initial release

README.md

@@ -0,0 +1,43 @@
+---
+language:
+- "ja"
+tags:
+- "japanese"
+- "token-classification"
+- "pos"
+- "dependency-parsing"
+base_model: KoichiYasuoka/modernbert-large-japanese-aozora
+datasets:
+- "universal_dependencies"
+license: "apache-2.0"
+pipeline_tag: "token-classification"
+widget:
+- text: "国境の長いトンネルを抜けると雪国であった。"
+---
+
+# modernbert-large-japanese-aozora-luw-upos
+
+## Model Description
+
+This is a ModernBERT model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [modernbert-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/modernbert-large-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/).
+
+## How to Use
+
+```py
+from transformers import pipeline
+nlp=pipeline("upos","KoichiYasuoka/modernbert-large-japanese-aozora-luw-upos",trust_remote_code=True,aggregation_strategy="simple")
+print(nlp("国境の長いトンネルを抜けると雪国であった。"))
+```
+
+or
+
+```py
+import esupar
+nlp=esupar.load("KoichiYasuoka/modernbert-large-japanese-aozora-luw-upos")
+print(nlp("国境の長いトンネルを抜けると雪国であった。"))
+```
+
+## See Also
+
+[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa/GPT models
+

config.json

@@ -0,0 +1,174 @@
+{
+  "architectures": [
+    "ModernBertForTokenClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_modernbert.ModernBertConfig",
+    "AutoModel": "modeling_modernbert.ModernBertModel",
+    "AutoModelForMaskedLM": "modeling_modernbert.ModernBertForMaskedLM",
+    "AutoModelForSequenceClassification": "modeling_modernbert.ModernBertForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_modernbert.ModernBertForTokenClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 0,
+  "custom_pipelines": {
+    "upos": {
+      "impl": "upos.BellmanFordTokenClassificationPipeline",
+      "pt": "AutoModelForTokenClassification"
+    }
+  },
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 2,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "ADJ",
+    "1": "ADP",
+    "2": "ADV",
+    "3": "AUX",
+    "4": "AUX|Polarity=Neg",
+    "5": "B-ADJ",
+    "6": "B-ADP",
+    "7": "B-ADV",
+    "8": "B-AUX",
+    "9": "B-AUX|Polarity=Neg",
+    "10": "B-CCONJ",
+    "11": "B-DET",
+    "12": "B-INTJ",
+    "13": "B-NOUN",
+    "14": "B-NOUN|Polarity=Neg",
+    "15": "B-NUM",
+    "16": "B-PART",
+    "17": "B-PRON",
+    "18": "B-PROPN",
+    "19": "B-PUNCT",
+    "20": "B-SCONJ",
+    "21": "B-SYM",
+    "22": "B-VERB",
+    "23": "B-X",
+    "24": "CCONJ",
+    "25": "I-ADJ",
+    "26": "I-ADP",
+    "27": "I-ADV",
+    "28": "I-AUX",
+    "29": "I-AUX|Polarity=Neg",
+    "30": "I-CCONJ",
+    "31": "I-DET",
+    "32": "I-INTJ",
+    "33": "I-NOUN",
+    "34": "I-NOUN|Polarity=Neg",
+    "35": "I-NUM",
+    "36": "I-PART",
+    "37": "I-PRON",
+    "38": "I-PROPN",
+    "39": "I-PUNCT",
+    "40": "I-SCONJ",
+    "41": "I-SYM",
+    "42": "I-VERB",
+    "43": "I-X",
+    "44": "INTJ",
+    "45": "NOUN",
+    "46": "NUM",
+    "47": "PART",
+    "48": "PRON",
+    "49": "PROPN",
+    "50": "PUNCT",
+    "51": "SCONJ",
+    "52": "SYM",
+    "53": "VERB",
+    "54": "X"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "label2id": {
+    "ADJ": 0,
+    "ADP": 1,
+    "ADV": 2,
+    "AUX": 3,
+    "AUX|Polarity=Neg": 4,
+    "B-ADJ": 5,
+    "B-ADP": 6,
+    "B-ADV": 7,
+    "B-AUX": 8,
+    "B-AUX|Polarity=Neg": 9,
+    "B-CCONJ": 10,
+    "B-DET": 11,
+    "B-INTJ": 12,
+    "B-NOUN": 13,
+    "B-NOUN|Polarity=Neg": 14,
+    "B-NUM": 15,
+    "B-PART": 16,
+    "B-PRON": 17,
+    "B-PROPN": 18,
+    "B-PUNCT": 19,
+    "B-SCONJ": 20,
+    "B-SYM": 21,
+    "B-VERB": 22,
+    "B-X": 23,
+    "CCONJ": 24,
+    "I-ADJ": 25,
+    "I-ADP": 26,
+    "I-ADV": 27,
+    "I-AUX": 28,
+    "I-AUX|Polarity=Neg": 29,
+    "I-CCONJ": 30,
+    "I-DET": 31,
+    "I-INTJ": 32,
+    "I-NOUN": 33,
+    "I-NOUN|Polarity=Neg": 34,
+    "I-NUM": 35,
+    "I-PART": 36,
+    "I-PRON": 37,
+    "I-PROPN": 38,
+    "I-PUNCT": 39,
+    "I-SCONJ": 40,
+    "I-SYM": 41,
+    "I-VERB": 42,
+    "I-X": 43,
+    "INTJ": 44,
+    "NOUN": 45,
+    "NUM": 46,
+    "PART": 47,
+    "PRON": 48,
+    "PROPN": 49,
+    "PUNCT": 50,
+    "SCONJ": 51,
+    "SYM": 52,
