Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use lmattingly/imdb__text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmattingly/imdb__text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lmattingly/imdb__text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lmattingly/imdb__text_classification") model = AutoModelForSequenceClassification.from_pretrained("lmattingly/imdb__text_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6b9dbf21c53fcfcd4cc82c2970d67b3f02930c9cf2a7667ae5f4bda7639ccf7
- Size of remote file:
- 268 MB
- SHA256:
- 603eb7cc922492b72fc53dada6cd88fa24cbbd42dd7847661b721d154c091822
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