Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use kaisuke/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaisuke/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaisuke/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaisuke/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("kaisuke/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b32be04dd1bbd5b8d96e6243468359d52eb1a398f20dcab7e44a6c43bf1f46f
- Size of remote file:
- 268 MB
- SHA256:
- 4d175a53977cddb111c2cba8a6bfe3dd43aaf346d90a16bef12446c417141ca4
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