Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Manishkalra/discourse_classification_using_robrta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manishkalra/discourse_classification_using_robrta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Manishkalra/discourse_classification_using_robrta_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Manishkalra/discourse_classification_using_robrta_base") model = AutoModelForSequenceClassification.from_pretrained("Manishkalra/discourse_classification_using_robrta_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Manishkalra/discourse_classification_using_robrta_base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Manishkalra/discourse_classification_using_robrta_base/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Manishkalra/discourse_classification_using_robrta_base@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Manishkalra/discourse_classification_using_robrta_base/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 220a375b41349126d578ba648bdad3a239a128f26fac542586606ad5e5ae9f95
- Size of remote file:
- 499 MB
- SHA256:
- bc31a98b752a38c7a6c1504ec3b1e976f72dca2cbb8d6d0b4bd9f13d4e9d5c7c
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