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 training_args.bin from AdamCodd/tinybert-emotion-balanced: direct link, hf CLI and curl.
- Browser
- Download file 3.26 kB
-
https://huggingface.co/AdamCodd/tinybert-emotion-balanced/resolve/main/training_args.bin
- Command line
-
hf download hf://AdamCodd/tinybert-emotion-balanced/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AdamCodd/tinybert-emotion-balanced/resolve/main/training_args.bin
3.26 kB
- Xet hash:
- 5d267bfa6e02c3dd4e9ad65ab3c8043c34e1b2136565613b79499f791c5df44b
- Size of remote file:
- 3.26 kB
- SHA256:
- 103a8b9af6f283899f86a83a9d876d30a6b1f25f2215d00625ae3a5b758d5e82
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