Add new SentenceTransformer model.

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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+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- loss:AdaptiveLayerLoss
+- loss:MultipleNegativesRankingLoss
+base_model: distilbert/distilroberta-base
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: Certainly.
+  sentences:
+  - '''Of course.'''
+  - The idea is a good one.
+  - the woman is asleep at home
+- source_sentence: He walked.
+  sentences:
+  - The man was walking.
+  - The people are running.
+  - The women are making pizza.
+- source_sentence: Double pig.
+  sentences:
+  - Ah, triple pig!
+  - He had no real answer.
+  - Do you not know?
+- source_sentence: Very simply.
+  sentences:
+  - Not complicatedly.
+  - People are on a beach.
+  - The man kicks the umpire.
+- source_sentence: Introduction
+  sentences:
+  - Analytical Perspectives.
+  - A man reads the paper.
+  - No one wanted Singapore.
+pipeline_tag: sentence-similarity
+co2_eq_emissions:
+  emissions: 94.69690706493431
+  energy_consumed: 0.24362341090329948
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.849
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: SentenceTransformer based on distilbert/distilroberta-base
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.845554152020916
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8486455482928023
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8475103134032791
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8505660318245544
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8494883021932786
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8526835635349959
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7866563719943611
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7816258810453734
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8494883021932786
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8526835635349959
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.8182808182081737
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8148039503538166
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8132463174874629
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8088248622918064
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8148200486691981
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8105059611031759
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7499699563291125
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7350068244681712
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8182808182081737
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8148039503538166
+      name: Spearman Max
+---
+
+# SentenceTransformer based on distilbert/distilroberta-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **Language:** en
+<!-- - **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: RobertaModel 
+  (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})
+)
+```
+
+## 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("tomaarsen/distilroberta-base-nli-adaptive-layer")
+# Run inference
+sentences = [
+    'Introduction',
+    'Analytical Perspectives.',
+    'A man reads the paper.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(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
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8456     |
+| **spearman_cosine** | **0.8486** |
+| pearson_manhattan   | 0.8475     |
+| spearman_manhattan  | 0.8506     |
+| pearson_euclidean   | 0.8495     |
+| spearman_euclidean  | 0.8527     |
+| pearson_dot         | 0.7867     |
+| spearman_dot        | 0.7816     |
+| pearson_max         | 0.8495     |
+| spearman_max        | 0.8527     |
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8183     |
+| **spearman_cosine** | **0.8148** |
+| pearson_manhattan   | 0.8132     |
+| spearman_manhattan  | 0.8088     |
+| pearson_euclidean   | 0.8148     |
+| spearman_euclidean  | 0.8105     |
+| pearson_dot         | 0.75       |
+| spearman_dot        | 0.735      |
+| pearson_max         | 0.8183     |
+| spearman_max        | 0.8148     |
+
+<!--
+## 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
+
+#### sentence-transformers/all-nli
+
+* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [e587f0c](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/e587f0c494c20fb9a1853cdfb43d42576d60a7e5)
+* Size: 557,850 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                         | negative                                                   |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code>             |
+* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/losses.html#adaptivelayerloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "n_layers_per_step": 1,
+      "last_layer_weight": 1.0,
+      "prior_layers_weight": 1.0,
+      "kl_div_weight": 1.0,
+      "kl_temperature": 0.3
+  }
+  ```
+
+### Evaluation Dataset
+
+#### sentence-transformers/all-nli
+
+* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [e587f0c](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/e587f0c494c20fb9a1853cdfb43d42576d60a7e5)
+* Size: 6,584 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
+  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
+  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
+  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</code> |
+* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/losses.html#adaptivelayerloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "n_layers_per_step": 1,
+      "last_layer_weight": 1.0,
+      "prior_layers_weight": 1.0,
+      "kl_div_weight": 1.0,
+      "kl_temperature": 0.3
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: False
