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:
- 273e2261aacfae0321bdfb7cc01cfcc9e7e0203014b57fbe801b34ef76ba0c7e
- Size of remote file:
- 268 MB
- SHA256:
- 27c68391357cee0bf1859e2b7a6bbd335bbf2351c43c8ff8512ea9e6e698902b
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