Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 640,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+language:
+- 'no'
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:556367
+- loss:CachedMultipleNegativesRankingLoss
+base_model: Murhaf/ltg-norbert4-base_ndla
+widget:
+- source_sentence: Inne i igloen gjør den unge mannen seg klar for sitt overnattingsopphold.
+  sentences:
+  - Folk danser i gaten.
+  - Den unge mannen gjør seg klar for sitt overnattingsopphold.
+  - Den unge mannen gjør seg klar til å dra.
+- source_sentence: En kvinne i rullestol snakker med vennen sin mens hun er omgitt
+    av andre mennesker som går i parken.
+  sentences:
+  - Barna blir fotografert.
+  - Kvinnen er utendørs.
+  - Kvinnen spiser en pølse midt i soverommet sitt.
+- source_sentence: En kvinne løper langs en steinete strand.
+  sentences:
+  - En mann og en kvinne ser på frukt og grønnsaker.
+  - En kvinne løper.
+  - En kvinne sitter ved et piknikbord nær den steinete kysten.
+- source_sentence: To basketballspillere i svart og hvitt antrekk står på en basketballbane
+    og snakker.
+  sentences:
+  - De to basketballspillerne snakker sammen.
+  - Den unge gutten multitasker.
+  - De to basketballspillerne sitter på benken.
+- source_sentence: En mann lager et sandmaleri på gulvet.
+  sentences:
+  - En mann lager kunst.
+  - På fornøyelsesturen var det to jenter som smilte og lo
+  - En kvinne ødelegger et sandmaleri.
+datasets:
+- Murhaf/all-nli-norwegian
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on Murhaf/ltg-norbert4-base_ndla
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: nob all nli test
+      type: nob_all_nli_test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9470000267028809
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on Murhaf/ltg-norbert4-base_ndla
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Murhaf/ltg-norbert4-base_ndla](https://huggingface.co/Murhaf/ltg-norbert4-base_ndla) on the [all-nli-norwegian](https://huggingface.co/datasets/Murhaf/all-nli-norwegian) dataset. It maps sentences & paragraphs to a 640-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Murhaf/ltg-norbert4-base_ndla](https://huggingface.co/Murhaf/ltg-norbert4-base_ndla) <!-- at revision 762fb095e1c571e52d8690bf07ec8b65d3551026 -->
+- **Maximum Sequence Length:** 75 tokens
+- **Output Dimensionality:** 640 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli-norwegian](https://huggingface.co/datasets/Murhaf/all-nli-norwegian)
+- **Language:** no
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 75, 'do_lower_case': False, 'architecture': 'GptBertModel'})
+  (1): Pooling({'word_embedding_dimension': 640, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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 SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Murhaf/ltg-norbert4-base_ndla-all-nli")
+# Run inference
+sentences = [
+    'En mann lager et sandmaleri på gulvet.',
+    'En mann lager kunst.',
+    'En kvinne ødelegger et sandmaleri.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 640]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.5608, 0.3858],
+#         [0.5608, 1.0000, 0.2424],
+#         [0.3858, 0.2424, 1.0000]])
+```
+
+<!--
+### 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
+
+#### Triplet
+
+* Dataset: `nob_all_nli_test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| **cosine_accuracy** | **0.947** |
+
+<!--
+## 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
+
+#### all-nli-norwegian
+
+* Dataset: [all-nli-norwegian](https://huggingface.co/datasets/Murhaf/all-nli-norwegian) at [98cabde](https://huggingface.co/datasets/Murhaf/all-nli-norwegian/tree/98cabded09bfe5f505757840026ecdf6a357a04c)
+* Size: 556,367 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          | negative                                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 9.53 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.03 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.7 tokens</li><li>max: 49 tokens</li></ul> |
+* Samples:
+  | anchor                                                         | positive                                        | negative                                                       |
+  |:---------------------------------------------------------------|:------------------------------------------------|:---------------------------------------------------------------|
+  | <code>En person på en hest hopper over et havarert fly.</code> | <code>En person er utendørs, på en hest.</code> | <code>En person er på en diner og bestiller en omelett.</code> |
+  | <code>Barn smiler og vinker til kameraet</code>                | <code>Det er barn til stede</code>              | <code>Barna rynker pannen</code>                               |
+  | <code>En gutt hopper på skateboard midt på en rød bro.</code>  | <code>Gutten gjør et skateboardtriks.</code>    | <code>Gutten skater nedover fortauet.</code>                   |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 32,
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### all-nli-norwegian
+
+* Dataset: [all-nli-norwegian](https://huggingface.co/datasets/Murhaf/all-nli-norwegian) at [98cabde](https://huggingface.co/datasets/Murhaf/all-nli-norwegian/tree/98cabded09bfe5f505757840026ecdf6a357a04c)
+* Size: 6,561 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         | negative                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 17.72 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.98 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.5 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                          | positive                                                  | negative                                                    |
+  |:--------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------|:------------------------------------------------------------|
