Duplicate from dicta-il/neodictabert-bilingual

Co-authored-by: Shaltiel Shmidman <Shaltiel@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-4.0
+language:
+- he
+- en
+---
+# NeoDictaBERT-bilingual: Pushing the Frontier of BERT models in Hebrew
+
+Following the success of [ModernBERT](https://huggingface.co/blog/modernbert) and [NeoBERT](https://huggingface.co/chandar-lab/NeoBERT), we set out to train a Hebrew version of NeoBERT. 
+
+Introducing **NeoDictaBERT-bilingual**: A Next-Generation BERT-style model trained on a mixture of Hebrew and English data, technical report coming soon.  
+
+Supported Context Length: *4,096* (~**2,700** Hebrew words)
+
+Trained on a total of 612B tokens with a context length of 1,024, and another 122B tokens with a context length of 4,096. 
+
+This is the base model pretrained on both English and Hebrew. You can access the base model pretrained *only* on Hebrew [here](https://huggingface.co/dicta-il/neodictabert). 
+
+Sample usage:
+
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('dicta-il/neodictabert-bilingual')
+model = AutoModelForMaskedLM.from_pretrained('dicta-il/neodictabert-bilingual', trust_remote_code=True)
+
+model.eval()
+
+sentence = 'בשנת 1948 השלים אפרים קישון את [MASK] בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים'
+
+output = model(tokenizer.encode(sentence, return_tensors='pt'))
+# the [MASK] is the 7th token (including [CLS])
+import torch
+top_2 = torch.topk(output.logits[0, 7, :], 2)[1]
+print('\n'.join(tokenizer.convert_ids_to_tokens(top_2))) # should print לימודיו / הכשרתו
+```
+
+## Performance
+
+Please see our technical report for performance metrics. The model outperforms previous SOTA models on almost all benchmarks, with a noticeable jump in the QA scores which indicate a much deeper semantic understanding. 
+
+In addition the model shows strong results on retrieval tasks, outperforming other multilingual models of similar size. See technical report [here](https://arxiv.org/abs/2510.20386) for more details. 
+
+
+## Citation
+
+If you use NeoDictaBERT in your research, please cite ```NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew```
+
+**BibTeX:**
+
+```bibtex
+@misc{shmidman2025neodictabertpushingfrontierbert,
+      title={NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew}, 
+      author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
+      year={2025},
+      eprint={2510.20386},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.20386}, 
+}
+```
+
+## License
+
+Shield: [![CC BY 4.0][cc-by-shield]][cc-by]
+
+This work is licensed under a
+[Creative Commons Attribution 4.0 International License][cc-by].
+
+[![CC BY 4.0][cc-by-image]][cc-by]
+
+[cc-by]: http://creativecommons.org/licenses/by/4.0/
+[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
+[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg

config.json

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+{
+  "architectures": [
+    "NeoBERTLMHead"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_neobert.NeoBERTConfig",
+    "AutoModel": "modeling_neobert.NeoBERT",
+    "AutoModelForMaskedLM": "modeling_neobert.NeoBERTLMHead",
+    "AutoModelForSequenceClassification": "modeling_neobert.NeoBERTForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_neobert.NeoBERTForTokenClassification",
+    "AutoModelForQuestionAnswering": "modeling_neobert.NeoBERTForQuestionAnswering"
+  },
+  "decoder_init_range": 0.02,
+  "dim_head": 64,
+  "embedding_init_range": 0.02,
+  "encoder_init_range": 0.02,
+  "hidden_size": 768,
+  "intermediate_size": 3072,
+  "kwargs": {
+    "decoder_init_range": 0.02
+  },
+  "max_length": 4096,
+  "model_type": "neobert",
+  "norm_eps": 1e-06,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "pad_token_id": 3,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.0",
+  "vocab_size": 128000
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65364c0b5df91da558634abdcdbcc06cae6ef81d93246b250a5ba87246a6ce37
+size 922061824

modeling_neobert.py

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+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.nn.functional import scaled_dot_product_attention
+
+from typing import Optional, Tuple, Union
+import numpy as np
+
+try:
+    from xformers.ops import SwiGLU
+except:
+    class SwiGLU(nn.Module):
+        """
+        A Module that mimicks the call to :attr:`xformers.ops.swiglu`,
+        and holds the weights for the 3 linear layers
+        """
+        def __init__(
+            self,
+            in_features: int,
+            hidden_features: int,
+            out_features: Optional[int] = None,
+            bias: bool = True,
+            *,
+            _pack_weights: bool = True,
+        ) -> None:
+            """Create a SwiGLU module
+
+            Args:
+                in_features (int): Number of features of the input
+                hidden_features (int): Number of hidden features
+                out_features (Optional[int], optional): Number of features of the input. Defaults to None.
