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---
license: apache-2.0
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
---
This GPT-2 model was fine-tuned using +400k nuclear energy tweets from twitter/X. The classification accuracy is 95%.
The number of labels is 3: {0: Negative, 1: Neutral, 2: Positive}
This is an example to use it
```bash
from transformers import AutoTokenizer
from transformers import pipeline
from transformers import AutoModelForSequenceClassification
import torch
checkpoint = 'kumo24/gpt2-sentiment-nuclear'
tokenizer=AutoTokenizer.from_pretrained(checkpoint)
id2label = {0: "negative", 1: "neutral", 2: "positive"}
label2id = {"negative": 0, "neutral": 1, "positive": 2}
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model = AutoModelForSequenceClassification.from_pretrained(checkpoint,
num_labels=3,
id2label=id2label,
label2id=label2id)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
sentiment_task = pipeline("sentiment-analysis",
model=model,
tokenizer=tokenizer)
print(sentiment_task("Michigan Wolverines are Champions, Go Blue!")) |