+  | <code>To kvinner klemmer mens de holder take-away pakker.</code>                                                                | <code>To kvinner holder pakker.</code>                    | <code>Mennene slåss utenfor en deli.</code>                 |
+  | <code>To små barn i blå drakter, en med nummer 9 og en med nummer 2, står på trinn i et bad og vasker hendene i en vask.</code> | <code>To barn i nummererte drakter vasker hendene.</code> | <code>To barn i jakker går til skolen.</code>               |
+  | <code>En mann selger donuts til en kunde under et verdensutstillingsarrangement holdt i byen Angeles</code>                     | <code>En mann selger donuts til en kunde.</code>          | <code>En kvinne drikker kaffen sin på en liten kafé.</code> |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 32,
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 512
+- `per_device_eval_batch_size`: 256
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### 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`: 512
+- `per_device_eval_batch_size`: 256
+- `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`: 5e-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`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 2
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `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}
+- `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}
+- `parallelism_config`: 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
+- `hub_revision`: None
+- `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
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | nob_all_nli_test_cosine_accuracy |
+|:------:|:----:|:-------------:|:---------------:|:--------------------------------:|
+| 0.3690 | 100  | 1.8282        | 0.6138          | 0.9420                           |
+| 0.7380 | 200  | 1.1887        | 0.5645          | 0.9470                           |
+
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.1
+- Transformers: 4.56.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.10.1
+- Datasets: 4.1.1
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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 +1,43 @@
+{
+  "architectures": [
+    "GptBertModel"
+  ],
+  "attention_dropout": 0.0,
+  "attn_implementation": null,
+  "auto_map": {
+    "AutoConfig": "configuration_gptbert.GptBertConfig",
+    "AutoModel": "modeling_gptbert.GptBertModel",
+    "AutoModelForCausalLM": "modeling_gptbert.GptBertForCausalLM",
+    "AutoModelForMaskedLM": "modeling_gptbert.GptBertForMaskedLM",
+    "AutoModelForMultipleChoice": "modeling_gptbert.GptBertForMultipleChoice",
+    "AutoModelForQuestionAnswering": "modeling_gptbert.GptBertForQuestionAnswering",
+    "AutoModelForSequenceClassification": "modeling_gptbert.GptBertForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_gptbert.GptBertForTokenClassification"
+  },
+  "bos_token_id": 1,
+  "classifier_dropout": 0.2,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.1,
+  "eos_token_id": 2,
+  "global_window_length": 8192,
+  "hidden_dropout": 0.0,
+  "hidden_size": 640,
+  "intermediate_size": 1664,
+  "layer_norm_eps": 1e-07,
+  "local_global_ratio": 4,
+  "local_window_length": 256,
+  "mask_token_id": 4,
+  "max_sequence_length": 16384,
+  "model": "norbert4",
+  "num_attention_heads": 10,
+  "num_layers": 24,
+  "pad_token_id": 3,
+  "query_key_head_size": 64,
+  "rope_theta": 160000,
+  "transformers_version": "4.56.2",
+  "unk_token_id": 0,
+  "use_cache": false,
+  "value_head_size": 64,
+  "vocab_size": 51200
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

configuration_gptbert.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import copy
+from transformers.configuration_utils import PretrainedConfig
+
+
+class GptBertConfig(PretrainedConfig):
+
+    def __init__(
+        self,
+        config_file: Path | str | None = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.model = "norbert4"
+
+        if config_file is not None:
+            if type(config_file) is str:
+                config_file = Path(config_file)
+            assert type(config_file) is not Path, "The config_file should either be a Path or str"
+            with config_file.open("r") as file:
+                config = json.load(file)
+
+            for attr, value in config.items():
+                if isinstance(value, str):
+                    value = value.lower()
+                setattr(self, attr, value)
+
+        for attr, value in kwargs.items():
+            if isinstance(value, str):
+                value = value.lower()
+            setattr(self, attr, value)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:041900178a3b1d3d04eaeafe056d49541f203dc201c591bebf88f3d735e004f8
+size 595640976

modeling_gptbert.py

@@ -0,0 +1,1105 @@
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch import _softmax_backward_data as _softmax_backward_data
+
+from functools import partial, lru_cache
+
+from .configuration_gptbert import GptBertConfig
+from transformers.modeling_utils import PreTrainedModel
+from transformers.activations import gelu_new
+from transformers.utils import is_flash_attn_2_available, logging
+from transformers.modeling_outputs import (
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+    BaseModelOutput,
+    CausalLMOutput
+)
+import math
+from typing import TYPE_CHECKING, Optional, Union, Tuple, List
+
+logger = logging.get_logger(__name__)
+
+
+# Workaround for transformers < 4.36.0 check_imports issue
+# See: https://github.com/huggingface/transformers/issues/28459
+try:
+    if is_flash_attn_2_available():
+        from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+        from flash_attn.layers.rotary import RotaryEmbedding
+        from flash_attn.ops.triton.rotary import apply_rotary
+    else:
+        flash_attn_varlen_qkvpacked_func, RotaryEmbedding, apply_rotary = None, object, None
+        logger.warning_once(
+            "NorBERT4 støtter FlashAttention, men det er ikke funnet i miljøet ditt. Du bør vurdere å oppdatere miljøet ditt for å få raskere og mindre minnekrevende behandling."