+                bias (bool, optional): Whether linear layers also include a bias. Defaults to True.
+            """
+            super().__init__()
+            out_features = out_features or in_features
+            hidden_features = hidden_features or in_features
+
+            self.w12: Optional[nn.Linear]
+            if _pack_weights:
+                self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+            else:
+                self.w12 = None
+                self.w1 = nn.Linear(in_features, hidden_features, bias=bias)
+                self.w2 = nn.Linear(in_features, hidden_features, bias=bias)
+            self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+            self.hidden_features = hidden_features
+            self.out_features = out_features
+            self.in_features = in_features
+            self.op: Optional[SwiGLUOp] = None
+
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
+            """Computes :attr:`swiglu` with the module's weights
+
+            Args:
+                x (torch.Tensor): A Tensor of shape ``[..., in_features]``
+
+            Returns:
+                torch.Tensor: A Tensor of shape ``[..., out_features]``
+            """
+            if self.w12 is not None:
+                gate, x = self.w12(x).chunk(2, dim=-1)
+                hidden = F.silu(gate) * x
+            else:
+                x1 = self.w1(x)
+                x2 = self.w2(x)
+                hidden = F.silu(x1) * x2
+
+            return self.w3(hidden)
+
+
+try:
+    from flash_attn.flash_attn_interface import flash_attn_varlen_func
+
+    FLASH_ATTN_AVAILABLE = True
+except ImportError:
+    FLASH_ATTN_AVAILABLE = False
+
+from transformers import (
+    PreTrainedModel,
+    PretrainedConfig,
+    DataCollatorForLanguageModeling,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+    QuestionAnsweringModelOutput
+)
+
+import torch
+from typing import Tuple
+
+def precompute_freqs(dim: int, end: int, theta: float = 10000.0, *, device=None, dtype=torch.float32):
+    """
+    Returns (cos, sin) tensors of shape [end, dim//2], no complex dtype.
+    """
+    h = dim // 2
+    idx = torch.arange(0, h, device=device, dtype=dtype)
+    inv_freq = 1.0 / (theta ** ((2.0 * idx) / dim))
+    t = torch.arange(end, device=device, dtype=dtype)
+    angles = torch.outer(t, inv_freq)                         # [L, h]
+    return angles.cos(), angles.sin()   # ([L, h], [L, h])
+
+def reshape_for_broadcast(freqs: torch.Tensor, x: torch.Tensor):
+    # freqs: [L, h]; x: [B, L, H, h] for the half-dim tensors
+    assert freqs.shape == (x.shape[1], x.shape[-1]), (freqs.shape, x.shape)
+    return freqs[None, :, None, :]                            # [1, L, 1, h]
+
+# Rotary embedding without complex numbers (megatron-core pairing: first half with second half)
+def apply_rotary_emb(xq: torch.Tensor, xk: torch.Tensor, freqs: tuple[torch.Tensor, torch.Tensor]):
+    # x*: [B, L, H, D]; freqs = (cos[L,h], sin[L,h])
+    D = xq.shape[-1]
+    h = D // 2
+    xq1, xq2 = xq[..., :h], xq[..., h:]
+    xk1, xk2 = xk[..., :h], xk[..., h:]
+
+    cos, sin = freqs
+    cos = reshape_for_broadcast(cos.type_as(xq1), xq1)        # [1, L, 1, h]
+    sin = reshape_for_broadcast(sin.type_as(xq1), xq1)        # [1, L, 1, h]
+
+    q1 = xq1 * cos - xq2 * sin
+    q2 = xq1 * sin + xq2 * cos
+    k1 = xk1 * cos - xk2 * sin
+    k2 = xk1 * sin + xk2 * cos
+
+    return torch.cat([q1, q2], dim=-1), torch.cat([k1, k2], dim=-1)
+
+class NeoBERTEagerRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        NeoBERTEagerRMSNorm is equivalent to nn.RMSNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class NeoBERTConfig(PretrainedConfig):
+    model_type = "neobert"
+
+    # All config parameters must have a default value.
