Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:72
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: What do the packets contain that is essential for their travel?
+  sentences:
+  - 'Packet switching breaks data into small packets, each containing a destination
+    address. These packets travel independently across the network, taking different
+    paths if necessary, and reassemble at the destination. This method proved to be
+    more efficient and resilient, making it the backbone of modern internet communication.
+
+
+    The Birth of ARPANET'
+  - Early Concepts of Networking
+  - . Researchers such as Paul Baran at RAND Corporation and Donald Davies at the
+    National Physical Laboratory in the UK independently developed the concept of
+    packet switching.
+- source_sentence: Which laboratory was Donald Davies associated with when he developed
+    packet switching?
+  sentences:
+  - . Understanding the beginning of the internet requires an exploration of the early
+    concepts of networking, the establishment of ARPANET, and the development of key
+    protocols that laid the foundation for the modern internet.
+  - . Researchers such as Paul Baran at RAND Corporation and Donald Davies at the
+    National Physical Laboratory in the UK independently developed the concept of
+    packet switching.
+  - 'The Beginning of the Internet: A Journey Through Innovation
+
+
+    Introduction'
+- source_sentence: What role did commercial networking play in relation to the internet?
+  sentences:
+  - Beyond ARPANET, various institutions contributed to the internet’s expansion.
+    The emergence of local area networks (LANs), the Domain Name System (DNS), and
+    the rise of commercial networking played significant roles in shaping the internet.
+  - Beyond ARPANET, various institutions contributed to the internet’s expansion.
+    The emergence of local area networks (LANs), the Domain Name System (DNS), and
+    the rise of commercial networking played significant roles in shaping the internet.
+  - The TCP/IP Protocol SuiteIn 1973, Vinton Cerf and Robert Kahn developed the Transmission
+    Control Protocol (TCP) and later, the Internet Protocol (IP), collectively known
+    as TCP/IP. This protocol suite allowed networks of different architectures to
+    communicate, forming the foundation of the modern internet.
+- source_sentence: What type of communication system did the United States government
+    seek?
+  sentences:
+  - Packet Switching and Its RoleTraditional telephone networks relied on circuit-switching,
+    which established a direct connection between two parties. However, circuit-switching
+    was inefficient for data communication, as it required dedicated lines for each
+    connection
+  - 'Packet switching breaks data into small packets, each containing a destination
+    address. These packets travel independently across the network, taking different
+    paths if necessary, and reassemble at the destination. This method proved to be
+    more efficient and resilient, making it the backbone of modern internet communication.
+
+
+    The Birth of ARPANET'
+  - The idea of interconnected networks dates back to the 1950s and 1960s, during
+    the height of the Cold War. The United States government, concerned with maintaining
+    communication in the event of a nuclear attack, sought a decentralized communication
+    system that could withstand disruptions. This vision led to research in packet-switching
+    technology and distributed networks.
+- source_sentence: What are the origins of the internet said to be rooted in?
+  sentences:
+  - The internet is one of the most transformative technological advancements in human
+    history, shaping the way we communicate, work, and interact with the world. However,
+    its origins are rooted in decades of research, experimentation, and collaboration
+    among scientists, engineers, and visionaries
+  - 'The Beginning of the Internet: A Journey Through Innovation
+
+
+    Introduction'
+  - 'The first successful ARPANET message was sent on October 29, 1969, from UCLA
+    to SRI. The intended message was “LOGIN,” but the system crashed after transmitting
+    only “LO.” This marked the first instance of networked digital communication,
+    paving the way for the modern internet.
+
+
+    Expansion and Development of Protocols'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 1.0
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 1.0
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 1.0
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.2
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.0
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 1.0
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 1.0
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 1.0
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 1.0
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 1.0
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 1.0
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 1.0
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.2
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.0
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 1.0
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 1.0
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 1.0
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 1.0
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 1.0
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 1.0
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 1.0
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.2
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.0
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 1.0
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 1.0
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 1.0
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 1.0
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.875
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.875
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.875
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.875
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.29166666666666663
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17500000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.875
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.875
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.875
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9195258983885027
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8958333333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8958333333333334
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.625
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.625
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.875
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.625
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.20833333333333331
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17500000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.625
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.625
