Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use harshitaskh/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshitaskh/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harshitaskh/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harshitaskh/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("harshitaskh/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harshitaskh/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/harshitaskh/test_trainer/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://harshitaskh/test_trainer@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harshitaskh/test_trainer/resolve/refs%2Fpr%2F1/pytorch_model.bin
433 MB
- Xet hash:
- f66022e69801ae000f86067be78ce5d4180f04700aa259f8bbfdcba128a04dc0
- Size of remote file:
- 433 MB
- SHA256:
- ac2d4f83d0cfe9fa496179e3a0ab7c14f302b6d6f2b1b9e052f763f87f84f30e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.