+    def __init__(
+        self,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 28,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        embedding_init_range: float = 0.02,
+        encoder_init_range: float = 0.02,
+        norm_eps: float = 1e-06,
+        vocab_size: int = 30522,
+        pad_token_id: int = 0,
+        max_length: int = 1024,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        if hidden_size % num_attention_heads != 0:
+            raise ValueError("Hidden size must be divisible by the number of heads.")
+        self.dim_head = hidden_size // num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.embedding_init_range = embedding_init_range
+        self.encoder_init_range = encoder_init_range
+        self.norm_eps = norm_eps
+        self.vocab_size = vocab_size
+        self.pad_token_id = pad_token_id
+        self.max_length = max_length
+        self.kwargs = kwargs
+
+
+class EncoderBlock(nn.Module):
+    """Transformer encoder block."""
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__()
+
+        self.config = config
+
+        # Attention
+        self.qkv = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size * 3, bias=False)
+        self.wo = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=False)
+
+        # Feedforward network
+        # Original NeoBERT:
+        # multiple_of = 8
+        # intermediate_size = int(2 * config.intermediate_size / 3)
+        # intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of)
+        intermediate_size = config.intermediate_size
+        self.ffn = SwiGLU(config.hidden_size, intermediate_size, config.hidden_size, bias=False)
+
+        # Layer norms
+        rms_norm_cls = nn.RMSNorm if hasattr(nn, 'RMSNorm') else NeoBERTEagerRMSNorm
+        self.attention_norm = rms_norm_cls(config.hidden_size, config.norm_eps)
+        self.ffn_norm = rms_norm_cls(config.hidden_size, config.norm_eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        output_attentions: bool,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+    ):
+        # Attention
+        attn_output, attn_weights = self._att_block(
+            self.attention_norm(x), attention_mask, freqs_cis, output_attentions, max_seqlen, cu_seqlens
+        )
+
+        # Residual
+        x = x + attn_output
+
+        # Feed-forward
+        x = x + self.ffn(self.ffn_norm(x))
+
+        return x, attn_weights
+
+    def _att_block(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        output_attentions: bool,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+    ):
+        batch_size, seq_len, _ = x.shape
+
+        xq, xk, xv = self.qkv(x).view(batch_size, seq_len, self.config.num_attention_heads, self.config.dim_head * 3).chunk(3, axis=-1)
+
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
+
+        # Attn block
+        attn_weights = None
+
+        # Flash attention if the tensors are packed
+        if cu_seqlens is not None:
+            attn = flash_attn_varlen_func(
+                q=xq.squeeze(0),
+                k=xk.squeeze(0),
+                v=xv.squeeze(0),
+                cu_seqlens_q=cu_seqlens,
+                cu_seqlens_k=cu_seqlens,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                dropout_p=0.0,
+                causal=False,
+            )
+        # Eager attention if attention weights are needed in the output (avoid using this unless needed - e.g., for onnx export)
+        elif output_attentions or self.config._attn_implementation == 'eager':
