Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use LianZhang/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LianZhang/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LianZhang/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LianZhang/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("LianZhang/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6c6497c42eefd0e28db50d29fcbee75cfa6bdbd21a9a22c11f66fcd77a09abb
- Size of remote file:
- 268 MB
- SHA256:
- 7743947629c94ce18172f9db3f9101cadf31e90ca9d3b023d26b05b01a329093
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