Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mmeet611/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmeet611/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mmeet611/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mmeet611/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mmeet611/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e717b346dcfa197e4f683b7a3d51a13714a68521a4fe0e707e635404c2180e0b
- Size of remote file:
- 3.31 kB
- SHA256:
- 05bf7ec3354838e2221619ca005d28b44eba91965d0b951805a6695a3bea6a71
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