Refactor requirements.txt to remove finbert dependency and maintain timedelta. This change streamlines the project's dependencies while ensuring necessary libraries for trading and data analysis remain intact.

finbert_utils.py

@@ -0,0 +1,28 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from typing import Tuple 
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
+model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert").to(device)
+labels = ["positive", "negative", "neutral"]
+
+def estimate_sentiment(news):
+    if news:
+        tokens = tokenizer(news, return_tensors="pt", padding=True).to(device)
+
+        result = model(tokens["input_ids"], attention_mask=tokens["attention_mask"])[
+            "logits"
+        ]
+        result = torch.nn.functional.softmax(torch.sum(result, 0), dim=-1)
+        probability = result[torch.argmax(result)]
+        sentiment = labels[torch.argmax(result)]
+        return probability, sentiment
+    else:
+        return 0, labels[-1]
+
+
+if __name__ == "__main__":
+    tensor, sentiment = estimate_sentiment(['markets responded negatively to the news!','traders were displeased!'])
+    print(tensor, sentiment)
+    print(torch.cuda.is_available())

requirements.txt

@@ -10,5 +10,4 @@ torch
 transformers
 lumibot
 alpaca-trade-api
-timedelta
-finbert
+timedelta