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Initial upload of EmoAxis model

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config.json ADDED
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+ {
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+ "architectures": [
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+ "RobertaEmoPillars"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "dtype": "float32",
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "25": "LABEL_25",
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+ "26": "LABEL_26",
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+ "27": "LABEL_27"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.57.1",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 50265
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+ }
merges.txt ADDED
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model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b4d28faea07e06face6719a197967d9d5ff4625356b2cdda3415080872ba61ee
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+ size 500245096
modeling.py ADDED
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+ import torch
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+ import torch.nn as nn
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+ import torch.nn.functional as F
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+ from transformers import PreTrainedModel, AutoModel, AutoConfig
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+
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+
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+ class Encoder(nn.Module):
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+ def __init__(self, base_encoder):
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+ super().__init__()
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+ self.encoder = base_encoder
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+
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+ def forward(self, inputs):
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+ outputs = self.encoder(**inputs, output_hidden_states=True)
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+ last_hidden = outputs.hidden_states[-1]
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+ mask = inputs["attention_mask"].unsqueeze(-1).float()
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+ pooled = (last_hidden * mask).sum(1) / mask.sum(1).clamp(min=1e-9)
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+ return F.normalize(pooled, p=2, dim=1)
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+
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+
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+ class Classifier(nn.Module):
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+ def __init__(self, input_dim=768, num_classes=28):
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+ super().__init__()
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+ self.mlp = nn.Sequential(
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+ nn.Linear(input_dim, 512),
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+ nn.LayerNorm(512),
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+ nn.GELU(),
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+ nn.Dropout(0.25),
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+ nn.Linear(512, num_classes),
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+ )
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+
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+ def forward(self, x):
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+ return self.mlp(x)
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+
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+
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+ class RobertaEmoPillars(PreTrainedModel):
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+ config_class = AutoConfig
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+
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+ def __init__(self, config):
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+ super().__init__(config)
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+ base_encoder = AutoModel.from_pretrained(config._name_or_path)
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+ self.encoder = Encoder(base_encoder)
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+ self.classifier = Classifier(
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+ input_dim=base_encoder.config.hidden_size,
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+ num_classes=config.num_labels,
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+ )
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+
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+ def forward(self, input_ids=None, attention_mask=None):
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+ inputs = {"input_ids": input_ids, "attention_mask": attention_mask}
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+ emb = self.encoder(inputs)
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+ logits = self.classifier(emb)
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+ return logits
special_tokens_map.json ADDED
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+ {
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+ "bos_token": "<s>",
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+ },
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+ "pad_token": "<pad>",
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+ "sep_token": "</s>",
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+ "unk_token": "<unk>"
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+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {
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+ "1": {
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+ }
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+ },
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": false,
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+ "errors": "replace",
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+ "tokenizer_class": "RobertaTokenizer",
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+ "trim_offsets": true,
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+ }
vocab.json ADDED
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