Add new CrossEncoder model

README.md

@@ -0,0 +1,391 @@
+---
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:1220
+- loss:ListNetLoss
+base_model: Alibaba-NLP/gte-reranker-modernbert-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+---
+
+# CrossEncoder based on Alibaba-NLP/gte-reranker-modernbert-base
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-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:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision 5e9cbf663291297cef19cd5fc58931e89f025e68 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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("yjoonjang/preranker-gte-reranker-modernbert-base-listnetloss")
+# Get scores for pairs of texts
+pairs = [
+    ['Hi, I need a new username. My first name is John and my last name is Doe.', '{\n    "name": "generate_username",\n    "description": "Generate a unique username for a new user",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "first_name": {\n                "type": "string",\n                "description": "The first name of the user"\n            },\n            "last_name": {\n                "type": "string",\n                "description": "The last name of the user"\n            }\n        },\n        "required": [\n            "first_name",\n            "last_name"\n        ]\n    }\n}'],
+    ['Hi, I need a new username. My first name is John and my last name is Doe.', '{\n    "name": "create_invoice",\n    "description": "Create a new invoice",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "items": {\n                "type": "array",\n                "items": {\n                    "type": "string"\n                },\n                "description": "The list of items in the invoice"\n            },\n            "total_amount": {\n                "type": "number",\n                "description": "The total amount of the invoice"\n            }\n        },\n        "required": [\n            "customer_name",\n            "items",\n            "total_amount"\n        ]\n    }\n}'],
+    ['Hi, I need a new username. My first name is John and my last name is Doe.', '{\n    "name": "create_invoice",\n    "description": "Create an invoice",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "amount": {\n                "type": "number",\n                "description": "The amount of the invoice"\n            },\n            "due_date": {\n                "type": "string",\n                "description": "The due date of the invoice"\n            }\n        },\n        "required": [\n            "customer_name",\n            "amount"\n        ]\n    }\n}'],
+    ['Hi, I need a new username. My first name is John and my last name is Doe.', '{\n    "name": "create_invoice",\n    "description": "Create an invoice with specified details",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "order_items": {\n                "type": "array",\n                "items": {\n                    "type": "object",\n                    "properties": {\n                        "item_name": {\n                            "type": "string",\n                            "description": "The name of the item"\n                        },\n                        "quantity": {\n                            "type": "integer",\n                            "description": "The quantity of the item"\n                        },\n                        "unit_price": {\n                            "type": "number",\n                            "description": "The unit price of the item"\n                        }\n                    },\n                    "required": [\n                        "item_name",\n                        "quantity",\n                        "unit_price"\n                    ]\n                }\n            }\n        },\n        "required": [\n            "customer_name",\n            "order_items"\n        ]\n    }\n}'],
+    ['Hi, I need a new username. My first name is John and my last name is Doe.', '{\n    "name": "generate_random_joke",\n    "description": "Generate a random joke",\n    "parameters": {}\n}'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'Hi, I need a new username. My first name is John and my last name is Doe.',
+    [
+        '{\n    "name": "generate_username",\n    "description": "Generate a unique username for a new user",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "first_name": {\n                "type": "string",\n                "description": "The first name of the user"\n            },\n            "last_name": {\n                "type": "string",\n                "description": "The last name of the user"\n            }\n        },\n        "required": [\n            "first_name",\n            "last_name"\n        ]\n    }\n}',
+        '{\n    "name": "create_invoice",\n    "description": "Create a new invoice",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "items": {\n                "type": "array",\n                "items": {\n                    "type": "string"\n                },\n                "description": "The list of items in the invoice"\n            },\n            "total_amount": {\n                "type": "number",\n                "description": "The total amount of the invoice"\n            }\n        },\n        "required": [\n            "customer_name",\n            "items",\n            "total_amount"\n        ]\n    }\n}',