+    "VERB": 53,
+    "X": 54
+  },
+  "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": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 2,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "tokenizer_class": "DebertaV2TokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "vocab_size": 65000
+}

configuration_modernbert.py

@@ -0,0 +1,213 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_modernbert.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Literal
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class ModernBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ModernBertModel`]. It is used to instantiate an ModernBert
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the ModernBERT-base.
+    e.g. [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50368):
+            Vocabulary size of the ModernBert model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ModernBertModel`]
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 1152):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 22):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
+            if not specified.
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
+            The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        norm_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the normalization layers.
+        pad_token_id (`int`, *optional*, defaults to 50283):
+            Padding token id.
+        eos_token_id (`int`, *optional*, defaults to 50282):
+            End of stream token id.
+        bos_token_id (`int`, *optional*, defaults to 50281):
+            Beginning of stream token id.
+        cls_token_id (`int`, *optional*, defaults to 50281):
+            Classification token id.
+        sep_token_id (`int`, *optional*, defaults to 50282):
+            Separation token id.
+        global_rope_theta (`float`, *optional*, defaults to 160000.0):
+            The base period of the global RoPE embeddings.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        global_attn_every_n_layers (`int`, *optional*, defaults to 3):
+            The number of layers between global attention layers.
+        local_attention (`int`, *optional*, defaults to 128):
+            The window size for local attention.
+        local_rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the local RoPE embeddings.
+        embedding_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the MLP layers.
+        mlp_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the MLP layers.
+        decoder_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the decoder layers.
+        classifier_pooling (`str`, *optional*, defaults to `"cls"`):
+            The pooling method for the classifier. Should be either `"cls"` or `"mean"`. In local attention layers, the
+            CLS token doesn't attend to all tokens on long sequences.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the classifier.
+        classifier_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the classifier.
+        classifier_activation (`str`, *optional*, defaults to `"gelu"`):
+            The activation function for the classifier.
+        deterministic_flash_attn (`bool`, *optional*, defaults to `False`):
+            Whether to use deterministic flash attention. If `False`, inference will be faster but not deterministic.
+        sparse_prediction (`bool`, *optional*, defaults to `False`):
+            Whether to use sparse prediction for the masked language model instead of returning the full dense logits.
+        sparse_pred_ignore_index (`int`, *optional*, defaults to -100):
+            The index to ignore for the sparse prediction.
+        reference_compile (`bool`, *optional*):
+            Whether to compile the layers of the model which were compiled during pretraining. If `None`, then parts of
+            the model will be compiled if 1) `triton` is installed, 2) the model is not on MPS, 3) the model is not
+            shared between devices, and 4) the model is not resized after initialization. If `True`, then the model may
+            be faster in some scenarios.