+            attn_weights = xq.permute(0, 2, 1, 3) @ xk.permute(0, 2, 3, 1) / (xq.size(-1) ** 0.5)
+            if attention_mask is not None:
+               attn_weights = attn_weights * attention_mask
+            attn_weights = attn_weights.softmax(-1)
+            attn = attn_weights @ xv.permute(0, 2, 1, 3)
+            attn = attn.transpose(1, 2)
+        # Fall back to SDPA otherwise
+        else:
+            attn = scaled_dot_product_attention(
+                query=xq.transpose(1, 2),
+                key=xk.transpose(1, 2),
+                value=xv.transpose(1, 2),
+                attn_mask=attention_mask.bool(),
+                dropout_p=0,
+            ).transpose(1, 2)
+
+        return self.wo(attn.reshape(batch_size, seq_len, self.config.num_attention_heads * self.config.dim_head)), attn_weights
+
+
+class NeoBERTPreTrainedModel(PreTrainedModel):
+    config_class = NeoBERTConfig
+    base_model_prefix = "model"
+    _supports_cache_class = True
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.uniform_(-self.config.encoder_init_range, self.config.encoder_init_range)
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.uniform_(-self.config.embedding_init_range, self.config.embedding_init_range)
+
+
+class NeoBERT(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.encoder = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+
+        # Ensures freqs_cis is moved to the same devices as the model. Non-persistent buffers are not saved in the state_dict.
+        cos, sin = precompute_freqs(config.hidden_size // config.num_attention_heads, config.max_length)
+        self.register_buffer("freqs_cos", cos, persistent=False)
+        self.register_buffer("freqs_sin", sin, persistent=False)
+
+        self.transformer_encoder = nn.ModuleList()
+        for _ in range(config.num_hidden_layers):
+            self.transformer_encoder.append(EncoderBlock(config))
+
+        rms_norm_cls = nn.RMSNorm if hasattr(nn, 'RMSNorm') else NeoBERTEagerRMSNorm
+        self.layer_norm = rms_norm_cls(config.hidden_size, config.norm_eps)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None, # kept in to not break compatibility with tokenizer(...), ignored
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        **kwargs,
+    ):
+        # Initialize
+        hidden_states, attentions = [], []
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        # Expand and repeat: (Batch, Length) -> (Batch, Heads, Length, Length)
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        attention_mask = attention_mask[:, None, None, :]
+            
+            # attention_mask = attention_mask.unsqueeze(1).unsqueeze(1).repeat(1, self.config.num_attention_heads, attention_mask.size(-1), 1)
+
+        # Checks to be done if inputs are packed sequences
+        if cu_seqlens is not None:
+            assert (
+                FLASH_ATTN_AVAILABLE
+            ), "Flash-attention is not available. Please ''pip install flash_attn'', or provide un-packed sequences."
+            assert not output_attentions, "Output attentions is not supported when sequences are packed."
+            assert max_seqlen is not None, "Missing max_seqlen. It must be provided when cu_seqlens are not None."
+            assert (input_ids if input_ids is not None else inputs_embeds).shape[
+                0
+            ] == 1, "Cumulative sequence lengths are provided but inputs are not packed."
+            assert (
+                input_ids if input_ids is not None else inputs_embeds
+            ).is_cuda, "Packing uses an implementation of flash-attention and is only supported on GPU."
