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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b1b7377f63f99cd9c74879812224ac60371f3c3648ce11f01324e0b29639d3a
- Size of remote file:
- 268 MB
- SHA256:
- 861e089f7dc21c2ef7c843fece6c56da19fc063862f4b99c16f1b4781a044708
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