Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Neha2608/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neha2608/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Neha2608/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Neha2608/results") model = AutoModelForSequenceClassification.from_pretrained("Neha2608/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Neha2608/results: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Neha2608/results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Neha2608/results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Neha2608/results/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- b4efdf1d06f563886217ebcf220bbada00e85cef830cbf4db57eebc8be26681e
- Size of remote file:
- 268 MB
- SHA256:
- eaa04adac2fb2d7949ac45b7cd299df383e8dd1f30d6fab2912981f73fdfaa13
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