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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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:784827
+- loss:ContrastiveLoss
+base_model: sentence-transformers/all-mpnet-base-v2
+widget:
+- source_sentence: 'Background: The study addresses the need for effective tools that
+    allow both novice and expert users to analyze the diversity of news coverage about
+    events. It highlights the importance of tailoring the interface to accommodate
+    non-expert users while also considering the insights of journalism-savvy users,
+    indicating a gap in existing systems that cater to varying levels of expertise
+    in news analysis.
+
+    Contribution: Combine ''a coordinated visualization interface tailored for visualization
+    non-expert users'' and '
+  sentences:
+  - a method considering lexical relationships
+  - cross-modality self-supervised learning via masked visual language modeling
+  - cognitive models of chaining
+- source_sentence: 'Background: Existing methods for anomaly detection on dynamic
+    graphs struggle with capturing complex time information in graph structures and
+    generating effective negative samples for unsupervised learning. These challenges
+    highlight the need for improved methodologies that can address the limitations
+    of current approaches in this field.
+
+    Contribution: Combine ''a message-passing framework'' and '
+  sentences:
+  - the grouping task
+  - a forecaster
+  - the optimisation algorithm producing the learnable model
+- source_sentence: 'Background: The accuracy of pixel flows is crucial for achieving
+    high-quality video enhancement, yet most prior works focus on estimating dense
+    flows that are generally less robust and computationally expensive. This highlights
+    a gap in existing methodologies that fail to prioritize accuracy over density,
+    necessitating a more efficient approach to flow estimation for video enhancement
+    tasks.
+
+    Contribution: Combine ''sparse point cloud data'' and '
+  sentences:
+  - a deep CNN
+  - a reinforcement learning view of the dialog generation task
+  - graphical models
+- source_sentence: 'Background: The optimal robot assembly planning problem is challenging
+    due to the necessity of finding the optimal solution amongst an exponentially
+    vast number of possible plans while satisfying a selection of constraints. Traditional
+    heuristic methods are limited as they are specific to a given objective structure
+    or set of problem parameters, indicating a need for more versatile and effective
+    approaches.
+
+    Contribution: ''pos[e] assembly sequencing'' inspired by '
+  sentences:
+  - 3D geometric neural field representation
+  - prompt learning
+  - gestures
+- source_sentence: 'Background: Patients find it difficult to use dexterous prosthetic
+    hands without a suitable control system, highlighting a need for improved grasp
+    performance and ease of operation. Existing methods may not adequately address
+    the challenges faced by users, particularly those with inferior myoelectric signals,
+    in effectively controlling prosthetic devices.
+
+    Contribution: Combine ''myoelectric signal'' and '
+  sentences:
+  - a unified framework for collaborative decoding between large and small language
+    models (Large Language Models and small language models)
+  - image understanding
+  - joint biomedical entity linking and event extraction
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
+- **Maximum Sequence Length:** 384 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/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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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 = [
+    "Background: Patients find it difficult to use dexterous prosthetic hands without a suitable control system, highlighting a need for improved grasp performance and ease of operation. Existing methods may not adequately address the challenges faced by users, particularly those with inferior myoelectric signals, in effectively controlling prosthetic devices.\nContribution: Combine 'myoelectric signal' and ",
