Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use moonzi/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonzi/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moonzi/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moonzi/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("moonzi/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 048febed2e1b9b077b98989bc57fe05c501ed46417cb3e1b1060657cb7ddad1f
- Size of remote file:
- 268 MB
- SHA256:
- 54e1d43d02999017311335c8fb6fc942874e4ddbf871345c92338def6f94567c
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