Upload model

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "SpecTModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_spect.SpecTConfig",
+    "AutoModel": "modeling_spect.SpecTModel"
+  },
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 64,
+  "initializer_range": 0.02,
+  "intermediate_size": 128,
+  "layer_norm_eps": 1e-12,
+  "model_type": "spect",
+  "num_attention_heads": 2,
+  "num_channels": 1,
+  "num_hidden_layers": 2,
+  "patch_size": 64,
+  "qkv_bias": true,
+  "spectral_length": 4096,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2"
+}

configuration_spect.py

@@ -0,0 +1,123 @@
+# coding=utf-8
+# Copyright 2021 Google AI 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.
+""" SpecT model configuration"""
+
+from collections import OrderedDict
+from typing import Mapping
+
+from packaging import version
+
+from transformers.utils import logging
+from transformers.onnx import OnnxConfig
+from transformers.configuration_utils import PretrainedConfig
+
+
+logger = logging.get_logger(__name__)
+
+# VIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+#     "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
+#     # See all ViT models at https://huggingface.co/models?filter=vit
+# }
+
+
+class SpecTConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SpecTModel`]. It is used to instantiate an SpecT
+    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 SpecT architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        spectral_length (`int`, *optional*, defaults to 4096):
+            The length of each spectral.
+        patch_size (`int`, *optional*, defaults to 64):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to 1):
+            The number of input channels.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+
+    Example:
+
+    ```python
+    >>> from transformers import ViTConfig, ViTModel
+
+    >>> # Initializing a ViT vit-base-patch16-224 style configuration
+    >>> configuration = ViTConfig()
+
+    >>> # Initializing a model (with random weights) from the vit-base-patch16-224 style configuration
+    >>> model = ViTModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "spect"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        spectral_length=4096,
+        patch_size=64,
+        num_channels=1,
+        qkv_bias=True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.spectral_length = spectral_length
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.qkv_bias = qkv_bias
+

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:441471a885417c5f0f51562b584dd68a22afc54202bf57e0e7e5b3f8c0ee2f98
+size 323264

modeling_spect.py

@@ -0,0 +1,635 @@
+import math
+from typing import Dict, List, Optional, Set, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from transformers import PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.file_utils import ModelOutput
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    SequenceClassifierOutput
+)
+from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+)
+
+from models.spect import SpecTConfig  
+
+
+logger = logging.get_logger(__name__)
+
+# General docstring
+_CONFIG_FOR_DOC = "SpecTConfig"
+
+# Base docstring
+_CHECKPOINT_FOR_DOC = "Maxwell-Jia/spect-base-patch64-4096-lamost"
+_EXPECTED_OUTPUT_SHAPE = [1, 65, 768]
+
+# # Image classification docstring
+# _IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224"
+# _IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"
+
+VIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "Maxwell-Jia/spect-base-patch64-4096-lamost",
+    # See all Spectral data models at your model repository or documentation page
+]
+
+
+class SpecTPatchEmbeddings(nn.Module):
+    """
+    This class turns `spectral_values` of shape `(batch_size, sequence_length)` into
+    `hidden_states` (segment embeddings) of shape `(batch_size, num_segments, hidden_size)` 
+    for a Transformer.
+    """
+
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        spectral_length, patch_size = config.spectral_length, config.patch_size
+        num_channels, hidden_size = config.num_channels, config.hidden_size
+
+        # Assuming spectral data is 1D, adjust dimensions accordingly
+        num_patches = spectral_length // patch_size
+        self.spectral_length = spectral_length
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.num_patches = num_patches
+
+        # Using Conv1d for patching the spectral sequence
+        self.projection = nn.Conv1d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, spectral_values: torch.Tensor) -> torch.Tensor:
+        batch_size, spectral_length = spectral_values.shape
+        if spectral_length != self.spectral_length:
+            raise ValueError(
+                f"Spectral sequence length ({spectral_length}) doesn't match model"
+                f" ({self.spectral_length})."
+            )
+        # Reshape and project the spectral segments to embeddings
+        spectral_values = spectral_values.unsqueeze(1)  # Add a channel dimension
+        embeddings = self.projection(spectral_values).transpose(1, 2)
+        return embeddings
+
+
+class SpecTEmbeddings(nn.Module):
+    """
+    Construct the CLS token, position embeddings for spectral data.
+    Optionally, also the mask token.
+    """
+
+    def __init__(self, config: SpecTConfig, use_mask_token: bool = False) -> None:
+        super().__init__()
+
+        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
+        self.patch_embeddings = SpecTPatchEmbeddings(config)
+        num_patches = self.patch_embeddings.num_patches
+        self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.config = config
+
+    def forward(
+        self,
+        flux_values: torch.Tensor,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+    ) -> torch.Tensor:
+        batch_size = flux_values.shape[0]
+        embeddings = self.patch_embeddings(flux_values)
+
+        if bool_masked_pos is not None:
+            seq_length = embeddings.shape[1]
+            mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
+            # replace the masked visual tokens by mask_tokens
+            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
+            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
+
+        # add the [CLS] token to the embedded patch tokens
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+        # add positional encoding to each token
+        embeddings = embeddings + self.position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+class SpecTSelfAttention(nn.Module):
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
+                f"heads {config.num_attention_heads}."
+            )
+        
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3)
+    
+    def forward(
+        self, 
+        hidden_states: torch.Tensor, 
+        head_mask: Optional[torch.Tensor] = None, 
+        output_attentions: bool = False
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        mixed_query_layer = self.query(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        return outputs
+    
+
+class SpecTSelfOutput(nn.Module):
+    """
+    The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the
+    layernorm applied before each block.
+    """
+
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+    
+
+class SpecTAttention(nn.Module):
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        self.attention = SpecTSelfAttention(config)
+        self.output = SpecTSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads: Set[int]) -> None:
