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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "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:499184
+- loss:MultipleNegativesRankingLoss
+base_model: answerdotai/ModernBERT-large
+widget:
+- source_sentence: how long will rotisserie chicken keep in refridgerator
+  sentences:
+  - '1 Meats with gravy or sauces: 1 to 2 days refrigerator or 6 months (freezer).
+    2  Rotisserie chicken: 3 to 4 days (refrigerator) or 2 to 3 months (freezer).
+    3  Opened package of hot dogs: 1 week (refrigerator) or 1 to 2 months (freezer).4  Opened
+    package of deli meat: 3 to 4 days (refrigerator) or 1 to 2 months (freezer). Rotisserie
+    chicken: 3 to 4 days (refrigerator) or 2 to 3 months (freezer). 2  Opened package
+    of hot dogs: 1 week (refrigerator) or 1 to 2 months (freezer). 3  Opened package
+    of deli meat: 3 to 4 days (refrigerator) or 1 to 2 months (freezer).'
+  - Can Spinach Cause Constipation? Those who have problems with constipation will
+    want to stay away from certain foods including spinach. Because spinach has so
+    much fiber in it, it can cause constipation in some people, especially those who
+    are already prone to it. Other foods which you will want to avoid if you problems
+    with constipation include apples, peaches, raw carrots, zucchini, kidney beans,
+    lima beans, and whole-grain cereal.
+  - Brush the chickens with oil and season the outside and cavities with salt and
+    pepper. Skewer the chickens onto the rotisserie rod and grill, on the rotisserie,
+    for 30 to 35 minutes, or until the chicken is golden brown and just cooked through.
+    Remove from grill and let rest for 10 minutes before serving.
+- source_sentence: empyema causes
+  sentences:
+  - "Causes of an Empyema. Most cases of an empyema are related to bacterial pneumonia\
+    \ (infection of the lung). Pneumonia tends to cause a pleural effusion â\x80\x93\
+    \ para-pneumonic effusion. This can be uncomplicated (containing exudate), complicated\
+    \ (exudate with high concentrations of neurophils) or empyema thoracis (pus in\
+    \ the pleural space)."
+  - empyema - a collection of pus in a body cavity (especially in the lung cavity)
+    inflammatory disease - a disease characterized by inflammation. purulent pleurisy
+    - a collection of pus in the lung cavity. Translations.
+  - Laminar Flow. The resistance to flow in a liquid can be characterized in terms
+    of the viscosity of the fluid if the flow is smooth. In the case of a moving plate
+    in a liquid, it is found that there is a layer or lamina which moves with the
+    plate, and a layer which is essentially stationary if it is next to a stationary
+    plate.
+- source_sentence: why is coal found in layers
+  sentences:
+  - Email the author | Follow on Twitter. on March 06, 2015 at 6:03 PM, updated March
+    06, 2015 at 6:35 PM. Comments. CLEVELAND, Ohio -- The first day of spring 2015
+    will be on March 20, with winter officially ending at 6:45 p.m. that day. Summer
+    2015 will begin on June 21, fall on Sept. 23 and winter on Dec. 21.
+  - EXPERT ANSWER. Coal if formed when dead animals and plants got buried inside the
+    layer of Earth. The layers increase form time to time and more dead plants and
+    animals get buried in the layers.Therefore, coal is found in layers.For example,
+    let us consider the layers of sandwich, on the first bread we apply the toppings
+    and cover it another slice. Then some more topping is added to second slice and
+    is covered by third slide.XPERT ANSWER. Coal if formed when dead animals and plants
+    got buried inside the layer of Earth. The layers increase form time to time and
+    more dead plants and animals get buried in the layers.
+  - Why is Coal not classified as a Mineral? July 8, 2011, shiela, Leave a comment.