+
+        # RoPE
+        if position_ids is not None:
+            freqs = (self.freqs_cos[position_ids], self.freqs_sin[position_ids])
+        else:
+            L = (input_ids if input_ids is not None else inputs_embeds).shape[1]
+            freqs = (self.freqs_cos[:L], self.freqs_sin[:L])
+
+        # Embedding
+        x = self.encoder(input_ids) if input_ids is not None else inputs_embeds
+
+        # Transformer encoder
+        for layer in self.transformer_encoder:
+            x, attn = layer(x, attention_mask, freqs, output_attentions, max_seqlen, cu_seqlens)
+            if output_hidden_states:
+                hidden_states.append(x)
+            if output_attentions:
+                attentions.append(attn)
+
+        # Final normalization layer
+        x = self.layer_norm(x)
+
+        # Return the output of the last hidden layer
+        return BaseModelOutput(
+            last_hidden_state=x,
+            hidden_states=hidden_states if output_hidden_states else None,
+            attentions=attentions if output_attentions else None,
+        )
+
+
+class NeoBERTLMHead(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.model = NeoBERT(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None, # kept in to not break compatibility with tokenizer(...), ignored
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        **kwargs,
+    ):
+
+        output = self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            max_seqlen=max_seqlen,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        logits = self.decoder(output.last_hidden_state)
+
+        return MaskedLMOutput(
+            hidden_states=output.hidden_states if output_hidden_states else None,
+            attentions=output.attentions if output_attentions else None,
+            logits=logits,
+        )
+
+
+class NeoBERTForTokenClassification(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.num_labels = getattr(config, "num_labels", 2)
+        self.classifier_dropout = getattr(config, "classifier_dropout", 0.1)
+        self.classifier_init_range = getattr(config, "classifier_init_range", 0.02)
+
+        self.model = NeoBERT(config)
+
+        self.dense = nn.Linear(self.config.hidden_size, self.config.hidden_size)
+        self.dropout = nn.Dropout(self.classifier_dropout)
+        self.classifier = nn.Linear(self.config.hidden_size, self.num_labels)
+
+        self.post_init()
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.classifier_init_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None, # kept in to not break compatibility with tokenizer(...), ignored
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        labels: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        output = self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            max_seqlen=max_seqlen,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        hidden_states = output.last_hidden_state
+
+        x = self.dropout(hidden_states)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+
+        logits = self.classifier(x)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)[active_loss]
+                active_labels = labels.view(-1)[active_loss]
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            result = (logits,)
+            return ((loss,) + result) if loss is not None else result
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=output.hidden_states if output_hidden_states else None,
+            attentions=output.attentions if output_attentions else None,
+        )
+
+
+class NeoBERTForSequenceClassification(NeoBERTPreTrainedModel):
+    config_class = NeoBERTConfig
+
+    def __init__(self, config: NeoBERTConfig):
+        super().__init__(config)
+
+        self.config = config
+
+        self.num_labels = getattr(config, "num_labels", 2)
+        self.classifier_dropout = getattr(config, "classifier_dropout", 0.1)
+        self.classifier_init_range = getattr(config, "classifier_init_range", 0.02)
+
+        self.model = NeoBERT(config)
+
+        self.dense = nn.Linear(self.config.hidden_size, self.config.hidden_size)
+        self.dropout = nn.Dropout(self.classifier_dropout)
+        self.classifier = nn.Linear(self.config.hidden_size, self.num_labels)
+
+        self.post_init()
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.classifier_init_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None, # kept in to not break compatibility with tokenizer(...), ignored
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        labels: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        output = self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            max_seqlen=max_seqlen,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        hidden_states = output.last_hidden_state
+
+        x = hidden_states[:, 0, :]
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+
+        logits = self.classifier(x)
+
+        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:
+            result = (logits,)
+            return ((loss,) + result) if loss is not None else result
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=output.hidden_states if output_hidden_states else None,
+            attentions=output.attentions if output_attentions else None,
+        )
+
+class NeoBERTForQuestionAnswering(NeoBERTPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = NeoBERT(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None, # kept in to not break compatibility with tokenizer(...), ignored
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if output_attentions or output_hidden_states: return_dict = True
+
+        output = self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            max_seqlen=max_seqlen,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=True
+        )
+        hidden_states = output.last_hidden_state
+
+        logits = self.qa_outputs(hidden_states)
+        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 = 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)
+            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=output.hidden_states,
+            attentions=output.attentions,
+        )

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
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "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": "[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
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}