cardiffnlp/tweet_eval
Viewer • Updated • 201k • 43.7k • 144
How to use mayapapaya/Sentiment-Analyzer with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="mayapapaya/Sentiment-Analyzer") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mayapapaya/Sentiment-Analyzer")
model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Sentiment-Analyzer")This model is meant to extract sentiments (positive, negative, or neutral) from a tweet text.
This model is a fine-tuned version of the BERT model.
Trained on tweet_eval from HuggingFace Hub.
Note: model inputs were tokenized using bert-base-uncased tokenizer
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Sentiment-Analyzer")