+    Why is Coal not classified as a Mineral? Coal is not a mineral because it does
+    not qualify to be one. A mineral is made of rocks. It is non-living and made up
+    of atoms of elements. Coals on the other hand are carbon-based and came from fossilized
+    plants. By just looking into the origin of coals, these are not qualified to be
+    minerals because they come from organic material and it has no definite chemical
+    composition. Minerals are not formed from living things such as plants or animals.
+    They are building blocks of rocks and are formed thousands of years ago. Coals
+    on the other hand came from dead plants and animals. The coals are formed when
+    these living creatures will decay. Again, it takes thousands of years to form
+    a coal.
+- source_sentence: where is the ford edge built
+  sentences:
+  - 'Amongst fruit-bearing cherry trees, there are two main types: Prunus avium (sweet
+    cherries), which are the kind sold in produce sections for eating, and Prunus
+    cerasus (sour cherries), which are the kind used in cooking and baking.mongst
+    fruit-bearing cherry trees, there are two main types: Prunus avium (sweet cherries),
+    which are the kind sold in produce sections for eating, and Prunus cerasus (sour
+    cherries), which are the kind used in cooking and baking.'
+  - Ford is recalling 204,448 Edge and Lincoln MKX crossovers in North America for
+    fuel-tank brackets that can rust and cause gas to leak, the automaker said.
+  - Ford Edge to be built at new $760 million plant in China. DETROIT, MI - Ford Motor
+    Co. announced Tuesday it has opened its sixth assembly plant in China, with a
+    $760 million investment for the Changan Ford Hangzhou Plant.
+- source_sentence: what is a tensilon universal testing instrument
+  sentences:
+  - Universal Material Testing Instrument. The TENSILON RTF is our newest universal
+    testing machine offering innovative measuring possibilities, based on A&D's newly-developed
+    and extensive technological knowledge.The RTF Series is a world-class Class 0.5
+    testing machine.Having improved the overall design and structure of the machine,
+    we achieved a very strong load frame stiffness enabling super-high accuracy in
+    measurement.he RTF Series is a world-class Class 0.5 testing machine. Having improved
+    the overall design and structure of the machine, we achieved a very strong load
+    frame stiffness enabling super-high accuracy in measurement.
+  - The term ectopic pregnancy frequently refers to a pregnancy that has occurred
+    in one of the fallopian tubes, instead of the uterus. This is the case about 95
+    percent of the time, but ectopic pregnancies can also be abdominal, ovarian, cornual,
+    or cervical.
+  - The McDonald Patent Universal String Tension Calculator (MPUSTC) is a handy calculator
+    to figure string tensions in steel-string instruments. If you plug in your scale
+    length, string gauges and tuning, it will give you a readout of the tension on
+    each of the strings. This is useful when you're trying to fine-tune a set of custom
+    gauges, or when you're working out how far you can push a drop tuning before it
+    becomes unmanageable.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on answerdotai/ModernBERT-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large). It maps sentences & paragraphs to a 1024-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:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+)
