Instructions to use tweettemposhift/topic-topic_temporal-bernice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tweettemposhift/topic-topic_temporal-bernice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tweettemposhift/topic-topic_temporal-bernice")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/topic-topic_temporal-bernice") model = AutoModelForSequenceClassification.from_pretrained("tweettemposhift/topic-topic_temporal-bernice") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91317a3caa8e0e14758721b3472f68c8113813e9ca3f812ccaf4b9b7a3ad770c
- Size of remote file:
- 1.11 GB
- SHA256:
- 8785b4d8ab166d3a777a40aa72251587df2cc52ae63c65d5df5c3cf7082970a1
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