+
+    Examples:
+
+    ```python
+    >>> from transformers import ModernBertModel, ModernBertConfig
+
+    >>> # Initializing a ModernBert style configuration
+    >>> configuration = ModernBertConfig()
+
+    >>> # Initializing a model from the modernbert-base style configuration
+    >>> model = ModernBertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "modernbert"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50368,
+        hidden_size=768,
+        intermediate_size=1152,
+        num_hidden_layers=22,
+        num_attention_heads=12,
+        hidden_activation="gelu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        initializer_cutoff_factor=2.0,
+        norm_eps=1e-5,
+        norm_bias=False,
+        pad_token_id=50283,
+        eos_token_id=50282,
+        bos_token_id=50281,
+        cls_token_id=50281,
+        sep_token_id=50282,
+        global_rope_theta=160000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        global_attn_every_n_layers=3,
+        local_attention=128,
+        local_rope_theta=10000.0,
+        embedding_dropout=0.0,
+        mlp_bias=False,
+        mlp_dropout=0.0,
+        decoder_bias=True,
+        classifier_pooling: Literal["cls", "mean"] = "cls",
+        classifier_dropout=0.0,
+        classifier_bias=False,
+        classifier_activation="gelu",
+        deterministic_flash_attn=False,
+        sparse_prediction=False,
+        sparse_pred_ignore_index=-100,
+        reference_compile=None,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            cls_token_id=cls_token_id,
+            sep_token_id=sep_token_id,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.initializer_range = initializer_range
+        self.initializer_cutoff_factor = initializer_cutoff_factor
+        self.norm_eps = norm_eps
+        self.norm_bias = norm_bias
+        self.global_rope_theta = global_rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.hidden_activation = hidden_activation
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.local_attention = local_attention
+        self.local_rope_theta = local_rope_theta
+        self.embedding_dropout = embedding_dropout
+        self.mlp_bias = mlp_bias
+        self.mlp_dropout = mlp_dropout
+        self.decoder_bias = decoder_bias
+        self.classifier_pooling = classifier_pooling
+        self.classifier_dropout = classifier_dropout
+        self.classifier_bias = classifier_bias
+        self.classifier_activation = classifier_activation
+        self.deterministic_flash_attn = deterministic_flash_attn
+        self.sparse_prediction = sparse_prediction
+        self.sparse_pred_ignore_index = sparse_pred_ignore_index
+        self.reference_compile = reference_compile
+
+        if self.classifier_pooling not in ["cls", "mean"]:
+            raise ValueError(
+                f'Invalid value for `classifier_pooling`, should be either "cls" or "mean", but is {self.classifier_pooling}.'
+            )
+
+
+__all__ = ["ModernBertConfig"]

maker.sh

@@ -0,0 +1,13 @@
+#! /bin/sh
+S=KoichiYasuoka/modernbert-large-japanese-aozora
+T=KoichiYasuoka/modernbert-large-japanese-aozora-luw-upos
+U=https://github.com/UniversalDependencies/UD_Japanese-GSDLUW
+
+D=`basename $U`
+test -d $D || git clone --depth=1 $U
+for F in train dev test
+do cp $D/*-$F.conllu $F.conllu
+done
+
+python3 -m esupar.train $S $T -32 /tmp train.conllu
+python3 -m esupar.train $T $T 32 /// train.conllu dev.conllu test.conllu

modeling_modernbert.py

@@ -0,0 +1,1351 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_modernbert.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    logging,
+)
+import importlib
+is_triton_available = lambda: importlib.util.find_spec("triton") is not None
+from .configuration_modernbert import ModernBertConfig
+
+
+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:
+    RotaryEmbedding = object
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "answerdotai/ModernBERT-base"
+_CONFIG_FOR_DOC = "ModernBertConfig"
+
+
+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
+
+
+def apply_rotary_unpadded(
+    qkv,
+    cos,
+    sin,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+):
+    """
+    Arguments:
+        qkv: (total_nnz, 3, nheads, headdim) - input tensor for packed QKV.
+        cos, sin: (seqlen_rotary, rotary_dim / 2)
+        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
+            of 1st half and 2nd half (GPT-NeoX style).
+        inplace: if True, apply rotary embedding in-place.
+        seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+        cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Return:
+        out: (total_nnz, dim)
+    rotary_dim must be <= headdim
+    Apply rotary embedding to the first rotary_dim of x.
+    """
+    return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)
+
+
+class ModernBertUnpaddedRotaryEmbedding(RotaryEmbedding):
+    """
+    The rotary position embeddings applied directly to unpadded sequences.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        base: float = 10000.0,
+        max_seqlen: Optional[int] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        """
+        max_seqlen: if max_seqlen, device, and dtype are provided, we precompute the cos_sin_cache
+            up to max_seqlen. If the max_seqlen, device, or dtype during training/inference differ,
+            the cos_sin_cache wll be recomputed during the forward pass.