+```
+
+## 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 = [
+    'what is a tensilon universal testing instrument',
+    "Universal Material Testing Instrument. The TENSILON RTF is our newest universal testing machine offering innovative measuring possibilities, based on A&D's newly-developed and extensive technological knowledge.The RTF Series is a world-class Class 0.5 testing machine.Having improved the overall design and structure of the machine, we achieved a very strong load frame stiffness enabling super-high accuracy in measurement.he RTF Series is a world-class Class 0.5 testing machine. Having improved the overall design and structure of the machine, we achieved a very strong load frame stiffness enabling super-high accuracy in measurement.",
+    "The McDonald Patent Universal String Tension Calculator (MPUSTC) is a handy calculator to figure string tensions in steel-string instruments. If you plug in your scale length, string gauges and tuning, it will give you a readout of the tension on each of the strings. This is useful when you're trying to fine-tune a set of custom gauges, or when you're working out how far you can push a drop tuning before it becomes unmanageable.",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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: 499,184 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: 9.07 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 80.89 tokens</li><li>max: 254 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 79.05 tokens</li><li>max: 226 tokens</li></ul> |
+* Samples:
+  | sentence_0                                   | sentence_1                                                                                                                                                                                                                                                                                                                                                                          | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:---------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what is a dependent person</code>      | <code>1. depending on a person or thing for aid, support, life, etc. 2. (postpositive; foll by on or upon) influenced or conditioned (by); contingent (on) 3. subordinate; subject: a dependent prince. 4. obsolete hanging down.</code>                                                                                                                                            | <code>Dependent personality disorder (DPD) is one of the most frequently diagnosed personality disorders. It occurs equally in men and women, usually becoming apparent in young adulthood or later as important adult relationships form. People with DPD become emotionally dependent on other people and spend great effort trying to please others. People with DPD tend to display needy, passive, and clinging behavior, and have a fear of separation. Other common characteristics of this personality disorder include:</code>       |
