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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]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## 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]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[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]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "_name_or_path": "/media/ssd/perelluis/relik_experiments/models/relik-reader-deberta-v3-large-re-wikipedia",
+  "activation": "gelu",
+  "add_entity_embedding": null,
+  "additional_special_symbols": 30,
+  "additional_special_symbols_types": 0,
+  "architectures": [
+    "RelikReaderREModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_relik.RelikReaderConfig",
+    "AutoModel": "modeling_relik.RelikReaderREModel"
+  },
+  "binary_end_logits": false,
+  "default_reader_class": null,
+  "entity_type_loss": null,
+  "linears_hidden_size": 512,
+  "model_type": "relik-reader",
+  "num_layers": null,
+  "threshold": 0.5,
+  "torch_dtype": "float32",
+  "training": true,
+  "transformer_model": "microsoft/deberta-v3-small",
+  "transformers_version": "4.41.2",
+  "use_last_k_layers": 1
+}

configuration_relik.py

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+from typing import Optional
+
+from transformers import AutoConfig
+from transformers.configuration_utils import PretrainedConfig
+
+
+class RelikReaderConfig(PretrainedConfig):
+    model_type = "relik-reader"
+
+    def __init__(
+        self,
+        transformer_model: str = "microsoft/deberta-v3-base",
+        additional_special_symbols: int = 101,
+        additional_special_symbols_types: Optional[int] = 0,
+        num_layers: Optional[int] = None,
+        activation: str = "gelu",
+        linears_hidden_size: Optional[int] = 512,
+        use_last_k_layers: int = 1,
+        entity_type_loss: bool = False,
+        add_entity_embedding: bool = None,
+        binary_end_logits: bool = False,
+        training: bool = False,
+        default_reader_class: Optional[str] = None,
+        threshold: Optional[float] = 0.5,
+        **kwargs
+    ) -> None:
+        # TODO: add name_or_path to kwargs
+        self.transformer_model = transformer_model
+        self.additional_special_symbols = additional_special_symbols
+        self.additional_special_symbols_types = additional_special_symbols_types
+        self.num_layers = num_layers
+        self.activation = activation
+        self.linears_hidden_size = linears_hidden_size
+        self.use_last_k_layers = use_last_k_layers
+        self.entity_type_loss = entity_type_loss
+        self.add_entity_embedding = (
+            True
+            if add_entity_embedding is None and entity_type_loss
+            else add_entity_embedding
+        )
+        self.threshold = threshold
+        self.binary_end_logits = binary_end_logits
+        self.training = training
+        self.default_reader_class = default_reader_class
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fcaad405b58a947631c5690834dad6d3dcaf1475446a90cbfe6ec5523a44eb8e
+size 585824816

modeling_relik.py

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+from typing import Any, Dict, Optional
+
+import torch
+from transformers import AutoModel, PreTrainedModel
+from transformers.activations import ClippedGELUActivation, GELUActivation
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_utils import PoolerEndLogits
+
+from .configuration_relik import RelikReaderConfig
+
+
+class RelikReaderSample:
+    def __init__(self, **kwargs):
+        super().__setattr__("_d", {})
+        self._d = kwargs
+
+    def __getattribute__(self, item):
+        return super(RelikReaderSample, self).__getattribute__(item)
+
+    def __getattr__(self, item):
+        if item.startswith("__") and item.endswith("__"):
+            # this is likely some python library-specific variable (such as __deepcopy__ for copy)
+            # better follow standard behavior here
+            raise AttributeError(item)
+        elif item in self._d:
+            return self._d[item]
+        else:
+            return None
+
+    def __setattr__(self, key, value):
+        if key in self._d:
+            self._d[key] = value
+        else:
+            super().__setattr__(key, value)
+            self._d[key] = value
+
+
+activation2functions = {
+    "relu": torch.nn.ReLU(),
+    "gelu": GELUActivation(),
+    "gelu_10": ClippedGELUActivation(-10, 10),
+}
+
+
+class PoolerEndLogitsBi(PoolerEndLogits):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__(config)
