Add new CrossEncoder model

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README.md +522 -0
config.json +57 -0
model.safetensors +3 -0
special_tokens_map.json +37 -0
tokenizer.json +0 -0
tokenizer_config.json +945 -0

README.md ADDED Viewed

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:578402
+- loss:BinaryCrossEntropyLoss
+base_model: answerdotai/ModernBERT-large
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: ModernBERT-large trained on GooAQ
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: gooaq dev
+      type: gooaq-dev
+    metrics:
+    - type: map
+      value: 0.7586
+      name: Map
+    - type: mrr@10
+      value: 0.7576
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.7946
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.5488
+      name: Map
+    - type: mrr@10
+      value: 0.5443
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6323
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3682
+      name: Map
+    - type: mrr@10
+      value: 0.5677
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4136
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.6103
+      name: Map
+    - type: mrr@10
+      value: 0.6108
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.657
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.5091
+      name: Map
+    - type: mrr@10
+      value: 0.5743
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5676
+      name: Ndcg@10
+---
+# ModernBERT-large trained on GooAQ
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+## Model Details
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+### Model Sources
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+## 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 CrossEncoder
+# Download from the 🤗 Hub
+model = CrossEncoder("tomaarsen/reranker-ModernBERT-large-gooaq-bce")
+# Get scores for pairs of texts
+pairs = [
+    ['what are the characteristics and elements of poetry?', 'The elements of poetry include meter, rhyme, form, sound, and rhythm (timing). Different poets use these elements in many different ways.'],
+    ['what are the characteristics and elements of poetry?', "What's the first rule of writing poetry? That there are no rules — it's all up to you! Of course there are different poetic forms and devices, and free verse poems are one of the many poetic styles; they have no structure when it comes to format or even rhyming."],
+    ['what are the characteristics and elements of poetry?', "['Blank verse. Blank verse is poetry written with a precise meter—almost always iambic pentameter—that does not rhyme. ... ', 'Rhymed poetry. In contrast to blank verse, rhymed poems rhyme by definition, although their scheme varies. ... ', 'Free verse. ... ', 'Epics. ... ', 'Narrative poetry. ... ', 'Haiku. ... ', 'Pastoral poetry. ... ', 'Sonnet.']"],
+    ['what are the characteristics and elements of poetry?', 'The main component of poetry is its meter (the regular pattern of strong and weak stress). When a poem has a recognizable but varying pattern of stressed and unstressed syllables, the poetry is written in verse. ... There are many possible patterns of verse, and the basic pattern of each unit is called a foot.'],
+    ['what are the characteristics and elements of poetry?', "Some poetry may not make sense to you. But that's because poets don't write to be understood by others. They write because they must. The feelings and emotions that reside within them need to be expressed."],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'what are the characteristics and elements of poetry?',
+    [
+        'The elements of poetry include meter, rhyme, form, sound, and rhythm (timing). Different poets use these elements in many different ways.',
+        "What's the first rule of writing poetry? That there are no rules — it's all up to you! Of course there are different poetic forms and devices, and free verse poems are one of the many poetic styles; they have no structure when it comes to format or even rhyming.",
+        "['Blank verse. Blank verse is poetry written with a precise meter—almost always iambic pentameter—that does not rhyme. ... ', 'Rhymed poetry. In contrast to blank verse, rhymed poems rhyme by definition, although their scheme varies. ... ', 'Free verse. ... ', 'Epics. ... ', 'Narrative poetry. ... ', 'Haiku. ... ', 'Pastoral poetry. ... ', 'Sonnet.']",
+        'The main component of poetry is its meter (the regular pattern of strong and weak stress). When a poem has a recognizable but varying pattern of stressed and unstressed syllables, the poetry is written in verse. ... There are many possible patterns of verse, and the basic pattern of each unit is called a foot.',
+        "Some poetry may not make sense to you. But that's because poets don't write to be understood by others. They write because they must. The feelings and emotions that reside within them need to be expressed.",
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+<!--
+### 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
+#### Cross Encoder Reranking
+* Dataset: `gooaq-dev`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": false
+  }
+  ```
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.7586 (+0.2275)     |
+| mrr@10      | 0.7576 (+0.2336)     |
+| **ndcg@10** | **0.7946 (+0.2034)** |
+#### Cross Encoder Reranking
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.5488 (+0.0592)     | 0.3682 (+0.1072)     | 0.6103 (+0.1907)     |
+| mrr@10      | 0.5443 (+0.0668)     | 0.5677 (+0.0678)     | 0.6108 (+0.1841)     |
+| **ndcg@10** | **0.6323 (+0.0918)** | **0.4136 (+0.0886)** | **0.6570 (+0.1564)** |
+#### Cross Encoder Nano BEIR
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.5091 (+0.1190)     |
+| mrr@10      | 0.5743 (+0.1063)     |
+| **ndcg@10** | **0.5676 (+0.1123)** |
+<!--
