Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Sahara/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sahara/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sahara/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sahara/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Sahara/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5ed5b6fa158ebd328fcd4b5fea2e1cf0898c4be6436e5cbec6550148b708d58
- Size of remote file:
- 3.31 kB
- SHA256:
- 73420ac1f1d8d3dfd405b9b25f083914c002f43e66dbb1137f500164b30d384c
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