Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use zwellington/microtest-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zwellington/microtest-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zwellington/microtest-2.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zwellington/microtest-2.0") model = AutoModelForSequenceClassification.from_pretrained("zwellington/microtest-2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zwellington/microtest-2.0: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/zwellington/microtest-2.0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zwellington/microtest-2.0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zwellington/microtest-2.0/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a098a0d05305d50056cc98dfb79cc0145ceed173552f062e7e26e9499c6a3547
- Size of remote file:
- 438 MB
- SHA256:
- b213492ab782b8e20c49227f1648939f4b16a2cc1242c8226224e18f34ace8a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.