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.875
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7662391001971381
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6958333333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6958333333333333
+      name: Cosine Map@100
+---
+
+# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: 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("onecd2000/modernbert-embed-test")
+# Run inference
+sentences = [
+    'What are the origins of the internet said to be rooted in?',
+    'The internet is one of the most transformative technological advancements in human history, shaping the way we communicate, work, and interact with the world. However, its origins are rooted in decades of research, experimentation, and collaboration among scientists, engineers, and visionaries',
+    'The first successful ARPANET message was sent on October 29, 1969, from UCLA to SRI. The intended message was “LOGIN,” but the system crashed after transmitting only “LO.” This marked the first instance of networked digital communication, paving the way for the modern internet.\n\nExpansion and Development of Protocols',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768 | dim_512 | dim_256 | dim_128    | dim_64     |
+|:--------------------|:--------|:--------|:--------|:-----------|:-----------|
+| cosine_accuracy@1   | 1.0     | 1.0     | 1.0     | 0.875      | 0.625      |
+| cosine_accuracy@3   | 1.0     | 1.0     | 1.0     | 0.875      | 0.625      |
+| cosine_accuracy@5   | 1.0     | 1.0     | 1.0     | 0.875      | 0.875      |
+| cosine_accuracy@10  | 1.0     | 1.0     | 1.0     | 1.0        | 1.0        |
+| cosine_precision@1  | 1.0     | 1.0     | 1.0     | 0.875      | 0.625      |
+| cosine_precision@3  | 0.3333  | 0.3333  | 0.3333  | 0.2917     | 0.2083     |
+| cosine_precision@5  | 0.2     | 0.2     | 0.2     | 0.175      | 0.175      |
+| cosine_precision@10 | 0.1     | 0.1     | 0.1     | 0.1        | 0.1        |
+| cosine_recall@1     | 1.0     | 1.0     | 1.0     | 0.875      | 0.625      |
+| cosine_recall@3     | 1.0     | 1.0     | 1.0     | 0.875      | 0.625      |
+| cosine_recall@5     | 1.0     | 1.0     | 1.0     | 0.875      | 0.875      |
+| cosine_recall@10    | 1.0     | 1.0     | 1.0     | 1.0        | 1.0        |
+| **cosine_ndcg@10**  | **1.0** | **1.0** | **1.0** | **0.9195** | **0.7662** |
+| cosine_mrr@10       | 1.0     | 1.0     | 1.0     | 0.8958     | 0.6958     |
+| cosine_map@100      | 1.0     | 1.0     | 1.0     | 0.8958     | 0.6958     |
+
+<!--
+## 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: 72 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 72 samples:
+  |         | anchor                                                                           | positive                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 14.6 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 57.92 tokens</li><li>max: 89 tokens</li></ul> |
+* Samples:
+  | anchor                                                                          | positive                                                                                                                                                                                                                                                                                                                                                                                         |
+  |:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What does the text focus on regarding the Internet?</code>                | <code>The Beginning of the Internet: A Journey Through Innovation<br><br>Introduction</code>                                                                                                                                                                                                                                                                                                     |
+  | <code>What was the primary purpose of the first web browser?</code>             | <code>The First Web Browser – A tool for accessing and navigating websites.<br><br>The World Wide Web revolutionized internet usage, making it more accessible and appealing to the general public. By the mid-1990s, web browsers like Netscape Navigator and Microsoft Internet Explorer fueled rapid internet adoption, leading to the digital age we live in today.<br><br>Conclusion</code> |
+  | <code>What system contributed to the organization of internet addresses?</code> | <code>Beyond ARPANET, various institutions contributed to the internet’s expansion. The emergence of local area networks (LANs), the Domain Name System (DNS), and the rise of commercial networking played significant roles in shaping the internet.</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,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `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`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `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`: True
+- `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_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
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step  | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 1.0     | 1     | 0.8827                 | 0.8827                 | 0.8827                 | 0.7413                 | 0.6383                |
+| 2.0     | 2     | 0.9288                 | 0.9539                 | 1.0                    | 0.8289                 | 0.7611                |
+| 3.0     | 3     | 1.0                    | 1.0                    | 1.0                    | 0.9167                 | 0.7634                |
+| **4.0** | **4** | **1.0**                | **1.0**                | **1.0**                | **0.9195**             | **0.7662**            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.9
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.0
+- PyTorch: 2.6.0+cu126
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### 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{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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

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+{
+  "architectures": [
+    "ModernBertModel"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
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+  "classifier_pooling": "mean",
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+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
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+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
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+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.0",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.50.0",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 596070136

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "type": "sentence_transformers.models.Pooling"
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+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 1024,
+  "do_lower_case": false
+}

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