+        """
+        super().__init__(dim=dim, base=base, pos_idx_in_fp32=True, device=device, interleaved=False)
+        self.max_seqlen = max_seqlen
+
+        if max_seqlen is not None and device is not None and dtype is not None:
+            self._update_cos_sin_cache(max_seqlen, device=device, dtype=dtype)
+
+    def forward(
+        self,
+        qkv: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: Optional[int] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Apply rotary embedding *inplace* to qkv.
+        qkv: (total_nnz, 3, nheads, headdim)
+        cu_seqlens: (batch + 1,) cumulative sequence lengths
+        max_seqlen: int max seq length in the batch
+        """
+        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
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, base={self.base}, scale_base={self.scale_base}"
+
+
+class ModernBertEmbeddings(nn.Module):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+
+    def __init__(self, config: ModernBertConfig):
+        super().__init__()
+        self.config = config
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.drop = nn.Dropout(config.embedding_dropout)
+
+    @torch.compile(dynamic=True)
+    def compiled_embeddings(self, input_ids: torch.LongTensor) -> torch.Tensor:
+        return self.drop(self.norm(self.tok_embeddings(input_ids)))
+
+    def forward(
+        self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        if inputs_embeds is not None:
+            hidden_states = self.drop(self.norm(inputs_embeds))
+        else:
+            hidden_states = (
+                self.compiled_embeddings(input_ids)
+                if self.config.reference_compile
+                else self.drop(self.norm(self.tok_embeddings(input_ids)))
+            )
+        return hidden_states
+
+
+class ModernBertMLP(nn.Module):
+    """Applies the GLU at the end of each ModernBERT layer.
+
+    Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
+    and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
+    """
+
+    def __init__(self, config: ModernBertConfig):
+        super().__init__()
+        self.config = config
+        self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=config.mlp_bias)
+        self.act = ACT2FN[config.hidden_activation]
+        self.drop = nn.Dropout(config.mlp_dropout)
+        self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
+        return self.Wo(self.drop(self.act(input) * gate))
+
+
+class ModernBertRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
+        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        self.inv_freq.to(x.device)
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def eager_attention_forward(
+    module: "ModernBertAttention",
+    qkv: torch.Tensor,
+    attention_mask: torch.Tensor,
+    sliding_window_mask: torch.Tensor,
+    position_ids: Optional[torch.LongTensor],
+    local_attention: Tuple[int, int],
+    bs: int,
+    dim: int,
+    output_attentions: Optional[bool] = False,
+    **_kwargs,
+) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+    # qkv: [batch_size, seqlen, 3, nheads, headdim]
+    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+    # query, key, value: [batch_size, heads, seq_len, head_dim]
+    query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+    scale = module.head_dim**-0.5
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scale
+
+    if local_attention != (-1, -1):
+        attention_mask = sliding_window_mask
+
+    attn_weights = attn_weights + attention_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=module.attention_dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.view(bs, -1, dim)
+    if output_attentions:
+        return (attn_output, attn_weights)
+    return (attn_output,)
+
+
+def flash_attention_forward(
+    module: "ModernBertAttention",
+    qkv: torch.Tensor,
+    rotary_emb: ModernBertUnpaddedRotaryEmbedding,
+    cu_seqlens: torch.Tensor,
+    max_seqlen: int,
+    local_attention: Tuple[int, int],
+    bs: int,
+    dim: int,
+    target_dtype: torch.dtype = torch.bfloat16,
+    **_kwargs,
+) -> Tuple[torch.Tensor]:
+    # (total_seqlen, 3, nheads, headdim)
+    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=module.attention_dropout if module.training else 0.0,
+            deterministic=module.deterministic_flash_attn,
+            window_size=local_attention,
+        )
+        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=module.attention_dropout if module.training else 0.0,
+            deterministic=module.deterministic_flash_attn,
+            window_size=local_attention,
+        )
+    return (attn.view(bs, dim),)
+
+
+def sdpa_attention_forward(
+    module: "ModernBertAttention",
+    qkv: torch.Tensor,
+    attention_mask: torch.Tensor,
+    sliding_window_mask: torch.Tensor,
+    position_ids: Optional[torch.LongTensor],
+    local_attention: Tuple[int, int],
+    bs: int,
+    dim: int,
+    **_kwargs,
+) -> Tuple[torch.Tensor]:
+    # qkv: [batch_size, seqlen, 3, nheads, headdim]
+    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+    # query, key, value: [batch_size, heads, seq_len, head_dim]
+    query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+    if local_attention != (-1, -1):
+        attention_mask = sliding_window_mask
+
+    attn_output = (
+        F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            dropout_p=module.attention_dropout if module.training else 0.0,
+            attn_mask=attention_mask,
+        )
+        .transpose(1, 2)
+        .contiguous()
+    )
+    attn_output = attn_output.view(bs, -1, dim)
+    return (attn_output,)
+
+
+MODERNBERT_ATTENTION_FUNCTION = {
+    "flash_attention_2": flash_attention_forward,
+    "eager": eager_attention_forward,
+    "sdpa": sdpa_attention_forward,
+}
+
+
+class ModernBertAttention(nn.Module):
+    """Performs multi-headed self attention on a batch of unpadded sequences.
