Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/roberta-base") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_checkpoints/checkpoint-643/optimizer.pt from xshubhamx/roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 997 MB
-
https://huggingface.co/xshubhamx/roberta-base/resolve/main/training_checkpoints/checkpoint-643/optimizer.pt
- Command line
-
hf download hf://xshubhamx/roberta-base/training_checkpoints/checkpoint-643/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/xshubhamx/roberta-base/resolve/main/training_checkpoints/checkpoint-643/optimizer.pt
997 MB
- Xet hash:
- 3cc71761f9ce5d7e66275c8b6a12471a5766c364b957fe045757808bcd23b825
- Size of remote file:
- 997 MB
- SHA256:
- a029b8ca38e3bc352d88e0f711525b30cd50606bd4dc2149937e21dd416f94d6
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