+        self.dense_1 = torch.nn.Linear(config.hidden_size, 2)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        start_states: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        p_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        if p_mask is not None:
+            p_mask = p_mask.unsqueeze(-1)
+        logits = super().forward(
+            hidden_states,
+            start_states,
+            start_positions,
+            p_mask,
+        )
+        return logits
+
+
+class RelikReaderSpanModel(PreTrainedModel):
+    config_class = RelikReaderConfig
+
+    def __init__(self, config: RelikReaderConfig, *args, **kwargs):
+        super().__init__(config)
+        # Transformer model declaration
+        self.config = config
+        self.transformer_model = (
+            AutoModel.from_pretrained(self.config.transformer_model)
+            if self.config.num_layers is None
+            else AutoModel.from_pretrained(
+                self.config.transformer_model, num_hidden_layers=self.config.num_layers
+            )
+        )
+        self.transformer_model.resize_token_embeddings(
+            self.transformer_model.config.vocab_size
+            + self.config.additional_special_symbols
+        )
+
+        self.activation = self.config.activation
+        self.linears_hidden_size = self.config.linears_hidden_size
+        self.use_last_k_layers = self.config.use_last_k_layers
+
+        # named entity detection layers
+        self.ned_start_classifier = self._get_projection_layer(
+            self.activation, last_hidden=2, layer_norm=False
+        )
+        if self.config.binary_end_logits:
+            self.ned_end_classifier = PoolerEndLogitsBi(self.transformer_model.config)
+        else:
+            self.ned_end_classifier = PoolerEndLogits(self.transformer_model.config)
+
+        # END entity disambiguation layer
+        self.ed_start_projector = self._get_projection_layer(self.activation)
+        self.ed_end_projector = self._get_projection_layer(self.activation)
+
+        self.training = self.config.training
+
+        # criterion
+        self.criterion = torch.nn.CrossEntropyLoss()
+
+    def _get_projection_layer(
+        self,
+        activation: str,
+        last_hidden: Optional[int] = None,
+        input_hidden=None,
+        layer_norm: bool = True,
+    ) -> torch.nn.Sequential:
+        head_components = [
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                (
+                    self.transformer_model.config.hidden_size * self.use_last_k_layers
+                    if input_hidden is None
+                    else input_hidden
+                ),
+                self.linears_hidden_size,
+            ),
+            activation2functions[activation],
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                self.linears_hidden_size,
+                self.linears_hidden_size if last_hidden is None else last_hidden,
+            ),
+        ]
+
+        if layer_norm:
+            head_components.append(
+                torch.nn.LayerNorm(
+                    self.linears_hidden_size if last_hidden is None else last_hidden,
+                    self.transformer_model.config.layer_norm_eps,
+                )
+            )
+
+        return torch.nn.Sequential(*head_components)
+
+    def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+        mask = mask.unsqueeze(-1)
+        if next(self.parameters()).dtype == torch.float16:
+            logits = logits * (1 - mask) - 65500 * mask
+        else:
+            logits = logits * (1 - mask) - 1e30 * mask
+        return logits
+
+    def _get_model_features(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor],
+    ):
+        model_input = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "output_hidden_states": self.use_last_k_layers > 1,
+        }
+
+        if token_type_ids is not None:
+            model_input["token_type_ids"] = token_type_ids
+
+        model_output = self.transformer_model(**model_input)
+
+        if self.use_last_k_layers > 1:
+            model_features = torch.cat(
+                model_output[1][-self.use_last_k_layers :], dim=-1
+            )
+        else:
+            model_features = model_output[0]
+
+        return model_features
+
+    def compute_ned_end_logits(
+        self,
+        start_predictions,
+        start_labels,
+        model_features,
+        prediction_mask,
+        batch_size,
+    ) -> Optional[torch.Tensor]:
+        # todo: maybe when constraining on the spans,
+        #  we should not use a prediction_mask for the end tokens.
