Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use aditira/text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aditira/text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aditira/text_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aditira/text_classification") model = AutoModelForSequenceClassification.from_pretrained("aditira/text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aditira/text_classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/aditira/text_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://aditira/text_classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aditira/text_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
268 MB
- Xet hash:
- f82ee79895273708f9ce7e1b2d2379413041f448ef1c7a0c08f8fcabb52cbec8
- Size of remote file:
- 268 MB
- SHA256:
- fa82d2c4ebe44ab52eabac191b0bcf3a220df200f70e0e10dc00e05bc3a123df
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