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 +952 -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: 0.7308
+      name: Map
+    - type: mrr@10
+      value: 0.7292
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.7713
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.4579
+      name: Map
+    - type: mrr@10
+      value: 0.4479
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5275
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3414
+      name: Map
+    - type: mrr@10
+      value: 0.534
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3821
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.3932
+      name: Map
+    - type: mrr@10
+      value: 0.3918
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.463
+      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: 0.3975
+      name: Map
+    - type: mrr@10
+      value: 0.4579
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4575
+      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")
+# Get scores for pairs of texts
+pairs = [
+    ['why are rye chips so good?', "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips."],
+    ['why are rye chips so good?', 'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.'],
+    ['why are rye chips so good?', 'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.'],
+    ['why are rye chips so good?', 'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.'],
+    ['why are rye chips so good?', 'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'why are rye chips so good?',
+    [
+        "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.",
+        'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.',
+        'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.',
+        'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.',
+        'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.',
+    ]
+)
+# [{'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         | 0.7308 (+0.1997)     |
+| mrr@10      | 0.7292 (+0.2052)     |
+| **ndcg@10** | **0.7713 (+0.1801)** |
+#### 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         | 0.4579 (-0.0317)     | 0.3414 (+0.0804)     | 0.3932 (-0.0264)     |
+| mrr@10      | 0.4479 (-0.0296)     | 0.5340 (+0.0342)     | 0.3918 (-0.0349)     |
+| **ndcg@10** | **0.5275 (-0.0130)** | **0.3821 (+0.0571)** | **0.4630 (-0.0377)** |
+#### 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         | 0.3975 (+0.0074)     |
+| mrr@10      | 0.4579 (-0.0101)     |
+| **ndcg@10** | **0.4575 (+0.0022)** |
+<!--
+## 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: 19 characters</li><li>mean: 45.14 characters</li><li>max: 85 characters</li></ul> | <ul><li>min: 65 characters</li><li>mean: 254.8 characters</li><li>max: 379 characters</li></ul> | <ul><li>0: ~82.90%</li><li>1: ~17.10%</li></ul> |
+* Samples:
+  | question                                | answer                                                                                                                                                                                                                                                                                                                  | label          |
+  |:----------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>why are rye chips so good?</code> | <code>It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.</code>                                                                                  | <code>1</code> |
+  | <code>why are rye chips so good?</code> | <code>There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.</code>                                                      | <code>0</code> |
+  | <code>why are rye chips so good?</code> | <code>Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.</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       | -             | 0.1288 (-0.4624)     | 0.0149 (-0.5255)         | 0.2278 (-0.0972)          | 0.0229 (-0.4777)     | 0.0885 (-0.3668)           |
+| 0.0001     | 1        | 1.0435        | -                    | -                        | -                         | -                    | -                          |
+| 0.0221     | 200      | 1.1924        | -                    | -                        | -                         | -                    | -                          |
+| 0.0443     | 400      | 1.1531        | -                    | -                        | -                         | -                    | -                          |
+| 0.0664     | 600      | 0.9371        | -                    | -                        | -                         | -                    | -                          |
+| 0.0885     | 800      | 0.6993        | -                    | -                        | -                         | -                    | -                          |
+| 0.1106     | 1000     | 0.669         | 0.7042 (+0.1130)     | 0.4353 (-0.1051)         | 0.3289 (+0.0039)          | 0.4250 (-0.0757)     | 0.3964 (-0.0590)           |
+| 0.1328     | 1200     | 0.6257        | -                    | -                        | -                         | -                    | -                          |
+| 0.1549     | 1400     | 0.6283        | -                    | -                        | -                         | -                    | -                          |
+| 0.1770     | 1600     | 0.6014        | -                    | -                        | -                         | -                    | -                          |
+| 0.1992     | 1800     | 0.5888        | -                    | -                        | -                         | -                    | -                          |
+| 0.2213     | 2000     | 0.5493        | 0.7425 (+0.1513)     | 0.4947 (-0.0457)         | 0.3568 (+0.0318)          | 0.4634 (-0.0373)     | 0.4383 (-0.0171)           |
+| 0.2434     | 2200     | 0.5479        | -                    | -                        | -                         | -                    | -                          |