+  | <code>what is the hat trick in hockey</code> | <code>Definition of hat trick. 1  1 : the retiring of three batsmen with three consecutive balls by a bowler in cricket. 2  2 : the scoring of three goals in one game (as of hockey or soccer) by a single player. 3  3 : a series of three victories, successes, or related accomplishments scored a hat trick when her three best steers corralled top honors — People.</code> | <code>Hat trick was first recorded in print in the 1870s, but has since been widened to apply to any sport in which the person competing carries off some feat three times in quick succession, such as scoring three goals in one game of soccer.</code>                                                                                                                                                                                                                                                                                     |
+  | <code>what is an egalitarian</code>          | <code>An egalitarian is defined as a person who believes all people were created equal and should be treated equal. An example of an egalitarian is a person who fights for civil rights, like Martin Luther King Jr.</code>                                                                                                                                                        | <code>About Egalitarian Companies. In the tradition hierarchical corporate structure, each employee operates under a specific job description. Each employee also reports to a superior who monitors his progress and issues instructions. Egalitarian-style companies eliminate most of this structure. Employees in an egalitarian company have general job descriptions, rather than specific ones. Instead of reporting to a superior, all employees in an egalitarian company work collaboratively on tasks and behave as equals.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 10
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss |
+|:------:|:------:|:-------------:|
+| 0.0321 | 500    | 1.1178        |
+| 0.0641 | 1000   | 0.293         |
+| 0.0962 | 1500   | 0.2542        |
+| 0.1282 | 2000   | 0.2357        |
+| 0.1603 | 2500   | 0.2187        |
+| 0.1923 | 3000   | 0.2107        |
+| 0.2244 | 3500   | 0.1959        |
+| 0.2564 | 4000   | 0.2049        |
+| 0.2885 | 4500   | 0.1945        |
+| 0.3205 | 5000   | 0.1848        |
+| 0.3526 | 5500   | 0.1846        |
+| 0.3846 | 6000   | 0.1736        |
+| 0.4167 | 6500   | 0.1795        |
+| 0.4487 | 7000   | 0.1767        |
+| 0.4808 | 7500   | 0.1727        |
+| 0.5128 | 8000   | 0.1688        |
+| 0.5449 | 8500   | 0.1708        |
+| 0.5769 | 9000   | 0.1663        |
+| 0.6090 | 9500   | 0.1654        |
+| 0.6410 | 10000  | 0.1637        |
+| 0.6731 | 10500  | 0.1651        |
+| 0.7051 | 11000  | 0.1625        |
+| 0.7372 | 11500  | 0.1584        |
+| 0.7692 | 12000  | 0.1607        |
+| 0.8013 | 12500  | 0.156         |
+| 0.8333 | 13000  | 0.1548        |
+| 0.8654 | 13500  | 0.1484        |
+| 0.8974 | 14000  | 0.1527        |
+| 0.9295 | 14500  | 0.1555        |
+| 0.9615 | 15000  | 0.1528        |
+| 0.9936 | 15500  | 0.1533        |
+| 1.0256 | 16000  | 0.0827        |
+| 1.0577 | 16500  | 0.0597        |
+| 1.0897 | 17000  | 0.0599        |
+| 1.1218 | 17500  | 0.0592        |
+| 1.1538 | 18000  | 0.0592        |
+| 1.1859 | 18500  | 0.0584        |
+| 1.2179 | 19000  | 0.0615        |
+| 1.25   | 19500  | 0.0589        |
+| 1.2821 | 20000  | 0.0612        |
+| 1.3141 | 20500  | 0.0618        |
+| 1.3462 | 21000  | 0.0606        |
+| 1.3782 | 21500  | 0.0587        |
+| 1.4103 | 22000  | 0.0611        |
+| 1.4423 | 22500  | 0.0616        |
+| 1.4744 | 23000  | 0.0623        |
+| 1.5064 | 23500  | 0.0615        |
+| 1.5385 | 24000  | 0.0602        |
+| 1.5705 | 24500  | 0.0658        |
+| 1.6026 | 25000  | 0.068         |
+| 1.6346 | 25500  | 0.0649        |
+| 1.6667 | 26000  | 0.0645        |
