Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use BSlinky/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSlinky/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BSlinky/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BSlinky/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("BSlinky/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 378728ecd4fca2a2ade709a4a2dd9019539d06a79f5393b1e9208ab98abd06b2
- Size of remote file:
- 3.12 kB
- SHA256:
- 00e3311f0b37e07b957d0e3e47bc9db1d70538e0a1efa7bffa09cad7e496a070
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