+        #  at least we should not during training imo
+        start_positions = start_labels if self.training else start_predictions
+        start_positions_indices = (
+            torch.arange(start_positions.size(1), device=start_positions.device)
+            .unsqueeze(0)
+            .expand(batch_size, -1)[start_positions > 0]
+        ).to(start_positions.device)
+
+        if len(start_positions_indices) > 0:
+            expanded_features = model_features.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            expanded_prediction_mask = prediction_mask.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            end_logits = self.ned_end_classifier(
+                hidden_states=expanded_features,
+                start_positions=start_positions_indices,
+                p_mask=expanded_prediction_mask,
+            )
+
+            return end_logits
+
+        return None
+
+    def compute_classification_logits(
+        self,
+        model_features_start,
+        model_features_end,
+        special_symbols_features,
+    ) -> torch.Tensor:
+        model_start_features = self.ed_start_projector(model_features_start)
+        model_end_features = self.ed_end_projector(model_features_end)
+        model_start_features_symbols = self.ed_start_projector(special_symbols_features)
+        model_end_features_symbols = self.ed_end_projector(special_symbols_features)
+
+        model_ed_features = torch.cat(
+            [model_start_features, model_end_features], dim=-1
+        )
+        special_symbols_representation = torch.cat(
+            [model_start_features_symbols, model_end_features_symbols], dim=-1
+        )
+
+        logits = torch.bmm(
+            model_ed_features,
+            torch.permute(special_symbols_representation, (0, 2, 1)),
+        )
+
+        logits = self._mask_logits(
+            logits, (model_features_start == -100).all(2).long()
+        )
+        return logits
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor] = None,
+        prediction_mask: Optional[torch.Tensor] = None,
+        special_symbols_mask: Optional[torch.Tensor] = None,
+        start_labels: Optional[torch.Tensor] = None,
+        end_labels: Optional[torch.Tensor] = None,
+        use_predefined_spans: bool = False,
+        *args,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        batch_size, seq_len = input_ids.shape
+
+        model_features = self._get_model_features(
+            input_ids, attention_mask, token_type_ids
+        )
+
+        ned_start_labels = None
+
+        # named entity detection if required
+        if use_predefined_spans:  # no need to compute spans
+            ned_start_logits, ned_start_probabilities, ned_start_predictions = (
+                None,
+                None,
+                (
+                    torch.clone(start_labels)
+                    if start_labels is not None
+                    else torch.zeros_like(input_ids)
+                ),
+            )
+            ned_end_logits, ned_end_probabilities, ned_end_predictions = (
+                None,
+                None,
+                (
+                    torch.clone(end_labels)
+                    if end_labels is not None
+                    else torch.zeros_like(input_ids)
+                ),
+            )
+            ned_start_predictions[ned_start_predictions > 0] = 1
+            ned_end_predictions[end_labels > 0] = 1 
+            ned_end_predictions = ned_end_predictions[~(end_labels == -100).all(2)]
+
+        else:  # compute spans
+            # start boundary prediction
+            ned_start_logits = self.ned_start_classifier(model_features)
+            ned_start_logits = self._mask_logits(ned_start_logits, prediction_mask)
+            ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1)
+            ned_start_predictions = ned_start_probabilities.argmax(dim=-1)
+
+            # end boundary prediction
+            ned_start_labels = (
+                torch.zeros_like(start_labels) if start_labels is not None else None
+            )
+
+            if ned_start_labels is not None:
+                ned_start_labels[start_labels == -100] = -100
+                ned_start_labels[start_labels > 0] = 1
+
+            ned_end_logits = self.compute_ned_end_logits(
+                ned_start_predictions,
+                ned_start_labels,
+                model_features,
+                prediction_mask,
+                batch_size,
+            )
+
+            if ned_end_logits is not None:
+                ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1)
+                if not self.config.binary_end_logits:
+                    ned_end_predictions = torch.argmax(ned_end_probabilities, dim=-1, keepdim=True)
+                    ned_end_predictions = torch.zeros_like(ned_end_probabilities).scatter_(1, ned_end_predictions, 1)
+                else:
+                    ned_end_predictions = torch.argmax(ned_end_probabilities, dim=-1)
+            else:
+                ned_end_logits, ned_end_probabilities = None, None
+                ned_end_predictions = ned_start_predictions.new_zeros(batch_size, seq_len)
+                
+            if not self.training:
+                # if len(ned_end_predictions.shape) < 2:
+                #     print(ned_end_predictions)
+                end_preds_count = ned_end_predictions.sum(1)
+                # If there are no end predictions for a start prediction, remove the start prediction
