Add new CrossEncoder model

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README.md +522 -0
config.json +56 -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-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: ModernBERT-base trained on GooAQ
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: gooaq dev
+      type: gooaq-dev
+    metrics:
+    - type: map
+      value: 0.7285
+      name: Map
+    - type: mrr@10
+      value: 0.727
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.77
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.4718
+      name: Map
+    - type: mrr@10
+      value: 0.4647
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5533
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3424
+      name: Map
+    - type: mrr@10
+      value: 0.5554
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3784
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.5178
+      name: Map
+    - type: mrr@10
+      value: 0.5159
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5882
+      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: 0.444
+      name: Map
+    - type: mrr@10
+      value: 0.512
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5066
+      name: Ndcg@10
+---
+# ModernBERT-base trained on GooAQ
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) 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-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **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-base-gooaq-bce-random")
+# Get scores for pairs of texts
+pairs = [
+    ['is esurance a reputable company?', "Esurance auto insurance earned 4.5 stars out of 5 for overall performance. ... Based on these ratings, Esurance is among NerdWallet's Best Car Insurance Companies for 2020. Esurance offers all the usual coverage options, plus optional coverage including: Emergency roadside assistance."],
+    ['is esurance a reputable company?', 'Coinsurance in property insurance is a means for insurers to obtain rate and premium equality. ... Rates are applied against a specified percentage (100, 90, or 80 percent, for example) of the value to the insured: building, contents, or business income.'],
+    ['is esurance a reputable company?', 'Some employers offer both term life insurance coverage and supplemental life insurance. Term life insurance through your employer generally works like regular term life insurance. ... Supplemental life insurance is similar to a group term life insurance policy, but is typically more limited.'],
+    ['is esurance a reputable company?', "Third party insurance is the legal minimum. This means you're covered if you have an accident causing damage or injury to any other person, vehicle, animal or property. It does not cover any other costs like repair to your own vehicle. You may want to use an insurance broker."],
+    ['is esurance a reputable company?', 'In the United States, corporations have limited liability and the expression corporation is preferred to limited company. A "limited liability company" (LLC) is a different entity. However, some states permit corporations to have the designation Ltd. (instead of the usual Inc.) to signify their corporate status.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'is esurance a reputable company?',
+    [
+        "Esurance auto insurance earned 4.5 stars out of 5 for overall performance. ... Based on these ratings, Esurance is among NerdWallet's Best Car Insurance Companies for 2020. Esurance offers all the usual coverage options, plus optional coverage including: Emergency roadside assistance.",
+        'Coinsurance in property insurance is a means for insurers to obtain rate and premium equality. ... Rates are applied against a specified percentage (100, 90, or 80 percent, for example) of the value to the insured: building, contents, or business income.',
+        'Some employers offer both term life insurance coverage and supplemental life insurance. Term life insurance through your employer generally works like regular term life insurance. ... Supplemental life insurance is similar to a group term life insurance policy, but is typically more limited.',
+        "Third party insurance is the legal minimum. This means you're covered if you have an accident causing damage or injury to any other person, vehicle, animal or property. It does not cover any other costs like repair to your own vehicle. You may want to use an insurance broker.",
+        'In the United States, corporations have limited liability and the expression corporation is preferred to limited company. A "limited liability company" (LLC) is a different entity. However, some states permit corporations to have the designation Ltd. (instead of the usual Inc.) to signify their corporate status.',
+    ]
+)
+# [{'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         | 0.7285 (+0.1974)     |
+| mrr@10      | 0.7270 (+0.2030)     |
+| **ndcg@10** | **0.7700 (+0.1787)** |
+#### 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         | 0.4718 (-0.0178)     | 0.3424 (+0.0814)     | 0.5178 (+0.0982)     |
+| mrr@10      | 0.4647 (-0.0128)     | 0.5554 (+0.0555)     | 0.5159 (+0.0892)     |
+| **ndcg@10** | **0.5533 (+0.0129)** | **0.3784 (+0.0534)** | **0.5882 (+0.0875)** |
+#### 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         | 0.4440 (+0.0539)     |
