add model

amc.py

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+import copy
+
+from transformers import PreTrainedModel, PretrainedConfig, AutoModel, AutoTokenizer
+import torch.nn as nn
+import torch
+from typing import List
+
+
+class AMC_OPT_conf(PretrainedConfig):
+    model_type = "bert"
+    def __init__(self,
+                 out_labels=3,
+                 emb_size=768,
+                 drop_out=0.1,
+                 pretrained_model="m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0",
+                 **kwargs):
+        self.out_labels = out_labels
+        self.drop_out = drop_out
+        self.emb_size = emb_size
+        self.pretrained_model = pretrained_model
+        self.fine_tuned_model = "data/models/amc_opt_msmd_nocoadapt.pt"
+        super().__init__(**kwargs)
+
+
+class AMC_OPT_sub(nn.Module):
+    def __init__(self, config):
+        super(AMC_OPT_sub, self).__init__()
+
+        self.model = copy.deepcopy(AutoModel.from_pretrained(config.pretrained_model))
+        self.dropout1 = nn.Dropout(config.drop_out)
+        self.linear1 = nn.Linear(config.emb_size, config.out_labels)
+
+        self.loss_fct = nn.CrossEntropyLoss()
+        self.hyper_params = {'learning_rate': 6.599917952321265e-05,
+                             'weight_decay': 0.02157165894420757,
+                             'warmup_steps': 0.8999999999999999,
+                             'num_epochs': 11}
+        self.params_name = "alberto_multiclass_opt_msmd.pt"
+
+    def forward(self, labels, input_ids, attention_mask, **args):
+        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, **args)
+        x = self.dropout1(outputs[1])
+        logits = self.linear1(x)
+        if labels != None:
+            loss = self.loss_fct(logits, labels)
+            return {'logits':logits, 'loss':loss}
+        else:
+            return {'logits':logits}
+
+class AMC_OPT(PreTrainedModel):
+    config_class = AMC_OPT_conf
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = AMC_OPT_sub(config)
+        self.model.load_state_dict(torch.load(config.fine_tuned_model))
+
+    def forward(self, labels, input_ids, attention_mask, **args):
+        return self.model(labels, input_ids, attention_mask, **args)
+
+tokenizer = AutoTokenizer.from_pretrained("m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0")
+tokenizer.model_max_length = 128

config.json

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+{
+  "architectures": [
+    "AMC_OPT"
+  ],
+  "auto_map": {
+    "AutoConfig": "amc.AMC_OPT_conf",
+    "AutoModelForSequenceClassification": "amc.AMC_OPT"
+  },
+  "drop_out": 0.1,
+  "emb_size": 768,
+  "fine_tuned_model": "data/models/amc_opt_msmd_nocoadapt.pt",
+  "model_type": "bert",
+  "out_labels": 3,
+  "pretrained_model": "m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0",
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e8e4602d1fabefb736c5ec5abd5de581f74a03fe58d653a958ec22b060dee6c
+size 737463021