+                if (end_preds_count == 0).any() and (ned_start_predictions > 0).any():
+                    ned_start_predictions[ned_start_predictions == 1] = (
+                        end_preds_count != 0
+                    ).long()
+                    ned_end_predictions = ned_end_predictions[end_preds_count != 0]
+
+        if end_labels is not None:
+            end_labels = end_labels[~(end_labels == -100).all(2)]
+
+        start_position, end_position = (
+            (start_labels, end_labels)
+            if self.training
+            else (ned_start_predictions, ned_end_predictions)
+        )
+        start_counts = (start_position > 0).sum(1)
+        if (start_counts > 0).any():
+            ned_end_predictions = ned_end_predictions.split(start_counts.tolist())
+        # Entity disambiguation
+        if (end_position > 0).sum() > 0:
+            ends_count = (end_position > 0).sum(1)
+            model_entity_start = torch.repeat_interleave(
+                        model_features[start_position > 0], ends_count, dim=0
+                    )
+            model_entity_end = torch.repeat_interleave(
+                        model_features, start_counts, dim=0)[
+                        end_position > 0
+                    ]
+            ents_count = torch.nn.utils.rnn.pad_sequence(
+                torch.split(ends_count, start_counts.tolist()),
+                batch_first=True,
+                padding_value=0,
+            ).sum(1)
+
+            model_entity_start = torch.nn.utils.rnn.pad_sequence(
+                torch.split(model_entity_start, ents_count.tolist()),
+                batch_first=True,
+                padding_value=-100,
+            )
+
+            model_entity_end = torch.nn.utils.rnn.pad_sequence(
+                torch.split(model_entity_end, ents_count.tolist()),
+                batch_first=True,
+                padding_value=-100,
+            )
+
+            ed_logits = self.compute_classification_logits(
+                model_entity_start,
+                model_entity_end,
+                model_features[special_symbols_mask].view(
+                    batch_size, -1, model_features.shape[-1]
+                ),
+            )
+            ed_probabilities = torch.softmax(ed_logits, dim=-1)
+            ed_predictions = torch.argmax(ed_probabilities, dim=-1)
+        else:
+            ed_logits, ed_probabilities, ed_predictions = (
+                None, 
+                ned_start_predictions.new_zeros(batch_size, seq_len),
+                ned_start_predictions.new_zeros(batch_size),
+            )
+        # output build
+        output_dict = dict(
+            batch_size=batch_size,
+            ned_start_logits=ned_start_logits,
+            ned_start_probabilities=ned_start_probabilities,
+            ned_start_predictions=ned_start_predictions,
+            ned_end_logits=ned_end_logits,
+            ned_end_probabilities=ned_end_probabilities,
+            ned_end_predictions=ned_end_predictions,
+            ed_logits=ed_logits,
+            ed_probabilities=ed_probabilities,
+            ed_predictions=ed_predictions,
+        )
+
+        # compute loss if labels
+        if start_labels is not None and end_labels is not None and self.training:
+            # named entity detection loss
+
+            # start
+            if ned_start_logits is not None:
+                ned_start_loss = self.criterion(
+                    ned_start_logits.view(-1, ned_start_logits.shape[-1]),
+                    ned_start_labels.view(-1),
+                )
+            else:
+                ned_start_loss = 0
+
+            # end
+            # use ents_count to assign the labels to the correct positions i.e. using end_labels -> [[0,0,4,0], [0,0,0,2]] -> [4,2] (this is just an element, for batch we need to mask it with ents_count), ie -> [[4,2,-100,-100], [3,1,2,-100], [1,3,2,5]]
+
+            if ned_end_logits is not None:
+                ed_labels = end_labels.clone()
+                ed_labels = torch.nn.utils.rnn.pad_sequence(
+                    torch.split(ed_labels[ed_labels > 0], ents_count.tolist()),
+                    batch_first=True,
+                    padding_value=-100,
+                )
+                end_labels[end_labels > 0] = 1
+                if not self.config.binary_end_logits:
+                    # transform label to position in the sequence
+                    end_labels = end_labels.argmax(dim=-1)
+                    ned_end_loss = self.criterion(
+                        ned_end_logits.view(-1, ned_end_logits.shape[-1]),
+                        end_labels.view(-1),
+                    )
+                else:
+                    ned_end_loss = self.criterion(ned_end_logits.reshape(-1, ned_end_logits.shape[-1]), end_labels.reshape(-1).long())
+                
+                # entity disambiguation loss
+                ed_loss = self.criterion(
+                    ed_logits.view(-1, ed_logits.shape[-1]),
+                    ed_labels.view(-1).long(),
+                )
+
+            else:
+                ned_end_loss = 0
+                ed_loss = 0
+
+            output_dict["ned_start_loss"] = ned_start_loss
+            output_dict["ned_end_loss"] = ned_end_loss
+            output_dict["ed_loss"] = ed_loss
+
+            output_dict["loss"] = ned_start_loss + ned_end_loss + ed_loss
+
+        return output_dict
+
+
+class RelikReaderREModel(PreTrainedModel):