+| mrr@10      | 0.5120 (+0.0440)     |
+| **ndcg@10** | **0.5066 (+0.0513)** |
+<!--
+## 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: 21 characters</li><li>mean: 44.5 characters</li><li>max: 101 characters</li></ul> | <ul><li>min: 54 characters</li><li>mean: 253.36 characters</li><li>max: 397 characters</li></ul> | <ul><li>0: ~83.00%</li><li>1: ~17.00%</li></ul> |
+* Samples:
+  | question                                      | answer                                                                                                                                                                                                                                                                                                            | label          |
+  |:----------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>is esurance a reputable company?</code> | <code>Esurance auto insurance earned 4.5 stars out of 5 for overall performance. ... Based on these ratings, Esurance is among NerdWallet's Best Car Insurance Companies for 2020. Esurance offers all the usual coverage options, plus optional coverage including: Emergency roadside assistance.</code>        | <code>1</code> |
+  | <code>is esurance a reputable company?</code> | <code>Coinsurance in property insurance is a means for insurers to obtain rate and premium equality. ... Rates are applied against a specified percentage (100, 90, or 80 percent, for example) of the value to the insured: building, contents, or business income.</code>                                       | <code>0</code> |
+  | <code>is esurance a reputable company?</code> | <code>Some employers offer both term life insurance coverage and supplemental life insurance. Term life insurance through your employer generally works like regular term life insurance. ... Supplemental life insurance is similar to a group term life insurance policy, but is typically more limited.</code> | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fct": "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       | -             | 0.1307 (-0.4605)     | 0.0867 (-0.4537)         | 0.3025 (-0.0226)          | 0.0200 (-0.4806)     | 0.1364 (-0.3190)           |
+| 0.0001     | 1        | 1.1444        | -                    | -                        | -                         | -                    | -                          |
+| 0.0221     | 200      | 1.182         | -                    | -                        | -                         | -                    | -                          |
+| 0.0443     | 400      | 0.9767        | -                    | -                        | -                         | -                    | -                          |
+| 0.0664     | 600      | 0.5736        | -                    | -                        | -                         | -                    | -                          |
+| 0.0885     | 800      | 0.4752        | -                    | -                        | -                         | -                    | -                          |
+| 0.1106     | 1000     | 0.4281        | 0.7180 (+0.1268)     | 0.4989 (-0.0415)         | 0.3655 (+0.0405)          | 0.5535 (+0.0529)     | 0.4726 (+0.0173)           |
+| 0.1328     | 1200     | 0.3803        | -                    | -                        | -                         | -                    | -                          |
+| 0.1549     | 1400     | 0.3646        | -                    | -                        | -                         | -                    | -                          |
+| 0.1770     | 1600     | 0.3535        | -                    | -                        | -                         | -                    | -                          |
+| 0.1992     | 1800     | 0.3498        | -                    | -                        | -                         | -                    | -                          |
+| 0.2213     | 2000     | 0.3237        | 0.7328 (+0.1416)     | 0.5173 (-0.0231)         | 0.3619 (+0.0368)          | 0.6429 (+0.1423)     | 0.5074 (+0.0520)           |
+| 0.2434     | 2200     | 0.3199        | -                    | -                        | -                         | -                    | -                          |
+| 0.2655     | 2400     | 0.3196        | -                    | -                        | -                         | -                    | -                          |
+| 0.2877     | 2600     | 0.317         | -                    | -                        | -                         | -                    | -                          |
+| 0.3098     | 2800     | 0.3134        | -                    | -                        | -                         | -                    | -                          |
+| 0.3319     | 3000     | 0.2915        | 0.7501 (+0.1589)     | 0.5589 (+0.0184)         | 0.3926 (+0.0676)          | 0.5667 (+0.0660)     | 0.5060 (+0.0507)           |
+| 0.3541     | 3200     | 0.3022        | -                    | -                        | -                         | -                    | -                          |
+| 0.3762     | 3400     | 0.2847        | -                    | -                        | -                         | -                    | -                          |
