metadata
license: apache-2.0
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
This MistralAI 7B was fined-tuned on nuclear energy data from twitter/X. The classification accuracy obtained is 94%.
The number of labels is 3: {0: Negative, 1: Neutral, 2: Positive}
Warning: You need sufficient GPU to run this model.
This is an example to use it, it worked on 8 GB Nvidia-RTX 4060
from transformers import AutoTokenizer
from transformers import pipeline
from transformers import AutoModelForSequenceClassification
import torch
checkpoint = 'kumo24/mistralai-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_map='auto')
sentiment_task = pipeline("sentiment-analysis",
model=model,
tokenizer=tokenizer)
print(sentiment_task("Michigan Wolverines are Champions, Go Blue!"))