Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ProceduralTree/final-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProceduralTree/final-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProceduralTree/final-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProceduralTree/final-model") model = AutoModelForSequenceClassification.from_pretrained("ProceduralTree/final-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProceduralTree/final-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/ProceduralTree/final-model/resolve/main/training_args.bin
- Command line
-
hf download hf://ProceduralTree/final-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProceduralTree/final-model/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 98c5a58164b7d0b8bcb24b2e9de3ca2c6f4bde3f7300c32627741b726550e52b
- Size of remote file:
- 3.39 kB
- SHA256:
- 840fba832f53f2e73e84703b9f66b70ed23f73466a2cd80eaabecd11356ba081
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