Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokuls/bert-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/bert-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/bert-base-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokuls/bert-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("gokuls/bert-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gokuls/bert-base-sst2: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/gokuls/bert-base-sst2/resolve/main/training_args.bin
- Command line
-
hf download hf://gokuls/bert-base-sst2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gokuls/bert-base-sst2/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 263753608afb2ce2664f0407b6608ef1b83acc5d150d4af33ffe4f6098b0f5fa
- Size of remote file:
- 3.38 kB
- SHA256:
- 04db0ef39b21ad42f013d8431e4704a62fd7a6c567d415c6c48e7f04b654ecf4
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