+        )
+except ImportError:
+    flash_attn_varlen_qkvpacked_func, RotaryEmbedding, apply_rotary = None, object, None
+    logger.warning_once(
+        "NorBERT4 støtter FlashAttention, men det er ikke funnet i miljøet ditt. Du bør vurdere å oppdatere miljøet ditt for å få raskere og mindre minnekrevende behandling."
+    )
+
+
+# from https://github.com/huggingface/transformers/blob/main/src/transformers/models/modernbert/modeling_modernbert.py
+@torch.compiler.disable()
+def _unpad_input(input_ids: torch.Tensor, attention_mask: torch.Tensor):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = int(seqlens_in_batch.max().item())
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+
+    if input_ids.dim() == 2:
+        unpadded_inputs = input_ids.flatten()[indices]
+    else:
+        batch_size, sequence_length, *rest = input_ids.shape
+        shape = batch_size * sequence_length
+        unpadded_inputs = input_ids.view(shape, *rest)[indices]
+
+    return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch
+
+
+# from https://github.com/huggingface/transformers/blob/main/src/transformers/models/modernbert/modeling_modernbert.py
+def _pad_output(input_ids: torch.Tensor, indices: torch.Tensor, batch_size: int, sequence_length: int) -> torch.Tensor:
+    if input_ids.dim() == 1:
+        output = torch.zeros(batch_size * sequence_length, dtype=input_ids.dtype, device=input_ids.device)
+        output[indices] = input_ids
+        padded_inputs = output.view(batch_size, sequence_length)
+    else:
+        _, *rest = input_ids.shape
+        output = torch.zeros(batch_size * sequence_length, *rest, dtype=input_ids.dtype, device=input_ids.device)
+        output[indices] = input_ids
+        padded_inputs = output.view(batch_size, sequence_length, *rest)
+
+    return padded_inputs
+
+
+class CastedLinear(nn.Linear):
+    def __init__(self, in_features, out_features, bias):
+        super().__init__(in_features, out_features, bias=bias)
+
+    def forward(self, x):
+        return F.linear(x, self.weight.type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class CastedLinearIn(nn.Linear):
+    def __init__(self, in_features, out_features, bias):
+        super().__init__(in_features, out_features, bias=bias)
+        self.scale = nn.Parameter(torch.ones(in_features))
+
+    def forward(self, x):
+        return F.linear(x, (self.weight * (self.scale + 1.0).unsqueeze(0)).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class MultiCastedLinearOrthoIn(nn.Module):
+    def __init__(self, in_features, out_features, bias):
+        super().__init__()
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+        self.weights = nn.ParameterList()
+        for out_feature in out_features:
+            self.weights.append(nn.Parameter(torch.empty((out_feature, in_features))))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(sum(out_features)))
+        else:
+            self.bias = self.register_parameter("bias", None)
+
+        self.scale = nn.Parameter(torch.ones(in_features))
+
+    def forward(self, x):
+        return F.linear(x, (torch.cat([weight for weight in self.weights], dim=0) * (self.scale + 1.0).unsqueeze(0)).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class GeGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        return x * gelu_new(gate)
+
+
+class Embedding(nn.Module):
+    def __init__(self, config: GptBertConfig):
+        super().__init__()
+
+        self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.word_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False, bias=False)
+        self.word_scale = nn.Parameter(torch.zeros(config.hidden_size))
+        self.dropout = nn.Dropout(config.embedding_dropout)
+
+    def forward(self, input_ids: torch.Tensor):
+        word_embedding = self.word_embedding(input_ids)
+        word_embedding = self.word_norm(word_embedding)
+        word_embedding = word_embedding * (self.word_scale + 1.0)
+
+        return self.dropout(word_embedding)
+
+
+class LMClassifier(nn.Module):
+    def __init__(self, config: GptBertConfig, n_labels: int):
+        super().__init__()
+
+        self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.projection = CastedLinearIn(config.hidden_size, config.hidden_size, bias=False)
+        self.post_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.emb2vocab = CastedLinearIn(config.hidden_size, n_labels, bias=True)
+
+    def forward(self, x: torch.Tensor):
+        x = self.pre_norm(x.float()).type_as(x)
+        x = self.projection(x)
+        x = gelu_new(x)
+        x = self.post_norm(x.float()).type_as(x)
+        x = self.emb2vocab(x)
+        return x
+
+
+class Classifier(nn.Module):
+    def __init__(self, config: GptBertConfig, n_labels: int):
+        super().__init__()
+
+        self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.projection = CastedLinearIn(config.hidden_size, config.hidden_size, bias=False)
+        self.post_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.output_projection = CastedLinearIn(config.hidden_size, n_labels, bias=True)
+
+    def forward(self, x: torch.Tensor):
