Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Heer/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heer/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Heer/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Heer/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Heer/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2205e22347b9ac75b9a60d1058c0e9a1885fde9c0ffd21885b899494a3e69a2
- Size of remote file:
- 3.38 kB
- SHA256:
- 405a3b184538f35b84a6d89f33dddabb18ecb9c1f34b8b27ff73511c87ef9537
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