Views
No views yet
labelbase_reference.csv (1797 samples)| Metric | Value |
|---|---|
| Loss | 0.0000 |
| Accuracy | 1.0000 |
| F1 Score | 1.0000 |
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4tokenizer = AutoTokenizer.from_pretrained("Bencode92/tradepulse-finbert-sentiment")
5model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse-finbert-sentiment")
6
7# Example prediction
8text = "Apple reported strong quarterly earnings beating expectations"
9inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
10outputs = model(**inputs)
11
12predictions = outputs.logits.softmax(dim=-1)