Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use GMW123/finetuning-classification-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GMW123/finetuning-classification-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GMW123/finetuning-classification-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GMW123/finetuning-classification-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("GMW123/finetuning-classification-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from GMW123/finetuning-classification-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/GMW123/finetuning-classification-model-3000-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://GMW123/finetuning-classification-model-3000-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/GMW123/finetuning-classification-model-3000-samples/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- a80e3a1778199a8f5ab64c6a67473b7fc6a033ecaa4777d1b0d2750fabbc5ac7
- Size of remote file:
- 4.03 kB
- SHA256:
- 8c09ad16b2bd2eb631072e523a00f5d1e860116a868b82767ae4eb29bbd13a1d
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