+        '{\n    "name": "create_invoice",\n    "description": "Create an invoice",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "amount": {\n                "type": "number",\n                "description": "The amount of the invoice"\n            },\n            "due_date": {\n                "type": "string",\n                "description": "The due date of the invoice"\n            }\n        },\n        "required": [\n            "customer_name",\n            "amount"\n        ]\n    }\n}',
+        '{\n    "name": "create_invoice",\n    "description": "Create an invoice with specified details",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "order_items": {\n                "type": "array",\n                "items": {\n                    "type": "object",\n                    "properties": {\n                        "item_name": {\n                            "type": "string",\n                            "description": "The name of the item"\n                        },\n                        "quantity": {\n                            "type": "integer",\n                            "description": "The quantity of the item"\n                        },\n                        "unit_price": {\n                            "type": "number",\n                            "description": "The unit price of the item"\n                        }\n                    },\n                    "required": [\n                        "item_name",\n                        "quantity",\n                        "unit_price"\n                    ]\n                }\n            }\n        },\n        "required": [\n            "customer_name",\n            "order_items"\n        ]\n    }\n}',
+        '{\n    "name": "generate_random_joke",\n    "description": "Generate a random joke",\n    "parameters": {}\n}',
+    ]
+)
+# [{'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.*
+-->
+
+<!--
+## 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: 1,220 training samples
+* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                            | docs                                                                                  | labels                                                                                |
+  |:--------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
+  | type    | string                                                                                           | list                                                                                  | list                                                                                  |
+  | details | <ul><li>min: 23 characters</li><li>mean: 113.88 characters</li><li>max: 264 characters</li></ul> | <ul><li>min: 2 elements</li><li>mean: 5.80 elements</li><li>max: 9 elements</li></ul> | <ul><li>min: 2 elements</li><li>mean: 5.80 elements</li><li>max: 9 elements</li></ul> |
+* Samples:
+  | query                                                                                                                                                                   | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>Hi, I need a new username. My first name is John and my last name is Doe.</code>                                                                                  | <code>['{\n    "name": "generate_username",\n    "description": "Generate a unique username for a new user",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "first_name": {\n                "type": "string",\n                "description": "The first name of the user"\n            },\n            "last_name": {\n                "type": "string",\n                "description": "The last name of the user"\n            }\n        },\n        "required": [\n            "first_name",\n            "last_name"\n        ]\n    }\n}', '{\n    "name": "create_invoice",\n    "description": "Create a new invoice",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "customer_name": {\n                "type": "string",\n                "description": "The name of the customer"\n            },\n            "items": {\n                "type": "array",\n                "items": {\n                    "type": "string"\n                }...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>Hey, I'm feeling a bit bored. Can you tell me a random fact?