+
+    If Flash Attention 2 is installed, this module uses Flash Attention to improve throughput.
+    If Flash Attention 2 is not installed, the implementation will use PyTorch's SDPA kernel,
+    which requires padding and unpadding inputs, adding some overhead.
+
+    See `forward` method for additional details.
+    """
+
+    def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_id = layer_id
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.deterministic_flash_attn = config.deterministic_flash_attn
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.all_head_size = self.head_dim * self.num_heads
+        self.Wqkv = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=config.attention_bias)
+
+        if layer_id % config.global_attn_every_n_layers != 0:
+            self.local_attention = (config.local_attention // 2, config.local_attention // 2)
+        else:
+            self.local_attention = (-1, -1)
+
+        rope_theta = config.global_rope_theta
+        max_position_embeddings = config.max_position_embeddings
+        if self.local_attention != (-1, -1):
+            if config.local_rope_theta is not None:
+                rope_theta = config.local_rope_theta
+            max_position_embeddings = config.local_attention
+
+        if config._attn_implementation == "flash_attention_2":
+            self.rotary_emb = ModernBertUnpaddedRotaryEmbedding(
+                dim=self.head_dim, max_seqlen=max_position_embeddings, base=rope_theta
+            )
+        else:
+            self.rotary_emb = ModernBertRotaryEmbedding(
+                dim=self.head_dim, max_position_embeddings=max_position_embeddings, base=rope_theta
+            )
+
+        self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+        self.out_drop = nn.Dropout(config.attention_dropout) if config.attention_dropout > 0.0 else nn.Identity()
+        self.pruned_heads = set()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv = self.Wqkv(hidden_states)
+
+        bs = hidden_states.shape[0]
+        if self.config._attn_implementation == "flash_attention_2":
+            qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
+        else:
+            qkv = qkv.view(bs, -1, 3, self.num_heads, self.head_dim)
+
+        attn_outputs = MODERNBERT_ATTENTION_FUNCTION[self.config._attn_implementation](
+            self,
+            qkv=qkv,
+            rotary_emb=self.rotary_emb,
+            local_attention=self.local_attention,
+            bs=bs,
+            dim=self.all_head_size,
+            output_attentions=output_attentions,
+            **kwargs,
+        )
+        hidden_states = attn_outputs[0]
+        hidden_states = self.out_drop(self.Wo(hidden_states))
+
+        return (hidden_states,) + attn_outputs[1:]  # add attentions if outputted
+
+
+class ModernBertEncoderLayer(nn.Module):
+    def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        if layer_id == 0:
+            self.attn_norm = nn.Identity()
+        else:
+            self.attn_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.attn = ModernBertAttention(config=config, layer_id=layer_id)
+        self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.mlp = ModernBertMLP(config)
+
+    @torch.compile(dynamic=True)
+    def compiled_mlp(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.mlp(self.mlp_norm(hidden_states))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> torch.Tensor:
+        attn_outputs = self.attn(
+            self.attn_norm(hidden_states),
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            output_attentions=output_attentions,
+        )
+        hidden_states = hidden_states + attn_outputs[0]
+        mlp_output = (
+            self.compiled_mlp(hidden_states)
+            if self.config.reference_compile
+            else self.mlp(self.mlp_norm(hidden_states))
+        )
+        hidden_states = hidden_states + mlp_output
+
+        return (hidden_states,) + attn_outputs[1:]  # add attentions if outputted
+
+
+MODERNBERT_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`ModernBertConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare ModernBert Model outputting raw hidden-states without any specific head on top.",
+    MODERNBERT_START_DOCSTRING,
+)
+class ModernBertPreTrainedModel(PreTrainedModel):
+    config_class = ModernBertConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["ModernBertEmbeddings", "ModernBertEncoderLayer"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = False
+
+    def _init_weights(self, module: nn.Module):
+        cutoff_factor = self.config.initializer_cutoff_factor
+        if cutoff_factor is None:
+            cutoff_factor = 3
+
+        def init_weight(module: nn.Module, std: float):
+            nn.init.trunc_normal_(
+                module.weight,
+                mean=0.0,
+                std=std,
+                a=-cutoff_factor * std,
+                b=cutoff_factor * std,
+            )
+
+            if isinstance(module, nn.Linear):