+        x = self.pre_norm(x.float()).type_as(x)
+        x = self.projection(x)
+        x = gelu_new(x)
+        x = self.post_norm(x.float()).type_as(x)
+        x = self.dropout(x)
+        x = self.output_projection(x)
+        return x
+
+
+# from https://github.com/huggingface/transformers/blob/main/src/transformers/models/modernbert/modeling_modernbert.py
+def flash_attention_forward(qkv: torch.Tensor, rotary_emb: UnpaddedRotaryEmbedding, cu_seqlens: torch.Tensor, max_seqlen: int, causal: bool, local_attention: Tuple[int, int], dropout_p: float, deterministic: bool, target_dtype: torch.dtype = torch.bfloat16, **_kwargs):
+    qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
+
+    convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
+    if convert_dtype:
+        # FA2 implementation only supports fp16 and bf16. If FA2 is supported,
+        # bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
+        orig_dtype = qkv.dtype
+        qkv = qkv.to(target_dtype)
+
+        attn = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            dropout_p=dropout_p,
+            deterministic=deterministic,
+            window_size=local_attention,
+            causal=False
+        )
+        attn = attn.to(orig_dtype)  # type: ignore
+    else:
+        attn = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            dropout_p=dropout_p,
+            deterministic=deterministic,
+            window_size=local_attention,
+            causal=False
+        )
+    return attn
+
+
+# from https://github.com/huggingface/transformers/blob/main/src/transformers/models/modernbert/modeling_modernbert.py
+class ApplyRotaryEmbUnpad(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, qkv, cos, sin, cu_seqlens: Optional[torch.Tensor] = None, max_seqlen: Optional[int] = None):
+        # (total_nnz, 3, nheads, headdim)
+        qkv = qkv.contiguous()
+        total_nnz, _three, _nheads, headdim = qkv.shape
+        # We need qkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+        # we get the same tensor
+        # qk = rearrange(qkv[:, :2], "b_s t h d -> b_s (t h) d")
+        qk = qkv[:, :2].view(total_nnz, -1, headdim)
+        apply_rotary(qk, cos, sin, seqlen_offsets=0, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, interleaved=False, inplace=True)
+
+        ctx.save_for_backward(cos, sin, cu_seqlens)
+        ctx.max_seqlen = max_seqlen
+        return qkv
+
+    @staticmethod
+    def backward(ctx, do):
+        cos, sin, cu_seqlens = ctx.saved_tensors
+        do = do.contiguous()
+        total_nnz, _three, _nheads, headdim = do.shape
+        # We need dqkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+        # we get the same tensor
+        dqk = do[:, :2].view(total_nnz, -1, headdim)
+        apply_rotary(
+            dqk,
+            cos,
+            sin,
+            seqlen_offsets=0,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=ctx.max_seqlen,
+            interleaved=False,
+            inplace=True,
+            conjugate=True,
+        )
+
+        return do, None, None, None, None, None, None
+
+
+# from https://github.com/huggingface/transformers/blob/main/src/transformers/models/modernbert/modeling_modernbert.py
+def apply_rotary_unpadded(qkv, cos, sin, cu_seqlens: Optional[torch.Tensor] = None, max_seqlen: Optional[int] = None):
+    return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)
+
+
+# from https://github.com/huggingface/transformers/blob/main/src/transformers/models/modernbert/modeling_modernbert.py
+class UnpaddedRotaryEmbedding(RotaryEmbedding):
+    def __init__(self, dim: int, base: float = 10000.0, max_seqlen: Optional[int] = None):
+        super().__init__(dim=dim, base=base, device=None, interleaved=False)
+        self.max_seqlen = max_seqlen
+
+    def forward(self, qkv: torch.Tensor, cu_seqlens: torch.Tensor, max_seqlen: Optional[int] = None) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        if max_seqlen is not None:
+            self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
+
+        qkv = apply_rotary_unpadded(
+            qkv,
+            self._cos_cached,
+            self._sin_cached,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+
+        return qkv
+
+
+class RotaryPositionalEmbeddings(nn.Module):
+    def __init__(self, config, theta: int):
+        super().__init__()
+
+        head_size = config.query_key_head_size
+        assert head_size % 2 == 0
+        max_seq_len = config.max_sequence_length
+
+        inv_freq = 1.0 / (theta ** (torch.arange(0, head_size, 2, dtype=torch.float32) / head_size))
+        pos = torch.arange(max_seq_len, dtype=torch.float32)
+        embedding = torch.einsum('n, d -> nd', pos, inv_freq)
+        embedding = torch.cat([embedding, embedding], dim=-1).unsqueeze(0)
+        self.register_buffer("cos_matrix", embedding.cos(), persistent=False)
+        self.register_buffer("sin_matrix", embedding.sin(), persistent=False)
+
+    def forward(self, x: torch.Tensor):
+        hidden_layer = x.float()
+
+        seq_len = x.shape[2]
+
+        cos_matrix = self.cos_matrix[:, None, :seq_len, :]
