Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Krisna/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Krisna/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Krisna/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Krisna/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Krisna/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1b244406fd2a7533ca1a68d117c1042e3c1402c1a9908bd2b42d81aba72b491
- Size of remote file:
- 3.31 kB
- SHA256:
- e72a3ddef8a81b45984e7b4bbaf810c823b2757b3c8f534385609aa159eb99f9
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