</code>                                                                                               | <code>['{\n    "name": "get_random_fact",\n    "description": "Get a random fact",\n    "parameters": {}\n}', '{\n    "name": "get_news",\n    "description": "Get the latest news articles",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "category": {\n                "type": "string",\n                "description": "The category of news articles to retrieve"\n            },\n            "country": {\n                "type": "string",\n                "description": "The country for which to retrieve news articles"\n            }\n        },\n        "required": [\n            "category",\n            "country"\n        ]\n    }\n}', '{\n    "name": "get_news_headlines",\n    "description": "Get the latest news headlines",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "country": {\n                "type": "string",\n                "description": "The country for which news headlines are requested"\n            }\...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>I need to create a poll for my team to decide our next team building activity. The options are "Bowling", "Movie Night", "Paintball", and "Cooking Class".</code> | <code>['{\n    "name": "create_poll",\n    "description": "Create a new poll",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "question": {\n                "type": "string",\n                "description": "The question of the poll"\n            },\n            "options": {\n                "type": "array",\n                "items": {\n                    "type": "string"\n                },\n                "description": "The options for the poll"\n            }\n        },\n        "required": [\n            "question",\n            "options"\n        ]\n    }\n}', '{\n    "name": "schedule_meeting",\n    "description": "Schedule a meeting with specified participants",\n    "parameters": {\n        "type": "object",\n        "properties": {\n            "participants": {\n                "type": "array",\n                "items": {\n                    "type": "string"\n                },\n                "description": "The participants of the meet...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+* Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "mini_batch_size": 4
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `seed`: 12
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 8
+- `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`: 0.0
+- `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`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `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 |
+|:------:|:----:|:-------------:|
+| 0.0033 | 1    | 2.4675        |
+| 0.0164 | 5    | 2.1357        |
+| 0.0328 | 10   | 2.1358        |
+| 0.0492 | 15   | 2.1071        |
+| 0.0656 | 20   | 2.1268        |
+| 0.0820 | 25   | 2.1258        |
+| 0.0984 | 30   | 2.1272        |
+| 0.1148 | 35   | 2.1288        |
+| 0.1311 | 40   | 2.1298        |
+| 0.1475 | 45   | 2.125         |
+| 0.1639 | 50   | 2.1249        |
+| 0.1803 | 55   | 2.0922        |
+| 0.1967 | 60   | 2.1357        |
+| 0.2131 | 65   | 2.1275        |
+| 0.2295 | 70   | 2.1204        |
+| 0.2459 | 75   | 2.1348        |
+| 0.2623 | 80   | 2.1246        |
+| 0.2787 | 85   | 2.1251        |
+| 0.2951 | 90   | 2.128         |
+| 0.3115 | 95   | 2.1261        |
+| 0.3279 | 100  | 2.124         |
+| 0.3443 | 105  | 2.1313        |
+| 0.3607 | 110  | 2.1196        |
+| 0.3770 | 115  | 2.1201        |
+| 0.3934 | 120  | 2.1227        |
+| 0.4098 | 125  | 2.1317        |
+| 0.4262 | 130  | 2.1209        |
+| 0.4426 | 135  | 2.0453        |
+| 0.4590 | 140  | 2.1211        |
+| 0.4754 | 145  | 2.1193        |
+| 0.4918 | 150  | 2.1233        |
+| 0.5082 | 155  | 2.1227        |
+| 0.5246 | 160  | 2.1229        |
+| 0.5410 | 165  | 2.1313        |
+| 0.5574 | 170  | 2.1218        |
+| 0.5738 | 175  | 2.1241        |
+| 0.5902 | 180  | 2.1213        |
+| 0.6066 | 185  | 2.1215        |
+| 0.6230 | 190  | 2.1196        |
+| 0.6393 | 195  | 2.1211        |
+| 0.6557 | 200  | 2.1252        |
+| 0.6721 | 205  | 2.1018        |
+| 0.6885 | 210  | 2.1209        |
+| 0.7049 | 215  | 2.1201        |
+| 0.7213 | 220  | 2.1232        |
+| 0.7377 | 225  | 2.1259        |
+| 0.7541 | 230  | 2.1235        |
+| 0.7705 | 235  | 2.125         |
+| 0.7869 | 240  | 2.1188        |
+| 0.8033 | 245  | 2.1305        |
+| 0.8197 | 250  | 2.1273        |
+| 0.8361 | 255  | 2.1193        |
+| 0.8525 | 260  | 2.1255        |
+| 0.8689 | 265  | 2.0802        |
+| 0.8852 | 270  | 2.1238        |
+| 0.9016 | 275  | 2.125         |
+| 0.9180 | 280  | 2.1268        |
+| 0.9344 | 285  | 2.1237        |
+| 0.9508 | 290  | 2.1189        |
+| 0.9672 | 295  | 2.1244        |
+| 0.9836 | 300  | 2.1131        |
+| 1.0    | 305  | 2.1242        |
+
+
+### Framework Versions
+- Python: 3.10.16
+- Sentence Transformers: 4.0.1
+- Transformers: 4.50.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### ListNetLoss
+```bibtex
+@inproceedings{cao2007learning,
+    title={Learning to Rank: From Pairwise Approach to Listwise Approach},
+    author={Cao, Zhe and Qin, Tao and Liu, Tie-Yan and Tsai, Ming-Feng and Li, Hang},
+    booktitle={Proceedings of the 24th international conference on Machine learning},
+    pages={129--136},
+    year={2007}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,56 @@
+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": true,
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