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:
- dfc0f5fb8c10dfe5ce2ac0c479de6b9e48e1b3883ffb5fe813196c73a91d0239
- Size of remote file:
- 3.5 kB
- SHA256:
- 94e2760b6a36d7b943dae3b9405cf57f499efcf32ed057132f43dbe77f9440ba
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