Add roberta

bert/__init__.py

@@ -0,0 +1,2 @@
+from .bert_model import BERTClass
+from .bert_utils import score, process_tweet, load_torch_model, model_path, EMOTIONS

bert/bert_model.py

@@ -0,0 +1,16 @@
+import torch
+from transformers import RobertaModel
+
+
+class BERTClass(torch.nn.Module):
+    def __init__(self, num_of_cols, path='roberta-large', do=0.3):
+        super(BERTClass, self).__init__()
+        self.l1 = RobertaModel.from_pretrained(path)
+        self.l2 = torch.nn.Dropout(do)
+        self.l3 = torch.nn.Linear(1024, num_of_cols)
+
+    def forward(self, ids, mask, token_type_ids):
+        _, output_1 = self.l1(ids, attention_mask=mask, token_type_ids=token_type_ids)
+        output_2 = self.l2(output_1)
+        output = self.l3(output_2)
+        return output

bert/bert_utils.py

@@ -0,0 +1,43 @@
+import torch
+
+
+EMOTIONS = ('anger', 'anticipation',
+            'disgust', 'fear', 'joy',
+            'love', 'optimism', 'pessimism',
+            'sadness', 'surprise', 'trust',
+            'positive', 'negative')
+emotion_dict = {w: i for i, w in enumerate(EMOTIONS)}
+model_path = "pytorch_model/roberta.mdl"
+
+
+def load_torch_model(path):
+    return torch.load(path)
+
+
+def process_tweet(tweet_text, tokenizer, max_len):
+    tweet_text = " ".join(tweet_text.split())
+
+    inputs = tokenizer.encode_plus(
+        tweet_text,
+        None,
+        add_special_tokens=True,
+        max_length=max_len,
+        pad_to_max_length=True,
+        return_token_type_ids=True,
+        truncation=True)
+    ids = torch.tensor(inputs['input_ids'], dtype=torch.long)
+
+    mask = torch.tensor(inputs['attention_mask'], dtype=torch.long)
+    token_type_ids = torch.tensor(inputs["token_type_ids"], dtype=torch.long)
+    return {'ids': ids, 'mask': mask, 'token_type_ids': token_type_ids}
+
+
+def score(model, processed_tweet, emotions=EMOTIONS):
+    scores = model(processed_tweet['ids'].unsqueeze(0),
+                   processed_tweet['mask'].unsqueeze(0),
+                   processed_tweet['token_type_ids'].unsqueeze(0))[0]
+    scores = torch.sigmoid(scores).cpu().detach().numpy()
+    return {em: sc for em, sc in zip(emotions, scores)}
+
+
+

docs/DATA/Alabama.csv

@@ -1,6 +1,2 @@
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docs/DATA/Arizona.csv

@@ -1,6 +1,4 @@
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docs/DATA/Arkansas.csv

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docs/DATA/California.csv

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docs/DATA/Colorado.csv

@@ -1,6 +1,4 @@
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docs/DATA/Connecticut.csv

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docs/DATA/Florida.csv

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docs/DATA/Georgia.csv

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docs/DATA/Hawaii.csv

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docs/DATA/Ilinois.csv

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docs/DATA/Indiana.csv

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docs/DATA/Iowa.csv

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docs/DATA/Louisiana.csv

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docs/DATA/Massachusetts.csv

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docs/DATA/Michigan.csv

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docs/DATA/Minnesota.csv

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docs/DATA/Missouri.csv

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