Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use fabf21/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabf21/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fabf21/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fabf21/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("fabf21/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80af0db85d5d0f180f221eb81bb17bff28df21cf676cc12a10a3c66bd9ce9728
- Size of remote file:
- 3.38 kB
- SHA256:
- afbb03f5c466b2dc68926b4f74e396afcaae540dbab11e60a77c0931a34a6fcf
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