Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Daniel246/test4_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daniel246/test4_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Daniel246/test4_distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Daniel246/test4_distilbert") model = AutoModelForSequenceClassification.from_pretrained("Daniel246/test4_distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Daniel246/test4_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Daniel246/test4_distilbert/resolve/main/training_args.bin
- Command line
-
hf download hf://Daniel246/test4_distilbert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Daniel246/test4_distilbert/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 67c62f39594d917fa50319c53220a6ff2ddbd70f298bfc7cf51d9c50efd508ca
- Size of remote file:
- 4.73 kB
- SHA256:
- 0c7f24097dbe9ac22de170d2c0994f453431df54d3090743d10fae525fd1b4f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.