Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use johntang/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johntang/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="johntang/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("johntang/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("johntang/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b98f8107613190547b7c89716d85d68d7dd3030c4c0228e13988e450f3dd83a
- Size of remote file:
- 268 MB
- SHA256:
- f6442554679ea1af0f98216a27695c4a713f27058044c2a5588b01703e636970
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