Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use sgraf202/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgraf202/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgraf202/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgraf202/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("sgraf202/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77b01d01d8bbf0e6c3ae73202f856b2da9867a160615eca3a5f890fbcccfb5ff
- Size of remote file:
- 268 MB
- SHA256:
- e1283f539ec8ed3875daafbb570cfc6bfc4f44a73fc3577cb2446dbcbd7761d0
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