+    config_class = RelikReaderConfig
+
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config)
+        # Transformer model declaration
+        # self.transformer_model_name = transformer_model
+        self.config = config
+        self.transformer_model = (
+            AutoModel.from_pretrained(config.transformer_model)
+            if config.num_layers is None
+            else AutoModel.from_pretrained(
+                config.transformer_model, num_hidden_layers=config.num_layers
+            )
+        )
+        self.transformer_model.resize_token_embeddings(
+            self.transformer_model.config.vocab_size
+            + config.additional_special_symbols
+            + config.additional_special_symbols_types,
+        )
+
+        # named entity detection layers
+        self.ned_start_classifier = self._get_projection_layer(
+            config.activation, last_hidden=2, layer_norm=False
+        )
+
+        self.ned_end_classifier = PoolerEndLogitsBi(self.transformer_model.config)
+
+        self.relation_disambiguation_loss = (
+            config.relation_disambiguation_loss
+            if hasattr(config, "relation_disambiguation_loss")
+            else False
+        )
+
+        if self.config.entity_type_loss and self.config.add_entity_embedding:
+            input_hidden_ents = 3
+        else:
+            input_hidden_ents = 2
+
+        self.re_projector = self._get_projection_layer(
+            config.activation,
+            input_hidden=input_hidden_ents * self.transformer_model.config.hidden_size,
+            hidden=input_hidden_ents * self.config.linears_hidden_size,
+            last_hidden=2 * self.config.linears_hidden_size,
+        )
+
+        self.re_relation_projector = self._get_projection_layer(
+            config.activation,
+            input_hidden=self.transformer_model.config.hidden_size,
+        )
+
+        if self.config.entity_type_loss or self.relation_disambiguation_loss:
+            self.re_entities_projector = self._get_projection_layer(
+                config.activation,
+                input_hidden=2 * self.transformer_model.config.hidden_size,
+            )
+            self.re_definition_projector = self._get_projection_layer(
+                config.activation,
+            )
+
+        self.re_classifier = self._get_projection_layer(
+            config.activation,
+            input_hidden=config.linears_hidden_size,
+            last_hidden=2,
+            layer_norm=False,
+        )
+
+        self.training = config.training
+
+        # criterion
+        self.criterion = torch.nn.CrossEntropyLoss()
+        self.criterion_type = torch.nn.BCEWithLogitsLoss()
+
+    def _get_projection_layer(
+        self,
+        activation: str,
+        last_hidden: Optional[int] = None,
+        hidden: Optional[int] = None,
+        input_hidden=None,
+        layer_norm: bool = True,
+    ) -> torch.nn.Sequential:
+        head_components = [
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                (
+                    self.transformer_model.config.hidden_size
+                    * self.config.use_last_k_layers
+                    if input_hidden is None
+                    else input_hidden
+                ),
+                self.config.linears_hidden_size if hidden is None else hidden,
+            ),
+            activation2functions[activation],
+            torch.nn.Dropout(0.1),
+            torch.nn.Linear(
+                self.config.linears_hidden_size if hidden is None else hidden,
+                self.config.linears_hidden_size if last_hidden is None else last_hidden,
+            ),
+        ]
+
+        if layer_norm:
+            head_components.append(
+                torch.nn.LayerNorm(
+                    (
+                        self.config.linears_hidden_size
+                        if last_hidden is None
+                        else last_hidden
+                    ),
+                    self.transformer_model.config.layer_norm_eps,
+                )
+            )
+
+        return torch.nn.Sequential(*head_components)
+
+    def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+        mask = mask.unsqueeze(-1)
+        if next(self.parameters()).dtype == torch.float16:
+            logits = logits * (1 - mask) - 65500 * mask
+        else:
+            logits = logits * (1 - mask) - 1e30 * mask
+        return logits
+
+    def _get_model_features(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor],
+    ):
+        model_input = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "output_hidden_states": self.config.use_last_k_layers > 1,
+        }
+
+        if token_type_ids is not None:
+            model_input["token_type_ids"] = token_type_ids
+
+        model_output = self.transformer_model(**model_input)
+
+        if self.config.use_last_k_layers > 1:
+            model_features = torch.cat(
+                model_output[1][-self.config.use_last_k_layers :], dim=-1
+            )
+        else:
+            model_features = model_output[0]
+
+        return model_features
+
+    def compute_ned_end_logits(
+        self,
+        start_predictions,
+        start_labels,
+        model_features,
+        prediction_mask,
+        batch_size,
+        mask_preceding: bool = False,
+    ) -> Optional[torch.Tensor]:
+        # todo: maybe when constraining on the spans,
+        #  we should not use a prediction_mask for the end tokens.