+| 1.6987 | 26500  | 0.0652        |
+| 1.7308 | 27000  | 0.0632        |
+| 1.7628 | 27500  | 0.0631        |
+| 1.7949 | 28000  | 0.0655        |
+| 1.8269 | 28500  | 0.0633        |
+| 1.8590 | 29000  | 0.0607        |
+| 1.8910 | 29500  | 0.0633        |
+| 1.9231 | 30000  | 0.0612        |
+| 1.9551 | 30500  | 0.0631        |
+| 1.9872 | 31000  | 0.0616        |
+| 2.0192 | 31500  | 0.0382        |
+| 2.0513 | 32000  | 0.0178        |
+| 2.0833 | 32500  | 0.0177        |
+| 2.1154 | 33000  | 0.0178        |
+| 2.1474 | 33500  | 0.0171        |
+| 2.1795 | 34000  | 0.0188        |
+| 2.2115 | 34500  | 0.0186        |
+| 2.2436 | 35000  | 0.0177        |
+| 2.2756 | 35500  | 0.0183        |
+| 2.3077 | 36000  | 0.0195        |
+| 2.3397 | 36500  | 0.0202        |
+| 2.3718 | 37000  | 0.0199        |
+| 2.4038 | 37500  | 0.0197        |
+| 2.4359 | 38000  | 0.019         |
+| 2.4679 | 38500  | 0.021         |
+| 2.5    | 39000  | 0.0195        |
+| 2.5321 | 39500  | 0.0211        |
+| 2.5641 | 40000  | 0.0205        |
+| 2.5962 | 40500  | 0.0207        |
+| 2.6282 | 41000  | 0.0222        |
+| 2.6603 | 41500  | 0.0204        |
+| 2.6923 | 42000  | 0.0205        |
+| 2.7244 | 42500  | 0.0211        |
+| 2.7564 | 43000  | 0.0232        |
+| 2.7885 | 43500  | 0.0202        |
+| 2.8205 | 44000  | 0.0207        |
+| 2.8526 | 44500  | 0.0225        |
+| 2.8846 | 45000  | 0.0224        |
+| 2.9167 | 45500  | 0.0203        |
+| 2.9487 | 46000  | 0.0215        |
+| 2.9808 | 46500  | 0.0218        |
+| 3.0128 | 47000  | 0.0159        |
+| 3.0449 | 47500  | 0.0064        |
+| 3.0769 | 48000  | 0.0069        |
+| 3.1090 | 48500  | 0.0074        |
+| 3.1410 | 49000  | 0.0075        |
+| 3.1731 | 49500  | 0.0066        |
+| 3.2051 | 50000  | 0.0076        |
+| 3.2372 | 50500  | 0.0073        |
+| 3.2692 | 51000  | 0.0077        |
+| 3.3013 | 51500  | 0.0075        |
+| 3.3333 | 52000  | 0.0079        |
+| 3.3654 | 52500  | 0.008         |
+| 3.3974 | 53000  | 0.0087        |
+| 3.4295 | 53500  | 0.0077        |
+| 3.4615 | 54000  | 0.0084        |
+| 3.4936 | 54500  | 0.0086        |
+| 3.5256 | 55000  | 0.009         |
+| 3.5577 | 55500  | 0.0082        |
+| 3.5897 | 56000  | 0.0084        |
+| 3.6218 | 56500  | 0.0084        |
+| 3.6538 | 57000  | 0.008         |
+| 3.6859 | 57500  | 0.0079        |
+| 3.7179 | 58000  | 0.0085        |
+| 3.75   | 58500  | 0.0096        |
+| 3.7821 | 59000  | 0.0087        |
+| 3.8141 | 59500  | 0.0086        |
+| 3.8462 | 60000  | 0.0089        |
+| 3.8782 | 60500  | 0.0081        |
+| 3.9103 | 61000  | 0.0087        |
+| 3.9423 | 61500  | 0.0085        |
+| 3.9744 | 62000  | 0.0082        |
+| 4.0064 | 62500  | 0.0076        |
+| 4.0385 | 63000  | 0.0037        |
+| 4.0705 | 63500  | 0.0035        |
+| 4.1026 | 64000  | 0.0037        |
+| 4.1346 | 64500  | 0.004         |
+| 4.1667 | 65000  | 0.0037        |
+| 4.1987 | 65500  | 0.0036        |
+| 4.2308 | 66000  | 0.0042        |
+| 4.2628 | 66500  | 0.0044        |
+| 4.2949 | 67000  | 0.0041        |
+| 4.3269 | 67500  | 0.004         |
+| 4.3590 | 68000  | 0.0037        |
+| 4.3910 | 68500  | 0.0043        |
+| 4.4231 | 69000  | 0.0035        |
+| 4.4551 | 69500  | 0.0045        |
+| 4.4872 | 70000  | 0.0042        |
+| 4.5192 | 70500  | 0.0043        |
+| 4.5513 | 71000  | 0.0042        |
+| 4.5833 | 71500  | 0.0049        |
+| 4.6154 | 72000  | 0.0041        |
+| 4.6474 | 72500  | 0.0041        |
+| 4.6795 | 73000  | 0.0044        |
+| 4.7115 | 73500  | 0.0038        |