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+        stds = {
+            "in": self.config.initializer_range,
+            "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
+            "embedding": self.config.initializer_range,
+            "final_out": self.config.hidden_size**-0.5,
+        }
+
+        if isinstance(module, ModernBertEmbeddings):
+            init_weight(module.tok_embeddings, stds["embedding"])
+        elif isinstance(module, ModernBertMLP):
+            init_weight(module.Wi, stds["in"])
+            init_weight(module.Wo, stds["out"])
+        elif isinstance(module, ModernBertAttention):
+            init_weight(module.Wqkv, stds["in"])
+            init_weight(module.Wo, stds["out"])
+        elif isinstance(module, ModernBertPredictionHead):
+            init_weight(module.dense, stds["out"])
+        elif isinstance(module, ModernBertForMaskedLM):
+            init_weight(module.decoder, stds["out"])
+        elif isinstance(module, (ModernBertForSequenceClassification, ModernBertForTokenClassification)):
+            init_weight(module.classifier, stds["final_out"])
+
+    @classmethod
+    def _autoset_attn_implementation(
+        cls,
+        config,
+        use_flash_attention_2: bool = False,
+        torch_dtype: Optional[torch.dtype] = None,
+        device_map: Optional[Union[str, Dict[str, int]]] = None,
+        check_device_map: bool = True,
+    ):
+        # If the user didn't specify anything, try to use flash_attention_2 if available.
+        # Otherwise we fall back to the default SDPA -> Eager from the super() method.
+        if config._attn_implementation_internal is None:
+            config._attn_implementation_internal = "flash_attention_2"
+            try:
+                return cls._check_and_enable_flash_attn_2(
+                    config,
+                    torch_dtype=torch_dtype,
+                    device_map=device_map,
+                    hard_check_only=False,
+                    check_device_map=check_device_map,
+                )
+            except (ValueError, ImportError):
+                config._attn_implementation_internal = None
+        return super()._autoset_attn_implementation(
+            config,
+            use_flash_attention_2=use_flash_attention_2,
+            torch_dtype=torch_dtype,
+            device_map=device_map,
+            check_device_map=check_device_map,
+        )
+
+    def _maybe_set_compile(self):
+        if self.config.reference_compile is False:
+            return
+
+        if hasattr(self, "hf_device_map") and len(self.hf_device_map) > 1:
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "If `accelerate` split the model across devices, `torch.compile` will not work. "
+                    "Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        if self.device.type == "mps":
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. "
+                    "Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        if self.config.reference_compile is None:
+            self.config.reference_compile = is_triton_available()
+
+    def resize_token_embeddings(self, *args, **kwargs):
+        model_embeds = super().resize_token_embeddings(*args, **kwargs)
+
+        if self.config.reference_compile in {True, None}:
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "Resizing token embeddings with `torch.compile` is not supported. Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        return model_embeds
+
+
+def _unpad_modernbert_input(
+    inputs: torch.Tensor,
+    attention_mask: torch.Tensor,
+    position_ids: Optional[torch.Tensor] = None,
+    labels: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
+    """
+    Remove padding from input sequences.
+
+    Args:
+        inputs: (batch, seqlen, ...) or (batch, seqlen)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+        position_ids: (batch, seqlen), int, position ids
+        labels: (batch, seqlen), int, labels
+
+    Returns:
+        unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
+        indices: (total_nnz)
+        cu_seqlens: (batch + 1), the cumulative sequence lengths
+        max_seqlen_in_batch: int
+        unpadded_position_ids: (total_nnz) or None
+        unpadded_labels: (total_nnz) or None
+    """
+    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 = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+
+    if inputs.dim() == 2:
+        unpadded_inputs = inputs.flatten()[indices]
+    else:
+        batch, seqlen, *rest = inputs.shape
+        shape = batch * seqlen
+        unpadded_inputs = inputs.view(shape, *rest)[indices]
+
+    unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
+    unpadded_labels = labels.flatten()[indices] if labels is not None else None
+
+    return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels
+
+
+def _pad_modernbert_output(
+    inputs: torch.Tensor,
+    indices: torch.Tensor,
+    batch: int,
+    seqlen: int,
+) -> torch.Tensor:
+    """
+    Add padding to sequences.
