Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use andersab/tweet_model_sentiment_andersab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andersab/tweet_model_sentiment_andersab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="andersab/tweet_model_sentiment_andersab")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("andersab/tweet_model_sentiment_andersab") model = AutoModelForSequenceClassification.from_pretrained("andersab/tweet_model_sentiment_andersab") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be0baf3d7f603bc8c5664cdbbad0f13ff6a7e71e041be409f5cd35c979de3507
- Size of remote file:
- 3.44 kB
- SHA256:
- da06d9d394be1f118d50766049ca4f228ec7979c79492e6532bc7819b0a0592d
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