Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use olivia371/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olivia371/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="olivia371/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("olivia371/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("olivia371/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fd09cefd5409abcd52d1f55f053b64d878226fc1c0af38643c7c252eefbbaa6
- Size of remote file:
- 3.25 kB
- SHA256:
- d37fe351a952f417bba0c9640bb37b8ffecdad30a73a480f070f98571c3ac70f
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