Upload EncT5ForSequenceClassification

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
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+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+[More Information Needed]
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+### Out-of-Scope Use
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+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
+## Bias, Risks, and Limitations
+
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+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
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+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+**BibTeX:**
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+**APA:**
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+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
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+## More Information [optional]
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+## Model Card Authors [optional]
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+[More Information Needed]
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+## Model Card Contact
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+[More Information Needed]
+
+

config.json

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+{
+  "_name_or_path": "/Users/hckyn/Desktop/enct5-glue-sst2/",
+  "architectures": [
+    "EncT5ForSequenceClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_enct5.EncT5Config",
+    "AutoModelForSequenceClassification": "modeling_enct5.EncT5ForSequenceClassification"
+  },
+  "classifier_dropout": 0.0,
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "decoder_vocab_size": 1,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "enct5",
+  "n_positions": 512,
+  "num_decoder_layers": 1,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

configuration_enct5.py

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+# coding=utf-8
+#
+# 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.
+""" EncT5 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class EncT5Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`EncT5`]. It is used to instantiate a EncT5 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 T5 [t5-small](https://huggingface.co/t5-small)
+    architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Arguments:
+        vocab_size (`int`, *optional*, defaults to 32128):
+            Vocabulary size of the EncT5 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
+        decoder_vocab_size (`int`, *optional*, defaults to 1):
+            Decoder vocabulary size of the EncT5 model. For regression and single-label classification, this should just
+            be 1 (the default). For multi-label classification, this should be the number of labels.
+        d_model (`int`, *optional*, defaults to 512):
+            Size of the encoder layers and the pooler layer.
+        d_kv (`int`, *optional*, defaults to 64):
+            Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will
+            be defined as `num_heads * d_kv`.
+        d_ff (`int`, *optional*, defaults to 2048):
+            Size of the intermediate feed forward layer in each `T5Block`.
+        num_layers (`int`, *optional*, defaults to 6):
+            Number of hidden layers in the Transformer encoder.
+        num_decoder_layers (`int`, *optional*, defaults to 1):
+            Number of hidden layers in the Transformer decoder.
+        num_heads (`int`, *optional*, defaults to 8):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
+            The number of buckets to use for each attention layer.
+        relative_attention_max_distance (`int`, *optional*, defaults to 128):
+            The maximum distance of the longer sequences for the bucket separation.
+        dropout_rate (`float`, *optional*, defaults to 0.1):
+            The ratio for all dropout layers.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for classifier.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        initializer_factor (`float`, *optional*, defaults to 1):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
+            Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
+            `"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+    """
+
+    model_type = "enct5"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
+
+    def __init__(
+        self,
+        vocab_size=32128,
+        decoder_vocab_size=1,
+        d_model=512,
+        d_kv=64,
+        d_ff=2048,
+        num_layers=6,
+        num_decoder_layers=1,
+        num_heads=8,
+        relative_attention_num_buckets=32,
+        relative_attention_max_distance=128,
+        dropout_rate=0.1,
+        layer_norm_epsilon=1e-6,
+        initializer_factor=1.0,
+        feed_forward_proj="relu",
+        is_encoder_decoder=True,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        classifier_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.decoder_vocab_size = decoder_vocab_size
+        self.d_model = d_model
+        self.d_kv = d_kv
+        self.d_ff = d_ff
+        self.num_layers = num_layers
+        self.num_decoder_layers = num_decoder_layers
+        self.num_heads = num_heads
+        self.relative_attention_num_buckets = relative_attention_num_buckets
+        self.relative_attention_max_distance = relative_attention_max_distance
+        self.dropout_rate = dropout_rate
+        self.classifier_dropout = classifier_dropout
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_factor = initializer_factor
+        self.feed_forward_proj = feed_forward_proj
+        self.use_cache = use_cache
+
+        act_info = self.feed_forward_proj.split("-")
+        self.dense_act_fn = act_info[-1]
+        self.is_gated_act = act_info[0] == "gated"
+
+        if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
+            raise ValueError(
+                f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer. "
+                "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
+                "'gated-gelu' or 'relu'"
+            )
+
+        # for backwards compatibility
+        if feed_forward_proj == "gated-gelu":
+            self.dense_act_fn = "gelu_new"
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            **kwargs,
+        )
+
+        # Override the default behavior to tie word embeddings.
+        self.tie_word_embeddings = False

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84f94588b91759227e5d958ff472fb38b2e24374a766f4d6402928414b631031
+size 476301088