+        #  at least we should not during training imo
+        start_positions = start_labels if self.training else start_predictions
+        start_positions_indices = (
+            torch.arange(start_positions.size(1), device=start_positions.device)
+            .unsqueeze(0)
+            .expand(batch_size, -1)[start_positions > 0]
+        ).to(start_positions.device)
+
+        if len(start_positions_indices) > 0:
+            expanded_features = model_features.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            expanded_prediction_mask = prediction_mask.repeat_interleave(
+                torch.sum(start_positions > 0, dim=-1), dim=0
+            )
+            if mask_preceding:
+                expanded_prediction_mask[
+                    torch.arange(
+                        expanded_prediction_mask.shape[1],
+                        device=expanded_prediction_mask.device,
+                    )
+                    < start_positions_indices.unsqueeze(1)
+                ] = 1
+            end_logits = self.ned_end_classifier(
+                hidden_states=expanded_features,
+                start_positions=start_positions_indices,
+                p_mask=expanded_prediction_mask,
+            )
+
+            return end_logits
+
+        return None
+
+    def compute_relation_logits(
+        self,
+        model_entity_features,
+        special_symbols_features,
+    ) -> torch.Tensor:
+        model_subject_object_features = self.re_projector(model_entity_features)
+        model_subject_features = model_subject_object_features[
+            :, :, : model_subject_object_features.shape[-1] // 2
+        ]
+        model_object_features = model_subject_object_features[
+            :, :, model_subject_object_features.shape[-1] // 2 :
+        ]
+        special_symbols_start_representation = self.re_relation_projector(
+            special_symbols_features
+        )
+        re_logits = torch.einsum(
+            "bse,bde,bfe->bsdfe",
+            model_subject_features,
+            model_object_features,
+            special_symbols_start_representation,
+        )
+        re_logits = self.re_classifier(re_logits)
+
+        return re_logits
+
+    def compute_entity_logits(
+        self,
+        model_entity_features,
+        special_symbols_features,
+    ) -> torch.Tensor:
+        model_ed_features = self.re_entities_projector(model_entity_features)
+        special_symbols_ed_representation = self.re_definition_projector(
+            special_symbols_features
+        )
+
+        logits = torch.bmm(
+            model_ed_features,
+            torch.permute(special_symbols_ed_representation, (0, 2, 1)),
+        )
+        logits = self._mask_logits(
+            logits, (model_entity_features == -100).all(2).long()
+        )
+        return logits
+
+    def compute_loss(self, logits, labels, mask=None):
+        logits = logits.reshape(-1, logits.shape[-1])
+        labels = labels.reshape(-1).long()
+        if mask is not None:
+            return self.criterion(logits[mask], labels[mask])
+        return self.criterion(logits, labels)
+
+    def compute_ned_type_loss(
+        self,
+        disambiguation_labels,
+        re_ned_entities_logits,
+        ned_type_logits,
+        re_entities_logits,
+        entity_types,
+        mask,
+    ):
+        if self.config.entity_type_loss and self.relation_disambiguation_loss:
+            return self.criterion_type(
+                re_ned_entities_logits[disambiguation_labels != -100],
+                disambiguation_labels[disambiguation_labels != -100],
+            )
+        if self.config.entity_type_loss:
+            return self.criterion_type(
+                ned_type_logits[mask],
+                disambiguation_labels[:, :, :entity_types][mask],
+            )
+
+        if self.relation_disambiguation_loss:
+            return self.criterion_type(
+                re_entities_logits[disambiguation_labels != -100],
+                disambiguation_labels[disambiguation_labels != -100],
+            )
+        return 0
+
+    def compute_relation_loss(self, relation_labels, re_logits):
+        return self.compute_loss(
+            re_logits, relation_labels, relation_labels.view(-1) != -100
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: torch.Tensor,
+        prediction_mask: Optional[torch.Tensor] = None,
+        special_symbols_mask: Optional[torch.Tensor] = None,
+        special_symbols_mask_entities: Optional[torch.Tensor] = None,
+        start_labels: Optional[torch.Tensor] = None,
+        end_labels: Optional[torch.Tensor] = None,
+        disambiguation_labels: Optional[torch.Tensor] = None,
+        relation_labels: Optional[torch.Tensor] = None,
+        relation_threshold: float = None,
+        is_validation: bool = False,
+        is_prediction: bool = False,
+        use_predefined_spans: bool = False,
+        *args,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        relation_threshold = (
+            self.config.threshold if relation_threshold is None else relation_threshold
+        )
+
+        batch_size = input_ids.shape[0]
+
+        model_features = self._get_model_features(
+            input_ids, attention_mask, token_type_ids
+        )
+
+        # named entity detection
+        if use_predefined_spans:
+            ned_start_logits, ned_start_probabilities, ned_start_predictions = (
+                None,
+                None,
+                torch.zeros_like(start_labels),
+            )
+            ned_end_logits, ned_end_probabilities, ned_end_predictions = (
+                None,
+                None,
+                torch.zeros_like(end_labels),
+            )
+
+            ned_start_predictions[start_labels > 0] = 1
+            ned_end_predictions[end_labels > 0] = 1
+            ned_end_predictions = ned_end_predictions[~(end_labels == -100).all(2)]
+            ned_start_labels = start_labels
+            ned_start_labels[start_labels > 0] = 1
+        else:
+            # start boundary prediction
+            ned_start_logits = self.ned_start_classifier(model_features)
+            if is_validation or is_prediction:
+                ned_start_logits = self._mask_logits(
+                    ned_start_logits, prediction_mask
+                )  # why?
