Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Etelis/Sentiment140_DistilBERT_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/Sentiment140_DistilBERT_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/Sentiment140_DistilBERT_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/Sentiment140_DistilBERT_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/Sentiment140_DistilBERT_5E") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 336632514a2487b221c1ff9bfd1a30bea76220a08ea40a690e8c8deaa324774c
- Size of remote file:
- 268 MB
- SHA256:
- 9d704ae37d34ef5d8732a105a3d0d8237ce41fe3f2ba862f89382c5dc8fb49c6
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