Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use linyangnyc/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linyangnyc/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="linyangnyc/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("linyangnyc/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("linyangnyc/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57d8109c95bfb01146fa81ad59e83b0df676f8fa8ae338219f7b9db69a6d06bd
- Size of remote file:
- 268 MB
- SHA256:
- 345aa72a272cc28af84b472985d4e8e8186abdfd789ade6d4f727197567f4715
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