+            ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1)
+            ned_start_predictions = ned_start_probabilities.argmax(dim=-1)
+
+            # end boundary prediction
+            ned_start_labels = (
+                torch.zeros_like(start_labels) if start_labels is not None else None
+            )
+
+            # start_labels contain entity id at their position, we just need 1 for start of entity
+            if ned_start_labels is not None:
+                ned_start_labels[start_labels == -100] = -100
+                ned_start_labels[start_labels > 0] = 1
+
+            # compute end logits only if there are any start predictions.
+            # For each start prediction, n end predictions are made
+            ned_end_logits = self.compute_ned_end_logits(
+                ned_start_predictions,
+                ned_start_labels,
+                model_features,
+                prediction_mask,
+                batch_size,
+                True,
+            )
+
+            if ned_end_logits is not None:
+                # For each start prediction, n end predictions are made based on
+                # binary classification ie. argmax at each position.
+                ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1)
+                ned_end_predictions = ned_end_probabilities.argmax(dim=-1)
+            else:
+                ned_end_logits, ned_end_probabilities = None, None
+                ned_end_predictions = torch.zeros_like(ned_start_predictions)
+
+            if is_prediction or is_validation:
+                end_preds_count = ned_end_predictions.sum(1)
+                # If there are no end predictions for a start prediction, remove the start prediction
+                if (end_preds_count == 0).any() and (ned_start_predictions > 0).any():
+                    ned_start_predictions[ned_start_predictions == 1] = (
+                        end_preds_count != 0
+                    ).long()
+                    ned_end_predictions = ned_end_predictions[end_preds_count != 0]
+
+        if end_labels is not None:
+            end_labels = end_labels[~(end_labels == -100).all(2)]
+
+        start_position, end_position = (
+            (start_labels, end_labels)
+            if (not is_prediction and not is_validation)
+            else (ned_start_predictions, ned_end_predictions)
+        )
+
+        start_counts = (start_position > 0).sum(1)
+        if (start_counts > 0).any():
+            ned_end_predictions = ned_end_predictions.split(start_counts.tolist())
+        # limit to 30 predictions per document using start_counts, by setting all po after sum is 30 to 0
+        # if is_validation or is_prediction:
+        #     ned_start_predictions[ned_start_predictions == 1] = start_counts
+        # We can only predict relations if we have start and end predictions
+        if (end_position > 0).sum() > 0:
+            ends_count = (end_position > 0).sum(1)
+            model_subject_features = torch.cat(
+                [
+                    torch.repeat_interleave(
+                        model_features[start_position > 0], ends_count, dim=0
+                    ),  # start position features
+                    torch.repeat_interleave(model_features, start_counts, dim=0)[
+                        end_position > 0
+                    ],  # end position features
+                ],
+                dim=-1,
+            )
+            ents_count = torch.nn.utils.rnn.pad_sequence(
+                torch.split(ends_count, start_counts.tolist()),
+                batch_first=True,
+                padding_value=0,
+            ).sum(1)
+            model_subject_features = torch.nn.utils.rnn.pad_sequence(
+                torch.split(model_subject_features, ents_count.tolist()),
+                batch_first=True,
+                padding_value=-100,
+            )
+
+            # if is_validation or is_prediction:
+            #     model_subject_features = model_subject_features[:, :30, :]
+
+            # entity disambiguation. Here relation_disambiguation_loss would only be useful to
+            # reduce the number of candidate relations for the next step, but currently unused.
