Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use peringe/finetuning-sentiment-model-3000-samples-pi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peringe/finetuning-sentiment-model-3000-samples-pi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peringe/finetuning-sentiment-model-3000-samples-pi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("peringe/finetuning-sentiment-model-3000-samples-pi") model = AutoModelForSequenceClassification.from_pretrained("peringe/finetuning-sentiment-model-3000-samples-pi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b43f703d2550653924345452eb06ff9b12327c65952854b200d285ba0e90dcc7
- Size of remote file:
- 268 MB
- SHA256:
- 4761936c550c8ab0b432a106ee45b64c3ee91767be7329bd33d998e247375c72
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