Text Classification
Transformers
PyTorch
English
distilbert
Pytorch
DistilBERT
Sentiment Analysis
2-classes
text-embeddings-inference
Instructions to use DreamyBeaver/distilBERT-SA-pietonium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DreamyBeaver/distilBERT-SA-pietonium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DreamyBeaver/distilBERT-SA-pietonium")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DreamyBeaver/distilBERT-SA-pietonium") model = AutoModelForSequenceClassification.from_pretrained("DreamyBeaver/distilBERT-SA-pietonium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38cff2019bc9395f55409c815d63e08c138e0a22947f180bc50afd9841d8b91c
- Size of remote file:
- 14.6 kB
- SHA256:
- c24ded61278d3bdb501c4e495dd10ddf01fa15e0368e52de2a102c5edb6db5a3
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