+| 4.7436 | 74000  | 0.0039        |
+| 4.7756 | 74500  | 0.0049        |
+| 4.8077 | 75000  | 0.0041        |
+| 4.8397 | 75500  | 0.0044        |
+| 4.8718 | 76000  | 0.0043        |
+| 4.9038 | 76500  | 0.0053        |
+| 4.9359 | 77000  | 0.0043        |
+| 4.9679 | 77500  | 0.0049        |
+| 5.0    | 78000  | 0.0042        |
+| 5.0321 | 78500  | 0.0022        |
+| 5.0641 | 79000  | 0.0023        |
+| 5.0962 | 79500  | 0.0021        |
+| 5.1282 | 80000  | 0.003         |
+| 5.1603 | 80500  | 0.0024        |
+| 5.1923 | 81000  | 0.0022        |
+| 5.2244 | 81500  | 0.0023        |
+| 5.2564 | 82000  | 0.0022        |
+| 5.2885 | 82500  | 0.0027        |
+| 5.3205 | 83000  | 0.0023        |
+| 5.3526 | 83500  | 0.0029        |
+| 5.3846 | 84000  | 0.0027        |
+| 5.4167 | 84500  | 0.0025        |
+| 5.4487 | 85000  | 0.0029        |
+| 5.4808 | 85500  | 0.0029        |
+| 5.5128 | 86000  | 0.0024        |
+| 5.5449 | 86500  | 0.0026        |
+| 5.5769 | 87000  | 0.0026        |
+| 5.6090 | 87500  | 0.0028        |
+| 5.6410 | 88000  | 0.0025        |
+| 5.6731 | 88500  | 0.0026        |
+| 5.7051 | 89000  | 0.0023        |
+| 5.7372 | 89500  | 0.0029        |
+| 5.7692 | 90000  | 0.0027        |
+| 5.8013 | 90500  | 0.0019        |
+| 5.8333 | 91000  | 0.0023        |
+| 5.8654 | 91500  | 0.0022        |
+| 5.8974 | 92000  | 0.003         |
+| 5.9295 | 92500  | 0.0023        |
+| 5.9615 | 93000  | 0.0026        |
+| 5.9936 | 93500  | 0.0027        |
+| 6.0256 | 94000  | 0.0015        |
+| 6.0577 | 94500  | 0.0012        |
+| 6.0897 | 95000  | 0.0016        |
+| 6.1218 | 95500  | 0.0018        |
+| 6.1538 | 96000  | 0.0017        |
+| 6.1859 | 96500  | 0.0014        |
+| 6.2179 | 97000  | 0.0013        |
+| 6.25   | 97500  | 0.0022        |
+| 6.2821 | 98000  | 0.0015        |
+| 6.3141 | 98500  | 0.002         |
+| 6.3462 | 99000  | 0.0021        |
+| 6.3782 | 99500  | 0.0016        |
+| 6.4103 | 100000 | 0.0024        |
+| 6.4423 | 100500 | 0.002         |
+| 6.4744 | 101000 | 0.0014        |
+| 6.5064 | 101500 | 0.0019        |
+| 6.5385 | 102000 | 0.0017        |
+| 6.5705 | 102500 | 0.0019        |
+| 6.6026 | 103000 | 0.0016        |
+| 6.6346 | 103500 | 0.0013        |
+| 6.6667 | 104000 | 0.0012        |
+| 6.6987 | 104500 | 0.0015        |
+| 6.7308 | 105000 | 0.0015        |
+| 6.7628 | 105500 | 0.0018        |
+| 6.7949 | 106000 | 0.0018        |
+| 6.8269 | 106500 | 0.0016        |
+| 6.8590 | 107000 | 0.0018        |
+| 6.8910 | 107500 | 0.0026        |
+| 6.9231 | 108000 | 0.0013        |
+| 6.9551 | 108500 | 0.0019        |
+| 6.9872 | 109000 | 0.0015        |
+| 7.0192 | 109500 | 0.0014        |
+| 7.0513 | 110000 | 0.0009        |
+| 7.0833 | 110500 | 0.0012        |
+| 7.1154 | 111000 | 0.0016        |
+| 7.1474 | 111500 | 0.0014        |
+| 7.1795 | 112000 | 0.0013        |
+| 7.2115 | 112500 | 0.0009        |
+| 7.2436 | 113000 | 0.0015        |
+| 7.2756 | 113500 | 0.0011        |
+| 7.3077 | 114000 | 0.0011        |
+| 7.3397 | 114500 | 0.0011        |
+| 7.3718 | 115000 | 0.0013        |
+| 7.4038 | 115500 | 0.001         |
+| 7.4359 | 116000 | 0.0012        |
+| 7.4679 | 116500 | 0.0012        |
+| 7.5    | 117000 | 0.0013        |
+| 7.5321 | 117500 | 0.0014        |
+| 7.5641 | 118000 | 0.0013        |
+| 7.5962 | 118500 | 0.0013        |
+| 7.6282 | 119000 | 0.0014        |
+| 7.6603 | 119500 | 0.001         |
+| 7.6923 | 120000 | 0.0012        |
+| 7.7244 | 120500 | 0.0018        |
+| 7.7564 | 121000 | 0.001         |
+| 7.7885 | 121500 | 0.0014        |
