Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Christabel/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Christabel/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Christabel/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Christabel/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Christabel/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34598a2e2b492acc91820ad7aa891bd2c16db3b985aeecc31f4a7b00d871de35
- Size of remote file:
- 268 MB
- SHA256:
- db87e9fd709396d8afcf48f4fabede082eb24b7442b862fdb76f46fde706ba78
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