+## 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: 578,402 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                                       | answer                                                                                           | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | int                                             |
+  | details | <ul><li>min: 22 characters</li><li>mean: 43.99 characters</li><li>max: 93 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 252.75 characters</li><li>max: 378 characters</li></ul> | <ul><li>0: ~82.30%</li><li>1: ~17.70%</li></ul> |
+* Samples:
+  | question                                                          | answer                                                                                                                                                                                                                                                                                                                                                                        | label          |
+  |:------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>what are the characteristics and elements of poetry?</code> | <code>The elements of poetry include meter, rhyme, form, sound, and rhythm (timing). Different poets use these elements in many different ways.</code>                                                                                                                                                                                                                        | <code>1</code> |
+  | <code>what are the characteristics and elements of poetry?</code> | <code>What's the first rule of writing poetry? That there are no rules — it's all up to you! Of course there are different poetic forms and devices, and free verse poems are one of the many poetic styles; they have no structure when it comes to format or even rhyming.</code>                                                                                           | <code>0</code> |
+  | <code>what are the characteristics and elements of poetry?</code> | <code>['Blank verse. Blank verse is poetry written with a precise meter—almost always iambic pentameter—that does not rhyme. ... ', 'Rhymed poetry. In contrast to blank verse, rhymed poems rhyme by definition, although their scheme varies. ... ', 'Free verse. ... ', 'Epics. ... ', 'Narrative poetry. ... ', 'Haiku. ... ', 'Pastoral poetry. ... ', 'Sonnet.']</code> | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": 5
+  }
+  ```
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `dataloader_num_workers`: 4
+- `load_best_model_at_end`: True
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 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
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 12
+- `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`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `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`: proportional
+</details>
+### Training Logs
+| Epoch      | Step     | Training Loss | gooaq-dev_ndcg@10    | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10  | NanoBEIR_R100_mean_ndcg@10 |
+|:----------:|:--------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
+| -1         | -1       | -             | 0.1279 (-0.4633)     | 0.0555 (-0.4849)         | 0.1735 (-0.1516)          | 0.0686 (-0.4320)     | 0.0992 (-0.3562)           |
+| 0.0001     | 1        | 1.2592        | -                    | -                        | -                         | -                    | -                          |
+| 0.0221     | 200      | 1.1826        | -                    | -                        | -                         | -                    | -                          |
+| 0.0443     | 400      | 0.7653        | -                    | -                        | -                         | -                    | -                          |
+| 0.0664     | 600      | 0.6423        | -                    | -                        | -                         | -                    | -                          |
+| 0.0885     | 800      | 0.6           | -                    | -                        | -                         | -                    | -                          |
+| 0.1106     | 1000     | 0.5753        | 0.7444 (+0.1531)     | 0.5365 (-0.0039)         | 0.4249 (+0.0998)          | 0.6111 (+0.1105)     | 0.5242 (+0.0688)           |
+| 0.1328     | 1200     | 0.5313        | -                    | -                        | -                         | -                    | -                          |
+| 0.1549     | 1400     | 0.5315        | -                    | -                        | -                         | -                    | -                          |
+| 0.1770     | 1600     | 0.5195        | -                    | -                        | -                         | -                    | -                          |
+| 0.1992     | 1800     | 0.5136        | -                    | -                        | -                         | -                    | -                          |
+| 0.2213     | 2000     | 0.4782        | 0.7774 (+0.1862)     | 0.6080 (+0.0676)         | 0.4371 (+0.1120)          | 0.6520 (+0.1513)     | 0.5657 (+0.1103)           |
+| 0.2434     | 2200     | 0.5026        | -                    | -                        | -                         | -                    | -                          |
+| 0.2655     | 2400     | 0.5011        | -                    | -                        | -                         | -                    | -                          |
+| 0.2877     | 2600     | 0.4893        | -                    | -                        | -                         | -                    | -                          |
+| 0.3098     | 2800     | 0.4855        | -                    | -                        | -                         | -                    | -                          |