+
+    Args:
+        inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
+        indices: (total_nnz)
+        batch: int, batch size
+        seqlen: int, max sequence length
+
+    Returns:
+        padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
+    """
+    if inputs.dim() == 1:
+        output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
+        output[indices] = inputs
+        padded_inputs = output.view(batch, seqlen)
+    else:
+        _, *rest = inputs.shape
+        output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
+        output[indices] = inputs
+        padded_inputs = output.view(batch, seqlen, *rest)
+
+    return padded_inputs
+
+
+MODERNBERT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch. Used to pad the output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences. Used to pad the output tensors.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare ModernBert Model outputting raw hidden-states without any specific head on top.",
+    MODERNBERT_START_DOCSTRING,
+)
+class ModernBertModel(ModernBertPreTrainedModel):
+    def __init__(self, config: ModernBertConfig):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = ModernBertEmbeddings(config)
+        self.layers = nn.ModuleList(
+            [ModernBertEncoderLayer(config, layer_id) for layer_id in range(config.num_hidden_layers)]
+        )
+        self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutput]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        self._maybe_set_compile()
+
+        if input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+
+        if batch_size is None and seq_len is None:
+            if inputs_embeds is not None:
+                batch_size, seq_len = inputs_embeds.shape[:2]
+            else:
+                batch_size, seq_len = input_ids.shape[:2]
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+        repad = False
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                repad = True
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask
+                    )
+        else:
+            if position_ids is None:
+                position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
+
+            attention_mask, sliding_window_mask = self._update_attention_mask(
+                attention_mask, output_attentions=output_attentions
+            )
+
+        hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    encoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    sliding_window_mask,
+                    position_ids,
+                    cu_seqlens,
+                    max_seqlen,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    sliding_window_mask=sliding_window_mask,
+                    position_ids=position_ids,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    output_attentions=output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions and len(layer_outputs) > 1:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        hidden_states = self.final_norm(hidden_states)
+
+        if repad:
+            hidden_states = _pad_modernbert_output(
+                inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
+            )
+            if all_hidden_states is not None:
+                all_hidden_states = tuple(
+                    _pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
+                    for hs in all_hidden_states
+                )
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+    def _update_attention_mask(self, attention_mask: torch.Tensor, output_attentions: bool) -> torch.Tensor:
+        if output_attentions:
+            if self.config._attn_implementation == "sdpa":
+                logger.warning_once(
+                    "Outputting attentions is only supported with the 'eager' attention implementation, "
+                    'not with "sdpa". Falling back to `attn_implementation="eager"`.'
+                )
+                self.config._attn_implementation = "eager"
+            elif self.config._attn_implementation != "eager":
+                logger.warning_once(
+                    "Outputting attentions is only supported with the eager attention implementation, "
+                    f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
+                    " Setting `output_attentions=False`."
