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:
- df28ba58d679d67ffaad646c17e16ef99dbff128c695b6c766d828b58f16ef90
- Size of remote file:
- 3.25 kB
- SHA256:
- 64fc6e4a6a006e7164a9eac80b461167410e0c2e7974ad48a4b2f7d7eb0d23e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.