+    'a unified framework for collaborative decoding between large and small language models (Large Language Models and small language models)',
+    'joint biomedical entity linking and event extraction',
+]
+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.*
+-->
+
+<!--
+## 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: 784,827 training samples
+* Columns: <code>query</code>, <code>answer</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | answer                                                                           | label                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------|
+  | type    | string                                                                             | string                                                                           | int                                            |
+  | details | <ul><li>min: 60 tokens</li><li>mean: 77.86 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.82 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>0: ~96.70%</li><li>1: ~3.30%</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | answer                                                        | label          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------|
+  | <code>Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.<br>Contribution: Combine 'a Hidden Markov Model' and </code> | <code>a multilayer perceptron</code>                          | <code>1</code> |
+  | <code>Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.<br>Contribution: Combine 'a Hidden Markov Model' and </code> | <code>synthetic occlusion augmentation during training</code> | <code>0</code> |
+  | <code>Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.<br>Contribution: Combine 'a Hidden Markov Model' and </code> | <code>robustness of deep learning methods</code>              | <code>0</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.5,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 1.9218937402834593e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.08278167292320517
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `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`: 1.9218937402834593e-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`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.08278167292320517
+- `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`: 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
+- `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
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0082 | 100   | 0.0104        |
+| 0.0163 | 200   | 0.0068        |
+| 0.0245 | 300   | 0.005         |
+| 0.0326 | 400   | 0.0041        |
+| 0.0408 | 500   | 0.0054        |
+| 0.0489 | 600   | 0.004         |
+| 0.0571 | 700   | 0.0037        |
+| 0.0652 | 800   | 0.0037        |
+| 0.0734 | 900   | 0.0049        |
+| 0.0815 | 1000  | 0.0038        |
+| 0.0897 | 1100  | 0.004         |
+| 0.0979 | 1200  | 0.0037        |
+| 0.1060 | 1300  | 0.004         |
+| 0.1142 | 1400  | 0.0049        |
+| 0.1223 | 1500  | 0.0038        |
+| 0.1305 | 1600  | 0.0036        |
+| 0.1386 | 1700  | 0.0037        |
+| 0.1468 | 1800  | 0.0045        |
+| 0.1549 | 1900  | 0.0038        |
+| 0.1631 | 2000  | 0.0034        |
+| 0.1712 | 2100  | 0.0034        |
+| 0.1794 | 2200  | 0.0035        |
+| 0.1876 | 2300  | 0.0045        |
+| 0.1957 | 2400  | 0.0036        |
+| 0.2039 | 2500  | 0.0036        |
+| 0.2120 | 2600  | 0.0033        |
+| 0.2202 | 2700  | 0.004         |
+| 0.2283 | 2800  | 0.0036        |
+| 0.2365 | 2900  | 0.0033        |
+| 0.2446 | 3000  | 0.0033        |
+| 0.2528 | 3100  | 0.0037        |
+| 0.2609 | 3200  | 0.0038        |
+| 0.2691 | 3300  | 0.0033        |
+| 0.2773 | 3400  | 0.0034        |
+| 0.2854 | 3500  | 0.0033        |
+| 0.2936 | 3600  | 0.0041        |
+| 0.3017 | 3700  | 0.0033        |