+            if self.config.entity_type_loss or self.relation_disambiguation_loss:
+                (re_ned_entities_logits) = self.compute_entity_logits(
+                    model_subject_features,
+                    model_features[
+                        special_symbols_mask | special_symbols_mask_entities
+                    ].view(batch_size, -1, model_features.shape[-1]),
+                )
+                entity_types = torch.sum(special_symbols_mask_entities, dim=1)[0].item()
+                ned_type_logits = re_ned_entities_logits[:, :, :entity_types]
+                re_entities_logits = re_ned_entities_logits[:, :, entity_types:]
+
+                if self.config.entity_type_loss:
+                    ned_type_probabilities = torch.sigmoid(ned_type_logits)
+                    ned_type_predictions = ned_type_probabilities.argmax(dim=-1)
+
+                    if self.config.add_entity_embedding:
+                        special_symbols_representation = model_features[
+                            special_symbols_mask_entities
+                        ].view(batch_size, entity_types, -1)
+
+                        entities_representation = torch.einsum(
+                            "bsp,bpe->bse",
+                            ned_type_probabilities,
+                            special_symbols_representation,
+                        )
+                        model_subject_features = torch.cat(
+                            [model_subject_features, entities_representation], dim=-1
+                        )
+                re_entities_probabilities = torch.sigmoid(re_entities_logits)
+                re_entities_predictions = re_entities_probabilities.round()
+            else:
+                (
+                    ned_type_logits,
+                    ned_type_probabilities,
+                    re_entities_logits,
+                    re_entities_probabilities,
+                ) = (None, None, None, None)
+                ned_type_predictions, re_entities_predictions = (
+                    torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+                    torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+                )
+
+            # Compute relation logits
+            re_logits = self.compute_relation_logits(
+                model_subject_features,
+                model_features[special_symbols_mask].view(
+                    batch_size, -1, model_features.shape[-1]
+                ),
+            )
+
+            re_probabilities = torch.softmax(re_logits, dim=-1)
+            # we set a thresshold instead of argmax in cause it needs to be tweaked
+            re_predictions = re_probabilities[:, :, :, :, 1] > relation_threshold
+            re_probabilities = re_probabilities[:, :, :, :, 1]
+        else:
+            (
+                ned_type_logits,
+                ned_type_probabilities,
+                re_entities_logits,
+                re_entities_probabilities,
+            ) = (None, None, None, None)
+            ned_type_predictions, re_entities_predictions = (
+                torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+                torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device),
+            )
+            re_logits, re_probabilities, re_predictions = (
+                torch.zeros(
+                    [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long
+                ).to(input_ids.device),
+                torch.zeros(
+                    [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long
+                ).to(input_ids.device),
+                torch.zeros(
+                    [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long
+                ).to(input_ids.device),
+            )
+
+        # output build
+        output_dict = dict(
+            batch_size=batch_size,
+            ned_start_logits=ned_start_logits,
+            ned_start_probabilities=ned_start_probabilities,
+            ned_start_predictions=ned_start_predictions,
+            ned_end_logits=ned_end_logits,
+            ned_end_probabilities=ned_end_probabilities,
+            ned_end_predictions=ned_end_predictions,
+            ned_type_logits=ned_type_logits,
+            ned_type_probabilities=ned_type_probabilities,
+            ned_type_predictions=ned_type_predictions,
+            re_entities_logits=re_entities_logits,
+            re_entities_probabilities=re_entities_probabilities,
+            re_entities_predictions=re_entities_predictions,
+            re_logits=re_logits,
+            re_probabilities=re_probabilities,
+            re_predictions=re_predictions,
+        )
+
+        if (
+            start_labels is not None
+            and end_labels is not None
+            and relation_labels is not None
+            and is_prediction is False
+        ):
+            ned_start_loss = self.compute_loss(ned_start_logits, ned_start_labels)
+            end_labels[end_labels > 0] = 1
+            ned_end_loss = self.compute_loss(ned_end_logits, end_labels)
+            if self.config.entity_type_loss or self.relation_disambiguation_loss:
+                ned_type_loss = self.compute_ned_type_loss(
+                    disambiguation_labels,
+                    re_ned_entities_logits,
+                    ned_type_logits,
+                    re_entities_logits,
+                    entity_types,
+                    (model_subject_features != -100).all(2),
+                )
+            relation_loss = self.compute_relation_loss(relation_labels, re_logits)
+            # compute loss. We can skip the relation loss if we are in the first epochs (optional)
+            if self.config.entity_type_loss or self.relation_disambiguation_loss:
+                output_dict["loss"] = (
+                    ned_start_loss + ned_end_loss + relation_loss + ned_type_loss
+                ) / 4
+                output_dict["ned_type_loss"] = ned_type_loss
+            else:
+                output_dict["loss"] = ((1 / 20) * (ned_start_loss + ned_end_loss)) + (
+                    (9 / 10) * relation_loss
+                )
+            output_dict["ned_start_loss"] = ned_start_loss
+            output_dict["ned_end_loss"] = ned_end_loss
+            output_dict["re_loss"] = relation_loss
+
+        return output_dict