Text Classification
Transformers
PyTorch
ONNX
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use AdamCodd/tinybert-emotion-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdamCodd/tinybert-emotion-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AdamCodd/tinybert-emotion-balanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AdamCodd/tinybert-emotion-balanced") model = AutoModelForSequenceClassification.from_pretrained("AdamCodd/tinybert-emotion-balanced", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AdamCodd/tinybert-emotion-balanced: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/AdamCodd/tinybert-emotion-balanced/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AdamCodd/tinybert-emotion-balanced/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AdamCodd/tinybert-emotion-balanced/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 19532265cfcddef1cda8be23b9639246c436a43c9da63770dbb26f91d427d21e
- Size of remote file:
- 17.6 MB
- SHA256:
- 0042c2c1b80c29c06352f31452a5878287d44ce7ece0a5713429584f72d597ad
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