Text Classification
Transformers
PyTorch
bert
Generated from Trainer
sibyl
Eval Results (legacy)
text-embeddings-inference
Instructions to use fabriceyhc/bert-base-uncased-amazon_polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabriceyhc/bert-base-uncased-amazon_polarity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fabriceyhc/bert-base-uncased-amazon_polarity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fabriceyhc/bert-base-uncased-amazon_polarity") model = AutoModelForSequenceClassification.from_pretrained("fabriceyhc/bert-base-uncased-amazon_polarity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the amazon_polarity config and test split of amazon_polarity
#2
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the amazon_polarity config and test split of the amazon_polarity dataset by @tts , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.
fabriceyhc changed pull request status to merged