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.attention.query = prune_linear_layer(self.attention.query, index)
+        self.attention.key = prune_linear_layer(self.attention.key, index)
+        self.attention.value = prune_linear_layer(self.attention.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
+        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        self_outputs = self.attention(hidden_states, head_mask, output_attentions)
+
+        attention_output = self.output(self_outputs[0], hidden_states)
+
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+    
+
+class SpecTIntermediate(nn.Module):
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+
+        return hidden_states
+    
+
+class SpecTOutput(nn.Module):
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        hidden_states = hidden_states + input_tensor
+
+        return hidden_states
+    
+
+class SpecTLayer(nn.Module):
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = SpecTAttention(config)
+        self.intermediate = SpecTIntermediate(config)
+        self.output = SpecTOutput(config)
+        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        self_attention_outputs = self.attention(
+            self.layernorm_before(hidden_states),  # in ViT, layernorm is applied before self-attention
+            head_mask,
+            output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        # first residual connection
+        hidden_states = attention_output + hidden_states
+
+        # in ViT, layernorm is also applied after self-attention
+        layer_output = self.layernorm_after(hidden_states)
+        layer_output = self.intermediate(layer_output)
+
+        # second residual connection is done here
+        layer_output = self.output(layer_output, hidden_states)
+
+        outputs = (layer_output,) + outputs
+
+        return outputs
+    
+
+class SpecTEncoder(nn.Module):
+    def __init__(self, config: SpecTConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([SpecTLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ) -> Union[tuple, BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    layer_head_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (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,
+        )
+    
+
+class SpecTPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = SpecTConfig
+    base_model_prefix = "spect"
+    main_input_name = "spectral_values"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["SpecTEmbeddings", "SpecTLayer"]
+
+    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
+            # `trunc_normal_cpu` not implemented in `half` issues
+            module.weight.data = nn.init.trunc_normal_(
+                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
+            ).to(module.weight.dtype)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, SpecTEmbeddings):
+            module.position_embeddings.data = nn.init.trunc_normal_(
+                module.position_embeddings.data.to(torch.float32),
+                mean=0.0,
+                std=self.config.initializer_range,
+            ).to(module.position_embeddings.dtype)
+
+            module.cls_token.data = nn.init.trunc_normal_(
+                module.cls_token.data.to(torch.float32),
+                mean=0.0,
+                std=self.config.initializer_range,
+            ).to(module.cls_token.dtype)
+
+
+SPECT_START_DOCSTRING = r"""
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass 
+    designed for spectral data analysis. Use it as a regular PyTorch Module and refer to the PyTorch documentation 
+    for all matters related to general usage and behavior.
+
+    Parameters:
+        config ([`SpecTConfig`]): Model configuration class with all the parameters of the model specific to spectral 
+            data analysis. 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.
+"""
+
+
+SPECT_INPUTS_DOCSTRING = r"""
+    Args:
+        flux_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
+            Spectral flux values across wavelengths for each sequence in the batch. 
+            Represents the input spectral data to be processed by the model.
+
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+            
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        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.
+
+        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). 
+            Relevant for models that incorporate some form of masked or self-supervised learning on spectral data.
+
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+
+@add_start_docstrings(
+    "The bare SpecT Model transformer outputting raw hidden-states without any specific head on top.",
+    SPECT_START_DOCSTRING,
+)
+class SpecTModel(SpecTPreTrainedModel):
+    config_class = SpecTConfig
+
+    def __init__(self, config: SpecTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = SpecTEmbeddings(config, use_mask_token=use_mask_token)
+        self.encoder = SpecTEncoder(config)
+
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.pooler = SpecTPooler(config) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> SpecTPatchEmbeddings:
+        return self.embeddings.patch_embeddings
+
+    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(SPECT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPooling,
+        config_class=_CONFIG_FOR_DOC,
+        modality="vision",
+        expected_output=_EXPECTED_OUTPUT_SHAPE,
+    )
+    def forward(
+        self,
+        flux_values: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
+        """
+        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 flux_values is None:
+            raise ValueError("You have to specify flux_values")
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
+        expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
+        if flux_values.dtype != expected_dtype:
+            flux_values = flux_values.to(expected_dtype)
+
+        embedding_output = self.embeddings(
+            flux_values, bool_masked_pos=bool_masked_pos
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
+            return head_outputs + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+    
+
+class SpecTPooler(nn.Module):
+    def __init__(self, config: SpecTConfig):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+    
+
+@add_start_docstrings(
+    """
+    SpecT Model transformer with an squence classification head on top (a linear layer on top of the final hidden state 
+    of the [CLS] token).
+    """,
+    SPECT_START_DOCSTRING,
+)
+class SpecTForSequenceClassification(PreTrainedModel):
+    """
+    This model is a modification of the SpecTModel for sequence classification tasks. It adds a classification head
+    on top of the SpecTModel, making it suitable for tasks like spectral type classification from stellar spectra.
+    """
+    config_class = SpecTConfig
+
+    def __init__(self, config: SpecTConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        # The base SpecTModel
+        self.spect = SpecTModel(config, add_pooling_layer=False)
+
+        # Classification head
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        flux_values: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        outputs = self.spect(
+            flux_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        
+        sequence_output = outputs[0]
+        pooled_output = sequence_output[:, 0, :] 
+        
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:  # This is for binary classification
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )