Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use xiaoding/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaoding/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiaoding/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiaoding/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("xiaoding/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb9ee95cafa9fbecdfc668bb6ce26d19be8853a608bebf0dac480d4fb99737e4
- Size of remote file:
- 3.44 kB
- SHA256:
- 070de7377da7c66457d0423e397365c9b026a25003d72c28d43187dbf0cd32ef
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