Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Etelis/rtm_DistilBERT_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/rtm_DistilBERT_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/rtm_DistilBERT_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/rtm_DistilBERT_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/rtm_DistilBERT_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19d1e99bf8150dee69c8e0a981ef81a942b6f0ba7f64b7c87e163ccc4f130589
- Size of remote file:
- 263 MB
- SHA256:
- b2b6af14929c4bbeb6b21988686401118ed1037b03e5636d4ab608495018144e
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