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `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}
+- `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`: None
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
+|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
+| 0.0229 | 100  | 7.0517        | 3.9378 | 0.7889                  | -                        |
+| 0.0459 | 200  | 4.4877        | 3.8105 | 0.7906                  | -                        |
+| 0.0688 | 300  | 4.0315        | 3.6401 | 0.7966                  | -                        |
+| 0.0918 | 400  | 3.822         | 3.3537 | 0.7883                  | -                        |
+| 0.1147 | 500  | 3.0608        | 2.5975 | 0.7973                  | -                        |
+| 0.1376 | 600  | 2.6304        | 2.3956 | 0.7943                  | -                        |
+| 0.1606 | 700  | 2.7723        | 2.0379 | 0.8009                  | -                        |
+| 0.1835 | 800  | 2.3556        | 1.9645 | 0.7984                  | -                        |
+| 0.2065 | 900  | 2.4998        | 1.9086 | 0.8017                  | -                        |
+| 0.2294 | 1000 | 2.1834        | 1.8400 | 0.7973                  | -                        |
+| 0.2524 | 1100 | 2.2793        | 1.5831 | 0.8102                  | -                        |
+| 0.2753 | 1200 | 2.1042        | 1.6485 | 0.8004                  | -                        |
+| 0.2982 | 1300 | 2.1365        | 1.7084 | 0.8013                  | -                        |
+| 0.3212 | 1400 | 2.0096        | 1.5520 | 0.8064                  | -                        |
+| 0.3441 | 1500 | 2.0492        | 1.4917 | 0.8084                  | -                        |
+| 0.3671 | 1600 | 1.8764        | 1.5447 | 0.8018                  | -                        |
+| 0.3900 | 1700 | 1.8611        | 1.5480 | 0.8046                  | -                        |
+| 0.4129 | 1800 | 1.972         | 1.5353 | 0.8075                  | -                        |
+| 0.4359 | 1900 | 1.8062        | 1.4633 | 0.8039                  | -                        |
+| 0.4588 | 2000 | 1.8565        | 1.4213 | 0.8027                  | -                        |
+| 0.4818 | 2100 | 1.8852        | 1.3860 | 0.8002                  | -                        |
+| 0.5047 | 2200 | 1.7939        | 1.5468 | 0.7910                  | -                        |
+| 0.5276 | 2300 | 1.7398        | 1.6041 | 0.7888                  | -                        |
+| 0.5506 | 2400 | 1.8535        | 1.5791 | 0.7949                  | -                        |
+| 0.5735 | 2500 | 1.8486        | 1.4871 | 0.7951                  | -                        |
+| 0.5965 | 2600 | 1.7379        | 1.5427 | 0.8019                  | -                        |
+| 0.6194 | 2700 | 1.7325        | 1.4585 | 0.8087                  | -                        |
+| 0.6423 | 2800 | 1.7664        | 1.5264 | 0.7965                  | -                        |
+| 0.6653 | 2900 | 1.7517        | 1.6344 | 0.7930                  | -                        |
+| 0.6882 | 3000 | 1.8329        | 1.4947 | 0.8008                  | -                        |
+| 0.7112 | 3100 | 1.7206        | 1.4917 | 0.8089                  | -                        |
+| 0.7341 | 3200 | 1.7138        | 1.4185 | 0.8065                  | -                        |
+| 0.7571 | 3300 | 1.3705        | 1.2040 | 0.8446                  | -                        |
+| 0.7800 | 3400 | 1.1289        | 1.1363 | 0.8447                  | -                        |
+| 0.8029 | 3500 | 1.0174        | 1.1049 | 0.8464                  | -                        |
+| 0.8259 | 3600 | 1.0188        | 1.0362 | 0.8466                  | -                        |
+| 0.8488 | 3700 | 0.9841        | 1.1391 | 0.8470                  | -                        |
+| 0.8718 | 3800 | 0.8466        | 1.0116 | 0.8485                  | -                        |
+| 0.8947 | 3900 | 0.9268        | 1.1323 | 0.8488                  | -                        |
+| 0.9176 | 4000 | 0.8686        | 1.0296 | 0.8495                  | -                        |
+| 0.9406 | 4100 | 0.9255        | 1.1737 | 0.8484                  | -                        |
+| 0.9635 | 4200 | 0.7991        | 1.0609 | 0.8486                  | -                        |
+| 0.9865 | 4300 | 0.8431        | 0.9976 | 0.8486                  | -                        |
+| 1.0    | 4359 | -             | -      | -                       | 0.8148                   |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.244 kWh
+- **Carbon Emitted**: 0.095 kg of CO2
+- **Hours Used**: 0.849 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 2.8.0.dev0
+- Transformers: 4.41.0.dev0
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.26.1
+- Datasets: 2.18.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### AdaptiveLayerLoss
+```bibtex
+@misc{li20242d,
+    title={2D Matryoshka Sentence Embeddings}, 
+    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
+    year={2024},
+    eprint={2402.14776},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### 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 +1,27 @@
+{
+  "_name_or_path": "distilroberta-base",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "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": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0.dev0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.8.0.dev0",
+    "transformers": "4.41.0.dev0",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3adfb442d77419b9e21f5f9e217b84e0f8618b1ff9359db68c5db55f4d8fefe
+size 328485128

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 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}