Instructions to use Tobias/bert-base-uncased_English_Hotel_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tobias/bert-base-uncased_English_Hotel_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tobias/bert-base-uncased_English_Hotel_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tobias/bert-base-uncased_English_Hotel_sentiment") model = AutoModelForSequenceClassification.from_pretrained("Tobias/bert-base-uncased_English_Hotel_sentiment") - Notebooks
- Google Colab
- Kaggle
English Hotel Review Sentiment Classification
A model trained on English Hotel Reviews from Switzerland. The base model is the bert-base-uncased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for 5 epochs on our dataset.
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