+                )
+
+        global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
+
+        # Create position indices
+        rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
+        # Calculate distance between positions
+        distance = torch.abs(rows - rows.T)
+
+        # Create sliding window mask (1 for positions within window, 0 outside)
+        window_mask = (
+            (distance <= self.config.local_attention // 2).unsqueeze(0).unsqueeze(0).to(attention_mask.device)
+        )
+        # Combine with existing mask
+        sliding_window_mask = global_attention_mask.masked_fill(window_mask.logical_not(), torch.finfo(self.dtype).min)
+
+        return global_attention_mask, sliding_window_mask
+
+
+class ModernBertPredictionHead(nn.Module):
+    def __init__(self, config: ModernBertConfig):
+        super().__init__()
+        self.config = config
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
+        self.act = ACT2FN[config.classifier_activation]
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.norm(self.act(self.dense(hidden_states)))
+
+
+@add_start_docstrings(
+    "The ModernBert Model with a decoder head on top that is used for masked language modeling.",
+    MODERNBERT_START_DOCSTRING,
+)
+class ModernBertForMaskedLM(ModernBertPreTrainedModel):
+    _tied_weights_keys = ["decoder.weight"]
+
+    def __init__(self, config: ModernBertConfig):
+        super().__init__(config)
+        self.config = config
+        self.model = ModernBertModel(config)
+        self.head = ModernBertPredictionHead(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+        self.sparse_prediction = self.config.sparse_prediction
+        self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.decoder
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear):
+        self.decoder = new_embeddings
+
+    @torch.compile(dynamic=True)
+    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.head(output))
+
+    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+                if attention_mask is None:
+                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                    )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        if self.sparse_prediction and labels is not None:
+            # flatten labels and output first
+            labels = labels.view(-1)
+            last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
+
+            # then filter out the non-masked tokens
+            mask_tokens = labels != self.sparse_pred_ignore_index
+            last_hidden_state = last_hidden_state[mask_tokens]
+            labels = labels[mask_tokens]
+
+        logits = (
+            self.compiled_head(last_hidden_state)
+            if self.config.reference_compile
+            else self.decoder(self.head(last_hidden_state))
+        )
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size)
+
+        if self.config._attn_implementation == "flash_attention_2":
+            with torch.no_grad():
+                logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The ModernBert Model with a sequence classification head on top that performs pooling.",
+    MODERNBERT_START_DOCSTRING,
+)
+class ModernBertForSequenceClassification(ModernBertPreTrainedModel):
+    def __init__(self, config: ModernBertConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.model = ModernBertModel(config)
+        self.head = ModernBertPredictionHead(config)
+        self.drop = torch.nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        if self.config.classifier_pooling == "cls":
+            last_hidden_state = last_hidden_state[:, 0]
+        elif self.config.classifier_pooling == "mean":
+            last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+                dim=1, keepdim=True
+            )
+
+        pooled_output = self.head(last_hidden_state)
+        pooled_output = self.drop(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        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 = 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 = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.",
+    MODERNBERT_START_DOCSTRING,
+)
+class ModernBertForTokenClassification(ModernBertPreTrainedModel):
+    def __init__(self, config: ModernBertConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = ModernBertModel(config)
+        self.head = ModernBertPredictionHead(config)
+        self.drop = torch.nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        last_hidden_state = self.head(last_hidden_state)
+        last_hidden_state = self.drop(last_hidden_state)
+        logits = self.classifier(last_hidden_state)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "ModernBertModel",
+    "ModernBertPreTrainedModel",
+    "ModernBertForMaskedLM",
+    "ModernBertForSequenceClassification",
+    "ModernBertForTokenClassification",
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc6a3a71511fc96fc18fc3fdd03289eac4aeba5a252d959237860baa69098533
+size 1643540546

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "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": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

supar.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad2e657c8787e27591918837a649b61fb629af6365a8ac3418c57ee80d7e3f9b
+size 1688703466

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "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
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "keep_accents": true,
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "split_by_punct": true,
+  "tokenizer_class": "DebertaV2TokenizerFast",
+  "unk_token": "[UNK]"
+}

upos.py

@@ -0,0 +1,41 @@
+from transformers import TokenClassificationPipeline
+
+class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
+  def __init__(self,**kwargs):
+    import numpy
+    super().__init__(**kwargs)
+    x=self.model.config.label2id
+    y=[k for k in x if not k.startswith("I-")]
+    self.transition=numpy.full((len(x),len(x)),numpy.nan)
+    for k,v in x.items():
+      for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
+        self.transition[v,x[j]]=0
+  def check_model_type(self,supported_models):
+    pass
+  def postprocess(self,model_outputs,**kwargs):
+    import numpy
+    if "logits" not in model_outputs:
+      return self.postprocess(model_outputs[0],**kwargs)
+    m=model_outputs["logits"][0].numpy()
+    e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
+    z=e/e.sum(axis=-1,keepdims=True)
+    for i in range(m.shape[0]-1,0,-1):
+      m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
+    k=[numpy.nanargmax(m[0]+self.transition[0])]
+    for i in range(1,m.shape[0]):
+      k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
+    w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
+    if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
+      for i,t in reversed(list(enumerate(w))):
+        p=t.pop("entity")
+        if p.startswith("I-"):
+          w[i-1]["score"]=min(w[i-1]["score"],t["score"])
+          w[i-1]["end"]=w.pop(i)["end"]
+        elif p.startswith("B-"):
+          t["entity_group"]=p[2:]
+        else:
+          t["entity_group"]=p
+    for t in w:
+      t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
+    return w
+