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---
license: apache-2.0
---
---
license: apache-2.0
---
This Falcon 7B was fined-tuned on 672k tweets from twitter/X for text classification task. The classification accuracy obtained is 98%. \
The number of labels is 3: {0: Negative, 1: Neutral, 2: Positive} \
Warning: You need enough GPU to use Falcon.
This is an example to use it, it worked on 8 GB VRAM Nvidia RTX-4060
```bash
from transformers import AutoTokenizer
from transformers import pipeline
from transformers import AutoModelForSequenceClassification
import torch
checkpoint = 'kumo24/mistralai-sentiment'
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!")) |