+| 0.3319     | 3000     | 0.4687        | 0.7692 (+0.1779)     | 0.6181 (+0.0777)         | 0.4273 (+0.1023)          | 0.6686 (+0.1679)     | 0.5713 (+0.1160)           |
+| 0.3541     | 3200     | 0.4619        | -                    | -                        | -                         | -                    | -                          |
+| 0.3762     | 3400     | 0.4626        | -                    | -                        | -                         | -                    | -                          |
+| 0.3983     | 3600     | 0.4504        | -                    | -                        | -                         | -                    | -                          |
+| 0.4204     | 3800     | 0.4435        | -                    | -                        | -                         | -                    | -                          |
+| 0.4426     | 4000     | 0.4573        | 0.7776 (+0.1864)     | 0.6589 (+0.1184)         | 0.4262 (+0.1012)          | 0.6634 (+0.1628)     | 0.5828 (+0.1275)           |
+| 0.4647     | 4200     | 0.4608        | -                    | -                        | -                         | -                    | -                          |
+| 0.4868     | 4400     | 0.4275        | -                    | -                        | -                         | -                    | -                          |
+| 0.5090     | 4600     | 0.4317        | -                    | -                        | -                         | -                    | -                          |
+| 0.5311     | 4800     | 0.4427        | -                    | -                        | -                         | -                    | -                          |
+| 0.5532     | 5000     | 0.4245        | 0.7795 (+0.1883)     | 0.6021 (+0.0617)         | 0.4387 (+0.1137)          | 0.6560 (+0.1553)     | 0.5656 (+0.1102)           |
+| 0.5753     | 5200     | 0.4243        | -                    | -                        | -                         | -                    | -                          |
+| 0.5975     | 5400     | 0.4295        | -                    | -                        | -                         | -                    | -                          |
+| 0.6196     | 5600     | 0.422         | -                    | -                        | -                         | -                    | -                          |
+| 0.6417     | 5800     | 0.4165        | -                    | -                        | -                         | -                    | -                          |
+| 0.6639     | 6000     | 0.4281        | 0.7859 (+0.1946)     | 0.6404 (+0.1000)         | 0.4449 (+0.1199)          | 0.6458 (+0.1451)     | 0.5770 (+0.1217)           |
+| 0.6860     | 6200     | 0.4155        | -                    | -                        | -                         | -                    | -                          |
+| 0.7081     | 6400     | 0.4189        | -                    | -                        | -                         | -                    | -                          |
+| 0.7303     | 6600     | 0.4066        | -                    | -                        | -                         | -                    | -                          |
+| 0.7524     | 6800     | 0.4114        | -                    | -                        | -                         | -                    | -                          |
+| 0.7745     | 7000     | 0.4111        | 0.7875 (+0.1963)     | 0.6358 (+0.0954)         | 0.4289 (+0.1038)          | 0.6358 (+0.1351)     | 0.5668 (+0.1114)           |
+| 0.7966     | 7200     | 0.3949        | -                    | -                        | -                         | -                    | -                          |
+| 0.8188     | 7400     | 0.4019        | -                    | -                        | -                         | -                    | -                          |
+| 0.8409     | 7600     | 0.395         | -                    | -                        | -                         | -                    | -                          |
+| 0.8630     | 7800     | 0.3885        | -                    | -                        | -                         | -                    | -                          |
+| **0.8852** | **8000** | **0.3991**    | **0.7946 (+0.2034)** | **0.6323 (+0.0918)**     | **0.4136 (+0.0886)**      | **0.6570 (+0.1564)** | **0.5676 (+0.1123)**       |
+| 0.9073     | 8200     | 0.3894        | -                    | -                        | -                         | -                    | -                          |
+| 0.9294     | 8400     | 0.392         | -                    | -                        | -                         | -                    | -                          |
+| 0.9515     | 8600     | 0.3853        | -                    | -                        | -                         | -                    | -                          |
+| 0.9737     | 8800     | 0.3691        | -                    | -                        | -                         | -                    | -                          |
+| 0.9958     | 9000     | 0.3784        | 0.7936 (+0.2024)     | 0.6481 (+0.1077)         | 0.4211 (+0.0961)          | 0.6439 (+0.1433)     | 0.5711 (+0.1157)           |
+| -1         | -1       | -             | 0.7946 (+0.2034)     | 0.6323 (+0.0918)         | 0.4136 (+0.0886)          | 0.6570 (+0.1564)     | 0.5676 (+0.1123)           |
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.5.0.dev0
+- Transformers: 4.49.0
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.5.2
+- Datasets: 2.21.0
+- 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",
+}
+```
+<!--
+## Glossary
+*Clearly define terms in order to be accessible across audiences.*
+-->
+<!--
+## Model Card Authors
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+<!--
+## Model Card Contact
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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