Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Percival/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Percival/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Percival/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Percival/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Percival/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbc99cbd799a402cd5d239af903391f27f764e6dab9800a84260d08d685db1d9
- Size of remote file:
- 268 MB
- SHA256:
- 62a44b42e6b18c96f26e8e890626e8bebd99fa1ba9bdf088900e06c991e0f7b2
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