Upload BERT_CRF

config.json

@@ -0,0 +1,61 @@
+{
+  "_name_or_path": "/notebooks/src/hugging_face_pipeline/BERT-CRF/out/model",
+  "architectures": [
+    "BERT_CRF"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.BERT_CRF_Config",
+    "AutoModelForTokenClassification": "model.BERT_CRF"
+  },
+  "bert_name": "neuralmind/bert-large-portuguese-cased",
+  "id2label": {
+    "0": "O",
+    "1": "B-PESSOA",
+    "2": "I-PESSOA",
+    "3": "B-ORGANIZACAO",
+    "4": "I-ORGANIZACAO",
+    "5": "B-LOCAL",
+    "6": "I-LOCAL",
+    "7": "B-TEMPO",
+    "8": "I-TEMPO",
+    "9": "B-VALOR",
+    "10": "I-VALOR",
+    "11": "B-ABSTRACCAO",
+    "12": "I-ABSTRACCAO",
+    "13": "B-ACONTECIMENTO",
+    "14": "I-ACONTECIMENTO",
+    "15": "B-COISA",
+    "16": "I-COISA",
+    "17": "B-OBRA",
+    "18": "I-OBRA",
+    "19": "B-OUTRO",
+    "20": "I-OUTRO"
+  },
+  "label2id": {
+    "B-ABSTRACCAO": 11,
+    "B-ACONTECIMENTO": 13,
+    "B-COISA": 15,
+    "B-LOCAL": 5,
+    "B-OBRA": 17,
+    "B-ORGANIZACAO": 3,
+    "B-OUTRO": 19,
+    "B-PESSOA": 1,
+    "B-TEMPO": 7,
+    "B-VALOR": 9,
+    "I-ABSTRACCAO": 12,
+    "I-ACONTECIMENTO": 14,
+    "I-COISA": 16,
+    "I-LOCAL": 6,
+    "I-OBRA": 18,
+    "I-ORGANIZACAO": 4,
+    "I-OUTRO": 20,
+    "I-PESSOA": 2,
+    "I-TEMPO": 8,
+    "I-VALOR": 10,
+    "O": 0
+  },
+  "model_name": "BERT_CRF",
+  "model_type": "BERT_CRF",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.1"
+}

model.py

@@ -0,0 +1,85 @@
+from torch import nn
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers import BertModel, BertConfig
+from transformers import AutoModelForTokenClassification, AutoConfig
+from torchcrf import CRF
+
+class BERT_CRF_Config(PretrainedConfig):
+    model_type = "BERT_CRF"
+
+    def __init__(self, **kwarg):
+        super().__init__(**kwarg)
+        self.model_name = "BERT_CRF"
+
+
+class BERT_CRF(PreTrainedModel):
+    config_class = BERT_CRF_Config
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        bert_config = BertConfig.from_pretrained(config.bert_name)
+
+        bert_config.output_attentions = True
+        bert_config.output_hidden_states = True
+
+        self.bert = BertModel.from_pretrained(config.bert_name, config=bert_config)
+
+        self.dropout = nn.Dropout(p=0.5)
+
+        self.linear = nn.Linear(
+            self.bert.config.hidden_size, config.num_labels)
+
+        self.crf = CRF(config.num_labels, batch_first=True)
+
+    def forward(self, input_ids, token_type_ids, attention_mask, labels, labels_mask):
+
+        last_hidden_layer = self.bert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)[
+            'last_hidden_state']
+
+        last_hidden_layer = self.dropout(last_hidden_layer)
+
+        logits = self.linear(last_hidden_layer)
+
+        batch_size = logits.shape[0]
+
+        output_tags = []
+
+        if labels is not None:
+            loss = 0
+
+            for seq_logits, seq_labels, seq_mask in zip(logits, labels, labels_mask):
+                # Index logits and labels using prediction mask to pass only the
+                # first subtoken of each word to CRF.
+                seq_logits = seq_logits[seq_mask].unsqueeze(0)
+                seq_labels = seq_labels[seq_mask].unsqueeze(0)
+
+                if seq_logits.numel() != 0:
+                    loss -= self.crf(seq_logits, seq_labels,
+                                     reduction='token_mean')
+
+            return loss / batch_size
+        else:
+            for seq_logits, seq_mask in zip(logits, labels_mask):
+                seq_logits = seq_logits[seq_mask].unsqueeze(0)
+
+                if seq_logits.numel() != 0:
+                    tags = self.crf.decode(seq_logits)
+                else:
+                    tags = [[]]
+
+                # Unpack "batch" results
+                output_tags.append(tags[0])
+
+            return output_tags
+
+
+class ModelRegisterStep():
+    def __call__(self, args):
+
+        AutoConfig.register("BERT_CRF", BERT_CRF_Config)
+        AutoModelForTokenClassification.register(BERT_CRF_Config, BERT_CRF)
+
+        return {
+            **args,
+        }

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66a60d10ec43c534b098fdfd34bad5889637c62c0840c83f1404f6843b38cbb9
+size 1337804903