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-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: 0.7323
+      name: Map
+    - type: mrr@10
+      value: 0.7309
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.7731
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.4464
+      name: Map
+    - type: mrr@10
+      value: 0.4352
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.525
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3794
+      name: Map
+    - type: mrr@10
+      value: 0.5704
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4269
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.5135
+      name: Map
+    - type: mrr@10
+      value: 0.518
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5685
+      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: 0.4464
+      name: Map
+    - type: mrr@10
+      value: 0.5079
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5068
+      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-no-pos-weight")
+# Get scores for pairs of texts
+pairs = [
+    ['what is a default final judgment?', 'Default judgment is a binding judgment in favor of either party based on some failure to take action by the other party. Most often, it is a judgment in favor of a plaintiff when the defendant has not responded to a summons or has failed to appear before a court of law. The failure to take action is the default.'],
+    ['what is a default final judgment?', "A default judgment is a judgment issued against a party that doesn't bother to defend itself at all. ... A summary judgment is a judgment issued against a party that doesn't have any evidence to support its claims. Summary judgment means: “You can't prove it; therefore you lose.”"],
+    ['what is a default final judgment?', 'This judgment is seen as being mentioned in Hebrews 9:27, which states that "it is appointed unto men once to die, but after this the judgment".'],
+    ['what is a default final judgment?', "If you don't file an answer or go to court, your landlord can ask the judge to find you in default. Then the judge may let your landlord show there is reason for you to be evicted. If the landlord does that, the judge can enter a default judgment against you."],
+    ['what is a default final judgment?', 'What can High Court Enforcement Officers do to enforce judgment? HCEOs can take control of goods or possessions to the value of the unpaid judgment, and may also attempt to take goods to cover the costs of enforcement, court costs, and interest on the debt.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'what is a default final judgment?',
+    [
+        'Default judgment is a binding judgment in favor of either party based on some failure to take action by the other party. Most often, it is a judgment in favor of a plaintiff when the defendant has not responded to a summons or has failed to appear before a court of law. The failure to take action is the default.',
+        "A default judgment is a judgment issued against a party that doesn't bother to defend itself at all. ... A summary judgment is a judgment issued against a party that doesn't have any evidence to support its claims. Summary judgment means: “You can't prove it; therefore you lose.”",
+        'This judgment is seen as being mentioned in Hebrews 9:27, which states that "it is appointed unto men once to die, but after this the judgment".',
+        "If you don't file an answer or go to court, your landlord can ask the judge to find you in default. Then the judge may let your landlord show there is reason for you to be evicted. If the landlord does that, the judge can enter a default judgment against you.",
+        'What can High Court Enforcement Officers do to enforce judgment? HCEOs can take control of goods or possessions to the value of the unpaid judgment, and may also attempt to take goods to cover the costs of enforcement, court costs, and interest on the debt.',
+    ]
+)
+# [{'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         | 0.7323 (+0.2012)     |
+| mrr@10      | 0.7309 (+0.2069)     |
+| **ndcg@10** | **0.7731 (+0.1818)** |
+#### 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         | 0.4464 (-0.0431)     | 0.3794 (+0.1184)     | 0.5135 (+0.0939)     |
+| mrr@10      | 0.4352 (-0.0423)     | 0.5704 (+0.0706)     | 0.5180 (+0.0913)     |
+| **ndcg@10** | **0.5250 (-0.0154)** | **0.4269 (+0.1018)** | **0.5685 (+0.0679)** |
+#### 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         | 0.4464 (+0.0564)     |
+| mrr@10      | 0.5079 (+0.0399)     |
+| **ndcg@10** | **0.5068 (+0.0514)** |
+<!--
+## 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: 45.16 characters</li><li>max: 84 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 252.6 characters</li><li>max: 361 characters</li></ul> | <ul><li>0: ~82.80%</li><li>1: ~17.20%</li></ul> |
+* Samples:
+  | question                                       | answer                                                                                                                                                                                                                                                                                                                                 | label          |
+  |:-----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>what is a default final judgment?</code> | <code>Default judgment is a binding judgment in favor of either party based on some failure to take action by the other party. Most often, it is a judgment in favor of a plaintiff when the defendant has not responded to a summons or has failed to appear before a court of law. The failure to take action is the default.</code> | <code>1</code> |
+  | <code>what is a default final judgment?</code> | <code>A default judgment is a judgment issued against a party that doesn't bother to defend itself at all. ... A summary judgment is a judgment issued against a party that doesn't have any evidence to support its claims. Summary judgment means: “You can't prove it; therefore you lose.”</code>                                  | <code>0</code> |
+  | <code>what is a default final judgment?</code> | <code>This judgment is seen as being mentioned in Hebrews 9:27, which states that "it is appointed unto men once to die, but after this the judgment".</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": null
+  }
+  ```
+### 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       | -             | 0.1386 (-0.4527)     | 0.0206 (-0.5198)         | 0.2387 (-0.0863)          | 0.0515 (-0.4491)     | 0.1036 (-0.3517)           |
+| 0.0001     | 1        | 1.0425        | -                    | -                        | -                         | -                    | -                          |
+| 0.0221     | 200      | 0.5627        | -                    | -                        | -                         | -                    | -                          |
+| 0.0443     | 400      | 0.4593        | -                    | -                        | -                         | -                    | -                          |
+| 0.0664     | 600      | 0.3714        | -                    | -                        | -                         | -                    | -                          |
+| 0.0885     | 800      | 0.2955        | -                    | -                        | -                         | -                    | -                          |
