twitter-roberta-base-sentiment-earthquake
This is an "extension" of the twitter-roberta-base-sentiment-latest
model, further finetuned with original Twitter data posted in English about the 10th anniversary of the 2010 Haiti Earthquake.
Full classification example
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
class_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
MODEL = "antypasd/twitter-roberta-base-sentiment-earthquake"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "$202 million of $1.14 billion in United States (US) recovery aid went to a new 'industrial park' in Caracol, an area unaffected by the Haiti earthquake. The plan was to invite foreign garment companies to take advantage of extremely low-wage labor"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
prediction = np.argmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# prediction = np.argmax(scores)
# Print label
print(class_mapping[prediction])
Output:
Negative
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