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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

config.json

@@ -0,0 +1,39 @@
+{
+  "_name_or_path": "google/mt5-small",
+  "architectures": [
+    "MT5EncoderForSequenceClassification"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": false,
+  "is_gated_act": true,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": null,
+  "num_heads": 6,
+  "num_layers": 8,
+  "pad_token_id": 0,
+  "pooling_mode": "mean",
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "use_cache": false,
+  "vocab_size": 250112
+}

mmarco_smalldev_scores.csv

@@ -0,0 +1,7 @@
+epoch,steps,cutoff,mrr,recall,r-precision
+0,20000,5,0.2660458452722063,0.4084885386819484,0.17775787965616047
+0,20000,10,0.28002393459771685,0.5111747851002866,0.17775787965616047
+0,20000,20,0.2868085426794964,0.6066738299904489,0.17775787965616047
+0,20000,50,0.2905402339116507,0.7201050620821395,0.17775787965616047
+0,20000,100,0.29166554225030883,0.7998089780324736,0.17775787965616047
+0,20000,500,0.2923872138566636,0.9454393505253104,0.17775787965616047

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0881c2af779ac4c32a290af6f6b97699b1e1adc2b56e05cb3b27914e0dc809c6
+size 588824300

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spiece.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef78f86560d809067d12bac6c09f19a462cb3af3f54d2b8acbba26e1433125d6
+size 4309802

t5.py

@@ -0,0 +1,195 @@
+import torch
+from torch import nn
+from typing import Optional, Union, Tuple
+
+from transformers.modeling_outputs import SequenceClassifierOutput
+from transformers.models.t5.modeling_t5 import T5Config, T5ClassificationHead, T5EncoderModel
+from transformers.models.mt5.modeling_mt5 import MT5Config, MT5ClassificationHead, MT5EncoderModel
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+
+
+def clean_t5_config(config: Union[T5Config, MT5Config], model_type: str):
+    assert model_type in ['t5', 'mt5']
+    setattr(config, 'pooling_mode', 'mean')
+    setattr(config, 'model_type', model_type)
+    setattr(config, 'use_cache', False)
+    setattr(config, 'is_encoder_decoder', False)
+    setattr(config, 'num_decoder_layers', None)
+    delattr(config, 'task_specific_params') if hasattr(config, 'task_specific_params') else None
+
+
+class T5EncoderForSequenceClassification(T5EncoderModel):
+    """
+    T5 encoder for sequence classification tasks.
+
+    :param config: The T5 configuration object.
+    """
+    def __init__(self, config: T5Config):
+        super().__init__(config)
+        self.pool_layer = PoolLayer(config.pooling_mode)
+        self.classification_head = T5ClassificationHead(config)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        """
+        Forward pass of the T5 encoder for sequence classification tasks.
+
+        :param input_ids: The input token IDs.
+        :param attention_mask: The attention mask.
+        :param head_mask: The head mask.
+        :param inputs_embeds: The input embeddings.
+        :param labels: The target labels.
+        :param output_attentions: Whether to output attentions.
+        :param output_hidden_states: Whether to output hidden states.
+        :param return_dict: Whether to return a dictionary.
+        :returns: The logits for the classification task or a dictionary containing the outputs.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        loss = None
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = self.pool_layer(outputs.last_hidden_state, attention_mask)
+        logits = self.classification_head(sequence_output)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+        
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class MT5EncoderForSequenceClassification(MT5EncoderModel):
+    """
+    mT5 encoder for sequence classification tasks.
+
+    :param config: The mT5 configuration object.
+    """
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.pool_layer = PoolLayer(config.pooling_mode)
+        self.classification_head = MT5ClassificationHead(config)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        """
+        Forward pass of the mT5 encoder for sequence classification tasks.
+
+        :param input_ids: The input token IDs.
+        :param attention_mask: The attention mask.
+        :param head_mask: The head mask.
+        :param inputs_embeds: The input embeddings.
+        :param labels: The target labels.
+        :param output_attentions: Whether to output attentions.
+        :param output_hidden_states: Whether to output hidden states.
+        :param return_dict: Whether to return a dictionary.
+        :returns: The logits for the classification task or a dictionary containing the outputs.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        loss = None
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = self.pool_layer(outputs.last_hidden_state, attention_mask)
+        logits = self.classification_head(sequence_output)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+        
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class PoolLayer(nn.Module):
+    """
+    Pooling layer on top of the commputed token embeddings. 
+
+    :param pooling_mode: The pooling strategy to use.
+    """
+    def __init__(self, pooling_mode: str):
+        super().__init__()
+        assert pooling_mode in ['first', 'mean', 'max'], f"ERROR: Unknown pooling strategy '{pooling_mode}'"
+        self.pooling_mode = pooling_mode
+
+    def forward(self, token_embeddings: torch.Tensor, attention_masks: torch.Tensor) -> torch.Tensor:
+        """
+        Compute the passage vector by pooling the token embeddings.
+
+        :param token_embeddings: A 3D tensor of size [batch_size, seq_len, d_model].
+        :param attention_masks: A 2D tensor of size [batch_size, seq_len].
+        :returns: A 2D tensor of size [batch_size, d_model].
+        """
+        if self.pooling_mode == 'first':
+            text_vectors = token_embeddings[:, 0, :]
+        elif self.pooling_mode == 'max':
+            # Set all values of the [PAD] embeddings to large negative values (so that they are never considered as maximum for a channel).
+            attention_masks_expanded = attention_masks.unsqueeze(-1).expand(token_embeddings.size())
+            token_embeddings[attention_masks_expanded == 0] = -1e+9 if token_embeddings.dtype == torch.float32 else -1e+4
+            # Compute the maxima along the 'seq_length' dimension (-> Tensor[batch_size, d_model]).
+            text_vectors = torch.max(token_embeddings, dim=1).values
+        else:
+            # Set all values of the [PAD] embeddings to zeros (so that they are not taken into account in the sum for a channel).
+            attention_masks_expanded = attention_masks.unsqueeze(-1).expand(token_embeddings.size())
+            token_embeddings[attention_masks_expanded == 0] = 0.0
+            # Compute the means by first summing along the 'seq_length' dimension (-> Tensor[batch_size, d_model]).
+            sum_embeddings = torch.sum(token_embeddings, dim=1)
+            # Then, divide all values of a passage vector by the original passage length.
+            sum_mask = attention_masks_expanded.sum(dim=1) # -> Tensor[batch_size, d_model] where each value is the length of the corresponding passage.
+            sum_mask = torch.clamp(sum_mask, min=1e-7) # Make sure not to have zeros by lower bounding all elements to 1e-7.
+            text_vectors = sum_embeddings / sum_mask # Divide each dimension by the sequence length.
+        return text_vectors
+
+
+if __name__ == "__main__":
+    config = AutoConfig.from_pretrained(model_name)
+    if isinstance(config, T5Config):
+        clean_t5_config(self.config, model_type='t5')
+        model = T5EncoderForSequenceClassification.from_pretrained(model_name, config=config)
+    elif isinstance(config, MT5Config):
+        clean_t5_config(self.config, model_type='t5')
+        model = MT5EncoderForSequenceClassification.from_pretrained(model_name, config=config)
+    else:
+        model = AutoModelForSequenceClassification.from_pretrained(model_name, config=config)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_args)

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1ae0f5ccb75b6fbb45cda86ea3c1cc20beb32758bebc9629efc9207c8b08f06
+size 16330714

tokenizer_config.json

@@ -0,0 +1,38 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 0,
+  "legacy": true,
+  "model_max_length": 256,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}