+| 0.3099 | 3800  | 0.0033        |
+| 0.3180 | 3900  | 0.0032        |
+| 0.3262 | 4000  | 0.004         |
+| 0.3343 | 4100  | 0.0035        |
+| 0.3425 | 4200  | 0.0031        |
+| 0.3506 | 4300  | 0.0033        |
+| 0.3588 | 4400  | 0.0033        |
+| 0.3670 | 4500  | 0.0039        |
+| 0.3751 | 4600  | 0.0032        |
+| 0.3833 | 4700  | 0.0034        |
+| 0.3914 | 4800  | 0.0031        |
+| 0.3996 | 4900  | 0.004         |
+| 0.4077 | 5000  | 0.0032        |
+| 0.4159 | 5100  | 0.0031        |
+| 0.4240 | 5200  | 0.0031        |
+| 0.4322 | 5300  | 0.0032        |
+| 0.4403 | 5400  | 0.0039        |
+| 0.4485 | 5500  | 0.0031        |
+| 0.4567 | 5600  | 0.003         |
+| 0.4648 | 5700  | 0.0032        |
+| 0.4730 | 5800  | 0.0038        |
+| 0.4811 | 5900  | 0.0033        |
+| 0.4893 | 6000  | 0.0031        |
+| 0.4974 | 6100  | 0.0032        |
+| 0.5056 | 6200  | 0.0033        |
+| 0.5137 | 6300  | 0.0033        |
+| 0.5219 | 6400  | 0.0032        |
+| 0.5300 | 6500  | 0.0031        |
+| 0.5382 | 6600  | 0.0032        |
+| 0.5464 | 6700  | 0.0038        |
+| 0.5545 | 6800  | 0.003         |
+| 0.5627 | 6900  | 0.003         |
+| 0.5708 | 7000  | 0.0029        |
+| 0.5790 | 7100  | 0.0038        |
+| 0.5871 | 7200  | 0.0032        |
+| 0.5953 | 7300  | 0.0031        |
+| 0.6034 | 7400  | 0.003         |
+| 0.6116 | 7500  | 0.003         |
+| 0.6198 | 7600  | 0.0039        |
+| 0.6279 | 7700  | 0.0031        |
+| 0.6361 | 7800  | 0.0031        |
+| 0.6442 | 7900  | 0.0031        |
+| 0.6524 | 8000  | 0.0039        |
+| 0.6605 | 8100  | 0.003         |
+| 0.6687 | 8200  | 0.003         |
+| 0.6768 | 8300  | 0.003         |
+| 0.6850 | 8400  | 0.0028        |
+| 0.6931 | 8500  | 0.0035        |
+| 0.7013 | 8600  | 0.0031        |
+| 0.7095 | 8700  | 0.003         |
+| 0.7176 | 8800  | 0.0026        |
+| 0.7258 | 8900  | 0.0034        |
+| 0.7339 | 9000  | 0.0033        |
+| 0.7421 | 9100  | 0.003         |
+| 0.7502 | 9200  | 0.0027        |
+| 0.7584 | 9300  | 0.0029        |
+| 0.7665 | 9400  | 0.0034        |
+| 0.7747 | 9500  | 0.0029        |
+| 0.7828 | 9600  | 0.0028        |
+| 0.7910 | 9700  | 0.0027        |
+| 0.7992 | 9800  | 0.0033        |
+| 0.8073 | 9900  | 0.0031        |
+| 0.8155 | 10000 | 0.0029        |
+| 0.8236 | 10100 | 0.0028        |
+| 0.8318 | 10200 | 0.0031        |
+| 0.8399 | 10300 | 0.0031        |
+| 0.8481 | 10400 | 0.003         |
+| 0.8562 | 10500 | 0.0029        |
+| 0.8644 | 10600 | 0.0028        |
+| 0.8725 | 10700 | 0.0033        |
+| 0.8807 | 10800 | 0.003         |
+| 0.8889 | 10900 | 0.0029        |
+| 0.8970 | 11000 | 0.0027        |
+| 0.9052 | 11100 | 0.0033        |
+| 0.9133 | 11200 | 0.0029        |
+| 0.9215 | 11300 | 0.0029        |
+| 0.9296 | 11400 | 0.0029        |
+| 0.9378 | 11500 | 0.003         |
+| 0.9459 | 11600 | 0.0034        |
+| 0.9541 | 11700 | 0.0031        |
+| 0.9622 | 11800 | 0.0027        |
+| 0.9704 | 11900 | 0.0029        |
+| 0.9786 | 12000 | 0.0034        |
+| 0.9867 | 12100 | 0.0032        |
+| 0.9949 | 12200 | 0.003         |
+| 1.0030 | 12300 | 0.0032        |
+| 1.0112 | 12400 | 0.0028        |
+| 1.0193 | 12500 | 0.003         |
+| 1.0275 | 12600 | 0.0027        |
+| 1.0356 | 12700 | 0.0034        |
+| 1.0438 | 12800 | 0.0029        |
+| 1.0519 | 12900 | 0.0025        |
+| 1.0601 | 13000 | 0.0028        |
+| 1.0683 | 13100 | 0.0026        |
+| 1.0764 | 13200 | 0.0035        |
+| 1.0846 | 13300 | 0.0026        |
+| 1.0927 | 13400 | 0.0028        |
+| 1.1009 | 13500 | 0.0026        |
+| 1.1090 | 13600 | 0.0034        |
+| 1.1172 | 13700 | 0.0028        |
+| 1.1253 | 13800 | 0.0027        |
+| 1.1335 | 13900 | 0.0026        |
+| 1.1416 | 14000 | 0.0031        |
+| 1.1498 | 14100 | 0.0025        |
+| 1.1580 | 14200 | 0.0025        |
+| 1.1661 | 14300 | 0.0025        |
+| 1.1743 | 14400 | 0.0024        |
+| 1.1824 | 14500 | 0.0031        |
+| 1.1906 | 14600 | 0.0025        |
+| 1.1987 | 14700 | 0.0024        |
+| 1.2069 | 14800 | 0.0025        |
+| 1.2150 | 14900 | 0.0029        |
+| 1.2232 | 15000 | 0.0025        |
+| 1.2313 | 15100 | 0.0025        |
