Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use QMMMS/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QMMMS/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="QMMMS/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("QMMMS/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("QMMMS/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from QMMMS/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/QMMMS/my_awesome_model/resolve/main/training_args.bin
- Command line
-
hf download hf://QMMMS/my_awesome_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/QMMMS/my_awesome_model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- f1e589c13cd2cb9136f7f6dead16aa9c1c98c7c28ab45637ed424531fdecea8e
- Size of remote file:
- 5.3 kB
- SHA256:
- 0a39879f91f4787a18230d38585144d2cf7f794157ca5f707b2f83d23a6f45ab
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