+| 0.2655     | 2400     | 0.5329        | -                    | -                        | -                         | -                    | -                          |
+| 0.2877     | 2600     | 0.5208        | -                    | -                        | -                         | -                    | -                          |
+| 0.3098     | 2800     | 0.5259        | -                    | -                        | -                         | -                    | -                          |
+| 0.3319     | 3000     | 0.5221        | 0.7479 (+0.1567)     | 0.5146 (-0.0258)         | 0.3710 (+0.0460)          | 0.4846 (-0.0160)     | 0.4568 (+0.0014)           |
+| 0.3541     | 3200     | 0.4977        | -                    | -                        | -                         | -                    | -                          |
+| 0.3762     | 3400     | 0.4965        | -                    | -                        | -                         | -                    | -                          |
+| 0.3983     | 3600     | 0.4985        | -                    | -                        | -                         | -                    | -                          |
+| 0.4204     | 3800     | 0.4907        | -                    | -                        | -                         | -                    | -                          |
+| 0.4426     | 4000     | 0.5058        | 0.7624 (+0.1712)     | 0.5166 (-0.0238)         | 0.3665 (+0.0415)          | 0.4868 (-0.0138)     | 0.4567 (+0.0013)           |
+| 0.4647     | 4200     | 0.4885        | -                    | -                        | -                         | -                    | -                          |
+| 0.4868     | 4400     | 0.495         | -                    | -                        | -                         | -                    | -                          |
+| 0.5090     | 4600     | 0.4839        | -                    | -                        | -                         | -                    | -                          |
+| 0.5311     | 4800     | 0.4983        | -                    | -                        | -                         | -                    | -                          |
+| 0.5532     | 5000     | 0.4778        | 0.7603 (+0.1691)     | 0.5110 (-0.0294)         | 0.3540 (+0.0290)          | 0.4809 (-0.0197)     | 0.4487 (-0.0067)           |
+| 0.5753     | 5200     | 0.4726        | -                    | -                        | -                         | -                    | -                          |
+| 0.5975     | 5400     | 0.477         | -                    | -                        | -                         | -                    | -                          |
+| 0.6196     | 5600     | 0.4613        | -                    | -                        | -                         | -                    | -                          |
+| 0.6417     | 5800     | 0.4492        | -                    | -                        | -                         | -                    | -                          |
+| 0.6639     | 6000     | 0.4506        | 0.7643 (+0.1731)     | 0.5275 (-0.0129)         | 0.3639 (+0.0389)          | 0.4913 (-0.0094)     | 0.4609 (+0.0055)           |
+| 0.6860     | 6200     | 0.4618        | -                    | -                        | -                         | -                    | -                          |
+| 0.7081     | 6400     | 0.463         | -                    | -                        | -                         | -                    | -                          |
+| 0.7303     | 6600     | 0.4585        | -                    | -                        | -                         | -                    | -                          |
+| 0.7524     | 6800     | 0.4612        | -                    | -                        | -                         | -                    | -                          |
+| 0.7745     | 7000     | 0.4621        | 0.7649 (+0.1736)     | 0.5105 (-0.0299)         | 0.3688 (+0.0437)          | 0.4552 (-0.0454)     | 0.4448 (-0.0105)           |
+| 0.7966     | 7200     | 0.4536        | -                    | -                        | -                         | -                    | -                          |
+| 0.8188     | 7400     | 0.4515        | -                    | -                        | -                         | -                    | -                          |
+| 0.8409     | 7600     | 0.4396        | -                    | -                        | -                         | -                    | -                          |
+| 0.8630     | 7800     | 0.4542        | -                    | -                        | -                         | -                    | -                          |
+| 0.8852     | 8000     | 0.4332        | 0.7669 (+0.1757)     | 0.5247 (-0.0157)         | 0.3794 (+0.0544)          | 0.4370 (-0.0637)     | 0.4470 (-0.0083)           |
+| 0.9073     | 8200     | 0.447         | -                    | -                        | -                         | -                    | -                          |
+| 0.9294     | 8400     | 0.4335        | -                    | -                        | -                         | -                    | -                          |
+| 0.9515     | 8600     | 0.4179        | -                    | -                        | -                         | -                    | -                          |
+| 0.9737     | 8800     | 0.4459        | -                    | -                        | -                         | -                    | -                          |
+| **0.9958** | **9000** | **0.4196**    | **0.7713 (+0.1801)** | **0.5275 (-0.0130)**     | **0.3821 (+0.0571)**      | **0.4630 (-0.0377)** | **0.4575 (+0.0022)**       |
+| -1         | -1       | -             | 0.7713 (+0.1801)     | 0.5275 (-0.0130)         | 0.3821 (+0.0571)          | 0.4630 (-0.0377)     | 0.4575 (+0.0022)           |
+* 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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