650 | 0.493         | -               |
+| 2.0390 | 15700 | 0.4937        | -               |
+| 2.0455 | 15750 | 0.5143        | -               |
+| 2.0519 | 15800 | 0.4471        | -               |
+| 2.0584 | 15850 | 0.5013        | -               |
+| 2.0649 | 15900 | 0.4686        | -               |
+| 2.0714 | 15950 | 0.4246        | -               |
+| 2.0779 | 16000 | 0.4193        | -               |
+| 2.0844 | 16050 | 0.4438        | -               |
+| 2.0909 | 16100 | 0.4426        | -               |
+| 2.0974 | 16150 | 0.4511        | -               |
+| 2.1039 | 16200 | 0.3932        | -               |
+| 2.1104 | 16250 | 0.4574        | -               |
+| 2.1169 | 16300 | 0.4363        | -               |
+| 2.1234 | 16350 | 0.4181        | -               |
+| 2.1299 | 16400 | 0.4237        | -               |
+| 2.1364 | 16450 | 0.4611        | -               |
+| 2.1429 | 16500 | 0.4072        | -               |
+| 2.1494 | 16550 | 0.4382        | -               |
+| 2.1558 | 16600 | 0.4325        | -               |
+| 2.1623 | 16650 | 0.4315        | -               |
+| 2.1688 | 16700 | 0.4194        | -               |
+| 2.1753 | 16750 | 0.41          | -               |
+| 2.1818 | 16800 | 0.395         | -               |
+| 2.1883 | 16850 | 0.4141        | -               |
+| 2.1948 | 16900 | 0.4234        | -               |
+| 2.2013 | 16950 | 0.3706        | -               |
+| 2.2078 | 17000 | 0.375         | -               |
+| 2.2143 | 17050 | 0.3856        | -               |
+| 2.2208 | 17100 | 0.4104        | -               |
+| 2.2273 | 17150 | 0.3682        | -               |
+| 2.2338 | 17200 | 0.3849        | -               |
+| 2.2403 | 17250 | 0.3607        | -               |
+| 2.2468 | 17300 | 0.3821        | -               |
+| 2.2532 | 17350 | 0.3749        | -               |
+| 2.2597 | 17400 | 0.3548        | -               |
+| 2.2662 | 17450 | 0.3684        | -               |
+| 2.2727 | 17500 | 0.3649        | -               |
+| 2.2792 | 17550 | 0.3547        | -               |
+| 2.2857 | 17600 | 0.3308        | -               |
+| 2.2922 | 17650 | 0.3417        | -               |
+| 2.2987 | 17700 | 0.3414        | -               |
+| 2.3052 | 17750 | 0.3372        | -               |
+| 2.3117 | 17800 | 0.348         | -               |
+| 2.3182 | 17850 | 0.3391        | -               |
+| 2.3247 | 17900 | 0.3172        | -               |
+| 2.3312 | 17950 | 0.3336        | -               |
+| 2.3377 | 18000 | 0.3228        | -               |
+| 2.3442 | 18050 | 0.3643        | -               |
+| 2.3506 | 18100 | 0.3257        | -               |
+| 2.3571 | 18150 | 0.328         | -               |
+| 2.3636 | 18200 | 0.3218        | -               |
+| 2.3701 | 18250 | 0.3208        | -               |
+| 2.3766 | 18300 | 0.3085        | -               |
+| 2.3831 | 18350 | 0.3118        | -               |
+| 2.3896 | 18400 | 0.3165        | -               |
+| 2.3961 | 18450 | 0.3058        | -               |
+| 2.4026 | 18500 | 0.3082        | -               |
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+| 2.4416 | 18800 | 0.2694        | -               |
+| 2.4481 | 18850 | 0.281         | -               |
+| 2.4545 | 18900 | 0.2493        | -               |
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+| 2.4805 | 19100 | 0.2637        | -               |
+| 2.4870 | 19150 | 0.2639        | -               |
+| 2.4935 | 19200 | 0.2666        | -               |
+| 2.5    | 19250 | 0.2672        | 6.0399          |
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+| 2.6623 | 20500 | 0.2017        | -               |
+| 2.6688 | 20550 | 0.2067        | -               |
+| 2.6753 | 20600 | 0.2019        | -               |
+| 2.6818 | 20650 | 0.204         | -               |
+| 2.6883 | 20700 | 0.1931        | -               |
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+| 2.7273 | 21000 | 0.1757        | -               |
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+| 2.8052 | 21600 | 0.1653        | -               |
+| 2.8117 | 21650 | 0.1599        | -               |
+| 2.8182 | 21700 | 0.1625        | -               |
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+| 2.8442 | 21900 | 0.158         | -               |
+| 2.8506 | 21950 | 0.1525        | -               |
+| 2.8571 | 22000 | 0.1458        | -               |
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+| 2.8831 | 22200 | 0.1572        | -               |
+| 2.8896 | 22250 | 0.1451        | -               |
+| 2.8961 | 22300 | 0.1502        | -               |
+| 2.9026 | 22350 | 0.1422        | -               |
+| 2.9091 | 22400 | 0.1495        | -               |
+| 2.9156 | 22450 | 0.1446        | -               |
+| 2.9221 | 22500 | 0.1422        | -               |
+| 2.9286 | 22550 | 0.1416        | -               |
+| 2.9351 | 22600 | 0.1592        | -               |
+| 2.9416 | 22650 | 0.1379        | -               |
+| 2.9481 | 22700 | 0.1412        | -               |
+| 2.9545 | 22750 | 0.1422        | -               |
+| 2.9610 | 22800 | 0.1251        | -               |
+| 2.9675 | 22850 | 0.1481        | -               |
+| 2.9740 | 22900 | 0.1256        | -               |
+| 2.9805 | 22950 | 0.1343        | -               |
+| 2.9870 | 23000 | 0.1304        | -               |
+| 2.9935 | 23050 | 0.1278        | -               |
+| 3.0    | 23100 | 0.1453        | 6.1832          |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.15
+- Sentence Transformers: 5.3.0
+- Transformers: 4.57.6
+- PyTorch: 2.11.0+cu130
+- Accelerate: 1.13.0
+- Datasets: 3.6.0
+- Tokenizers: 0.22.2
+
+## 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},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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+}

checkpoints/checkpoint-23100/config_sentence_transformers.json

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