+| 0.1106     | 1000     | 0.2829        | 0.7083 (+0.1171)     | 0.4992 (-0.0412)         | 0.3110 (-0.0141)          | 0.4795 (-0.0211)     | 0.4299 (-0.0255)           |
+| 0.1328     | 1200     | 0.2696        | -                    | -                        | -                         | -                    | -                          |
+| 0.1549     | 1400     | 0.2548        | -                    | -                        | -                         | -                    | -                          |
+| 0.1770     | 1600     | 0.2485        | -                    | -                        | -                         | -                    | -                          |
+| 0.1992     | 1800     | 0.2326        | -                    | -                        | -                         | -                    | -                          |
+| 0.2213     | 2000     | 0.241         | 0.7461 (+0.1549)     | 0.5350 (-0.0054)         | 0.3701 (+0.0451)          | 0.5339 (+0.0332)     | 0.4797 (+0.0243)           |
+| 0.2434     | 2200     | 0.2373        | -                    | -                        | -                         | -                    | -                          |
+| 0.2655     | 2400     | 0.2356        | -                    | -                        | -                         | -                    | -                          |
+| 0.2877     | 2600     | 0.2207        | -                    | -                        | -                         | -                    | -                          |
+| 0.3098     | 2800     | 0.222         | -                    | -                        | -                         | -                    | -                          |
+| 0.3319     | 3000     | 0.2258        | 0.7443 (+0.1531)     | 0.5554 (+0.0150)         | 0.3921 (+0.0671)          | 0.5368 (+0.0361)     | 0.4948 (+0.0394)           |
+| 0.3541     | 3200     | 0.2182        | -                    | -                        | -                         | -                    | -                          |
+| 0.3762     | 3400     | 0.215         | -                    | -                        | -                         | -                    | -                          |
+| 0.3983     | 3600     | 0.2161        | -                    | -                        | -                         | -                    | -                          |
+| 0.4204     | 3800     | 0.2202        | -                    | -                        | -                         | -                    | -                          |
+| 0.4426     | 4000     | 0.2147        | 0.7542 (+0.1629)     | 0.5465 (+0.0061)         | 0.4047 (+0.0797)          | 0.5199 (+0.0193)     | 0.4904 (+0.0350)           |
+| 0.4647     | 4200     | 0.2177        | -                    | -                        | -                         | -                    | -                          |
+| 0.4868     | 4400     | 0.2129        | -                    | -                        | -                         | -                    | -                          |
+| 0.5090     | 4600     | 0.2099        | -                    | -                        | -                         | -                    | -                          |
+| 0.5311     | 4800     | 0.2105        | -                    | -                        | -                         | -                    | -                          |
+| 0.5532     | 5000     | 0.2101        | 0.7644 (+0.1731)     | 0.5448 (+0.0044)         | 0.4157 (+0.0907)          | 0.5746 (+0.0739)     | 0.5117 (+0.0563)           |
+| 0.5753     | 5200     | 0.2034        | -                    | -                        | -                         | -                    | -                          |
+| 0.5975     | 5400     | 0.2047        | -                    | -                        | -                         | -                    | -                          |
+| 0.6196     | 5600     | 0.2043        | -                    | -                        | -                         | -                    | -                          |
+| 0.6417     | 5800     | 0.2029        | -                    | -                        | -                         | -                    | -                          |
+| 0.6639     | 6000     | 0.2021        | 0.7699 (+0.1786)     | 0.5250 (-0.0154)         | 0.4264 (+0.1013)          | 0.5491 (+0.0484)     | 0.5002 (+0.0448)           |
+| 0.6860     | 6200     | 0.2048        | -                    | -                        | -                         | -                    | -                          |
+| 0.7081     | 6400     | 0.2033        | -                    | -                        | -                         | -                    | -                          |
+| 0.7303     | 6600     | 0.2017        | -                    | -                        | -                         | -                    | -                          |
+| 0.7524     | 6800     | 0.1976        | -                    | -                        | -                         | -                    | -                          |
+| 0.7745     | 7000     | 0.1989        | 0.7722 (+0.1810)     | 0.5732 (+0.0328)         | 0.4206 (+0.0956)          | 0.6013 (+0.1007)     | 0.5317 (+0.0763)           |
+| 0.7966     | 7200     | 0.1925        | -                    | -                        | -                         | -                    | -                          |
+| 0.8188     | 7400     | 0.1917        | -                    | -                        | -                         | -                    | -                          |
+| 0.8409     | 7600     | 0.2002        | -                    | -                        | -                         | -                    | -                          |
+| 0.8630     | 7800     | 0.1913        | -                    | -                        | -                         | -                    | -                          |
+| 0.8852     | 8000     | 0.191         | 0.7707 (+0.1794)     | 0.5412 (+0.0007)         | 0.4332 (+0.1082)          | 0.5508 (+0.0502)     | 0.5084 (+0.0530)           |
+| 0.9073     | 8200     | 0.1929        | -                    | -                        | -                         | -                    | -                          |
+| 0.9294     | 8400     | 0.1989        | -                    | -                        | -                         | -                    | -                          |
+| 0.9515     | 8600     | 0.1889        | -                    | -                        | -                         | -                    | -                          |
+| 0.9737     | 8800     | 0.1874        | -                    | -                        | -                         | -                    | -                          |
+| **0.9958** | **9000** | **0.1863**    | **0.7731 (+0.1818)** | **0.5250 (-0.0154)**     | **0.4269 (+0.1018)**      | **0.5685 (+0.0679)** | **0.5068 (+0.0514)**       |
+| -1         | -1       | -             | 0.7731 (+0.1818)     | 0.5250 (-0.0154)         | 0.4269 (+0.1018)          | 0.5685 (+0.0679)     | 0.5068 (+0.0514)           |
+* 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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