+| 7.8205 | 122000 | 0.0011        |
+| 7.8526 | 122500 | 0.0012        |
+| 7.8846 | 123000 | 0.0012        |
+| 7.9167 | 123500 | 0.0008        |
+| 7.9487 | 124000 | 0.0013        |
+| 7.9808 | 124500 | 0.0014        |
+| 8.0128 | 125000 | 0.001         |
+| 8.0449 | 125500 | 0.0007        |
+| 8.0769 | 126000 | 0.001         |
+| 8.1090 | 126500 | 0.0009        |
+| 8.1410 | 127000 | 0.0007        |
+| 8.1731 | 127500 | 0.0007        |
+| 8.2051 | 128000 | 0.001         |
+| 8.2372 | 128500 | 0.0011        |
+| 8.2692 | 129000 | 0.0008        |
+| 8.3013 | 129500 | 0.0007        |
+| 8.3333 | 130000 | 0.0013        |
+| 8.3654 | 130500 | 0.0012        |
+| 8.3974 | 131000 | 0.001         |
+| 8.4295 | 131500 | 0.001         |
+| 8.4615 | 132000 | 0.0007        |
+| 8.4936 | 132500 | 0.001         |
+| 8.5256 | 133000 | 0.001         |
+| 8.5577 | 133500 | 0.001         |
+| 8.5897 | 134000 | 0.0011        |
+| 8.6218 | 134500 | 0.0013        |
+| 8.6538 | 135000 | 0.0007        |
+| 8.6859 | 135500 | 0.001         |
+| 8.7179 | 136000 | 0.0008        |
+| 8.75   | 136500 | 0.001         |
+| 8.7821 | 137000 | 0.0008        |
+| 8.8141 | 137500 | 0.0006        |
+| 8.8462 | 138000 | 0.0006        |
+| 8.8782 | 138500 | 0.0009        |
+| 8.9103 | 139000 | 0.0007        |
+| 8.9423 | 139500 | 0.0009        |
+| 8.9744 | 140000 | 0.0006        |
+| 9.0064 | 140500 | 0.0018        |
+| 9.0385 | 141000 | 0.0008        |
+| 9.0705 | 141500 | 0.0008        |
+| 9.1026 | 142000 | 0.0009        |
+| 9.1346 | 142500 | 0.0006        |
+| 9.1667 | 143000 | 0.0009        |
+| 9.1987 | 143500 | 0.0007        |
+| 9.2308 | 144000 | 0.0007        |
+| 9.2628 | 144500 | 0.0006        |
+| 9.2949 | 145000 | 0.0008        |
+| 9.3269 | 145500 | 0.0009        |
+| 9.3590 | 146000 | 0.0005        |
+| 9.3910 | 146500 | 0.001         |
+| 9.4231 | 147000 | 0.001         |
+| 9.4551 | 147500 | 0.0011        |
+| 9.4872 | 148000 | 0.0011        |
+| 9.5192 | 148500 | 0.0012        |
+| 9.5513 | 149000 | 0.0011        |
+| 9.5833 | 149500 | 0.0007        |
+| 9.6154 | 150000 | 0.0008        |
+| 9.6474 | 150500 | 0.0005        |
+| 9.6795 | 151000 | 0.0007        |
+| 9.7115 | 151500 | 0.0008        |
+| 9.7436 | 152000 | 0.0007        |
+| 9.7756 | 152500 | 0.0009        |
+| 9.8077 | 153000 | 0.0007        |
+| 9.8397 | 153500 | 0.0012        |
+| 9.8718 | 154000 | 0.0005        |
+| 9.9038 | 154500 | 0.0008        |
+| 9.9359 | 155000 | 0.0007        |
+| 9.9679 | 155500 | 0.0007        |
+| 10.0   | 156000 | 0.0011        |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- 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",
+}
+```
+
+#### 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

@@ -0,0 +1,46 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "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": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.50.3",
+    "pytorch": "2.6.0+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:dcb4ce4870ed1eb434d3e15080338d8835211b0d544e8ec41b286f989717cfde
+size 1579143688

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "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": "[SEP]",
+    "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

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "|||IP_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
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+      "single_word": false,
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