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 pytorch_model.bin from GMW123/finetuning-classification-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/GMW123/finetuning-classification-model-3000-samples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GMW123/finetuning-classification-model-3000-samples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GMW123/finetuning-classification-model-3000-samples/resolve/main/pytorch_model.bin
90.9 MB
- Xet hash:
- 37743569290caed125c66d5aa0410ec4037cbc004762d81346ed7bd5f66b1ae8
- Size of remote file:
- 90.9 MB
- SHA256:
- 33fb3f14c954b0711796da01bb32598d18b827c1b90cdb45433ab41474c25b7d
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