+        sin_matrix = self.sin_matrix[:, None, :seq_len, :]
+
+        x_rotate_half = torch.cat(
+            [
+                -hidden_layer[:, :, :, x.size(-1) // 2:],
+                hidden_layer[:, :, :, :x.size(-1) // 2]
+            ],
+            dim=-1
+        )
+
+        out = hidden_layer * cos_matrix + x_rotate_half * sin_matrix
+        return out.type_as(x)
+
+
+class MaskedSoftmax(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: torch.Tensor, mask: torch.BoolTensor, dim: int) -> torch.Tensor:
+        ctx.dim = dim
+        x.masked_fill_(mask, float('-inf'))
+        x = torch.softmax(x, ctx.dim)
+        x.masked_fill_(mask, 0.0)
+        ctx.save_for_backward(x)
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> tuple[torch.Tensor, None, None]:
+        output: torch.Tensor
+
+        output, = ctx.saved_tensors
+        inputGrad: torch.Tensor = _softmax_backward_data(grad_output, output, ctx.dim, output.dtype)
+        return inputGrad, None, None
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, config: GptBertConfig, layer_idx: int):
+        super().__init__()
+
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.d_qk = config.query_key_head_size
+        self.d_v = config.value_head_size
+        self.num_attention_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+
+        self.q_out_dim = self.d_qk * self.num_attention_heads
+        self.k_out_dim = self.d_qk * self.num_kv_heads
+        self.v_out_dim = self.d_v * self.num_kv_heads
+
+        self.qk_proj = MultiCastedLinearOrthoIn(self.hidden_size, [self.q_out_dim, self.k_out_dim], bias=False)
+        self.v_proj = CastedLinearIn(self.hidden_size, self.v_out_dim, bias=False)
+        self.out_proj = CastedLinearIn(self.d_v*self.num_attention_heads, self.hidden_size, bias=False)
+
+        self.pre_v_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.pre_qk_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.inter_norm = nn.LayerNorm(self.d_v * self.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.q_norm = nn.LayerNorm(self.d_qk, eps=config.layer_norm_eps, elementwise_affine=False, bias=False)
+        self.k_norm = nn.LayerNorm(self.d_qk, eps=config.layer_norm_eps, elementwise_affine=False, bias=False)
+        self.k_scale = nn.Parameter(torch.ones(self.num_kv_heads, self.d_qk))
+        self.q_scale = nn.Parameter(torch.ones(self.num_attention_heads, self.d_qk))
+
+        self.attention_dropout = nn.Dropout(config.attention_dropout)
+        self.dropout = nn.Dropout(config.hidden_dropout)
+
+        theta = 160_000 if (layer_idx + 1) % config.local_global_ratio == 0 else 10_000
+
+        # Initialize rotary embeddings based on whether FlashAttention is available
+        if flash_attn_varlen_qkvpacked_func is not None:
+            self.rope_embedding = UnpaddedRotaryEmbedding(dim=self.d_qk, base=theta, max_seqlen=config.max_sequence_length)
+        else:
+            self.rope_embedding = RotaryPositionalEmbeddings(config, theta)
+
+        self.scale = 1.0 / math.sqrt(self.d_qk)
+        self.lambdas = nn.Parameter(torch.tensor([0.5]))
+
+        self.sequence_length = config.max_sequence_length
+        self.is_causal = config.is_decoder
+        self.window_length = None
+
+    def set_window_length(self, window_length: int):
+        self.window_length = window_length
+
+    def _get_window_mask(self, query_length: int, key_length: int, device: torch.device):
+        """Create and cache window attention mask."""
+        if self.is_causal:
+            mask = torch.ones(query_length, key_length, dtype=torch.bool, device=device)
+            mask = mask.tril().triu(diagonal=-self.window_length)
+        else:
+            mask = torch.ones(query_length, key_length, dtype=torch.bool, device=device)
+            mask = mask.tril(diagonal=self.window_length).triu(diagonal=-self.window_length)
+        return mask.view(1, 1, query_length, key_length)
+
+    def attention_operation(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, padding_mask: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Standard attention computation with masking."""
+        batch_size, _, query_length, _ = query.size()
+        _, _, key_length, _ = key.size()
+
+        # Use cached window mask
+        with torch.no_grad():
+            window_mask = self._get_window_mask(query_length, key_length, query.device)
+            if padding_mask is not None:
+                attention_mask = padding_mask & window_mask
+            else:
+                attention_mask = window_mask
+
+        attention_scores = torch.bmm(query.flatten(0, 1), key.transpose(-1, -2).flatten(0, 1)) * self.scale  # shape: [B*H, Q_T, K_T]
+        attention_scores = attention_scores.view(batch_size, self.num_attention_heads, query_length, key_length)
+
+        attention_probabilities = MaskedSoftmax.apply(attention_scores, ~attention_mask, -1)
+        attention_probabilities = self.attention_dropout(attention_probabilities)
+
+        output = torch.bmm(attention_probabilities.flatten(0, 1), value.flatten(0, 1))
