Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/HATE_Pfinal_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/HATE_Pfinal_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/HATE_Pfinal_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/HATE_Pfinal_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/HATE_Pfinal_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2ec2480df6f4b7ad20cb4352f240588cb266093137450bb5db86c1dfe59b335
- Size of remote file:
- 3.58 kB
- SHA256:
- 837780a74efc9f79f885d383aa0f3a26ae0f8ce685aa3a566ce036c0bb6c1389
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.