Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use praptishadmaan/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use praptishadmaan/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="praptishadmaan/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("praptishadmaan/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("praptishadmaan/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ba7f77585ca4fadf87e567a3d44f329d1d59b2cec1c236aeb4f24104e289900
- Size of remote file:
- 268 MB
- SHA256:
- 27adaca856117a9454a129f3e371fd77ee0ff47ec6d5e86b0bf73b4ac6b87616
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