+| 0.3983     | 3600     | 0.2903        | -                    | -                        | -                         | -                    | -                          |
+| 0.4204     | 3800     | 0.2882        | -                    | -                        | -                         | -                    | -                          |
+| 0.4426     | 4000     | 0.2916        | 0.7516 (+0.1604)     | 0.5858 (+0.0454)         | 0.3933 (+0.0683)          | 0.5949 (+0.0943)     | 0.5247 (+0.0693)           |
+| 0.4647     | 4200     | 0.2763        | -                    | -                        | -                         | -                    | -                          |
+| 0.4868     | 4400     | 0.2834        | -                    | -                        | -                         | -                    | -                          |
+| 0.5090     | 4600     | 0.2747        | -                    | -                        | -                         | -                    | -                          |
+| 0.5311     | 4800     | 0.26          | -                    | -                        | -                         | -                    | -                          |
+| 0.5532     | 5000     | 0.2594        | 0.7556 (+0.1643)     | 0.5432 (+0.0028)         | 0.3748 (+0.0497)          | 0.6275 (+0.1268)     | 0.5152 (+0.0598)           |
+| 0.5753     | 5200     | 0.273         | -                    | -                        | -                         | -                    | -                          |
+| 0.5975     | 5400     | 0.2616        | -                    | -                        | -                         | -                    | -                          |
+| 0.6196     | 5600     | 0.2573        | -                    | -                        | -                         | -                    | -                          |
+| 0.6417     | 5800     | 0.2426        | -                    | -                        | -                         | -                    | -                          |
+| 0.6639     | 6000     | 0.279         | 0.7605 (+0.1693)     | 0.5431 (+0.0026)         | 0.3907 (+0.0656)          | 0.5926 (+0.0919)     | 0.5088 (+0.0534)           |
+| 0.6860     | 6200     | 0.2519        | -                    | -                        | -                         | -                    | -                          |
+| 0.7081     | 6400     | 0.2506        | -                    | -                        | -                         | -                    | -                          |
+| 0.7303     | 6600     | 0.241         | -                    | -                        | -                         | -                    | -                          |
+| 0.7524     | 6800     | 0.2373        | -                    | -                        | -                         | -                    | -                          |
+| 0.7745     | 7000     | 0.2488        | 0.7641 (+0.1728)     | 0.5753 (+0.0349)         | 0.3897 (+0.0647)          | 0.5988 (+0.0981)     | 0.5213 (+0.0659)           |
+| 0.7966     | 7200     | 0.2462        | -                    | -                        | -                         | -                    | -                          |
+| 0.8188     | 7400     | 0.2234        | -                    | -                        | -                         | -                    | -                          |
+| 0.8409     | 7600     | 0.235         | -                    | -                        | -                         | -                    | -                          |
+| 0.8630     | 7800     | 0.2209        | -                    | -                        | -                         | -                    | -                          |
+| 0.8852     | 8000     | 0.2267        | 0.7695 (+0.1783)     | 0.5509 (+0.0105)         | 0.3849 (+0.0598)          | 0.5975 (+0.0969)     | 0.5111 (+0.0557)           |
+| 0.9073     | 8200     | 0.2322        | -                    | -                        | -                         | -                    | -                          |
+| 0.9294     | 8400     | 0.2273        | -                    | -                        | -                         | -                    | -                          |
+| 0.9515     | 8600     | 0.2111        | -                    | -                        | -                         | -                    | -                          |
+| 0.9737     | 8800     | 0.2371        | -                    | -                        | -                         | -                    | -                          |
+| **0.9958** | **9000** | **0.2328**    | **0.7700 (+0.1787)** | **0.5533 (+0.0129)**     | **0.3784 (+0.0534)**      | **0.5882 (+0.0875)** | **0.5066 (+0.0513)**       |
+| -1         | -1       | -             | 0.7700 (+0.1787)     | 0.5533 (+0.0129)         | 0.3784 (+0.0534)          | 0.5882 (+0.0875)     | 0.5066 (+0.0513)           |
+* 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.*
+-->

config.json ADDED Viewed

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+{
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+  "architectures": [
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+  "pad_token_id": 50283,
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+  "vocab_size": 50368
+}

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