Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JeremiahZ/bert-base-uncased-sst2: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/JeremiahZ/bert-base-uncased-sst2/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://JeremiahZ/bert-base-uncased-sst2@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JeremiahZ/bert-base-uncased-sst2/resolve/refs%2Fpr%2F2/training_args.bin
3.31 kB
- Xet hash:
- 44ac20ceef10beec1d013a1a4c207dd9b5c83302e96b2ab5276cef433a339763
- Size of remote file:
- 3.31 kB
- SHA256:
- 47f660a3467b8f0e4dca441abcd56e1f7913db6db3630d21aa991fce4e79bfd6
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