Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ViktorDo/EcoBERT-POWO_Climber_Scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViktorDo/EcoBERT-POWO_Climber_Scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViktorDo/EcoBERT-POWO_Climber_Scratch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViktorDo/EcoBERT-POWO_Climber_Scratch") model = AutoModelForSequenceClassification.from_pretrained("ViktorDo/EcoBERT-POWO_Climber_Scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ViktorDo/EcoBERT-POWO_Climber_Scratch: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/ViktorDo/EcoBERT-POWO_Climber_Scratch/resolve/88af546c3bd7adede485fa7a1eb53f1bd4ba3499/training_args.bin
- Command line
-
hf download hf://ViktorDo/EcoBERT-POWO_Climber_Scratch@88af546c3bd7adede485fa7a1eb53f1bd4ba3499/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ViktorDo/EcoBERT-POWO_Climber_Scratch/resolve/88af546c3bd7adede485fa7a1eb53f1bd4ba3499/training_args.bin
3.52 kB
- Xet hash:
- 612b0f5063c034d5031e195e8f5434b1932f09b543217e3edab8d040b40b670b
- Size of remote file:
- 3.52 kB
- SHA256:
- 76a3e8a729da3344ccb3cb02c231080242978c24d7e2940f8c0b5130b9ec1f80
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