+        output = output.view(batch_size, self.num_attention_heads, query_length, self.d_v)
+
+        return output
+
+    def forward(self, hidden_layer: torch.Tensor, qk_layer: torch.Tensor, v1: torch.Tensor | None, padding_info):
+        # Get original shape info
+        if flash_attn_varlen_qkvpacked_func is not None:
+            # Unpadded case
+            indices, cu_seqlens, max_seqlen = padding_info
+            total_seqlen = hidden_layer.size(0)
+            batch_size = cu_seqlens.size(0) - 1
+        else:
+            # Padded case
+            batch_size, seq_length = hidden_layer.size(0), hidden_layer.size(1)
+
+        hidden_layer = self.pre_v_norm(hidden_layer.float()).type_as(hidden_layer)
+        qk_layer = self.pre_qk_norm(qk_layer.float()).type_as(qk_layer)
+
+        query, key = self.qk_proj(qk_layer).tensor_split([self.q_out_dim], dim=-1)
+        value = self.v_proj(hidden_layer)
+
+        if flash_attn_varlen_qkvpacked_func is not None:
+            # Reshape for FlashAttention: (total_seqlen, num_heads, head_dim)
+            query = query.view(total_seqlen, self.num_attention_heads, self.d_qk)
+            key = key.view(total_seqlen, self.num_kv_heads, self.d_qk)
+            value = value.view(total_seqlen, self.num_kv_heads, self.d_v)
+
+            # Apply layer norm and scaling
+            query = ((self.q_scale + 1.0).unsqueeze(0) * self.q_norm(query.float())).type_as(query)
+            key = ((self.k_scale + 1.0).unsqueeze(0) * self.k_norm(key.float())).type_as(key)
+
+            if v1 is None:
+                v1 = value
+            value = (1 - self.lambdas[0]) * value + self.lambdas[0] * v1
+
+            # Prepare qkv for FlashAttention
+            qkv = torch.stack([query, key, value], dim=1)  # (total_seqlen, 3, num_heads, head_dim)
+
+            # Determine window size for local attention
+            if self.window_length is not None and self.window_length > 0:
+                if self.is_causal:
+                    local_attention = (self.window_length - 1, 0)
+                else:
+                    local_attention = (self.window_length - 1, self.window_length - 1)
+            else:
+                local_attention = (-1, -1)
+
+            # Apply FlashAttention
+            output = flash_attention_forward(
+                qkv,
+                self.rope_embedding,
+                cu_seqlens,
+                max_seqlen,
+                self.is_causal,
+                local_attention,
+                self.config.attention_dropout if self.training else 0.0,
+                self.config.deterministic_flash_attn
+            )
+
+            # Reshape output back
+            output = output.view(total_seqlen, self.d_v * self.num_attention_heads)
+
+        else:
+            # Standard attention path
+            query_length = query.size(1)
+            key_length = key.size(1)
+
+            query = query.reshape(batch_size, query_length, self.num_attention_heads, self.d_qk).transpose(1, 2)
+            key = key.reshape(batch_size, key_length, self.num_kv_heads, self.d_qk).transpose(1, 2)
+            value = value.reshape(batch_size, key_length, self.num_kv_heads, self.d_v).transpose(1, 2)
+
+            query = ((self.q_scale + 1.0).unsqueeze(1).unsqueeze(0) * self.q_norm(query.float())).type_as(query)
+            key = ((self.k_scale + 1.0).unsqueeze(1).unsqueeze(0) * self.k_norm(key.float())).type_as(key)
+
+            if v1 is None:
+                v1 = value
+            else:
+                value = (1 - self.lambdas[0]) * value + self.lambdas[0] * v1
+
+            # Apply rotary embeddings
+            query = self.rope_embedding(query)
+            key = self.rope_embedding(key)
+
+            output = self.attention_operation(query, key, value, padding_info)
+            output = output.transpose(1, 2).flatten(2, 3)  # shape: [B, T, H*D]
+
+        output = self.inter_norm(output.float()).type_as(output)
+        output = self.out_proj(output)
+        output = self.dropout(output)
+
+        return output, v1
+
+
+class FeedForward(nn.Module):    
+    def __init__(self, config: GptBertConfig):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.up_proj = MultiCastedLinearOrthoIn(config.hidden_size, [config.intermediate_size, config.intermediate_size], bias=False)
+        self.activation = GeGLU()
+        self.inter_norm = nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.down_proj = CastedLinearIn(config.intermediate_size, config.hidden_size, bias=False)
+        self.dropout = nn.Dropout(config.hidden_dropout)
+        
+    def forward(self, x: torch.Tensor):
+        x = self.pre_norm(x.float()).type_as(x)
+        x = self.up_proj(x)
+        x = self.activation(x)
+        x = self.inter_norm(x.float()).type_as(x)
+        x = self.down_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+class Layer(nn.Module):
+    def __init__(self, config: GptBertConfig, layer_idx: int):
+        super().__init__()
+
+        self.attention = SelfAttention(config, layer_idx)
+        self.mlp = FeedForward(config)
+        self.lambdas = nn.Parameter(torch.tensor([0., 0., 1., 0., 1., 0.]))
