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:
- abfedb839a7a309831ef724d70a7cfab598b83854806dbc67a38eb2c1422b07a
- Size of remote file:
- 3.39 kB
- SHA256:
- 93037cc693a17f9e75363cd91962cb28fd8b17d4ad0898f78286bf4b13b8c852
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