Text Classification
Transformers
PyTorch
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use madmancity/bert2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madmancity/bert2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="madmancity/bert2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("madmancity/bert2") model = AutoModelForSequenceClassification.from_pretrained("madmancity/bert2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from madmancity/bert2: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/madmancity/bert2/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://madmancity/bert2@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/madmancity/bert2/resolve/refs%2Fpr%2F1/tokenizer.json
712 kB
- Xet hash:
- 08b6467b4451c91c5e5546aa8657fb66c3c25196545ded9defd42fe1529f395d
- Size of remote file:
- 712 kB
- SHA256:
- 41b9d12a4c559e4098f30173bc0f76d092c43d7b1f873e7027da45a256f30f87
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