+
+    def set_window_length(self, window_length: int):
+        self.attention.set_window_length(window_length)
+
+    def forward(self, hidden_layer: torch.Tensor, embeddings: torch.Tensor, v1: torch.Tensor | None, padding_info):
+        attention_output = (1 - self.lambdas[0]) * hidden_layer + self.lambdas[0] * embeddings
+        qk_layer = (1 - self.lambdas[1]) * hidden_layer + self.lambdas[1] * embeddings
+        mlp_layer = F.softplus(self.lambdas[2]) * ((1 - self.lambdas[3]) * hidden_layer + self.lambdas[3] * embeddings)
+
+        attention_output, v1 = self.attention(attention_output, qk_layer, v1, padding_info)
+        mlp_layer = mlp_layer + attention_output
+        hidden_layer = F.softplus(self.lambdas[4]) * ((1 - self.lambdas[5]) * hidden_layer + self.lambdas[5] * embeddings)
+        output = hidden_layer + attention_output + self.mlp(mlp_layer)
+
+        return output, v1
+
+
+class Encoder(nn.Module):
+    def __init__(self, config: GptBertConfig):
+        super().__init__()
+        self.layers = nn.ModuleList([Layer(config, i) for i in range(config.num_layers)])
+        self.local_global_ratio = config.local_global_ratio
+
+    def set_window_length(self, config: GptBertConfig):
+        for i, layer in enumerate(self.layers):
+            if (i + 1) % self.local_global_ratio == 0:
+                layer.set_window_length(config.global_window_length)
+            else:
+                layer.set_window_length(config.local_window_length)
+
+    def forward(self, hidden_layer: torch.Tensor, padding_info, output_hidden_states=False, checkpoint_activations=False):
+        hidden_layers = [hidden_layer] if output_hidden_states else None
+        v1 = None
+        embeddings = hidden_layer
+
+        for layer in self.layers:
+            if checkpoint_activations:
+                hidden_layer, v1 = torch.utils.checkpoint.checkpoint(layer, hidden_layer, embeddings, v1, padding_info, use_reentrant=True)
+            else:
+                hidden_layer, v1 = layer(hidden_layer, embeddings, v1, padding_info)
+
+            if output_hidden_states:
+                hidden_layers.append(hidden_layer)
+
+        return hidden_layer, hidden_layers
+
+
+#
+# HuggingFace wrappers
+#
+
+class GptBertPreTrainedModel(PreTrainedModel):
+    config_class = GptBertConfig
+    supports_gradient_checkpointing = True
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = False
+
+    def _init_weights(self, module):
+        std = math.sqrt(2.0 / (5.0 * self.hidden_size))
+
+        if isinstance(module, nn.Linear) or isinstance(module, CastedLinearIn):
+            nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class GptBertModel(GptBertPreTrainedModel):
+    def __init__(self, config: GptBertConfig, add_mlm_layer=False, **kwargs):
+        super().__init__(config, **kwargs)
+        self.config = config
+        self.hidden_size = config.hidden_size
+
+        self.embedding = Embedding(config)
+        self.encoder = Encoder(config)
+        self.classifier = LMClassifier(config, config.vocab_size) if add_mlm_layer else None
+        self.set_window_length(config)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def set_window_length(self, config) -> None:
+        self.encoder.set_window_length(config)
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embedding = value
+
+    def get_contextualized_embeddings(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            raise ValueError("You have to specify input_ids")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(batch_size, seq_length, dtype=torch.bool, device=device)
+        else:
+            attention_mask = attention_mask.bool()
+
+        if flash_attn_varlen_qkvpacked_func is not None:
+            if len(attention_mask.size()) != 2:
+                raise ValueError("Bare `attention_mask` med to dimensjoner støttes nå for FlashAttention.")
+            with torch.no_grad():
+                input_ids, indices, cu_seqlens, max_seqlen_in_batch = _unpad_input(input_ids, attention_mask)
+            padding_info = (indices, cu_seqlens, max_seqlen_in_batch)
+        else:
+            if len(attention_mask.size()) == 2:
+                attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            elif len(attention_mask.size()) == 3:
+                attention_mask = attention_mask.unsqueeze(1)
+            padding_info = attention_mask
+
+        static_embeddings = self.embedding(input_ids)
+
+        original_dtype = static_embeddings.dtype
+        if torch.cuda.is_available() and torch.cuda.is_bf16_supported() and static_embeddings.dtype == torch.float32:
+            static_embeddings = static_embeddings.bfloat16()
+
+        last_layer, contextualized_embeddings = self.encoder(
+            static_embeddings,
+            padding_info,
+            output_hidden_states=output_hidden_states,
+            checkpoint_activations=self.gradient_checkpointing and self.training
+        )
+
+        last_layer = last_layer.to(original_dtype)
+        if output_hidden_states:
+            contextualized_embeddings = [layer.to(original_dtype) for layer in contextualized_embeddings]
+
+        # Pad output if using FlashAttention
+        if flash_attn_varlen_qkvpacked_func is not None:
+            last_layer = _pad_output(last_layer, indices, batch_size, seq_length)
+            if output_hidden_states:
+                contextualized_embeddings = [_pad_output(layer, indices, batch_size, seq_length) for layer in contextualized_embeddings]
+            else:
+                contextualized_embeddings = None
+
+        return last_layer, contextualized_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(input_ids, attention_mask, output_hidden_states)
+
+        if not return_dict:
+            return (
+                sequence_output,
+                *([contextualized_embeddings] if output_hidden_states else [])
+            )
+
+        return BaseModelOutput(
+            last_hidden_state=sequence_output,
+            hidden_states=contextualized_embeddings if output_hidden_states else None
+        )
+
+
+class GptBertForMaskedLM(GptBertModel):
+    _tied_weights_keys = ["classifier.emb2vocab.weight"]
+
+    def __init__(self, config: GptBertConfig, **kwargs):
+        super().__init__(config, add_mlm_layer=True, **kwargs)
+
+    def get_output_embeddings(self):
+        return self.classifier.emb2vocab.weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.classifier.emb2vocab.weight = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(input_ids, attention_mask, output_hidden_states)
+        subword_prediction = self.classifier(sequence_output)
+        subword_prediction = 30 * torch.sigmoid(subword_prediction / 7.5)
+
+        masked_lm_loss = None
+        if labels is not None:
+            labels_flatten = labels[:, 1:].flatten()
+            subword_prediction_flatten = subword_prediction[:, :-1].flatten(0, 1)
+            masked_lm_loss = F.cross_entropy(subword_prediction_flatten, labels_flatten)
+