modeling_enct5.py

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+# coding=utf-8
+# 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.
+""" EncT5 model (based on HuggingFace T5 Model) """
+
+from typing import Optional, List, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.models.t5.modeling_t5 import T5Config, T5PreTrainedModel, T5Model
+from transformers.modeling_outputs import Seq2SeqSequenceClassifierOutput
+
+from .configuration_enct5 import EncT5Config
+
+
+class EncT5ClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config: EncT5Config):
+        super().__init__()
+        self.dropout = nn.Dropout(p=config.classifier_dropout)
+        self.out_proj = nn.Linear(config.d_model, config.num_labels)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
+class EncT5MultiLabelClassificationHead(nn.Module):
+    """Head for multi-label sentence-level classification tasks."""
+
+    def __init__(self, config: EncT5Config):
+        super().__init__()
+        self.weights = nn.Parameter(torch.Tensor(config.num_labels, config.d_model))
+        self.biases = nn.Parameter(torch.Tensor(config.num_labels))
+        self.dropout = nn.Dropout(p=config.classifier_dropout)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # The input hidden_states shape should be (batch_size, num_labels, d_model)
+        hidden_states = self.dropout(hidden_states)
+        # The following element-wise multiplication simulates multiple per-label classification heads (one head per
+        # label). The element-wise multiplication of the weights, followed by a summation and addition of biases, is
+        # equivalent to a linear projection from d_model down to 1 for each label (but with vectorization).
+        hidden_states = torch.sum(hidden_states * self.weights, dim=-1) + self.biases  # (batch_size, num_labels)
+        return hidden_states
+
+
+class EncT5PreTrainedModel(T5PreTrainedModel):
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        factor = self.config.initializer_factor  # Used for testing weights initialization
+        if isinstance(module, EncT5ClassificationHead):
+            module.out_proj.weight.data.normal_(mean=0.0, std=factor * (self.config.d_model ** -0.5))
+            if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
+                module.out_proj.bias.data.zero_()
+        elif isinstance(module, EncT5MultiLabelClassificationHead):
+            module.weights.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+            module.biases.data.zero_()
+        super()._init_weights(module)
+
+
+class EncT5ForSequenceClassification(EncT5PreTrainedModel):
+    r"""
+    The EncT5 model was proposed in [EncT5: A Framework for Fine-tuning T5 as Non-autoregressive
+    Models](https://arxiv.org/abs/2110.08426) by Frederick Liu, Terry Huang, Shihang Lyu, Siamak Shakeri, Hongkun Yu,
+    Jing Li.
+
+    EncT5 is a variant of T5 that uses mainly the encoder for non-autoregressive tasks. There are several special
+    features to EncT5: 1) there are less decoder layers (defaulting to 1 decoder layer), 2) there is a separate decoder
+    word embedding, with the decoder input ids being predefined constants, and 3) there is a classification head on top
+    of the output. Research has shown that this model can be more efficient and usable over T5 and BERT for
+    non-autoregressive tasks such as classification and regression.
+    """
+    config_class = EncT5Config
+    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+
+    def __init__(self, config: EncT5Config):
+        super().__init__(config)
+
+        # Initialize the base T5 model.
+        self.transformer = T5Model(T5Config.from_dict(config.to_dict()))
+
+        # Initiate decoder embedding from scratch and define the corresponding latent vector vocabulary size.
+        self.decoder_embeddings = nn.Embedding(config.decoder_vocab_size, config.d_model)
+        self.transformer.get_decoder().set_input_embeddings(self.decoder_embeddings)
+
+        # Initiate decoder projection head from scratch.
+        if config.problem_type == "multi_label_classification":
+            self.classification_head = EncT5MultiLabelClassificationHead(config)
+        else:
+            self.classification_head = EncT5ClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.model_parallel = False
+
+    def load_weights_from_pretrained_t5(self, model_path: str):
+        pretrained_t5_model = T5Model.from_pretrained(model_path)
+
+        # Override the decoder embedding weights to make them the correct shape.
+        pretrained_state_dict = pretrained_t5_model.state_dict()
+        pretrained_state_dict["decoder.embed_tokens.weight"] = self.decoder_embeddings.state_dict()["weight"]
+
+        self.transformer.load_state_dict(pretrained_state_dict, strict=False)
+
+    def prepare_for_fine_tuning(self):
+        r"""
+        Prepares the model for fine-tuning by re-initializing the necessary weights for fine-tuning. This step should be
+        performed after loading the pre-trained T5 model but before fine-tuning.
+        """
+        self.transformer.get_decoder().apply(self._init_weights)
+        self._init_weights(self.classification_head)
+
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
+        r"""
+        Arguments:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so
+                you should be able to pad the inputs on both the right and the left.
+
+                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for detail.
+
+                [What are input IDs?](../glossary#input-ids)
+
+                To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
+            attention_mask (`torch.FloatTensor` 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)
+            decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+                Indices of decoder input sequence tokens in the vocabulary.
+
+                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+                T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+                `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+                `past_key_values`).
+
+                To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
+                Training](./t5#training).
+            decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+                Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will
+                also be used by default.
+            head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+                Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in
+                `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+                Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in
+                `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+                    Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected
+                    in `[0, 1]`:
+
+                    - 1 indicates the head is **not masked**,
+                    - 0 indicates the head is **masked**.
+
+            encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
+                Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
+                `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states
+                at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+            past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4
+                tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+                Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
+                decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+            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.
+            decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
+                representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to
+                be input (see `past_key_values`). This is useful if you want more control over how to convert
+                `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
+
+                If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the
+                value of `inputs_embeds`.
+            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 classification loss is computed (Cross-Entropy).
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            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.
+        Returns:
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        if input_ids is None and inputs_embeds is None:
+            raise ValueError("You have to specify either input_ids or inputs_embeds.")
+        batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            if self.config.problem_type == "multi_label_classification":
+                decoder_input_ids = torch.arange(end=self.config.num_labels, device=device, dtype=torch.long)
+                decoder_input_ids = decoder_input_ids.repeat(batch_size, 1)  # Shape: (batch_size, num_labels)
+                # Provide a 3-dimensional attention mask by default to suppress the default causal mask.
+                if decoder_attention_mask is None:
+                    decoder_attention_mask = torch.ones(
+                        (batch_size, self.config.num_labels, self.config.num_labels), device=device, dtype=torch.long
+                    )
+            else:
+                decoder_input_ids = torch.zeros(batch_size, 1, device=device, dtype=torch.long)
+
+        outputs = self.transformer(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            head_mask=head_mask,
+            decoder_head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            encoder_outputs=encoder_outputs,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]  # Shape: (batch_size, 1 or num_labels, d_model)
+
+        logits = self.classification_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    # The classification head for multi-label classification is different, and so we need the
+                    # problem_type to be set during initialization to select the proper classification head.
+                    raise ValueError(
+                        "For multi-label classification, the config.problem_type must be set to "
+                        "'multi_label_classification' when initializing the model.")
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.config.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.config.num_labels), labels.view(-1))
+            else:
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )