Instructions to use finiteautomata/beto-headlines-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use finiteautomata/beto-headlines-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="finiteautomata/beto-headlines-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("finiteautomata/beto-headlines-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("finiteautomata/beto-headlines-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Targeted Sentiment Analysis in News Headlines
BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.
(details to be published)
Examples
Input is as follows
Headline [SEP] Target
where headline is the news title and target is an entity present in the headline.
Try
Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri (should be NEG)
and
Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández (POS or NEU)
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