+        bos_logits = torch.zeros(subword_prediction.size(0), 1, self.config.vocab_size, dtype=subword_prediction.dtype, device=subword_prediction.device)
+        bos_logits[:, :, self.config.bos_token_id] = 1.0
+        subword_prediction = torch.cat([bos_logits, subword_prediction[:, :-1]], dim=1)
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else [])
+            )
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=subword_prediction,
+            hidden_states=contextualized_embeddings if output_hidden_states else None
+        )
+
+
+class GptBertForCausalLM(GptBertModel):
+    _tied_weights_keys = ["classifier.emb2vocab.weight"]
+
+    def __init__(self, config: GptBertConfig, **kwargs):
+        config.is_decoder = True
+        super().__init__(config, add_mlm_layer=True, **kwargs)
+
+    def get_output_embeddings(self):
+        return self.classifier.emb2vocab.weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.classifier.emb2vocab.weight = new_embeddings
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embedding = value
+
+    def set_decoder(self, decoder):
+        self.encoder = decoder
+
+    def get_decoder(self):
+        return self.encoder
+
+    def can_generate(self):
+        return True
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None
+    ) -> Union[Tuple, CausalLMOutput]:
+
+        assert inputs_embeds is None, "inputs_embeds is not supported for now"
+        assert past_key_values is None, "past_key_values is not supported for now"
+        assert not use_cache, "use_cache is not supported for now"
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(input_ids, attention_mask, output_hidden_states)
+        subword_prediction = self.classifier(sequence_output)
+        subword_prediction = 30 * torch.sigmoid(subword_prediction / 7.5)
+
+        causal_lm_loss = None
+        if labels is not None:
+            labels_flatten = labels[:, 1:].flatten()
+            subword_prediction_flatten = subword_prediction[:, :-1].flatten(0, 1)
+            causal_lm_loss = F.cross_entropy(subword_prediction_flatten, labels_flatten)
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else [])
+            )
+            return ((causal_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return CausalLMOutput(
+            loss=causal_lm_loss,
+            logits=subword_prediction,
+            hidden_states=contextualized_embeddings if output_hidden_states else None
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.Tensor,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        use_cache: bool = True,
+        num_logits_to_keep: Optional[int] = None,
+        **kwargs,
+    ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if past_key_values is not None:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids.contiguous()}  # `contiguous()` needed for compilation use cases
+
+        if num_logits_to_keep is not None:
+            model_inputs["num_logits_to_keep"] = num_logits_to_keep
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+
+class GptBertForSequenceClassification(GptBertModel):
+    _keys_to_ignore_on_load_missing = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+    _keys_to_ignore_on_load_unexpected = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+
+    def __init__(self, config: GptBertConfig, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.classifier = Classifier(config, self.num_labels)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(input_ids, attention_mask, output_hidden_states)
+        logits = self.classifier(sequence_output[:, 0, :])
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (
+                logits,
+                *([contextualized_embeddings] if output_hidden_states else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None
+        )
+
+
+class GptBertForTokenClassification(GptBertModel):
+    _keys_to_ignore_on_load_missing = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+    _keys_to_ignore_on_load_unexpected = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+
+    def __init__(self, config: GptBertConfig, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.classifier = Classifier(config, self.num_labels)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(input_ids, attention_mask, output_hidden_states)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (
+                logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class GptBertForQuestionAnswering(GptBertModel):
+    _keys_to_ignore_on_load_missing = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+    _keys_to_ignore_on_load_unexpected = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+
+    def __init__(self, config: GptBertConfig, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.classifier = Classifier(config, self.num_labels)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(input_ids, attention_mask, output_hidden_states)
+        logits = self.classifier(sequence_output)
+
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (
+                start_logits,
+                end_logits,
+                *([contextualized_embeddings] if output_hidden_states else [])
+            )
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None
+        )
+
+
+class GptBertForMultipleChoice(GptBertModel):
+    _keys_to_ignore_on_load_missing = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+    _keys_to_ignore_on_load_unexpected = ["classifier.emb2vocab.weight", "classifier.emb2vocab.bias"]
+
+    def __init__(self, config: GptBertConfig, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = getattr(config, "num_labels", 2)
+        self.classifier = Classifier(config, self.num_labels)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1]
+
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+
+        sequence_output, contextualized_embeddings = self.get_contextualized_embeddings(flat_input_ids, flat_attention_mask, output_hidden_states)
+        logits = self.classifier(sequence_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (
+                reshaped_logits,
+                *([contextualized_embeddings] if output_hidden_states else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None
+        )

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 75,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,150 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<special_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<special_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<special_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<special_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<special_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<special_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<special_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<special_7>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<special_8>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<special_9>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<special_10>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}