+| 1.2395 | 15200 | 0.0023        |
+| 1.2477 | 15300 | 0.0024        |
+| 1.2558 | 15400 | 0.0029        |
+| 1.2640 | 15500 | 0.0023        |
+| 1.2721 | 15600 | 0.0023        |
+| 1.2803 | 15700 | 0.0023        |
+| 1.2884 | 15800 | 0.0032        |
+| 1.2966 | 15900 | 0.0023        |
+| 1.3047 | 16000 | 0.0023        |
+| 1.3129 | 16100 | 0.0024        |
+| 1.3210 | 16200 | 0.0025        |
+| 1.3292 | 16300 | 0.0028        |
+| 1.3374 | 16400 | 0.0023        |
+| 1.3455 | 16500 | 0.0021        |
+| 1.3537 | 16600 | 0.0023        |
+| 1.3618 | 16700 | 0.0029        |
+| 1.3700 | 16800 | 0.0023        |
+| 1.3781 | 16900 | 0.0023        |
+| 1.3863 | 17000 | 0.0025        |
+| 1.3944 | 17100 | 0.0028        |
+| 1.4026 | 17200 | 0.0023        |
+| 1.4107 | 17300 | 0.0023        |
+| 1.4189 | 17400 | 0.0023        |
+| 1.4271 | 17500 | 0.0023        |
+| 1.4352 | 17600 | 0.0029        |
+| 1.4434 | 17700 | 0.0022        |
+| 1.4515 | 17800 | 0.0022        |
+| 1.4597 | 17900 | 0.0023        |
+| 1.4678 | 18000 | 0.0026        |
+| 1.4760 | 18100 | 0.0024        |
+| 1.4841 | 18200 | 0.0023        |
+| 1.4923 | 18300 | 0.0024        |
+| 1.5004 | 18400 | 0.0024        |
+| 1.5086 | 18500 | 0.0026        |
+| 1.5168 | 18600 | 0.0022        |
+| 1.5249 | 18700 | 0.0023        |
+| 1.5331 | 18800 | 0.0023        |
+| 1.5412 | 18900 | 0.003         |
+| 1.5494 | 19000 | 0.002         |
+| 1.5575 | 19100 | 0.0022        |
+| 1.5657 | 19200 | 0.0023        |
+| 1.5738 | 19300 | 0.0023        |
+| 1.5820 | 19400 | 0.0028        |
+| 1.5901 | 19500 | 0.0022        |
+| 1.5983 | 19600 | 0.0023        |
+| 1.6065 | 19700 | 0.0022        |
+| 1.6146 | 19800 | 0.0028        |
+| 1.6228 | 19900 | 0.0022        |
+| 1.6309 | 20000 | 0.0023        |
+| 1.6391 | 20100 | 0.0025        |
+| 1.6472 | 20200 | 0.0028        |
+| 1.6554 | 20300 | 0.0023        |
+| 1.6635 | 20400 | 0.0021        |
+| 1.6717 | 20500 | 0.0022        |
+| 1.6798 | 20600 | 0.0022        |
+| 1.6880 | 20700 | 0.0025        |
+| 1.6962 | 20800 | 0.0024        |
+| 1.7043 | 20900 | 0.0023        |
+| 1.7125 | 21000 | 0.0021        |
+| 1.7206 | 21100 | 0.0024        |
+| 1.7288 | 21200 | 0.0024        |
+| 1.7369 | 21300 | 0.0023        |
+| 1.7451 | 21400 | 0.0022        |
+| 1.7532 | 21500 | 0.0021        |
+| 1.7614 | 21600 | 0.0025        |
+| 1.7696 | 21700 | 0.0023        |
+| 1.7777 | 21800 | 0.002         |
+| 1.7859 | 21900 | 0.0022        |
+| 1.7940 | 22000 | 0.0025        |
+| 1.8022 | 22100 | 0.0022        |
+| 1.8103 | 22200 | 0.0023        |
+| 1.8185 | 22300 | 0.0022        |
+| 1.8266 | 22400 | 0.0021        |
+| 1.8348 | 22500 | 0.0025        |
+| 1.8429 | 22600 | 0.0025        |
+| 1.8511 | 22700 | 0.0022        |
+| 1.8593 | 22800 | 0.0023        |
+| 1.8674 | 22900 | 0.0026        |
+| 1.8756 | 23000 | 0.0022        |
+| 1.8837 | 23100 | 0.0022        |
+| 1.8919 | 23200 | 0.0022        |
+| 1.9000 | 23300 | 0.0024        |
+| 1.9082 | 23400 | 0.0022        |
+| 1.9163 | 23500 | 0.0022        |
+| 1.9245 | 23600 | 0.0023        |
+| 1.9326 | 23700 | 0.0023        |
+| 1.9408 | 23800 | 0.0027        |
+| 1.9490 | 23900 | 0.0023        |
+| 1.9571 | 24000 | 0.0023        |
+| 1.9653 | 24100 | 0.0022        |
+| 1.9734 | 24200 | 0.0027        |
+| 1.9816 | 24300 | 0.0025        |
+| 1.9897 | 24400 | 0.0023        |
+| 1.9979 | 24500 | 0.0025        |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.2
+- Sentence Transformers: 3.3.1
+- Transformers: 4.49.0
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.0.1
+- Datasets: 3.1.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
+    title={Dimensionality Reduction by Learning an Invariant Mapping},
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## 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

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdeeeff8db3c0d7dbbbf8f3886638937aa8364363c4e1641c648739c25883412
+size 437967672

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,73 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}